Welcome to the ZenML SDK Docs
Actions
Alerter
Alerters allow you to send alerts from within your pipeline.
This is useful to immediately get notified when failures happen, and also for general monitoring / reporting.
BaseAlerter
Bases: StackComponent
, ABC
Base class for all ZenML alerters.
Source code in src/zenml/alerter/base_alerter.py
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|
config
property
Returns the BaseAlerterConfig
config.
Returns:
Type | Description |
---|---|
BaseAlerterConfig
|
The configuration. |
ask(question, params=None)
Post a message to a chat service and wait for approval.
This can be useful to easily get a human in the loop, e.g., when deploying models.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
question
|
str
|
Question to ask (message to be posted). |
required |
params
|
Optional[BaseAlerterStepParameters]
|
Optional parameters of this function. |
None
|
Returns:
Name | Type | Description |
---|---|---|
bool |
bool
|
True if operation succeeded and was approved, else False. |
Source code in src/zenml/alerter/base_alerter.py
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|
post(message, params=None)
Post a message to a chat service.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
str
|
Message to be posted. |
required |
params
|
Optional[BaseAlerterStepParameters]
|
Optional parameters of this function. |
None
|
Returns:
Name | Type | Description |
---|---|---|
bool |
bool
|
True if operation succeeded, else False. |
Source code in src/zenml/alerter/base_alerter.py
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|
BaseAlerterConfig
Bases: StackComponentConfig
Base config for alerters.
Source code in src/zenml/alerter/base_alerter.py
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|
BaseAlerterFlavor
Bases: Flavor
, ABC
Base class for all ZenML alerter flavors.
Source code in src/zenml/alerter/base_alerter.py
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|
config_class
property
Returns BaseAlerterConfig class.
Returns:
Type | Description |
---|---|
Type[BaseAlerterConfig]
|
The BaseAlerterConfig class. |
implementation_class
property
type
property
BaseAlerterStepParameters
Bases: BaseModel
Step parameters definition for all alerters.
Source code in src/zenml/alerter/base_alerter.py
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|
Analytics
The 'analytics' module of ZenML.
alias(user_id, previous_id)
Alias user IDs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
user_id
|
UUID
|
The user ID. |
required |
previous_id
|
UUID
|
Previous ID for the alias. |
required |
Returns:
Type | Description |
---|---|
bool
|
True if event is sent successfully, False is not. |
Source code in src/zenml/analytics/__init__.py
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|
group(group_id, group_metadata=None)
Attach metadata to a segment group.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
group_id
|
UUID
|
ID of the group. |
required |
group_metadata
|
Optional[Dict[str, Any]]
|
Metadata to attach to the group. |
None
|
Returns:
Type | Description |
---|---|
bool
|
True if event is sent successfully, False if not. |
Source code in src/zenml/analytics/__init__.py
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|
identify(metadata=None)
Attach metadata to user directly.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
metadata
|
Optional[Dict[str, Any]]
|
Dict of metadata to attach to the user. |
None
|
Returns:
Type | Description |
---|---|
bool
|
True if event is sent successfully, False is not. |
Source code in src/zenml/analytics/__init__.py
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|
track(event, metadata=None)
Track segment event if user opted-in.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
event
|
AnalyticsEvent
|
Name of event to track in segment. |
required |
metadata
|
Optional[Dict[str, Any]]
|
Dict of metadata to track. |
None
|
Returns:
Type | Description |
---|---|
bool
|
True if event is sent successfully, False if not. |
Source code in src/zenml/analytics/__init__.py
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Annotators
Initialization of the ZenML annotator stack component.
BaseAnnotator
Bases: StackComponent
, ABC
Base class for all ZenML annotators.
Source code in src/zenml/annotators/base_annotator.py
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|
config
property
Returns the BaseAnnotatorConfig
config.
Returns:
Type | Description |
---|---|
BaseAnnotatorConfig
|
The configuration. |
add_dataset(**kwargs)
abstractmethod
Registers a dataset for annotation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
Any
|
keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
Any
|
The dataset or confirmation object on adding the dataset. |
Source code in src/zenml/annotators/base_annotator.py
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|
delete_dataset(**kwargs)
abstractmethod
Deletes a dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
Any
|
keyword arguments. |
{}
|
Source code in src/zenml/annotators/base_annotator.py
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|
get_dataset(**kwargs)
abstractmethod
Gets the dataset with the given name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
Any
|
keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
Any
|
The dataset with the given name. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_dataset_names()
abstractmethod
Gets the names of the datasets currently available for annotation.
Returns:
Type | Description |
---|---|
List[str]
|
The names of the datasets currently available for annotation. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_dataset_stats(dataset_name)
abstractmethod
Gets the statistics of a dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset_name
|
str
|
name of the dataset. |
required |
Returns:
Type | Description |
---|---|
Tuple[int, int]
|
A tuple containing (labeled_task_count, unlabeled_task_count) for the dataset. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_datasets()
abstractmethod
Gets the datasets currently available for annotation.
Returns:
Type | Description |
---|---|
List[Any]
|
The datasets currently available for annotation. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_labeled_data(**kwargs)
abstractmethod
Gets the labeled data for the given dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
Any
|
keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
Any
|
The labeled data for the given dataset. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_unlabeled_data(**kwargs)
abstractmethod
Gets the unlabeled data for the given dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
str
|
Additional keyword arguments to pass to the Label Studio client. |
{}
|
Returns:
Type | Description |
---|---|
Any
|
The unlabeled data for the given dataset. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_url()
abstractmethod
Gets the URL of the annotation interface.
Returns:
Type | Description |
---|---|
str
|
The URL of the annotation interface. |
Source code in src/zenml/annotators/base_annotator.py
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|
get_url_for_dataset(dataset_name)
abstractmethod
Gets the URL of the annotation interface for a specific dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset_name
|
str
|
name of the dataset. |
required |
Returns:
Type | Description |
---|---|
str
|
The URL of the dataset annotation interface. |
Source code in src/zenml/annotators/base_annotator.py
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|
launch(**kwargs)
abstractmethod
Launches the annotation interface.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
Any
|
Additional keyword arguments to pass to the annotation client. |
{}
|
Source code in src/zenml/annotators/base_annotator.py
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Artifact Stores
ZenML's artifact-store stores artifacts in a file system.
In ZenML, the inputs and outputs which go through any step is treated as an
artifact and as its name suggests, an ArtifactStore
is a place where these
artifacts get stored.
Out of the box, ZenML comes with the BaseArtifactStore
and
LocalArtifactStore
implementations. While the BaseArtifactStore
establishes
an interface for people who want to extend it to their needs, the
LocalArtifactStore
is a simple implementation for a local setup.
Moreover, additional artifact stores can be found in specific integrations
modules, such as the GCPArtifactStore
in the gcp
integration and the
AzureArtifactStore
in the azure
integration.
BaseArtifactStore
Bases: StackComponent
Base class for all ZenML artifact stores.
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
config
property
Returns the BaseArtifactStoreConfig
config.
Returns:
Type | Description |
---|---|
BaseArtifactStoreConfig
|
The configuration. |
custom_cache_key
property
Custom cache key.
Any artifact store can override this property in case they need additional control over the caching behavior.
Returns:
Type | Description |
---|---|
Optional[bytes]
|
Custom cache key. |
path
property
The path to the artifact store.
Returns:
Type | Description |
---|---|
str
|
The path. |
__init__(*args, **kwargs)
Initiate the Pydantic object and register the corresponding filesystem.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
*args
|
Any
|
The positional arguments to pass to the Pydantic object. |
()
|
**kwargs
|
Any
|
The keyword arguments to pass to the Pydantic object. |
{}
|
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
copyfile(src, dst, overwrite=False)
abstractmethod
Copy a file from the source to the destination.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
src
|
PathType
|
The source path. |
required |
dst
|
PathType
|
The destination path. |
required |
overwrite
|
bool
|
Whether to overwrite the destination file if it exists. |
False
|
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
exists(path)
abstractmethod
Checks if a path exists.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to check. |
required |
Returns:
Type | Description |
---|---|
bool
|
|
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
glob(pattern)
abstractmethod
Gets the paths that match a glob pattern.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
pattern
|
PathType
|
The glob pattern. |
required |
Returns:
Type | Description |
---|---|
List[PathType]
|
The list of paths that match the pattern. |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
isdir(path)
abstractmethod
Returns whether the given path points to a directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to check. |
required |
Returns:
Type | Description |
---|---|
bool
|
|
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
listdir(path)
abstractmethod
Returns a list of files under a given directory in the filesystem.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to list. |
required |
Returns:
Type | Description |
---|---|
List[PathType]
|
The list of files under the given path. |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
makedirs(path)
abstractmethod
Make a directory at the given path, recursively creating parents.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to create. |
required |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
mkdir(path)
abstractmethod
Make a directory at the given path; parent directory must exist.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to create. |
required |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
open(path, mode='r')
abstractmethod
Open a file at the given path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path of the file to open. |
required |
mode
|
str
|
The mode to open the file. |
'r'
|
Returns:
Type | Description |
---|---|
Any
|
The file object. |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
remove(path)
abstractmethod
Remove the file at the given path. Dangerous operation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to remove. |
required |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
rename(src, dst, overwrite=False)
abstractmethod
Rename source file to destination file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
src
|
PathType
|
The source path. |
required |
dst
|
PathType
|
The destination path. |
required |
overwrite
|
bool
|
Whether to overwrite the destination file if it exists. |
False
|
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
rmtree(path)
abstractmethod
Deletes dir recursively. Dangerous operation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to delete. |
required |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
size(path)
abstractmethod
Get the size of a file in bytes.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to the file. |
required |
Returns:
Type | Description |
---|---|
Optional[int]
|
The size of the file in bytes or |
Optional[int]
|
does not implement the |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
stat(path)
abstractmethod
Return the stat descriptor for a given file path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
PathType
|
The path to check. |
required |
Returns:
Type | Description |
---|---|
Any
|
The stat descriptor. |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
walk(top, topdown=True, onerror=None)
abstractmethod
Return an iterator that walks the contents of the given directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top
|
PathType
|
The path to walk. |
required |
topdown
|
bool
|
Whether to walk the top-down or bottom-up. |
True
|
onerror
|
Optional[Callable[..., None]]
|
The error handler. |
None
|
Returns:
Type | Description |
---|---|
Iterable[Tuple[PathType, List[PathType], List[PathType]]]
|
The iterator that walks the contents of the given directory. |
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
BaseArtifactStoreConfig
Bases: StackComponentConfig
Config class for BaseArtifactStore
.
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
BaseArtifactStoreFlavor
Bases: Flavor
Base class for artifact store flavors.
Source code in src/zenml/artifact_stores/base_artifact_store.py
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|
config_class
property
Config class for this flavor.
Returns:
Type | Description |
---|---|
Type[StackComponentConfig]
|
The config class. |
implementation_class
abstractmethod
property
type
property
LocalArtifactStore
Bases: LocalFilesystem
, BaseArtifactStore
Artifact Store for local artifacts.
All methods are inherited from the default LocalFilesystem
.
Source code in src/zenml/artifact_stores/local_artifact_store.py
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|
custom_cache_key
property
Custom cache key.
The client ID is returned here to invalidate caching when using the same local artifact store on multiple client machines.
Returns:
Type | Description |
---|---|
Optional[bytes]
|
Custom cache key. |
local_path
property
Returns the local path of the artifact store.
Returns:
Type | Description |
---|---|
Optional[str]
|
The local path of the artifact store. |
path
property
Returns the path to the local artifact store.
If the user has not defined a path in the config, this will create a sub-folder in the global config directory.
Returns:
Type | Description |
---|---|
str
|
The path to the local artifact store. |
get_default_local_path(id_)
staticmethod
Returns the default local path for a local artifact store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_
|
UUID
|
The id of the local artifact store. |
required |
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
The default local path. |
Source code in src/zenml/artifact_stores/local_artifact_store.py
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|
LocalArtifactStoreConfig
Bases: BaseArtifactStoreConfig
Config class for the local artifact store.
Attributes:
Name | Type | Description |
---|---|---|
path |
str
|
The path to the local artifact store. |
Source code in src/zenml/artifact_stores/local_artifact_store.py
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|
is_local
property
Checks if this stack component is running locally.
Returns:
Type | Description |
---|---|
bool
|
True if this config is for a local component, False otherwise. |
ensure_path_local(path)
classmethod
Pydantic validator which ensures that the given path is a local path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
The path to validate. |
required |
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
The validated (local) path. |
Raises:
Type | Description |
---|---|
ArtifactStoreInterfaceError
|
If the given path is not a local path. |
Source code in src/zenml/artifact_stores/local_artifact_store.py
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|
LocalArtifactStoreFlavor
Bases: BaseArtifactStoreFlavor
Class for the LocalArtifactStoreFlavor
.
Source code in src/zenml/artifact_stores/local_artifact_store.py
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|
config_class
property
Config class for this flavor.
Returns:
Type | Description |
---|---|
Type[LocalArtifactStoreConfig]
|
The config class. |
docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
implementation_class
property
logo_url
property
A url to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
Returns the name of the artifact store flavor.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
The name of the artifact store flavor. |
sdk_docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
Artifacts
Client Lazy Loader
Lazy loading functionality for Client methods.
ClientLazyLoader
Bases: BaseModel
Lazy loader for Client methods.
Source code in src/zenml/client_lazy_loader.py
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|
__call__(*args, **kwargs)
Call mocked attribute.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
args
|
Any
|
Positional arguments. |
()
|
kwargs
|
Any
|
Keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
ClientLazyLoader
|
self |
Source code in src/zenml/client_lazy_loader.py
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|
__getattr__(name)
Get attribute not defined in ClientLazyLoader.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the attribute to get. |
required |
Returns:
Type | Description |
---|---|
ClientLazyLoader
|
self |
Source code in src/zenml/client_lazy_loader.py
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|
__getitem__(item)
Get item from mocked attribute.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
item
|
Any
|
Item to get. |
required |
Returns:
Type | Description |
---|---|
ClientLazyLoader
|
self |
Source code in src/zenml/client_lazy_loader.py
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|
evaluate()
Evaluate lazy loaded Client method.
Returns:
Type | Description |
---|---|
Any
|
Evaluated lazy loader chain of calls. |
Source code in src/zenml/client_lazy_loader.py
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|
client_lazy_loader(method_name, *args, **kwargs)
Lazy loader for Client methods helper.
Usage:
def get_something(self, arg1: Any)->SomeResponse:
if cll:=client_lazy_loader("get_something", arg1):
return cll # type: ignore[return-value]
return SomeResponse()
Parameters:
Name | Type | Description | Default |
---|---|---|---|
method_name
|
str
|
The name of the method to be called. |
required |
*args
|
Any
|
The arguments to be passed to the method. |
()
|
**kwargs
|
Any
|
The keyword arguments to be passed to the method. |
{}
|
Returns:
Type | Description |
---|---|
Optional[ClientLazyLoader]
|
The result of the method call. |
Source code in src/zenml/client_lazy_loader.py
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|
evaluate_all_lazy_load_args_in_client_methods(cls)
Class wrapper to evaluate lazy loader arguments of all methods.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cls
|
Type[Client]
|
The class to wrap. |
required |
Returns:
Type | Description |
---|---|
Type[Client]
|
Wrapped class. |
Source code in src/zenml/client_lazy_loader.py
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|
Client
Client implementation.
Client
ZenML client class.
The ZenML client manages configuration options for ZenML stacks as well as their components.
Source code in src/zenml/client.py
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active_project
property
Get the currently active project of the local client.
If no active project is configured locally for the client, the active project in the global configuration is used instead.
Returns:
Type | Description |
---|---|
ProjectResponse
|
The active project. |
Raises:
Type | Description |
---|---|
RuntimeError
|
If the active project is not set. |
active_stack
property
active_stack_model
property
The model of the active stack for this client.
If no active stack is configured locally for the client, the active stack in the global configuration is used instead.
Returns:
Type | Description |
---|---|
StackResponse
|
The model of the active stack for this client. |
Raises:
Type | Description |
---|---|
RuntimeError
|
If the active stack is not set. |
active_user
property
config_directory
property
The configuration directory of this client.
Returns:
Type | Description |
---|---|
Optional[Path]
|
The configuration directory of this client, or None, if the |
Optional[Path]
|
client doesn't have an active root. |
root
property
The root directory of this client.
Returns:
Type | Description |
---|---|
Optional[Path]
|
The root directory of this client, or None, if the client |
Optional[Path]
|
has not been initialized. |
uses_local_configuration
property
Check if the client is using a local configuration.
Returns:
Type | Description |
---|---|
bool
|
True if the client is using a local configuration, |
bool
|
False otherwise. |
zen_store
property
Shortcut to return the global zen store.
Returns:
Type | Description |
---|---|
BaseZenStore
|
The global zen store. |
__init__(root=None)
Initializes the global client instance.
Client is a singleton class: only one instance can exist. Calling this constructor multiple times will always yield the same instance (see the exception below).
The root
argument is only meant for internal use and testing purposes.
User code must never pass them to the constructor.
When a custom root
value is passed, an anonymous Client instance
is created and returned independently of the Client singleton and
that will have no effect as far as the rest of the ZenML core code is
concerned.
Instead of creating a new Client instance to reflect a different
repository root, to change the active root in the global Client,
call Client().activate_root(<new-root>)
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root
|
Optional[Path]
|
(internal use) custom root directory for the client. If
no path is given, the repository root is determined using the
environment variable |
None
|
Source code in src/zenml/client.py
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|
activate_root(root=None)
Set the active repository root directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root
|
Optional[Path]
|
The path to set as the active repository root. If not set,
the repository root is determined using the environment
variable |
None
|
Source code in src/zenml/client.py
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|
activate_stack(stack_name_id_or_prefix)
Sets the stack as active.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
stack_name_id_or_prefix
|
Union[str, UUID]
|
Model of the stack to activate. |
required |
Raises:
Type | Description |
---|---|
KeyError
|
If the stack is not registered. |
Source code in src/zenml/client.py
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|
attach_tag(tag_name_or_id, resources)
Attach a tag to resources.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tag_name_or_id
|
Union[str, UUID]
|
name or id of the tag to be attached. |
required |
resources
|
List[TagResource]
|
the resources to attach the tag to. |
required |
Source code in src/zenml/client.py
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|
backup_secrets(ignore_errors=True, delete_secrets=False)
Backs up all secrets to the configured backup secrets store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ignore_errors
|
bool
|
Whether to ignore individual errors during the backup process and attempt to backup all secrets. |
True
|
delete_secrets
|
bool
|
Whether to delete the secrets that have been successfully backed up from the primary secrets store. Setting this flag effectively moves all secrets from the primary secrets store to the backup secrets store. |
False
|
Source code in src/zenml/client.py
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|
create_action(name, flavor, action_type, configuration, service_account_id, auth_window=None, description='')
Create an action.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the action. |
required |
flavor
|
str
|
The flavor of the action, |
required |
action_type
|
PluginSubType
|
The action subtype. |
required |
configuration
|
Dict[str, Any]
|
The action configuration. |
required |
service_account_id
|
UUID
|
The service account that is used to execute the action. |
required |
auth_window
|
Optional[int]
|
The time window in minutes for which the service account is authorized to execute the action. Set this to 0 to authorize the service account indefinitely (not recommended). |
None
|
description
|
str
|
The description of the action. |
''
|
Returns:
Type | Description |
---|---|
ActionResponse
|
The created action |
Source code in src/zenml/client.py
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|
create_api_key(service_account_name_id_or_prefix, name, description='', set_key=False)
Create a new API key and optionally set it as the active API key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the service account to create the API key for. |
required |
name
|
str
|
Name of the API key. |
required |
description
|
str
|
The description of the API key. |
''
|
set_key
|
bool
|
Whether to set the created API key as the active API key. |
False
|
Returns:
Type | Description |
---|---|
APIKeyResponse
|
The created API key. |
Source code in src/zenml/client.py
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|
create_code_repository(name, config, source, description=None, logo_url=None)
Create a new code repository.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the code repository. |
required |
config
|
Dict[str, Any]
|
The configuration for the code repository. |
required |
source
|
Source
|
The code repository implementation source. |
required |
description
|
Optional[str]
|
The code repository description. |
None
|
logo_url
|
Optional[str]
|
URL of a logo (png, jpg or svg) for the code repository. |
None
|
Returns:
Type | Description |
---|---|
CodeRepositoryResponse
|
The created code repository. |
Source code in src/zenml/client.py
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|
create_event_source(name, configuration, flavor, event_source_subtype, description='')
Registers an event source.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the event source to create. |
required |
configuration
|
Dict[str, Any]
|
Configuration for this event source. |
required |
flavor
|
str
|
The flavor of event source. |
required |
event_source_subtype
|
PluginSubType
|
The event source subtype. |
required |
description
|
str
|
The description of the event source. |
''
|
Returns:
Type | Description |
---|---|
EventSourceResponse
|
The model of the registered event source. |
Source code in src/zenml/client.py
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|
create_flavor(source, component_type)
Creates a new flavor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
source
|
str
|
The flavor to create. |
required |
component_type
|
StackComponentType
|
The type of the flavor. |
required |
Returns:
Type | Description |
---|---|
FlavorResponse
|
The created flavor (in model form). |
Raises:
Type | Description |
---|---|
ValueError
|
in case the config_schema of the flavor is too large. |
Source code in src/zenml/client.py
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|
create_model(name, license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=None, save_models_to_registry=True)
Creates a new model in Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the model. |
required |
license
|
Optional[str]
|
The license under which the model is created. |
None
|
description
|
Optional[str]
|
The description of the model. |
None
|
audience
|
Optional[str]
|
The target audience of the model. |
None
|
use_cases
|
Optional[str]
|
The use cases of the model. |
None
|
limitations
|
Optional[str]
|
The known limitations of the model. |
None
|
trade_offs
|
Optional[str]
|
The tradeoffs of the model. |
None
|
ethics
|
Optional[str]
|
The ethical implications of the model. |
None
|
tags
|
Optional[List[str]]
|
Tags associated with the model. |
None
|
save_models_to_registry
|
bool
|
Whether to save the model to the registry. |
True
|
Returns:
Type | Description |
---|---|
ModelResponse
|
The newly created model. |
Source code in src/zenml/client.py
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|
create_model_version(model_name_or_id, name=None, description=None, tags=None, project=None)
Creates a new model version in Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Union[str, UUID]
|
the name or id of the model to create model version in. |
required |
name
|
Optional[str]
|
the name of the Model Version to be created. |
None
|
description
|
Optional[str]
|
the description of the Model Version to be created. |
None
|
tags
|
Optional[List[str]]
|
Tags associated with the model. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ModelVersionResponse
|
The newly created model version. |
Source code in src/zenml/client.py
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|
create_project(name, description, display_name=None)
Create a new project.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the project. |
required |
description
|
str
|
Description of the project. |
required |
display_name
|
Optional[str]
|
Display name of the project. |
None
|
Returns:
Type | Description |
---|---|
ProjectResponse
|
The created project. |
Source code in src/zenml/client.py
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|
create_run_metadata(metadata, resources, stack_component_id=None, publisher_step_id=None)
Create run metadata.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
metadata
|
Dict[str, MetadataType]
|
The metadata to create as a dictionary of key-value pairs. |
required |
resources
|
List[RunMetadataResource]
|
The list of IDs and types of the resources for that the metadata was produced. |
required |
stack_component_id
|
Optional[UUID]
|
The ID of the stack component that produced the metadata. |
None
|
publisher_step_id
|
Optional[UUID]
|
The ID of the step execution that publishes this metadata automatically. |
None
|
Source code in src/zenml/client.py
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|
create_run_template(name, deployment_id, description=None, tags=None)
Create a run template.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the run template. |
required |
deployment_id
|
UUID
|
ID of the deployment which this template should be based off of. |
required |
description
|
Optional[str]
|
The description of the run template. |
None
|
tags
|
Optional[List[str]]
|
Tags associated with the run template. |
None
|
Returns:
Type | Description |
---|---|
RunTemplateResponse
|
The created run template. |
Source code in src/zenml/client.py
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|
create_secret(name, values, private=False)
Creates a new secret.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the secret. |
required |
values
|
Dict[str, str]
|
The values of the secret. |
required |
private
|
bool
|
Whether the secret is private. A private secret is only accessible to the user who created it. |
False
|
Returns:
Type | Description |
---|---|
SecretResponse
|
The created secret (in model form). |
Raises:
Type | Description |
---|---|
NotImplementedError
|
If centralized secrets management is not enabled. |
Source code in src/zenml/client.py
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|
create_service(config, service_type, model_version_id=None)
Registers a service.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
config
|
ServiceConfig
|
The configuration of the service. |
required |
service_type
|
ServiceType
|
The type of the service. |
required |
model_version_id
|
Optional[UUID]
|
The ID of the model version to associate with the service. |
None
|
Returns:
Type | Description |
---|---|
ServiceResponse
|
The registered service. |
Source code in src/zenml/client.py
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|
create_service_account(name, description='')
Create a new service account.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the service account. |
required |
description
|
str
|
The description of the service account. |
''
|
Returns:
Type | Description |
---|---|
ServiceAccountResponse
|
The created service account. |
Source code in src/zenml/client.py
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|
create_service_connector(name, connector_type, resource_type=None, auth_method=None, configuration=None, resource_id=None, description='', expiration_seconds=None, expires_at=None, expires_skew_tolerance=None, labels=None, auto_configure=False, verify=True, list_resources=True, register=True)
Create, validate and/or register a service connector.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the service connector. |
required |
connector_type
|
str
|
The service connector type. |
required |
auth_method
|
Optional[str]
|
The authentication method of the service connector. May be omitted if auto-configuration is used. |
None
|
resource_type
|
Optional[str]
|
The resource type for the service connector. |
None
|
configuration
|
Optional[Dict[str, str]]
|
The configuration of the service connector. |
None
|
resource_id
|
Optional[str]
|
The resource id of the service connector. |
None
|
description
|
str
|
The description of the service connector. |
''
|
expiration_seconds
|
Optional[int]
|
The expiration time of the service connector. |
None
|
expires_at
|
Optional[datetime]
|
The expiration time of the service connector. |
None
|
expires_skew_tolerance
|
Optional[int]
|
The allowed expiration skew for the service connector credentials. |
None
|
labels
|
Optional[Dict[str, str]]
|
The labels of the service connector. |
None
|
auto_configure
|
bool
|
Whether to automatically configure the service connector from the local environment. |
False
|
verify
|
bool
|
Whether to verify that the service connector configuration and credentials can be used to gain access to the resource. |
True
|
list_resources
|
bool
|
Whether to also list the resources that the service connector can give access to (if verify is True). |
True
|
register
|
bool
|
Whether to register the service connector or not. |
True
|
Returns:
Type | Description |
---|---|
Optional[Union[ServiceConnectorResponse, ServiceConnectorRequest]]
|
The model of the registered service connector and the resources |
Optional[ServiceConnectorResourcesModel]
|
that the service connector can give access to (if verify is True). |
Raises:
Type | Description |
---|---|
ValueError
|
If the arguments are invalid. |
KeyError
|
If the service connector type is not found. |
NotImplementedError
|
If auto-configuration is not supported or not implemented for the service connector type. |
AuthorizationException
|
If the connector verification failed due to authorization issues. |
Source code in src/zenml/client.py
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|
create_stack(name, components, stack_spec_file=None, labels=None)
Registers a stack and its components.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the stack to register. |
required |
components
|
Mapping[StackComponentType, Union[str, UUID]]
|
dictionary which maps component types to component names |
required |
stack_spec_file
|
Optional[str]
|
path to the stack spec file |
None
|
labels
|
Optional[Dict[str, Any]]
|
The labels of the stack. |
None
|
Returns:
Type | Description |
---|---|
StackResponse
|
The model of the registered stack. |
Source code in src/zenml/client.py
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|
create_stack_component(name, flavor, component_type, configuration, labels=None)
Registers a stack component.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the stack component. |
required |
flavor
|
str
|
The flavor of the stack component. |
required |
component_type
|
StackComponentType
|
The type of the stack component. |
required |
configuration
|
Dict[str, str]
|
The configuration of the stack component. |
required |
labels
|
Optional[Dict[str, Any]]
|
The labels of the stack component. |
None
|
Returns:
Type | Description |
---|---|
ComponentResponse
|
The model of the registered component. |
Source code in src/zenml/client.py
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|
create_tag(name, exclusive=False, color=None)
Creates a new tag.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
the name of the tag. |
required |
exclusive
|
bool
|
the boolean to decide whether the tag is an exclusive tag. An exclusive tag means that the tag can exist only for a single: - pipeline run within the scope of a pipeline - artifact version within the scope of an artifact - run template |
False
|
color
|
Optional[Union[str, ColorVariants]]
|
the color of the tag |
None
|
Returns:
Type | Description |
---|---|
TagResponse
|
The newly created tag. |
Source code in src/zenml/client.py
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|
create_trigger(name, event_source_id, event_filter, action_id, description='')
Registers a trigger.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the trigger to create. |
required |
event_source_id
|
UUID
|
The id of the event source id |
required |
event_filter
|
Dict[str, Any]
|
The event filter |
required |
action_id
|
UUID
|
The ID of the action that should be triggered. |
required |
description
|
str
|
The description of the trigger |
''
|
Returns:
Type | Description |
---|---|
TriggerResponse
|
The created trigger. |
Source code in src/zenml/client.py
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|
create_user(name, password=None, is_admin=False)
Create a new user.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the user. |
required |
password
|
Optional[str]
|
The password of the user. If not provided, the user will be created with empty password. |
None
|
is_admin
|
bool
|
Whether the user should be an admin. |
False
|
Returns:
Type | Description |
---|---|
UserResponse
|
The model of the created user. |
Source code in src/zenml/client.py
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|
deactivate_user(name_id_or_prefix)
Deactivate a user and generate an activation token.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
str
|
The name or ID of the user to reset. |
required |
Returns:
Type | Description |
---|---|
UserResponse
|
The deactivated user. |
Source code in src/zenml/client.py
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|
delete_action(name_id_or_prefix, project=None)
Delete an action.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix id of the action to delete. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_all_model_version_artifact_links(model_version_id, only_links)
Delete all model version to artifact links in Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_version_id
|
UUID
|
The id of the model version holding the link. |
required |
only_links
|
bool
|
If true, only delete the link to the artifact. |
required |
Source code in src/zenml/client.py
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|
delete_api_key(service_account_name_id_or_prefix, name_id_or_prefix)
Delete an API key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the service account to delete the API key for. |
required |
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the API key. |
required |
Source code in src/zenml/client.py
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|
delete_artifact(name_id_or_prefix, project=None)
Delete an artifact.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the artifact to delete. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_artifact_version(name_id_or_prefix, version=None, delete_metadata=True, delete_from_artifact_store=False, project=None)
Delete an artifact version.
By default, this will delete only the metadata of the artifact from the database, not the actual object stored in the artifact store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The ID of artifact version or name or prefix of the artifact to delete. |
required |
version
|
Optional[str]
|
The version of the artifact to delete. |
None
|
delete_metadata
|
bool
|
If True, delete the metadata of the artifact version from the database. |
True
|
delete_from_artifact_store
|
bool
|
If True, delete the artifact object itself from the artifact store. |
False
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_authorized_device(id_or_prefix)
Delete an authorized device.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
Union[str, UUID]
|
The ID or ID prefix of the authorized device. |
required |
Source code in src/zenml/client.py
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|
delete_build(id_or_prefix, project=None)
Delete a build.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
str
|
The id or id prefix of the build. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_code_repository(name_id_or_prefix, project=None)
Delete a code repository.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the code repository. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_deployment(id_or_prefix, project=None)
Delete a deployment.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
str
|
The id or id prefix of the deployment. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_event_source(name_id_or_prefix, project=None)
Deletes an event_source.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix id of the event_source to deregister. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_flavor(name_id_or_prefix)
Deletes a flavor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
str
|
The name, id or prefix of the id for the flavor to delete. |
required |
Source code in src/zenml/client.py
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|
delete_model(model_name_or_id, project=None)
Deletes a model from Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Union[str, UUID]
|
name or id of the model to be deleted. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_model_version(model_version_id)
Deletes a model version from Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_version_id
|
UUID
|
Id of the model version to be deleted. |
required |
Source code in src/zenml/client.py
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|
delete_model_version_artifact_link(model_version_id, artifact_version_id)
Delete model version to artifact link in Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_version_id
|
UUID
|
The id of the model version holding the link. |
required |
artifact_version_id
|
UUID
|
The id of the artifact version to be deleted. |
required |
Raises:
Type | Description |
---|---|
RuntimeError
|
If more than one artifact link is found for given filters. |
Source code in src/zenml/client.py
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|
delete_pipeline(name_id_or_prefix, project=None)
Delete a pipeline.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or ID prefix of the pipeline. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_pipeline_run(name_id_or_prefix, project=None)
Deletes a pipeline run.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
Name, ID, or prefix of the pipeline run. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_project(name_id_or_prefix)
Delete a project.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
str
|
The name or ID of the project to delete. |
required |
Raises:
Type | Description |
---|---|
IllegalOperationError
|
If the project to delete is the active project. |
Source code in src/zenml/client.py
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|
delete_run_template(name_id_or_prefix, project=None)
Delete a run template.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
Name/ID/ID prefix of the template to delete. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_schedule(name_id_or_prefix, project=None)
Delete a schedule.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix id of the schedule to delete. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_secret(name_id_or_prefix, private=None)
Deletes a secret.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
str
|
The name or ID of the secret. |
required |
private
|
Optional[bool]
|
The private status of the secret to delete. |
None
|
Source code in src/zenml/client.py
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|
delete_service(name_id_or_prefix, project=None)
Delete a service.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
UUID
|
The name or ID of the service to delete. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_service_account(name_id_or_prefix)
Delete a service account.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name or ID of the service account to delete. |
required |
Source code in src/zenml/client.py
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|
delete_service_connector(name_id_or_prefix)
Deletes a registered service connector.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The ID or name of the service connector to delete. |
required |
Source code in src/zenml/client.py
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|
delete_stack(name_id_or_prefix, recursive=False)
Deregisters a stack.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix id of the stack to deregister. |
required |
recursive
|
bool
|
If |
False
|
Raises:
Type | Description |
---|---|
ValueError
|
If the stack is the currently active stack for this client. |
Source code in src/zenml/client.py
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|
delete_stack_component(name_id_or_prefix, component_type)
Deletes a registered stack component.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The model of the component to delete. |
required |
component_type
|
StackComponentType
|
The type of the component to delete. |
required |
Source code in src/zenml/client.py
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|
delete_tag(tag_name_or_id)
Deletes a tag.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tag_name_or_id
|
Union[str, UUID]
|
name or id of the tag to be deleted. |
required |
Source code in src/zenml/client.py
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|
delete_trigger(name_id_or_prefix, project=None)
Deletes an trigger.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix id of the trigger to deregister. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
delete_trigger_execution(trigger_execution_id)
Delete a trigger execution.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
trigger_execution_id
|
UUID
|
The ID of the trigger execution to delete. |
required |
Source code in src/zenml/client.py
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|
delete_user(name_id_or_prefix)
Delete a user.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
str
|
The name or ID of the user to delete. |
required |
Source code in src/zenml/client.py
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|
detach_tag(tag_name_or_id, resources)
Detach a tag from resources.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tag_name_or_id
|
Union[str, UUID]
|
name or id of the tag to be detached. |
required |
resources
|
List[TagResource]
|
the resources to detach the tag from. |
required |
Source code in src/zenml/client.py
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|
find_repository(path=None, enable_warnings=False)
staticmethod
Search for a ZenML repository directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
Optional[Path]
|
Optional path to look for the repository. If no path is
given, this function tries to find the repository using the
environment variable |
None
|
enable_warnings
|
bool
|
If |
False
|
Returns:
Type | Description |
---|---|
Optional[Path]
|
Absolute path to a ZenML repository directory or None if no |
Optional[Path]
|
repository directory was found. |
Source code in src/zenml/client.py
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|
get_action(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)
Get an action by name, ID or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, ID or prefix of the action. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ActionResponse
|
The action. |
Source code in src/zenml/client.py
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|
get_api_key(service_account_name_id_or_prefix, name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)
Get an API key by name, id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the service account to get the API key for. |
required |
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or ID prefix of the API key. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
APIKeyResponse
|
The API key. |
Source code in src/zenml/client.py
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|
get_artifact(name_id_or_prefix, project=None, hydrate=False)
Get an artifact by name, id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the artifact to get. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
ArtifactResponse
|
The artifact. |
Source code in src/zenml/client.py
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|
get_artifact_version(name_id_or_prefix, version=None, project=None, hydrate=True)
Get an artifact version by ID or artifact name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
Either the ID of the artifact version or the name of the artifact. |
required |
version
|
Optional[str]
|
The version of the artifact to get. Only used if
|
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ArtifactVersionResponse
|
The artifact version. |
Source code in src/zenml/client.py
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|
get_authorized_device(id_or_prefix, allow_id_prefix_match=True, hydrate=True)
Get an authorized device by id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
Union[UUID, str]
|
The ID or ID prefix of the authorized device. |
required |
allow_id_prefix_match
|
bool
|
If True, allow matching by ID prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
OAuthDeviceResponse
|
The requested authorized device. |
Raises:
Type | Description |
---|---|
KeyError
|
If no authorized device is found with the given ID or prefix. |
Source code in src/zenml/client.py
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|
get_build(id_or_prefix, project=None, hydrate=True)
Get a build by id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
Union[str, UUID]
|
The id or id prefix of the build. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
PipelineBuildResponse
|
The build. |
Raises:
Type | Description |
---|---|
KeyError
|
If no build was found for the given id or prefix. |
ZenKeyError
|
If multiple builds were found that match the given id or prefix. |
Source code in src/zenml/client.py
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|
get_code_repository(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)
Get a code repository by name, id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or ID prefix of the code repository. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
CodeRepositoryResponse
|
The code repository. |
Source code in src/zenml/client.py
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|
get_deployment(id_or_prefix, project=None, hydrate=True)
Get a deployment by id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
Union[str, UUID]
|
The id or id prefix of the deployment. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
PipelineDeploymentResponse
|
The deployment. |
Raises:
Type | Description |
---|---|
KeyError
|
If no deployment was found for the given id or prefix. |
ZenKeyError
|
If multiple deployments were found that match the given id or prefix. |
Source code in src/zenml/client.py
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|
get_event_source(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)
Get an event source by name, ID or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, ID or prefix of the stack. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
EventSourceResponse
|
The event_source. |
Source code in src/zenml/client.py
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|
get_flavor(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)
Get a stack component flavor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
str
|
The name, ID or prefix to the id of the flavor to get. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
FlavorResponse
|
The stack component flavor. |
Source code in src/zenml/client.py
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|
get_flavor_by_name_and_type(name, component_type)
Fetches a registered flavor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
component_type
|
StackComponentType
|
The type of the component to fetch. |
required |
name
|
str
|
The name of the flavor to fetch. |
required |
Returns:
Type | Description |
---|---|
FlavorResponse
|
The registered flavor. |
Raises:
Type | Description |
---|---|
KeyError
|
If no flavor exists for the given type and name. |
Source code in src/zenml/client.py
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|
get_flavors_by_type(component_type)
Fetches the list of flavor for a stack component type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
component_type
|
StackComponentType
|
The type of the component to fetch. |
required |
Returns:
Type | Description |
---|---|
Page[FlavorResponse]
|
The list of flavors. |
Source code in src/zenml/client.py
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|
get_instance()
classmethod
Return the Client singleton instance.
Returns:
Type | Description |
---|---|
Optional[Client]
|
The Client singleton instance or None, if the Client hasn't |
Optional[Client]
|
been initialized yet. |
Source code in src/zenml/client.py
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|
get_model(model_name_or_id, project=None, hydrate=True, bypass_lazy_loader=False)
Get an existing model from Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Union[str, UUID]
|
name or id of the model to be retrieved. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
bypass_lazy_loader
|
bool
|
Whether to bypass the lazy loader. |
False
|
Returns:
Type | Description |
---|---|
ModelResponse
|
The model of interest. |
Source code in src/zenml/client.py
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|
get_model_version(model_name_or_id=None, model_version_name_or_number_or_id=None, project=None, hydrate=True)
Get an existing model version from Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Optional[Union[str, UUID]]
|
name or id of the model containing the model version. |
None
|
model_version_name_or_number_or_id
|
Optional[Union[str, int, ModelStages, UUID]]
|
name, id, stage or number of the model version to be retrieved. If skipped - latest version is retrieved. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ModelVersionResponse
|
The model version of interest. |
Raises:
Type | Description |
---|---|
RuntimeError
|
In case method inputs don't adhere to restrictions. |
KeyError
|
In case no model version with the identifiers exists. |
ValueError
|
In case retrieval is attempted using non UUID model version identifier and no model identifier provided. |
Source code in src/zenml/client.py
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|
get_pipeline(name_id_or_prefix, project=None, hydrate=True)
Get a pipeline by name, id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or ID prefix of the pipeline. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
PipelineResponse
|
The pipeline. |
Source code in src/zenml/client.py
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|
get_pipeline_run(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)
Gets a pipeline run by name, ID, or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
Name, ID, or prefix of the pipeline run. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
PipelineRunResponse
|
The pipeline run. |
Source code in src/zenml/client.py
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|
get_project(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)
Gets a project.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Optional[Union[UUID, str]]
|
The name or ID of the project. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ProjectResponse
|
The project |
Source code in src/zenml/client.py
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|
get_run_step(step_run_id, hydrate=True)
Get a step run by ID.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
step_run_id
|
UUID
|
The ID of the step run to get. |
required |
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
StepRunResponse
|
The step run. |
Source code in src/zenml/client.py
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|
get_run_template(name_id_or_prefix, project=None, hydrate=True)
Get a run template.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
Name/ID/ID prefix of the template to get. |
required |
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
RunTemplateResponse
|
The run template. |
Source code in src/zenml/client.py
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|
get_schedule(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)
Get a schedule by name, id or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix of the schedule. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ScheduleResponse
|
The schedule. |
Source code in src/zenml/client.py
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|
get_secret(name_id_or_prefix, private=None, allow_partial_name_match=True, allow_partial_id_match=True, hydrate=True)
Get a secret.
Get a secret identified by a name, ID or prefix of the name or ID and optionally a scope.
If a private status is not provided, privately scoped secrets will be searched for first, followed by publicly scoped secrets. When a name or prefix is used instead of a UUID value, each scope is first searched for an exact match, then for a ID prefix or name substring match before moving on to the next scope.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix to the id of the secret to get. |
required |
private
|
Optional[bool]
|
Whether the secret is private. If not set, all secrets will be searched for, prioritizing privately scoped secrets. |
None
|
allow_partial_name_match
|
bool
|
If True, allow partial name matches. |
True
|
allow_partial_id_match
|
bool
|
If True, allow partial ID matches. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
SecretResponse
|
The secret. |
Raises:
Type | Description |
---|---|
KeyError
|
If no secret is found. |
ZenKeyError
|
If multiple secrets are found. |
NotImplementedError
|
If centralized secrets management is not enabled. |
Source code in src/zenml/client.py
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|
get_secret_by_name_and_private_status(name, private=None, hydrate=True)
Fetches a registered secret with a given name and optional private status.
This is a version of get_secret that restricts the search to a given name and an optional private status, without doing any prefix or UUID matching.
If no private status is provided, the search will be done first for private secrets, then for public secrets.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the secret to get. |
required |
private
|
Optional[bool]
|
The private status of the secret to get. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
SecretResponse
|
The registered secret. |
Raises:
Type | Description |
---|---|
KeyError
|
If no secret exists for the given name in the given scope. |
Source code in src/zenml/client.py
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|
get_service(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True, type=None, project=None)
Gets a service.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name or ID of the service. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
type
|
Optional[str]
|
The type of the service. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ServiceResponse
|
The Service |
Source code in src/zenml/client.py
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|
get_service_account(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)
Gets a service account.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name or ID of the service account. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ServiceAccountResponse
|
The ServiceAccount |
Source code in src/zenml/client.py
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|
get_service_connector(name_id_or_prefix, allow_name_prefix_match=True, load_secrets=False, hydrate=True)
Fetches a registered service connector.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The id of the service connector to fetch. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
load_secrets
|
bool
|
If True, load the secrets for the service connector. |
False
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ServiceConnectorResponse
|
The registered service connector. |
Source code in src/zenml/client.py
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|
get_service_connector_client(name_id_or_prefix, resource_type=None, resource_id=None, verify=False)
Get the client side of a service connector instance to use with a local client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the service connector to use. |
required |
resource_type
|
Optional[str]
|
The type of the resource to connect to. If not provided, the resource type from the service connector configuration will be used. |
None
|
resource_id
|
Optional[str]
|
The ID of a particular resource instance to configure
the local client to connect to. If the connector instance is
already configured with a resource ID that is not the same or
equivalent to the one requested, a |
None
|
verify
|
bool
|
Whether to verify that the service connector configuration and credentials can be used to gain access to the resource. |
False
|
Returns:
Type | Description |
---|---|
ServiceConnector
|
The client side of the indicated service connector instance that can |
ServiceConnector
|
be used to connect to the resource locally. |
Source code in src/zenml/client.py
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|
get_service_connector_type(connector_type)
Returns the requested service connector type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
connector_type
|
str
|
the service connector type identifier. |
required |
Returns:
Type | Description |
---|---|
ServiceConnectorTypeModel
|
The requested service connector type. |
Source code in src/zenml/client.py
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|
get_settings(hydrate=True)
Get the server settings.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ServerSettingsResponse
|
The server settings. |
Source code in src/zenml/client.py
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|
get_stack(name_id_or_prefix=None, allow_name_prefix_match=True, hydrate=True)
Get a stack by name, ID or prefix.
If no name, ID or prefix is provided, the active stack is returned.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Optional[Union[UUID, str]]
|
The name, ID or prefix of the stack. |
None
|
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
StackResponse
|
The stack. |
Source code in src/zenml/client.py
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|
get_stack_component(component_type, name_id_or_prefix=None, allow_name_prefix_match=True, hydrate=True)
Fetches a registered stack component.
If the name_id_or_prefix is provided, it will try to fetch the component with the corresponding identifier. If not, it will try to fetch the active component of the given type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
component_type
|
StackComponentType
|
The type of the component to fetch |
required |
name_id_or_prefix
|
Optional[Union[str, UUID]]
|
The id of the component to fetch. |
None
|
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
ComponentResponse
|
The registered stack component. |
Raises:
Type | Description |
---|---|
KeyError
|
If no name_id_or_prefix is provided and no such component is part of the active stack. |
Source code in src/zenml/client.py
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|
get_tag(tag_name_or_id, hydrate=True)
Get an existing tag.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tag_name_or_id
|
Union[str, UUID]
|
name or id of the tag to be retrieved. |
required |
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
TagResponse
|
The tag of interest. |
Source code in src/zenml/client.py
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|
get_trigger(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)
Get a trigger by name, ID or prefix.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, ID or prefix of the trigger. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
TriggerResponse
|
The trigger. |
Source code in src/zenml/client.py
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|
get_trigger_execution(trigger_execution_id, hydrate=True)
Get a trigger execution by ID.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
trigger_execution_id
|
UUID
|
The ID of the trigger execution to get. |
required |
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
TriggerExecutionResponse
|
The trigger execution. |
Source code in src/zenml/client.py
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|
get_user(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)
Gets a user.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name or ID of the user. |
required |
allow_name_prefix_match
|
bool
|
If True, allow matching by name prefix. |
True
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
True
|
Returns:
Type | Description |
---|---|
UserResponse
|
The User |
Source code in src/zenml/client.py
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|
initialize(root=None)
staticmethod
Initializes a new ZenML repository at the given path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root
|
Optional[Path]
|
The root directory where the repository should be created. If None, the current working directory is used. |
None
|
Raises:
Type | Description |
---|---|
InitializationException
|
If the root directory already contains a ZenML repository. |
Source code in src/zenml/client.py
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|
is_inside_repository(file_path)
staticmethod
Returns whether a file is inside the active ZenML repository.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
file_path
|
str
|
A file path. |
required |
Returns:
Type | Description |
---|---|
bool
|
True if the file is inside the active ZenML repository, False |
bool
|
otherwise. |
Source code in src/zenml/client.py
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|
is_repository_directory(path)
staticmethod
Checks whether a ZenML client exists at the given path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
Path
|
The path to check. |
required |
Returns:
Type | Description |
---|---|
bool
|
True if a ZenML client exists at the given path, |
bool
|
False otherwise. |
Source code in src/zenml/client.py
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|
list_actions(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, flavor=None, action_type=None, project=None, user=None, hydrate=False)
List actions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of the action to filter by. |
None
|
created
|
Optional[datetime]
|
Use to filter by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
name
|
Optional[str]
|
The name of the action to filter by. |
None
|
flavor
|
Optional[str]
|
The flavor of the action to filter by. |
None
|
action_type
|
Optional[str]
|
The type of the action to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ActionResponse]
|
A page of actions. |
Source code in src/zenml/client.py
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|
list_api_keys(service_account_name_id_or_prefix, sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, description=None, active=None, last_login=None, last_rotated=None, hydrate=False)
List all API keys.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the service account to list the API keys for. |
required |
sort_by
|
str
|
The column to sort by. |
'created'
|
page
|
int
|
The page of items. |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages. |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or]. |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of the API key to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation. |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering. |
None
|
name
|
Optional[str]
|
The name of the API key to filter by. |
None
|
description
|
Optional[str]
|
The description of the API key to filter by. |
None
|
active
|
Optional[bool]
|
Whether the API key is active or not. |
None
|
last_login
|
Optional[Union[datetime, str]]
|
The last time the API key was used. |
None
|
last_rotated
|
Optional[Union[datetime, str]]
|
The last time the API key was rotated. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[APIKeyResponse]
|
A page of API keys matching the filter description. |
Source code in src/zenml/client.py
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|
list_artifact_versions(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, artifact=None, name=None, version=None, version_number=None, artifact_store_id=None, type=None, data_type=None, uri=None, materializer=None, project=None, model_version_id=None, only_unused=False, has_custom_name=None, user=None, model=None, pipeline_run=None, run_metadata=None, tag=None, tags=None, hydrate=False)
Get a list of artifact versions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of artifact version to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
artifact
|
Optional[Union[str, UUID]]
|
The name or ID of the artifact to filter by. |
None
|
name
|
Optional[str]
|
The name of the artifact to filter by. |
None
|
version
|
Optional[Union[str, int]]
|
The version of the artifact to filter by. |
None
|
version_number
|
Optional[int]
|
The version number of the artifact to filter by. |
None
|
artifact_store_id
|
Optional[Union[str, UUID]]
|
The id of the artifact store to filter by. |
None
|
type
|
Optional[ArtifactType]
|
The type of the artifact to filter by. |
None
|
data_type
|
Optional[str]
|
The data type of the artifact to filter by. |
None
|
uri
|
Optional[str]
|
The uri of the artifact to filter by. |
None
|
materializer
|
Optional[str]
|
The materializer of the artifact to filter by. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
model_version_id
|
Optional[Union[str, UUID]]
|
Filter by model version ID. |
None
|
only_unused
|
Optional[bool]
|
Only return artifact versions that are not used in any pipeline runs. |
False
|
has_custom_name
|
Optional[bool]
|
Filter artifacts with/without custom names. |
None
|
tag
|
Optional[str]
|
A tag to filter by. |
None
|
tags
|
Optional[List[str]]
|
Tags to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name or ID. |
None
|
model
|
Optional[Union[UUID, str]]
|
Filter by model name or ID. |
None
|
pipeline_run
|
Optional[Union[UUID, str]]
|
Filter by pipeline run name or ID. |
None
|
run_metadata
|
Optional[List[str]]
|
Filter by run metadata. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ArtifactVersionResponse]
|
A list of artifact versions. |
Source code in src/zenml/client.py
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|
list_artifacts(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, has_custom_name=None, user=None, project=None, hydrate=False, tag=None, tags=None)
Get a list of artifacts.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of artifact to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
The name of the artifact to filter by. |
None
|
has_custom_name
|
Optional[bool]
|
Filter artifacts with/without custom names. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name or ID. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
tag
|
Optional[str]
|
Filter artifacts by tag. |
None
|
tags
|
Optional[List[str]]
|
Tags to filter by. |
None
|
Returns:
Type | Description |
---|---|
Page[ArtifactResponse]
|
A list of artifacts. |
Source code in src/zenml/client.py
4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 |
|
list_authorized_devices(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, expires=None, client_id=None, status=None, trusted_device=None, user=None, failed_auth_attempts=None, last_login=None, hydrate=False)
List all authorized devices.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by. |
'created'
|
page
|
int
|
The page of items. |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages. |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or]. |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of the code repository to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation. |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering. |
None
|
expires
|
Optional[Union[datetime, str]]
|
Use the expiration date for filtering. |
None
|
client_id
|
Union[UUID, str, None]
|
Use the client id for filtering. |
None
|
status
|
Union[OAuthDeviceStatus, str, None]
|
Use the status for filtering. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
trusted_device
|
Union[bool, str, None]
|
Use the trusted device flag for filtering. |
None
|
failed_auth_attempts
|
Union[int, str, None]
|
Use the failed auth attempts for filtering. |
None
|
last_login
|
Optional[Union[datetime, str, None]]
|
Use the last login date for filtering. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[OAuthDeviceResponse]
|
A page of authorized devices matching the filter. |
Source code in src/zenml/client.py
6792 6793 6794 6795 6796 6797 6798 6799 6800 6801 6802 6803 6804 6805 6806 6807 6808 6809 6810 6811 6812 6813 6814 6815 6816 6817 6818 6819 6820 6821 6822 6823 6824 6825 6826 6827 6828 6829 6830 6831 6832 6833 6834 6835 6836 6837 6838 6839 6840 6841 6842 6843 6844 6845 6846 6847 6848 6849 6850 6851 6852 |
|
list_builds(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, project=None, user=None, pipeline_id=None, stack_id=None, container_registry_id=None, is_local=None, contains_code=None, zenml_version=None, python_version=None, checksum=None, stack_checksum=None, duration=None, hydrate=False)
List all builds.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of build to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
pipeline_id
|
Optional[Union[str, UUID]]
|
The id of the pipeline to filter by. |
None
|
stack_id
|
Optional[Union[str, UUID]]
|
The id of the stack to filter by. |
None
|
container_registry_id
|
Optional[Union[UUID, str]]
|
The id of the container registry to filter by. |
None
|
is_local
|
Optional[bool]
|
Use to filter local builds. |
None
|
contains_code
|
Optional[bool]
|
Use to filter builds that contain code. |
None
|
zenml_version
|
Optional[str]
|
The version of ZenML to filter by. |
None
|
python_version
|
Optional[str]
|
The Python version to filter by. |
None
|
checksum
|
Optional[str]
|
The build checksum to filter by. |
None
|
stack_checksum
|
Optional[str]
|
The stack checksum to filter by. |
None
|
duration
|
Optional[Union[int, str]]
|
The duration of the build in seconds to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[PipelineBuildResponse]
|
A page with builds fitting the filter description |
Source code in src/zenml/client.py
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|
list_code_repositories(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, project=None, user=None, hydrate=False)
List all code repositories.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by. |
'created'
|
page
|
int
|
The page of items. |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages. |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or]. |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of the code repository to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation. |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering. |
None
|
name
|
Optional[str]
|
The name of the code repository to filter by. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[CodeRepositoryResponse]
|
A page of code repositories matching the filter description. |
Source code in src/zenml/client.py
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|
list_deployments(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, project=None, user=None, pipeline_id=None, stack_id=None, build_id=None, template_id=None, hydrate=False)
List all deployments.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of build to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
pipeline_id
|
Optional[Union[str, UUID]]
|
The id of the pipeline to filter by. |
None
|
stack_id
|
Optional[Union[str, UUID]]
|
The id of the stack to filter by. |
None
|
build_id
|
Optional[Union[str, UUID]]
|
The id of the build to filter by. |
None
|
template_id
|
Optional[Union[str, UUID]]
|
The ID of the template to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[PipelineDeploymentResponse]
|
A page with deployments fitting the filter description |
Source code in src/zenml/client.py
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|
list_event_sources(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, flavor=None, event_source_type=None, project=None, user=None, hydrate=False)
Lists all event_sources.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of event_sources to filter by. |
None
|
created
|
Optional[datetime]
|
Use to filter by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
name
|
Optional[str]
|
The name of the event_source to filter by. |
None
|
flavor
|
Optional[str]
|
The flavor of the event_source to filter by. |
None
|
event_source_type
|
Optional[str]
|
The subtype of the event_source to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[EventSourceResponse]
|
A page of event_sources. |
Source code in src/zenml/client.py
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|
list_flavors(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, type=None, integration=None, user=None, hydrate=False)
Fetches all the flavor models.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of flavors to filter by. |
None
|
created
|
Optional[datetime]
|
Use to flavors by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
name
|
Optional[str]
|
The name of the flavor to filter by. |
None
|
type
|
Optional[str]
|
The type of the flavor to filter by. |
None
|
integration
|
Optional[str]
|
The integration of the flavor to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[FlavorResponse]
|
A list of all the flavor models. |
Source code in src/zenml/client.py
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|
list_model_version_artifact_links(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, model_version_id=None, artifact_version_id=None, artifact_name=None, only_data_artifacts=None, only_model_artifacts=None, only_deployment_artifacts=None, has_custom_name=None, user=None, hydrate=False)
Get model version to artifact links by filter in Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
model_version_id
|
Optional[Union[UUID, str]]
|
Use the model version id for filtering |
None
|
artifact_version_id
|
Optional[Union[UUID, str]]
|
Use the artifact id for filtering |
None
|
artifact_name
|
Optional[str]
|
Use the artifact name for filtering |
None
|
only_data_artifacts
|
Optional[bool]
|
Use to filter by data artifacts |
None
|
only_model_artifacts
|
Optional[bool]
|
Use to filter by model artifacts |
None
|
only_deployment_artifacts
|
Optional[bool]
|
Use to filter by deployment artifacts |
None
|
has_custom_name
|
Optional[bool]
|
Filter artifacts with/without custom names. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ModelVersionArtifactResponse]
|
A page of all model version to artifact links. |
Source code in src/zenml/client.py
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|
list_model_version_pipeline_run_links(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, model_version_id=None, pipeline_run_id=None, pipeline_run_name=None, user=None, hydrate=False)
Get all model version to pipeline run links by filter.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
model_version_id
|
Optional[Union[UUID, str]]
|
Use the model version id for filtering |
None
|
pipeline_run_id
|
Optional[Union[UUID, str]]
|
Use the pipeline run id for filtering |
None
|
pipeline_run_name
|
Optional[str]
|
Use the pipeline run name for filtering |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name or ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response |
False
|
Returns:
Type | Description |
---|---|
Page[ModelVersionPipelineRunResponse]
|
A page of all model version to pipeline run links. |
Source code in src/zenml/client.py
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|
list_model_versions(model_name_or_id, sort_by='number', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, name=None, id=None, number=None, stage=None, run_metadata=None, user=None, hydrate=False, tag=None, tags=None, project=None)
Get model versions by filter from Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Union[str, UUID]
|
name or id of the model containing the model version. |
required |
sort_by
|
str
|
The column to sort by |
'number'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
name or id of the model version. |
None
|
id
|
Optional[Union[UUID, str]]
|
id of the model version. |
None
|
number
|
Optional[int]
|
number of the model version. |
None
|
stage
|
Optional[Union[str, ModelStages]]
|
stage of the model version. |
None
|
run_metadata
|
Optional[List[str]]
|
run metadata of the model version. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
tag
|
Optional[str]
|
The tag to filter by. |
None
|
tags
|
Optional[List[str]]
|
Tags to filter by. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
Page[ModelVersionResponse]
|
A page object with all model versions. |
Source code in src/zenml/client.py
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|
list_models(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, name=None, id=None, user=None, project=None, hydrate=False, tag=None, tags=None)
Get models by filter from Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
The name of the model to filter by. |
None
|
id
|
Optional[Union[UUID, str]]
|
The id of the model to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
tag
|
Optional[str]
|
The tag of the model to filter by. |
None
|
tags
|
Optional[List[str]]
|
Tags to filter by. |
None
|
Returns:
Type | Description |
---|---|
Page[ModelResponse]
|
A page object with all models. |
Source code in src/zenml/client.py
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|
list_pipeline_runs(sort_by='desc:created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, project=None, pipeline_id=None, pipeline_name=None, stack_id=None, schedule_id=None, build_id=None, deployment_id=None, code_repository_id=None, template_id=None, model_version_id=None, orchestrator_run_id=None, status=None, start_time=None, end_time=None, unlisted=None, templatable=None, tag=None, tags=None, user=None, run_metadata=None, pipeline=None, code_repository=None, model=None, stack=None, stack_component=None, hydrate=False)
List all pipeline runs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'desc:created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
The id of the runs to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
pipeline_id
|
Optional[Union[str, UUID]]
|
The id of the pipeline to filter by. |
None
|
pipeline_name
|
Optional[str]
|
DEPRECATED. Use |
None
|
stack_id
|
Optional[Union[str, UUID]]
|
The id of the stack to filter by. |
None
|
schedule_id
|
Optional[Union[str, UUID]]
|
The id of the schedule to filter by. |
None
|
build_id
|
Optional[Union[str, UUID]]
|
The id of the build to filter by. |
None
|
deployment_id
|
Optional[Union[str, UUID]]
|
The id of the deployment to filter by. |
None
|
code_repository_id
|
Optional[Union[str, UUID]]
|
The id of the code repository to filter by. |
None
|
template_id
|
Optional[Union[str, UUID]]
|
The ID of the template to filter by. |
None
|
model_version_id
|
Optional[Union[str, UUID]]
|
The ID of the model version to filter by. |
None
|
orchestrator_run_id
|
Optional[str]
|
The run id of the orchestrator to filter by. |
None
|
name
|
Optional[str]
|
The name of the run to filter by. |
None
|
status
|
Optional[str]
|
The status of the pipeline run |
None
|
start_time
|
Optional[Union[datetime, str]]
|
The start_time for the pipeline run |
None
|
end_time
|
Optional[Union[datetime, str]]
|
The end_time for the pipeline run |
None
|
unlisted
|
Optional[bool]
|
If the runs should be unlisted or not. |
None
|
templatable
|
Optional[bool]
|
If the runs should be templatable or not. |
None
|
tag
|
Optional[str]
|
Tag to filter by. |
None
|
tags
|
Optional[List[str]]
|
Tags to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
The name/ID of the user to filter by. |
None
|
run_metadata
|
Optional[List[str]]
|
The run_metadata of the run to filter by. |
None
|
pipeline
|
Optional[Union[UUID, str]]
|
The name/ID of the pipeline to filter by. |
None
|
code_repository
|
Optional[Union[UUID, str]]
|
Filter by code repository name/ID. |
None
|
model
|
Optional[Union[UUID, str]]
|
Filter by model name/ID. |
None
|
stack
|
Optional[Union[UUID, str]]
|
Filter by stack name/ID. |
None
|
stack_component
|
Optional[Union[UUID, str]]
|
Filter by stack component name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[PipelineRunResponse]
|
A page with Pipeline Runs fitting the filter description |
Source code in src/zenml/client.py
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|
list_pipelines(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, latest_run_status=None, project=None, user=None, tag=None, tags=None, hydrate=False)
List all pipelines.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of pipeline to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
The name of the pipeline to filter by. |
None
|
latest_run_status
|
Optional[str]
|
Filter by the status of the latest run of a pipeline. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
The name/ID of the user to filter by. |
None
|
tag
|
Optional[str]
|
Tag to filter by. |
None
|
tags
|
Optional[List[str]]
|
Tags to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[PipelineResponse]
|
A page with Pipeline fitting the filter description |
Source code in src/zenml/client.py
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|
list_projects(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, display_name=None, hydrate=False)
List all projects.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the project ID to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
Use the project name for filtering |
None
|
display_name
|
Optional[str]
|
Use the project display name for filtering |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ProjectResponse]
|
Page of projects |
Source code in src/zenml/client.py
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|
list_run_steps(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, cache_key=None, code_hash=None, status=None, start_time=None, end_time=None, pipeline_run_id=None, deployment_id=None, original_step_run_id=None, project=None, user=None, model_version_id=None, model=None, run_metadata=None, hydrate=False)
List all pipelines.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of runs to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
start_time
|
Optional[Union[datetime, str]]
|
Use to filter by the time when the step started running |
None
|
end_time
|
Optional[Union[datetime, str]]
|
Use to filter by the time when the step finished running |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
pipeline_run_id
|
Optional[Union[str, UUID]]
|
The id of the pipeline run to filter by. |
None
|
deployment_id
|
Optional[Union[str, UUID]]
|
The id of the deployment to filter by. |
None
|
original_step_run_id
|
Optional[Union[str, UUID]]
|
The id of the original step run to filter by. |
None
|
model_version_id
|
Optional[Union[str, UUID]]
|
The ID of the model version to filter by. |
None
|
model
|
Optional[Union[UUID, str]]
|
Filter by model name/ID. |
None
|
name
|
Optional[str]
|
The name of the step run to filter by. |
None
|
cache_key
|
Optional[str]
|
The cache key of the step run to filter by. |
None
|
code_hash
|
Optional[str]
|
The code hash of the step run to filter by. |
None
|
status
|
Optional[str]
|
The name of the run to filter by. |
None
|
run_metadata
|
Optional[List[str]]
|
Filter by run metadata. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[StepRunResponse]
|
A page with Pipeline fitting the filter description |
Source code in src/zenml/client.py
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|
list_run_templates(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, id=None, name=None, hidden=False, tag=None, project=None, pipeline_id=None, build_id=None, stack_id=None, code_repository_id=None, user=None, pipeline=None, stack=None, hydrate=False)
Get a page of run templates.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by. |
'created'
|
page
|
int
|
The page of items. |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages. |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or]. |
AND
|
created
|
Optional[Union[datetime, str]]
|
Filter by the creation date. |
None
|
updated
|
Optional[Union[datetime, str]]
|
Filter by the last updated date. |
None
|
id
|
Optional[Union[UUID, str]]
|
Filter by run template ID. |
None
|
name
|
Optional[str]
|
Filter by run template name. |
None
|
hidden
|
Optional[bool]
|
Filter by run template hidden status. |
False
|
tag
|
Optional[str]
|
Filter by run template tags. |
None
|
project
|
Optional[Union[str, UUID]]
|
Filter by project name/ID. |
None
|
pipeline_id
|
Optional[Union[str, UUID]]
|
Filter by pipeline ID. |
None
|
build_id
|
Optional[Union[str, UUID]]
|
Filter by build ID. |
None
|
stack_id
|
Optional[Union[str, UUID]]
|
Filter by stack ID. |
None
|
code_repository_id
|
Optional[Union[str, UUID]]
|
Filter by code repository ID. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
pipeline
|
Optional[Union[UUID, str]]
|
Filter by pipeline name/ID. |
None
|
stack
|
Optional[Union[UUID, str]]
|
Filter by stack name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[RunTemplateResponse]
|
A page of run templates. |
Source code in src/zenml/client.py
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|
list_schedules(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, project=None, user=None, pipeline_id=None, orchestrator_id=None, active=None, cron_expression=None, start_time=None, end_time=None, interval_second=None, catchup=None, hydrate=False, run_once_start_time=None)
List schedules.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of stacks to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
The name of the stack to filter by. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
pipeline_id
|
Optional[Union[str, UUID]]
|
The id of the pipeline to filter by. |
None
|
orchestrator_id
|
Optional[Union[str, UUID]]
|
The id of the orchestrator to filter by. |
None
|
active
|
Optional[Union[str, bool]]
|
Use to filter by active status. |
None
|
cron_expression
|
Optional[str]
|
Use to filter by cron expression. |
None
|
start_time
|
Optional[Union[datetime, str]]
|
Use to filter by start time. |
None
|
end_time
|
Optional[Union[datetime, str]]
|
Use to filter by end time. |
None
|
interval_second
|
Optional[int]
|
Use to filter by interval second. |
None
|
catchup
|
Optional[Union[str, bool]]
|
Use to filter by catchup. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
run_once_start_time
|
Optional[Union[datetime, str]]
|
Use to filter by run once start time. |
None
|
Returns:
Type | Description |
---|---|
Page[ScheduleResponse]
|
A list of schedules. |
Source code in src/zenml/client.py
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|
list_secrets(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, private=None, user=None, hydrate=False)
Fetches all the secret models.
The returned secrets do not contain the secret values. To get the
secret values, use get_secret
individually for each secret.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of secrets to filter by. |
None
|
created
|
Optional[datetime]
|
Use to secrets by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
The name of the secret to filter by. |
None
|
private
|
Optional[bool]
|
The private status of the secret to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[SecretResponse]
|
A list of all the secret models without the secret values. |
Raises:
Type | Description |
---|---|
NotImplementedError
|
If centralized secrets management is not enabled. |
Source code in src/zenml/client.py
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|
list_secrets_by_private_status(private, hydrate=False)
Fetches the list of secrets with a given private status.
The returned secrets do not contain the secret values. To get the
secret values, use get_secret
individually for each secret.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
private
|
bool
|
The private status of the secrets to search for. |
required |
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[SecretResponse]
|
The list of secrets in the given scope without the secret values. |
Source code in src/zenml/client.py
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|
list_service_accounts(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, description=None, active=None, hydrate=False)
List all service accounts.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of stacks to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
Use the service account name for filtering |
None
|
description
|
Optional[str]
|
Use the service account description for filtering |
None
|
active
|
Optional[bool]
|
Use the service account active status for filtering |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ServiceAccountResponse]
|
The list of service accounts matching the filter description. |
Source code in src/zenml/client.py
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|
list_service_connector_resources(connector_type=None, resource_type=None, resource_id=None)
List resources that can be accessed by service connectors.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
connector_type
|
Optional[str]
|
The type of service connector to filter by. |
None
|
resource_type
|
Optional[str]
|
The type of resource to filter by. |
None
|
resource_id
|
Optional[str]
|
The ID of a particular resource instance to filter by. |
None
|
Returns:
Type | Description |
---|---|
List[ServiceConnectorResourcesModel]
|
The matching list of resources that available service |
List[ServiceConnectorResourcesModel]
|
connectors have access to. |
Source code in src/zenml/client.py
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|
list_service_connector_types(connector_type=None, resource_type=None, auth_method=None)
Get a list of service connector types.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
connector_type
|
Optional[str]
|
Filter by connector type. |
None
|
resource_type
|
Optional[str]
|
Filter by resource type. |
None
|
auth_method
|
Optional[str]
|
Filter by authentication method. |
None
|
Returns:
Type | Description |
---|---|
List[ServiceConnectorTypeModel]
|
List of service connector types. |
Source code in src/zenml/client.py
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|
list_service_connectors(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, connector_type=None, auth_method=None, resource_type=None, resource_id=None, user=None, labels=None, secret_id=None, hydrate=False)
Lists all registered service connectors.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
The id of the service connector to filter by. |
None
|
created
|
Optional[datetime]
|
Filter service connectors by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
connector_type
|
Optional[str]
|
Use the service connector type for filtering |
None
|
auth_method
|
Optional[str]
|
Use the service connector auth method for filtering |
None
|
resource_type
|
Optional[str]
|
Filter service connectors by the resource type that they can give access to. |
None
|
resource_id
|
Optional[str]
|
Filter service connectors by the resource id that they can give access to. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
name
|
Optional[str]
|
The name of the service connector to filter by. |
None
|
labels
|
Optional[Dict[str, Optional[str]]]
|
The labels of the service connector to filter by. |
None
|
secret_id
|
Optional[Union[str, UUID]]
|
Filter by the id of the secret that is referenced by the service connector. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ServiceConnectorResponse]
|
A page of service connectors. |
Source code in src/zenml/client.py
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|
list_services(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, type=None, flavor=None, user=None, project=None, hydrate=False, running=None, service_name=None, pipeline_name=None, pipeline_run_id=None, pipeline_step_name=None, model_version_id=None, config=None)
List all services.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of services to filter by. |
None
|
created
|
Optional[datetime]
|
Use to filter by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
type
|
Optional[str]
|
Use the service type for filtering |
None
|
flavor
|
Optional[str]
|
Use the service flavor for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
running
|
Optional[bool]
|
Use the running status for filtering |
None
|
pipeline_name
|
Optional[str]
|
Use the pipeline name for filtering |
None
|
service_name
|
Optional[str]
|
Use the service name or model name for filtering |
None
|
pipeline_step_name
|
Optional[str]
|
Use the pipeline step name for filtering |
None
|
model_version_id
|
Optional[Union[str, UUID]]
|
Use the model version id for filtering |
None
|
config
|
Optional[Dict[str, Any]]
|
Use the config for filtering |
None
|
pipeline_run_id
|
Optional[str]
|
Use the pipeline run id for filtering |
None
|
Returns:
Type | Description |
---|---|
Page[ServiceResponse]
|
The Service response page. |
Source code in src/zenml/client.py
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|
list_stack_components(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, flavor=None, type=None, connector_id=None, stack_id=None, user=None, hydrate=False)
Lists all registered stack components.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of component to filter by. |
None
|
created
|
Optional[datetime]
|
Use to component by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
flavor
|
Optional[str]
|
Use the component flavor for filtering |
None
|
type
|
Optional[str]
|
Use the component type for filtering |
None
|
connector_id
|
Optional[Union[str, UUID]]
|
The id of the connector to filter by. |
None
|
stack_id
|
Optional[Union[str, UUID]]
|
The id of the stack to filter by. |
None
|
name
|
Optional[str]
|
The name of the component to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
The ID of name of the user to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[ComponentResponse]
|
A page of stack components. |
Source code in src/zenml/client.py
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|
list_stacks(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, description=None, component_id=None, user=None, component=None, hydrate=False)
Lists all stacks.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of stacks to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
description
|
Optional[str]
|
Use the stack description for filtering |
None
|
component_id
|
Optional[Union[str, UUID]]
|
The id of the component to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
The name/ID of the user to filter by. |
None
|
component
|
Optional[Union[UUID, str]]
|
The name/ID of the component to filter by. |
None
|
name
|
Optional[str]
|
The name of the stack to filter by. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[StackResponse]
|
A page of stacks. |
Source code in src/zenml/client.py
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|
list_tags(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, user=None, created=None, updated=None, name=None, color=None, exclusive=None, resource_type=None, hydrate=False)
Get tags by filter.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by. |
'created'
|
page
|
int
|
The page of items. |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or]. |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of stacks to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Use the user to filter by. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation. |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering. |
None
|
name
|
Optional[str]
|
The name of the tag. |
None
|
color
|
Optional[Union[str, ColorVariants]]
|
The color of the tag. |
None
|
exclusive
|
Optional[bool]
|
Flag indicating whether the tag is exclusive. |
None
|
resource_type
|
Optional[Union[str, TaggableResourceTypes]]
|
Filter tags associated with a specific resource type. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[TagResponse]
|
A page of all tags. |
Source code in src/zenml/client.py
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|
list_trigger_executions(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, trigger_id=None, user=None, project=None, hydrate=False)
List all trigger executions matching the given filter criteria.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by. |
'created'
|
page
|
int
|
The page of items. |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages. |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or]. |
AND
|
trigger_id
|
Optional[UUID]
|
ID of the trigger to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
project
|
Optional[Union[UUID, str]]
|
Filter by project name/ID. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[TriggerExecutionResponse]
|
A list of all trigger executions matching the filter criteria. |
Source code in src/zenml/client.py
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|
list_triggers(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, event_source_id=None, action_id=None, event_source_flavor=None, event_source_subtype=None, action_flavor=None, action_subtype=None, project=None, user=None, hydrate=False)
Lists all triggers.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of triggers to filter by. |
None
|
created
|
Optional[datetime]
|
Use to filter by time of creation |
None
|
updated
|
Optional[datetime]
|
Use the last updated date for filtering |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
user
|
Optional[Union[UUID, str]]
|
Filter by user name/ID. |
None
|
name
|
Optional[str]
|
The name of the trigger to filter by. |
None
|
event_source_id
|
Optional[UUID]
|
The event source associated with the trigger. |
None
|
action_id
|
Optional[UUID]
|
The action associated with the trigger. |
None
|
event_source_flavor
|
Optional[str]
|
Flavor of the event source associated with the trigger. |
None
|
event_source_subtype
|
Optional[str]
|
Type of the event source associated with the trigger. |
None
|
action_flavor
|
Optional[str]
|
Flavor of the action associated with the trigger. |
None
|
action_subtype
|
Optional[str]
|
Type of the action associated with the trigger. |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[TriggerResponse]
|
A page of triggers. |
Source code in src/zenml/client.py
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|
list_users(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, external_user_id=None, created=None, updated=None, name=None, full_name=None, email=None, active=None, email_opted_in=None, hydrate=False)
List all users.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sort_by
|
str
|
The column to sort by |
'created'
|
page
|
int
|
The page of items |
PAGINATION_STARTING_PAGE
|
size
|
int
|
The maximum size of all pages |
PAGE_SIZE_DEFAULT
|
logical_operator
|
LogicalOperators
|
Which logical operator to use [and, or] |
AND
|
id
|
Optional[Union[UUID, str]]
|
Use the id of stacks to filter by. |
None
|
external_user_id
|
Optional[str]
|
Use the external user id for filtering. |
None
|
created
|
Optional[Union[datetime, str]]
|
Use to filter by time of creation |
None
|
updated
|
Optional[Union[datetime, str]]
|
Use the last updated date for filtering |
None
|
name
|
Optional[str]
|
Use the username for filtering |
None
|
full_name
|
Optional[str]
|
Use the user full name for filtering |
None
|
email
|
Optional[str]
|
Use the user email for filtering |
None
|
active
|
Optional[bool]
|
User the user active status for filtering |
None
|
email_opted_in
|
Optional[bool]
|
Use the user opt in status for filtering |
None
|
hydrate
|
bool
|
Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. |
False
|
Returns:
Type | Description |
---|---|
Page[UserResponse]
|
The User |
Source code in src/zenml/client.py
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|
login_service_connector(name_id_or_prefix, resource_type=None, resource_id=None, **kwargs)
Use a service connector to authenticate a local client/SDK.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the service connector to use. |
required |
resource_type
|
Optional[str]
|
The type of the resource to connect to. If not provided, the resource type from the service connector configuration will be used. |
None
|
resource_id
|
Optional[str]
|
The ID of a particular resource instance to configure
the local client to connect to. If the connector instance is
already configured with a resource ID that is not the same or
equivalent to the one requested, a |
None
|
kwargs
|
Any
|
Additional implementation specific keyword arguments to use to configure the client. |
{}
|
Returns:
Type | Description |
---|---|
ServiceConnector
|
The service connector client instance that was used to configure the |
ServiceConnector
|
local client. |
Source code in src/zenml/client.py
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|
prune_artifacts(only_versions=True, delete_from_artifact_store=False, project=None)
Delete all unused artifacts and artifact versions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
only_versions
|
bool
|
Only delete artifact versions, keeping artifacts |
True
|
delete_from_artifact_store
|
bool
|
Delete data from artifact metadata |
False
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Source code in src/zenml/client.py
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|
restore_secrets(ignore_errors=False, delete_secrets=False)
Restore all secrets from the configured backup secrets store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ignore_errors
|
bool
|
Whether to ignore individual errors during the restore process and attempt to restore all secrets. |
False
|
delete_secrets
|
bool
|
Whether to delete the secrets that have been successfully restored from the backup secrets store. Setting this flag effectively moves all secrets from the backup secrets store to the primary secrets store. |
False
|
Source code in src/zenml/client.py
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|
rotate_api_key(service_account_name_id_or_prefix, name_id_or_prefix, retain_period_minutes=0, set_key=False)
Rotate an API key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the service account to rotate the API key for. |
required |
name_id_or_prefix
|
Union[UUID, str]
|
Name, ID or prefix of the API key to update. |
required |
retain_period_minutes
|
int
|
The number of minutes to retain the old API key for. If set to 0, the old API key will be invalidated. |
0
|
set_key
|
bool
|
Whether to set the rotated API key as the active API key. |
False
|
Returns:
Type | Description |
---|---|
APIKeyResponse
|
The updated API key. |
Source code in src/zenml/client.py
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|
set_active_project(project_name_or_id)
Set the project for the local client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_name_or_id
|
Union[str, UUID]
|
The name or ID of the project to set active. |
required |
Returns:
Type | Description |
---|---|
ProjectResponse
|
The model of the active project. |
Source code in src/zenml/client.py
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|
set_api_key(key)
Configure the client with an API key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
key
|
str
|
The API key to use. |
required |
Raises:
Type | Description |
---|---|
NotImplementedError
|
If the client is not connected to a ZenML server. |
Source code in src/zenml/client.py
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|
trigger_pipeline(pipeline_name_or_id=None, run_configuration=None, config_path=None, template_id=None, stack_name_or_id=None, synchronous=False, project=None)
Trigger a pipeline from the server.
Usage examples: * Run the latest runnable template for a pipeline:
Client().trigger_pipeline(pipeline_name_or_id=<NAME>)
- Run the latest runnable template for a pipeline on a specific stack:
Client().trigger_pipeline(
pipeline_name_or_id=<NAME>,
stack_name_or_id=<STACK_NAME_OR_ID>
)
- Run a specific template:
Client().trigger_pipeline(template_id=<ID>)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
pipeline_name_or_id
|
Union[str, UUID, None]
|
Name or ID of the pipeline. If this is specified, the latest runnable template for this pipeline will be used for the run (Runnable here means that the build associated with the template is for a remote stack without any custom flavor stack components). If not given, a template ID that should be run needs to be specified. |
None
|
run_configuration
|
Union[PipelineRunConfiguration, Dict[str, Any], None]
|
Configuration for the run. Either this or a path to a config file can be specified. |
None
|
config_path
|
Optional[str]
|
Path to a YAML configuration file. This file will be
parsed as a |
None
|
template_id
|
Optional[UUID]
|
ID of the template to run. Either this or a pipeline can be specified. |
None
|
stack_name_or_id
|
Union[str, UUID, None]
|
Name or ID of the stack on which to run the pipeline. If not specified, this method will try to find a runnable template on any stack. |
None
|
synchronous
|
bool
|
If |
False
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Raises:
Type | Description |
---|---|
RuntimeError
|
If triggering the pipeline failed. |
Returns:
Type | Description |
---|---|
PipelineRunResponse
|
Model of the pipeline run. |
Source code in src/zenml/client.py
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|
update_action(name_id_or_prefix, name=None, description=None, configuration=None, service_account_id=None, auth_window=None, project=None)
Update an action.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the action to update. |
required |
name
|
Optional[str]
|
The new name of the action. |
None
|
description
|
Optional[str]
|
The new description of the action. |
None
|
configuration
|
Optional[Dict[str, Any]]
|
The new configuration of the action. |
None
|
service_account_id
|
Optional[UUID]
|
The new service account that is used to execute the action. |
None
|
auth_window
|
Optional[int]
|
The new time window in minutes for which the service account is authorized to execute the action. Set this to 0 to authorize the service account indefinitely (not recommended). |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ActionResponse
|
The updated action. |
Source code in src/zenml/client.py
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|
update_api_key(service_account_name_id_or_prefix, name_id_or_prefix, name=None, description=None, active=None)
Update an API key.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the service account to update the API key for. |
required |
name_id_or_prefix
|
Union[UUID, str]
|
Name, ID or prefix of the API key to update. |
required |
name
|
Optional[str]
|
New name of the API key. |
None
|
description
|
Optional[str]
|
New description of the API key. |
None
|
active
|
Optional[bool]
|
Whether the API key is active or not. |
None
|
Returns:
Type | Description |
---|---|
APIKeyResponse
|
The updated API key. |
Source code in src/zenml/client.py
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|
update_artifact(name_id_or_prefix, new_name=None, add_tags=None, remove_tags=None, has_custom_name=None, project=None)
Update an artifact.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the artifact to update. |
required |
new_name
|
Optional[str]
|
The new name of the artifact. |
None
|
add_tags
|
Optional[List[str]]
|
Tags to add to the artifact. |
None
|
remove_tags
|
Optional[List[str]]
|
Tags to remove from the artifact. |
None
|
has_custom_name
|
Optional[bool]
|
Whether the artifact has a custom name. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ArtifactResponse
|
The updated artifact. |
Source code in src/zenml/client.py
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|
update_artifact_version(name_id_or_prefix, version=None, add_tags=None, remove_tags=None, project=None)
Update an artifact version.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, ID or prefix of the artifact to update. |
required |
version
|
Optional[str]
|
The version of the artifact to update. Only used if
|
None
|
add_tags
|
Optional[List[str]]
|
Tags to add to the artifact version. |
None
|
remove_tags
|
Optional[List[str]]
|
Tags to remove from the artifact version. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ArtifactVersionResponse
|
The updated artifact version. |
Source code in src/zenml/client.py
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|
update_authorized_device(id_or_prefix, locked=None)
Update an authorized device.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id_or_prefix
|
Union[UUID, str]
|
The ID or ID prefix of the authorized device. |
required |
locked
|
Optional[bool]
|
Whether to lock or unlock the authorized device. |
None
|
Returns:
Type | Description |
---|---|
OAuthDeviceResponse
|
The updated authorized device. |
Source code in src/zenml/client.py
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|
update_code_repository(name_id_or_prefix, name=None, description=None, logo_url=None, config=None, project=None)
Update a code repository.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
Name, ID or prefix of the code repository to update. |
required |
name
|
Optional[str]
|
New name of the code repository. |
None
|
description
|
Optional[str]
|
New description of the code repository. |
None
|
logo_url
|
Optional[str]
|
New logo URL of the code repository. |
None
|
config
|
Optional[Dict[str, Any]]
|
New configuration options for the code repository. Will
be used to update the existing configuration values. To remove
values from the existing configuration, set the value for that
key to |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
CodeRepositoryResponse
|
The updated code repository. |
Source code in src/zenml/client.py
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|
update_event_source(name_id_or_prefix, name=None, description=None, configuration=None, rotate_secret=None, is_active=None, project=None)
Updates an event_source.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the event_source to update. |
required |
name
|
Optional[str]
|
the new name of the event_source. |
None
|
description
|
Optional[str]
|
the new description of the event_source. |
None
|
configuration
|
Optional[Dict[str, Any]]
|
The event source configuration. |
None
|
rotate_secret
|
Optional[bool]
|
Allows rotating of secret, if true, the response will contain the new secret value |
None
|
is_active
|
Optional[bool]
|
Optional[bool] = Allows for activation/deactivating the event source |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
EventSourceResponse
|
The model of the updated event_source. |
Raises:
Type | Description |
---|---|
EntityExistsError
|
If the event_source name is already taken. |
Source code in src/zenml/client.py
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|
update_model(model_name_or_id, name=None, license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, add_tags=None, remove_tags=None, save_models_to_registry=None, project=None)
Updates an existing model in Model Control Plane.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Union[str, UUID]
|
name or id of the model to be deleted. |
required |
name
|
Optional[str]
|
The name of the model. |
None
|
license
|
Optional[str]
|
The license under which the model is created. |
None
|
description
|
Optional[str]
|
The description of the model. |
None
|
audience
|
Optional[str]
|
The target audience of the model. |
None
|
use_cases
|
Optional[str]
|
The use cases of the model. |
None
|
limitations
|
Optional[str]
|
The known limitations of the model. |
None
|
trade_offs
|
Optional[str]
|
The tradeoffs of the model. |
None
|
ethics
|
Optional[str]
|
The ethical implications of the model. |
None
|
add_tags
|
Optional[List[str]]
|
Tags to add to the model. |
None
|
remove_tags
|
Optional[List[str]]
|
Tags to remove from to the model. |
None
|
save_models_to_registry
|
Optional[bool]
|
Whether to save the model to the registry. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ModelResponse
|
The updated model. |
Source code in src/zenml/client.py
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|
update_model_version(model_name_or_id, version_name_or_id, stage=None, force=False, name=None, description=None, add_tags=None, remove_tags=None, project=None)
Get all model versions by filter.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name_or_id
|
Union[str, UUID]
|
The name or ID of the model containing model version. |
required |
version_name_or_id
|
Union[str, UUID]
|
The name or ID of model version to be updated. |
required |
stage
|
Optional[Union[str, ModelStages]]
|
Target model version stage to be set. |
None
|
force
|
bool
|
Whether existing model version in target stage should be silently archived or an error should be raised. |
False
|
name
|
Optional[str]
|
Target model version name to be set. |
None
|
description
|
Optional[str]
|
Target model version description to be set. |
None
|
add_tags
|
Optional[List[str]]
|
Tags to add to the model version. |
None
|
remove_tags
|
Optional[List[str]]
|
Tags to remove from to the model version. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
ModelVersionResponse
|
An updated model version. |
Source code in src/zenml/client.py
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|
update_project(name_id_or_prefix, new_name=None, new_display_name=None, new_description=None)
Update a project.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Optional[Union[UUID, str]]
|
Name, ID or prefix of the project to update. |
required |
new_name
|
Optional[str]
|
New name of the project. |
None
|
new_display_name
|
Optional[str]
|
New display name of the project. |
None
|
new_description
|
Optional[str]
|
New description of the project. |
None
|
Returns:
Type | Description |
---|---|
ProjectResponse
|
The updated project. |
Source code in src/zenml/client.py
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|
update_run_template(name_id_or_prefix, name=None, description=None, hidden=None, add_tags=None, remove_tags=None, project=None)
Update a run template.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
Name/ID/ID prefix of the template to update. |
required |
name
|
Optional[str]
|
The new name of the run template. |
None
|
description
|
Optional[str]
|
The new description of the run template. |
None
|
hidden
|
Optional[bool]
|
The new hidden status of the run template. |
None
|
add_tags
|
Optional[List[str]]
|
Tags to add to the run template. |
None
|
remove_tags
|
Optional[List[str]]
|
Tags to remove from the run template. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
RunTemplateResponse
|
The updated run template. |
Source code in src/zenml/client.py
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|
update_secret(name_id_or_prefix, private=None, new_name=None, update_private=None, add_or_update_values=None, remove_values=None)
Updates a secret.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name, id or prefix of the id for the secret to update. |
required |
private
|
Optional[bool]
|
The private status of the secret to update. |
None
|
new_name
|
Optional[str]
|
The new name of the secret. |
None
|
update_private
|
Optional[bool]
|
New value used to update the private status of the secret. |
None
|
add_or_update_values
|
Optional[Dict[str, str]]
|
The values to add or update. |
None
|
remove_values
|
Optional[List[str]]
|
The values to remove. |
None
|
Returns:
Type | Description |
---|---|
SecretResponse
|
The updated secret. |
Raises:
Type | Description |
---|---|
KeyError
|
If trying to remove a value that doesn't exist. |
ValueError
|
If a key is provided in both add_or_update_values and remove_values. |
Source code in src/zenml/client.py
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|
update_server_settings(updated_name=None, updated_logo_url=None, updated_enable_analytics=None, updated_enable_announcements=None, updated_enable_updates=None, updated_onboarding_state=None)
Update the server settings.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
updated_name
|
Optional[str]
|
Updated name for the server. |
None
|
updated_logo_url
|
Optional[str]
|
Updated logo URL for the server. |
None
|
updated_enable_analytics
|
Optional[bool]
|
Updated value whether to enable analytics for the server. |
None
|
updated_enable_announcements
|
Optional[bool]
|
Updated value whether to display announcements about ZenML. |
None
|
updated_enable_updates
|
Optional[bool]
|
Updated value whether to display updates about ZenML. |
None
|
updated_onboarding_state
|
Optional[Dict[str, Any]]
|
Updated onboarding state for the server. |
None
|
Returns:
Type | Description |
---|---|
ServerSettingsResponse
|
The updated server settings. |
Source code in src/zenml/client.py
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|
update_service(id, name=None, service_source=None, admin_state=None, status=None, endpoint=None, labels=None, prediction_url=None, health_check_url=None, model_version_id=None)
Update a service.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id
|
UUID
|
The ID of the service to update. |
required |
name
|
Optional[str]
|
The new name of the service. |
None
|
admin_state
|
Optional[ServiceState]
|
The new admin state of the service. |
None
|
status
|
Optional[Dict[str, Any]]
|
The new status of the service. |
None
|
endpoint
|
Optional[Dict[str, Any]]
|
The new endpoint of the service. |
None
|
service_source
|
Optional[str]
|
The new service source of the service. |
None
|
labels
|
Optional[Dict[str, str]]
|
The new labels of the service. |
None
|
prediction_url
|
Optional[str]
|
The new prediction url of the service. |
None
|
health_check_url
|
Optional[str]
|
The new health check url of the service. |
None
|
model_version_id
|
Optional[UUID]
|
The new model version id of the service. |
None
|
Returns:
Type | Description |
---|---|
ServiceResponse
|
The updated service. |
Source code in src/zenml/client.py
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|
update_service_account(name_id_or_prefix, updated_name=None, description=None, active=None)
Update a service account.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name or ID of the service account to update. |
required |
updated_name
|
Optional[str]
|
The new name of the service account. |
None
|
description
|
Optional[str]
|
The new description of the service account. |
None
|
active
|
Optional[bool]
|
The new active status of the service account. |
None
|
Returns:
Type | Description |
---|---|
ServiceAccountResponse
|
The updated service account. |
Source code in src/zenml/client.py
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|
update_service_connector(name_id_or_prefix, name=None, auth_method=None, resource_type=None, configuration=None, resource_id=None, description=None, expires_at=None, expires_skew_tolerance=None, expiration_seconds=None, labels=None, verify=True, list_resources=True, update=True)
Validate and/or register an updated service connector.
If the resource_type
, resource_id
and expiration_seconds
parameters are set to their "empty" values (empty string for resource
type and resource ID, 0 for expiration seconds), the existing values
will be removed from the service connector. Setting them to None or
omitting them will not affect the existing values.
If supplied, the configuration
parameter is a full replacement of the
existing configuration rather than a partial update.
Labels can be updated or removed by setting the label value to None.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the service connector to update. |
required |
name
|
Optional[str]
|
The new name of the service connector. |
None
|
auth_method
|
Optional[str]
|
The new authentication method of the service connector. |
None
|
resource_type
|
Optional[str]
|
The new resource type for the service connector. If set to the empty string, the existing resource type will be removed. |
None
|
configuration
|
Optional[Dict[str, str]]
|
The new configuration of the service connector. If set, this needs to be a full replacement of the existing configuration rather than a partial update. |
None
|
resource_id
|
Optional[str]
|
The new resource id of the service connector. If set to the empty string, the existing resource ID will be removed. |
None
|
description
|
Optional[str]
|
The description of the service connector. |
None
|
expires_at
|
Optional[datetime]
|
The new UTC expiration time of the service connector. |
None
|
expires_skew_tolerance
|
Optional[int]
|
The allowed expiration skew for the service connector credentials. |
None
|
expiration_seconds
|
Optional[int]
|
The expiration time of the service connector. If set to 0, the existing expiration time will be removed. |
None
|
labels
|
Optional[Dict[str, Optional[str]]]
|
The service connector to update or remove. If a label value is set to None, the label will be removed. |
None
|
verify
|
bool
|
Whether to verify that the service connector configuration and credentials can be used to gain access to the resource. |
True
|
list_resources
|
bool
|
Whether to also list the resources that the service connector can give access to (if verify is True). |
True
|
update
|
bool
|
Whether to update the service connector or not. |
True
|
Returns:
Type | Description |
---|---|
Optional[Union[ServiceConnectorResponse, ServiceConnectorUpdate]]
|
The model of the registered service connector and the resources |
Optional[ServiceConnectorResourcesModel]
|
that the service connector can give access to (if verify is True). |
Raises:
Type | Description |
---|---|
AuthorizationException
|
If the service connector verification fails due to invalid credentials or insufficient permissions. |
Source code in src/zenml/client.py
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|
update_stack(name_id_or_prefix=None, name=None, stack_spec_file=None, labels=None, description=None, component_updates=None)
Updates a stack and its components.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Optional[Union[UUID, str]]
|
The name, id or prefix of the stack to update. |
None
|
name
|
Optional[str]
|
the new name of the stack. |
None
|
stack_spec_file
|
Optional[str]
|
path to the stack spec file. |
None
|
labels
|
Optional[Dict[str, Any]]
|
The new labels of the stack component. |
None
|
description
|
Optional[str]
|
the new description of the stack. |
None
|
component_updates
|
Optional[Dict[StackComponentType, List[Union[UUID, str]]]]
|
dictionary which maps stack component types to lists of new stack component names or ids. |
None
|
Returns:
Type | Description |
---|---|
StackResponse
|
The model of the updated stack. |
Raises:
Type | Description |
---|---|
EntityExistsError
|
If the stack name is already taken. |
Source code in src/zenml/client.py
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|
update_stack_component(name_id_or_prefix, component_type, name=None, configuration=None, labels=None, disconnect=None, connector_id=None, connector_resource_id=None)
Updates a stack component.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Optional[Union[UUID, str]]
|
The name, id or prefix of the stack component to update. |
required |
component_type
|
StackComponentType
|
The type of the stack component to update. |
required |
name
|
Optional[str]
|
The new name of the stack component. |
None
|
configuration
|
Optional[Dict[str, Any]]
|
The new configuration of the stack component. |
None
|
labels
|
Optional[Dict[str, Any]]
|
The new labels of the stack component. |
None
|
disconnect
|
Optional[bool]
|
Whether to disconnect the stack component from its service connector. |
None
|
connector_id
|
Optional[UUID]
|
The new connector id of the stack component. |
None
|
connector_resource_id
|
Optional[str]
|
The new connector resource id of the stack component. |
None
|
Returns:
Type | Description |
---|---|
ComponentResponse
|
The updated stack component. |
Raises:
Type | Description |
---|---|
EntityExistsError
|
If the new name is already taken. |
Source code in src/zenml/client.py
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|
update_tag(tag_name_or_id, name=None, exclusive=None, color=None)
Updates an existing tag.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tag_name_or_id
|
Union[str, UUID]
|
name or UUID of the tag to be updated. |
required |
name
|
Optional[str]
|
the name of the tag. |
None
|
exclusive
|
Optional[bool]
|
the boolean to decide whether the tag is an exclusive tag. An exclusive tag means that the tag can exist only for a single: - pipeline run within the scope of a pipeline - artifact version within the scope of an artifact - run template |
None
|
color
|
Optional[Union[str, ColorVariants]]
|
the color of the tag |
None
|
Returns:
Type | Description |
---|---|
TagResponse
|
The updated tag. |
Source code in src/zenml/client.py
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|
update_trigger(name_id_or_prefix, name=None, description=None, event_filter=None, is_active=None, project=None)
Updates a trigger.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the trigger to update. |
required |
name
|
Optional[str]
|
the new name of the trigger. |
None
|
description
|
Optional[str]
|
the new description of the trigger. |
None
|
event_filter
|
Optional[Dict[str, Any]]
|
The event filter configuration. |
None
|
is_active
|
Optional[bool]
|
Whether the trigger is active or not. |
None
|
project
|
Optional[Union[str, UUID]]
|
The project name/ID to filter by. |
None
|
Returns:
Type | Description |
---|---|
TriggerResponse
|
The model of the updated trigger. |
Raises:
Type | Description |
---|---|
EntityExistsError
|
If the trigger name is already taken. |
Source code in src/zenml/client.py
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|
update_user(name_id_or_prefix, updated_name=None, updated_full_name=None, updated_email=None, updated_email_opt_in=None, updated_password=None, old_password=None, updated_is_admin=None, updated_metadata=None, updated_default_project_id=None, active=None)
Update a user.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[str, UUID]
|
The name or ID of the user to update. |
required |
updated_name
|
Optional[str]
|
The new name of the user. |
None
|
updated_full_name
|
Optional[str]
|
The new full name of the user. |
None
|
updated_email
|
Optional[str]
|
The new email of the user. |
None
|
updated_email_opt_in
|
Optional[bool]
|
The new email opt-in status of the user. |
None
|
updated_password
|
Optional[str]
|
The new password of the user. |
None
|
old_password
|
Optional[str]
|
The old password of the user. Required for password update. |
None
|
updated_is_admin
|
Optional[bool]
|
Whether the user should be an admin. |
None
|
updated_metadata
|
Optional[Dict[str, Any]]
|
The new metadata for the user. |
None
|
updated_default_project_id
|
Optional[UUID]
|
The new default project ID for the user. |
None
|
active
|
Optional[bool]
|
Use to activate or deactivate the user. |
None
|
Returns:
Type | Description |
---|---|
UserResponse
|
The updated user. |
Raises:
Type | Description |
---|---|
ValidationError
|
If the old password is not provided when updating the password. |
Source code in src/zenml/client.py
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|
verify_service_connector(name_id_or_prefix, resource_type=None, resource_id=None, list_resources=True)
Verifies if a service connector has access to one or more resources.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name_id_or_prefix
|
Union[UUID, str]
|
The name, id or prefix of the service connector to verify. |
required |
resource_type
|
Optional[str]
|
The type of the resource for which to verify access. If not provided, the resource type from the service connector configuration will be used. |
None
|
resource_id
|
Optional[str]
|
The ID of the resource for which to verify access. If not provided, the resource ID from the service connector configuration will be used. |
None
|
list_resources
|
bool
|
Whether to list the resources that the service connector has access to. |
True
|
Returns:
Type | Description |
---|---|
ServiceConnectorResourcesModel
|
The list of resources that the service connector has access to, |
ServiceConnectorResourcesModel
|
scoped to the supplied resource type and ID, if provided. |
Raises:
Type | Description |
---|---|
AuthorizationException
|
If the service connector does not have access to the resources. |
Source code in src/zenml/client.py
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|
ClientConfiguration
Bases: FileSyncModel
Pydantic object used for serializing client configuration options.
Source code in src/zenml/client.py
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|
active_project
property
Get the active project for the local client.
Returns:
Type | Description |
---|---|
ProjectResponse
|
The active project. |
Raises:
Type | Description |
---|---|
RuntimeError
|
If no active project is set. |
set_active_project(project)
Set the project for the local client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project
|
ProjectResponse
|
The project to set active. |
required |
Source code in src/zenml/client.py
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|
set_active_stack(stack)
Set the stack for the local client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
stack
|
StackResponse
|
The stack to set active. |
required |
Source code in src/zenml/client.py
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|
ClientMetaClass
Bases: ABCMeta
Client singleton metaclass.
This metaclass is used to enforce a singleton instance of the Client class with the following additional properties:
- the singleton Client instance is created on first access to reflect the global configuration and local client configuration.
- the Client shouldn't be accessed from within pipeline steps (a warning is logged if this is attempted).
Source code in src/zenml/client.py
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|
__call__(*args, **kwargs)
Create or return the global Client instance.
If the Client constructor is called with custom arguments, the singleton functionality of the metaclass is bypassed: a new Client instance is created and returned immediately and without saving it as the global Client singleton.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
*args
|
Any
|
Positional arguments. |
()
|
**kwargs
|
Any
|
Keyword arguments. |
{}
|
Returns:
Name | Type | Description |
---|---|---|
Client |
Client
|
The global Client instance. |
Source code in src/zenml/client.py
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|
__init__(*args, **kwargs)
Initialize the Client class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
*args
|
Any
|
Positional arguments. |
()
|
**kwargs
|
Any
|
Keyword arguments. |
{}
|
Source code in src/zenml/client.py
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|
Code Repositories
Initialization of the ZenML code repository base abstraction.
BaseCodeRepository
Bases: ABC
Base class for code repositories.
Code repositories are used to connect to a remote code repository and store information about the repository, such as the URL, the owner, the repository name, and the host. They also provide methods to download files from the repository when a pipeline is run remotely.
Source code in src/zenml/code_repositories/base_code_repository.py
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|
config
property
Config class for Code Repository.
Returns:
Type | Description |
---|---|
BaseCodeRepositoryConfig
|
The config class. |
id
property
ID of the code repository.
Returns:
Type | Description |
---|---|
UUID
|
The ID of the code repository. |
name
property
Name of the code repository.
Returns:
Type | Description |
---|---|
str
|
The name of the code repository. |
requirements
property
Set of PyPI requirements for the repository.
Returns:
Type | Description |
---|---|
Set[str]
|
A set of PyPI requirements for the repository. |
__init__(id, name, config)
Initializes a code repository.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id
|
UUID
|
The ID of the code repository. |
required |
name
|
str
|
The name of the code repository. |
required |
config
|
Dict[str, Any]
|
The config of the code repository. |
required |
Source code in src/zenml/code_repositories/base_code_repository.py
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|
download_files(commit, directory, repo_sub_directory)
abstractmethod
Downloads files from the code repository to a local directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
commit
|
str
|
The commit hash to download files from. |
required |
directory
|
str
|
The directory to download files to. |
required |
repo_sub_directory
|
Optional[str]
|
The subdirectory in the repository to download files from. |
required |
Raises:
Type | Description |
---|---|
RuntimeError
|
If the download fails. |
Source code in src/zenml/code_repositories/base_code_repository.py
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|
from_model(model)
classmethod
Loads a code repository from a model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
CodeRepositoryResponse
|
The CodeRepositoryResponseModel to load from. |
required |
Returns:
Type | Description |
---|---|
BaseCodeRepository
|
The loaded code repository object. |
Source code in src/zenml/code_repositories/base_code_repository.py
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|
get_local_context(path)
abstractmethod
Gets a local repository context from a path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
The path to the local repository. |
required |
Returns:
Type | Description |
---|---|
Optional[LocalRepositoryContext]
|
The local repository context object. |
Source code in src/zenml/code_repositories/base_code_repository.py
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|
login()
abstractmethod
Logs into the code repository.
This method is called when the code repository is initialized. It should be used to authenticate with the code repository.
Raises:
Type | Description |
---|---|
RuntimeError
|
If the login fails. |
Source code in src/zenml/code_repositories/base_code_repository.py
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|
validate_config(config)
classmethod
Validate the code repository config.
This method should check that the config/credentials are valid and the configured repository exists.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
config
|
Dict[str, Any]
|
The configuration. |
required |
Source code in src/zenml/code_repositories/base_code_repository.py
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|
LocalRepositoryContext
Bases: ABC
Base class for local repository contexts.
This class is used to represent a local repository. It is used to track the current state of the repository and to provide information about the repository, such as the root path, the current commit, and whether the repository is dirty.
Source code in src/zenml/code_repositories/local_repository_context.py
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|
code_repository
property
current_commit
abstractmethod
property
Returns the current commit of the local repository.
Returns:
Type | Description |
---|---|
str
|
The current commit of the local repository. |
has_local_changes
abstractmethod
property
Returns whether the local repository has local changes.
A repository has local changes if it is dirty or there are some commits which have not been pushed yet.
Returns:
Type | Description |
---|---|
bool
|
Whether the local repository has local changes. |
is_dirty
abstractmethod
property
Returns whether the local repository is dirty.
A repository counts as dirty if it has any untracked or uncommitted changes.
Returns:
Type | Description |
---|---|
bool
|
Whether the local repository is dirty. |
root
abstractmethod
property
Returns the root path of the local repository.
Returns:
Type | Description |
---|---|
str
|
The root path of the local repository. |
__init__(code_repository)
Initializes a local repository context.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
code_repository
|
BaseCodeRepository
|
The code repository. |
required |
Source code in src/zenml/code_repositories/local_repository_context.py
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|
Config
The config
module contains classes and functions that manage user-specific configuration.
ZenML's configuration is stored in a file called
config.yaml
, located on the user's directory for configuration files.
(The exact location differs from operating system to operating system.)
The GlobalConfiguration
class is the main class in this module. It provides
a Pydantic configuration object that is used to store and retrieve
configuration. This GlobalConfiguration
object handles the serialization and
deserialization of the configuration options that are stored in the file in
order to persist the configuration across sessions.
DockerSettings
Bases: BaseSettings
Settings for building Docker images to run ZenML pipelines.
Build process:
- No
dockerfile
specified: If any of the options regarding requirements, environment variables or copying files require us to build an image, ZenML will build this image. Otherwise, theparent_image
will be used to run the pipeline. dockerfile
specified: ZenML will first build an image based on the specified Dockerfile. If any of the options regarding requirements, environment variables or copying files require an additional image built on top of that, ZenML will build a second image. If not, the image build from the specified Dockerfile will be used to run the pipeline.
Requirements installation order:
Depending on the configuration of this object, requirements will be
installed in the following order (each step optional):
- The packages installed in your local python environment
- The packages required by the stack unless this is disabled by setting
install_stack_requirements=False
.
- The packages specified via the required_integrations
- The packages specified via the requirements
attribute
Attributes:
Name | Type | Description |
---|---|---|
parent_image |
Optional[str]
|
Full name of the Docker image that should be used as the parent for the image that will be built. Defaults to a ZenML image built for the active Python and ZenML version. Additional notes:
* If you specify a custom image here, you need to make sure it has
ZenML installed.
* If this is a non-local image, the environment which is running
the pipeline and building the Docker image needs to be able to pull
this image.
* If a custom |
dockerfile |
Optional[str]
|
Path to a custom Dockerfile that should be built. Depending on the other values you specify in this object, the resulting image will be used directly to run your pipeline or ZenML will use it as a parent image to build on top of. See the general docstring of this class for more information. Additional notes:
* If you specify this, the |
build_context_root |
Optional[str]
|
Build context root for the Docker build, only used
when the |
parent_image_build_config |
Optional[DockerBuildConfig]
|
Configuration for the parent image build. |
skip_build |
bool
|
If set to |
prevent_build_reuse |
bool
|
Prevent the reuse of an existing build. |
target_repository |
Optional[str]
|
Name of the Docker repository to which the image should be pushed. This repository will be appended to the registry URI of the container registry of your stack and should therefore not include any registry. If not specified, the default repository name configured in the container registry stack component settings will be used. |
python_package_installer |
PythonPackageInstaller
|
The package installer to use for python packages. |
python_package_installer_args |
Dict[str, Any]
|
Arguments to pass to the python package installer. |
replicate_local_python_environment |
Optional[Union[List[str], PythonEnvironmentExportMethod]]
|
If not |
requirements |
Union[None, str, List[str]]
|
Path to a requirements file or a list of required pip
packages. During the image build, these requirements will be
installed using pip. If you need to use a different tool to
resolve and/or install your packages, please use a custom parent
image or specify a custom |
required_integrations |
List[str]
|
List of ZenML integrations that should be installed. All requirements for the specified integrations will be installed inside the Docker image. |
required_hub_plugins |
List[str]
|
DEPRECATED/UNUSED. |
install_stack_requirements |
bool
|
If |
apt_packages |
List[str]
|
APT packages to install inside the Docker image. |
environment |
Dict[str, Any]
|
Dictionary of environment variables to set inside the Docker image. |
build_config |
Optional[DockerBuildConfig]
|
Configuration for the main image build. |
user |
Optional[str]
|
If not |
allow_including_files_in_images |
bool
|
If |
allow_download_from_code_repository |
bool
|
If |
allow_download_from_artifact_store |
bool
|
If |
build_options |
Dict[str, Any]
|
DEPRECATED, use parent_image_build_config.build_options instead. |
dockerignore |
Optional[str]
|
DEPRECATED, use build_config.dockerignore instead. |
copy_files |
bool
|
DEPRECATED/UNUSED. |
copy_global_config |
bool
|
DEPRECATED/UNUSED. |
source_files |
Optional[str]
|
DEPRECATED. Use allow_including_files_in_images, allow_download_from_code_repository and allow_download_from_artifact_store instead. |
Source code in src/zenml/config/docker_settings.py
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|
ResourceSettings
Bases: BaseSettings
Hardware resource settings.
Attributes:
Name | Type | Description |
---|---|---|
cpu_count |
Optional[PositiveFloat]
|
The amount of CPU cores that should be configured. |
gpu_count |
Optional[NonNegativeInt]
|
The amount of GPUs that should be configured. |
memory |
Optional[str]
|
The amount of memory that should be configured. |
Source code in src/zenml/config/resource_settings.py
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|
empty
property
Returns if this object is "empty" (=no values configured) or not.
Returns:
Type | Description |
---|---|
bool
|
|
get_memory(unit=ByteUnit.GB)
Gets the memory configuration in a specific unit.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
unit
|
Union[str, ByteUnit]
|
The unit to which the memory should be converted. |
GB
|
Raises:
Type | Description |
---|---|
ValueError
|
If the memory string is invalid. |
Returns:
Type | Description |
---|---|
Optional[float]
|
The memory configuration converted to the requested unit, or None |
Optional[float]
|
if no memory was configured. |
Source code in src/zenml/config/resource_settings.py
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|
StepRetryConfig
Bases: StrictBaseModel
Retry configuration for a step.
Delay is an integer (specified in seconds).
Source code in src/zenml/config/retry_config.py
19 20 21 22 23 24 25 26 27 |
|
Console
Constants
ZenML constants.
handle_bool_env_var(var, default=False)
Converts normal env var to boolean.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
var
|
str
|
The environment variable to convert. |
required |
default
|
bool
|
The default value to return if the env var is not set. |
False
|
Returns:
Type | Description |
---|---|
bool
|
The converted value. |
Source code in src/zenml/constants.py
96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
|
handle_int_env_var(var, default=0)
Converts normal env var to int.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
var
|
str
|
The environment variable to convert. |
required |
default
|
int
|
The default value to return if the env var is not set. |
0
|
Returns:
Type | Description |
---|---|
int
|
The converted value. |
Source code in src/zenml/constants.py
114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
|
handle_json_env_var(var, expected_type, default=None)
Converts a json env var into a Python object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
var
|
str
|
The environment variable to convert. |
required |
default
|
Optional[List[str]]
|
The default value to return if the env var is not set. |
None
|
expected_type
|
Type[T]
|
The type of the expected Python object. |
required |
Returns:
Type | Description |
---|---|
Any
|
The converted list value. |
Raises:
Type | Description |
---|---|
TypeError
|
In case the value of the environment variable is not of a valid type. |
Source code in src/zenml/constants.py
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|
is_false_string_value(value)
Checks if the given value is a string representation of 'False'.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
value
|
Any
|
the value to check. |
required |
Returns:
Type | Description |
---|---|
bool
|
Whether the input value represents a string version of 'False'. |
Source code in src/zenml/constants.py
84 85 86 87 88 89 90 91 92 93 |
|
is_true_string_value(value)
Checks if the given value is a string representation of 'True'.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
value
|
Any
|
the value to check. |
required |
Returns:
Type | Description |
---|---|
bool
|
Whether the input value represents a string version of 'True'. |
Source code in src/zenml/constants.py
72 73 74 75 76 77 78 79 80 81 |
|
Container Registries
Initialization for ZenML's container registries module.
A container registry is a store for (Docker) containers. A ZenML workflow involving a container registry would automatically containerize your code to be transported across stacks running remotely. As part of the deployment to the cluster, the ZenML base image would be downloaded (from a cloud container registry) and used as the basis for the deployed 'run'.
For instance, when you are running a local container-based stack, you would therefore have a local container registry which stores the container images you create that bundle up your pipeline code. You could also use a remote container registry like the Elastic Container Registry at AWS in a more production setting.
AzureContainerRegistryFlavor
Bases: BaseContainerRegistryFlavor
Class for Azure Container Registry.
Source code in src/zenml/container_registries/azure_container_registry.py
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
|
docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
logo_url
property
A url to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
Name of the flavor.
Returns:
Type | Description |
---|---|
str
|
The name of the flavor. |
sdk_docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
service_connector_requirements
property
Service connector resource requirements for service connectors.
Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.
Returns:
Type | Description |
---|---|
Optional[ServiceConnectorRequirements]
|
Requirements for compatible service connectors, if a service |
Optional[ServiceConnectorRequirements]
|
connector is required for this flavor. |
BaseContainerRegistry
Bases: AuthenticationMixin
Base class for all ZenML container registries.
Source code in src/zenml/container_registries/base_container_registry.py
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|
config
property
Returns the BaseContainerRegistryConfig
config.
Returns:
Type | Description |
---|---|
BaseContainerRegistryConfig
|
The configuration. |
credentials
property
Username and password to authenticate with this container registry.
Returns:
Type | Description |
---|---|
Optional[Tuple[str, str]]
|
Tuple with username and password if this container registry |
Optional[Tuple[str, str]]
|
requires authentication, |
docker_client
property
Returns a Docker client for this container registry.
Returns:
Type | Description |
---|---|
DockerClient
|
The Docker client. |
Raises:
Type | Description |
---|---|
RuntimeError
|
If the connector does not return a Docker client. |
requires_authentication
property
Returns whether the container registry requires authentication.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
|
prepare_image_push(image_name)
Preparation before an image gets pushed.
Subclasses can overwrite this to do any necessary checks or preparations before an image gets pushed.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
image_name
|
str
|
Name of the docker image that will be pushed. |
required |
Source code in src/zenml/container_registries/base_container_registry.py
163 164 165 166 167 168 169 170 171 |
|
push_image(image_name)
Pushes a docker image.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
image_name
|
str
|
Name of the docker image that will be pushed. |
required |
Returns:
Type | Description |
---|---|
str
|
The Docker repository digest of the pushed image. |
Raises:
Type | Description |
---|---|
ValueError
|
If the image name is not associated with this container registry. |
Source code in src/zenml/container_registries/base_container_registry.py
173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
|
DefaultContainerRegistryFlavor
Bases: BaseContainerRegistryFlavor
Class for default ZenML container registries.
Source code in src/zenml/container_registries/default_container_registry.py
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|
docs_url
property
A URL to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
logo_url
property
A URL to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
Name of the flavor.
Returns:
Type | Description |
---|---|
str
|
The name of the flavor. |
sdk_docs_url
property
A URL to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
DockerHubContainerRegistryFlavor
Bases: BaseContainerRegistryFlavor
Class for DockerHub Container Registry.
Source code in src/zenml/container_registries/dockerhub_container_registry.py
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
|
docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
logo_url
property
A url to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
Name of the flavor.
Returns:
Type | Description |
---|---|
str
|
The name of the flavor. |
sdk_docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
service_connector_requirements
property
Service connector resource requirements for service connectors.
Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.
Returns:
Type | Description |
---|---|
Optional[ServiceConnectorRequirements]
|
Requirements for compatible service connectors, if a service |
Optional[ServiceConnectorRequirements]
|
connector is required for this flavor. |
GCPContainerRegistryFlavor
Bases: BaseContainerRegistryFlavor
Class for GCP Container Registry.
Source code in src/zenml/container_registries/gcp_container_registry.py
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
|
docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
logo_url
property
A url to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
Name of the flavor.
Returns:
Type | Description |
---|---|
str
|
The name of the flavor. |
sdk_docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
service_connector_requirements
property
Service connector resource requirements for service connectors.
Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.
Returns:
Type | Description |
---|---|
Optional[ServiceConnectorRequirements]
|
Requirements for compatible service connectors, if a service |
Optional[ServiceConnectorRequirements]
|
connector is required for this flavor. |
GitHubContainerRegistryFlavor
Bases: BaseContainerRegistryFlavor
Class for GitHub Container Registry.
Source code in src/zenml/container_registries/github_container_registry.py
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
|
docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
logo_url
property
A url to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
Name of the flavor.
Returns:
Type | Description |
---|---|
str
|
The name of the flavor. |
sdk_docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
Data Validators
Data validators are stack components responsible for data profiling and validation.
BaseDataValidator
Bases: StackComponent
Base class for all ZenML data validators.
Source code in src/zenml/data_validators/base_data_validator.py
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
|
config
property
Returns the config of this data validator.
Returns:
Type | Description |
---|---|
BaseDataValidatorConfig
|
The config of this data validator. |
data_profiling(dataset, comparison_dataset=None, profile_list=None, **kwargs)
Analyze one or more datasets and generate a data profile.
This method should be implemented by data validators that support analyzing a dataset and generating a data profile (e.g. schema, statistical summary, data distribution profile, validation rules, data drift reports etc.). The method should return a data profile object.
This method also accepts an optional second dataset argument to accommodate different categories of data profiling, e.g.:
- profiles generated from a single dataset: schema inference, validation rules inference, statistical profiles, data integrity reports
- differential profiles that need a second dataset for comparison: differential statistical profiles, data drift reports
Data validators that support generating multiple categories of data
profiles should also take in a profile_list
argument that lists the
subset of profiles to be generated. If not supplied, the behavior is
implementation specific, but it is recommended to provide a good default
(e.g. a single default data profile type may be generated and returned,
or all available data profiles may be generated and returned as a single
result).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset
|
Any
|
Target dataset to be profiled. |
required |
comparison_dataset
|
Optional[Any]
|
Optional second dataset to be used for data comparison profiles (e.g data drift reports). |
None
|
profile_list
|
Optional[Sequence[Any]]
|
Optional list identifying the categories of data profiles to be generated. |
None
|
**kwargs
|
Any
|
Implementation specific keyword arguments. |
{}
|
Raises:
Type | Description |
---|---|
NotImplementedError
|
if data profiling is not supported by this data validator. |
Source code in src/zenml/data_validators/base_data_validator.py
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
|
data_validation(dataset, comparison_dataset=None, check_list=None, **kwargs)
Run data validation checks on a dataset.
This method should be implemented by data validators that support running data quality checks an input dataset (e.g. data integrity checks, data drift checks).
This method also accepts an optional second dataset argument to accommodate different categories of data validation tests, e.g.:
- single dataset checks: data integrity checks (e.g. missing values, conflicting labels, mixed data types etc.)
- checks that compare two datasets: data drift checks (e.g. new labels, feature drift, label drift etc.)
Data validators that support running multiple categories of data
integrity checks should also take in a check_list
argument that
lists the subset of checks to be performed. If not supplied, the
behavior is implementation specific, but it is recommended to provide a
good default (e.g. a single default validation check may be performed,
or all available validation checks may be performed and their results
returned as a list of objects).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset
|
Any
|
Target dataset to be validated. |
required |
comparison_dataset
|
Optional[Any]
|
Optional second dataset to be used for data comparison checks (e.g data drift checks). |
None
|
check_list
|
Optional[Sequence[Any]]
|
Optional list identifying the data checks to be performed. |
None
|
**kwargs
|
Any
|
Implementation specific keyword arguments. |
{}
|
Raises:
Type | Description |
---|---|
NotImplementedError
|
if data validation is not supported by this data validator. |
Source code in src/zenml/data_validators/base_data_validator.py
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
|
get_active_data_validator()
classmethod
Get the data validator registered in the active stack.
Returns:
Type | Description |
---|---|
BaseDataValidator
|
The data validator registered in the active stack. |
Raises:
Type | Description |
---|---|
TypeError
|
if a data validator is not part of the active stack. |
Source code in src/zenml/data_validators/base_data_validator.py
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
|
model_validation(dataset, model, comparison_dataset=None, check_list=None, **kwargs)
Run model validation checks.
This method should be implemented by data validators that support running model validation checks (e.g. confusion matrix validation, performance reports, model error analyzes, etc).
Unlike data_validation
, model validation checks require that a model
be present as an active component during the validation process.
This method also accepts an optional second dataset argument to accommodate different categories of data validation tests, e.g.:
- single dataset tests: confusion matrix validation, performance reports, model error analyzes, etc
- model comparison tests: tests that identify changes in a model behavior by comparing how it performs on two different datasets.
Data validators that support running multiple categories of model
validation checks should also take in a check_list
argument that
lists the subset of checks to be performed. If not supplied, the
behavior is implementation specific, but it is recommended to provide a
good default (e.g. a single default validation check may be performed,
or all available validation checks may be performed and their results
returned as a list of objects).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset
|
Any
|
Target dataset to be validated. |
required |
model
|
Any
|
Target model to be validated. |
required |
comparison_dataset
|
Optional[Any]
|
Optional second dataset to be used for model comparison checks (e.g model performance comparison checks). |
None
|
check_list
|
Optional[Sequence[Any]]
|
Optional list identifying the model validation checks to be performed. |
None
|
**kwargs
|
Any
|
Implementation specific keyword arguments. |
{}
|
Raises:
Type | Description |
---|---|
NotImplementedError
|
if model validation is not supported by this data validator. |
Source code in src/zenml/data_validators/base_data_validator.py
166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
|
BaseDataValidatorFlavor
Bases: Flavor
Base class for data validator flavors.
Source code in src/zenml/data_validators/base_data_validator.py
217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
|
config_class
property
Config class for data validator.
Returns:
Type | Description |
---|---|
Type[BaseDataValidatorConfig]
|
Config class for data validator. |
implementation_class
property
Implementation for data validator.
Returns:
Type | Description |
---|---|
Type[BaseDataValidator]
|
Implementation for data validator. |
type
property
Entrypoints
Initializations for ZenML entrypoints module.
PipelineEntrypointConfiguration
Bases: BaseEntrypointConfiguration
Base class for entrypoint configurations that run an entire pipeline.
Source code in src/zenml/entrypoints/pipeline_entrypoint_configuration.py
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
|
run()
Prepares the environment and runs the configured pipeline.
Source code in src/zenml/entrypoints/pipeline_entrypoint_configuration.py
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
|
StepEntrypointConfiguration
Bases: BaseEntrypointConfiguration
Base class for entrypoint configurations that run a single step.
If an orchestrator needs to run steps in a separate process or environment (e.g. a docker container), this class can either be used directly or subclassed if custom behavior is necessary.
How to subclass:
Passing additional arguments to the entrypoint:
If you need to pass additional arguments to the entrypoint, there are
two methods that you need to implement:
* get_entrypoint_options()
: This method should return all
the options that are required in the entrypoint. Make sure to
include the result from the superclass method so the options
are complete.
* `get_entrypoint_arguments(...)`: This method should return
a list of arguments that should be passed to the entrypoint.
Make sure to include the result from the superclass method so
the arguments are complete.
You'll be able to access the argument values from `self.entrypoint_args`
inside your `StepEntrypointConfiguration` subclass.
How to use:
After you created your StepEntrypointConfiguration
subclass, you only
have to run the entrypoint somewhere. To do this, you should execute the
command returned by the get_entrypoint_command()
method with the
arguments returned by the get_entrypoint_arguments(...)
method.
Example:
class MyStepEntrypointConfiguration(StepEntrypointConfiguration):
...
class MyOrchestrator(BaseOrchestrator):
def prepare_or_run_pipeline(
self,
deployment: "PipelineDeployment",
stack: "Stack",
environment: Dict[str, str],
placeholder_run: Optional["PipelineRunResponse"] = None,
) -> Any:
...
cmd = MyStepEntrypointConfiguration.get_entrypoint_command()
for step_name, step in pipeline.steps.items():
...
args = MyStepEntrypointConfiguration.get_entrypoint_arguments(
step_name=step_name
)
# Run the command and pass it the arguments. Our example
# orchestrator here executes the entrypoint in a separate
# process, but in a real-world scenario you would probably run
# it inside a docker container or a different environment.
import subprocess
subprocess.check_call(cmd + args)
Source code in src/zenml/entrypoints/step_entrypoint_configuration.py
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
|
get_entrypoint_arguments(**kwargs)
classmethod
Gets all arguments that the entrypoint command should be called with.
The argument list should be something that
argparse.ArgumentParser.parse_args(...)
can handle (e.g.
["--some_option", "some_value"]
or ["--some_option=some_value"]
).
It needs to provide values for all options returned by the
get_entrypoint_options()
method of this class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs
|
Any
|
Kwargs, must include the step name. |
{}
|
Returns:
Type | Description |
---|---|
List[str]
|
The superclass arguments as well as arguments for the name of the |
List[str]
|
step to run. |
Source code in src/zenml/entrypoints/step_entrypoint_configuration.py
125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
|
get_entrypoint_options()
classmethod
Gets all options required for running with this configuration.
Returns:
Type | Description |
---|---|
Set[str]
|
The superclass options as well as an option for the name of the |
Set[str]
|
step to run. |
Source code in src/zenml/entrypoints/step_entrypoint_configuration.py
115 116 117 118 119 120 121 122 123 |
|
post_run(pipeline_name, step_name)
Does cleanup or post-processing after the step finished running.
Subclasses should overwrite this method if they need to run any additional code after the step execution.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
pipeline_name
|
str
|
Name of the parent pipeline of the step that was executed. |
required |
step_name
|
str
|
Name of the step that was executed. |
required |
Source code in src/zenml/entrypoints/step_entrypoint_configuration.py
99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
|
run()
Prepares the environment and runs the configured step.
Source code in src/zenml/entrypoints/step_entrypoint_configuration.py
150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
|
Enums
ZenML enums.
APITokenType
Bases: StrEnum
The API token type.
Source code in src/zenml/enums.py
242 243 244 245 246 |
|
AnalyticsEventSource
Bases: StrEnum
Enum to identify analytics events source.
Source code in src/zenml/enums.py
207 208 209 210 211 212 |
|
AnnotationTasks
Bases: StrEnum
Supported annotation tasks.
Source code in src/zenml/enums.py
183 184 185 186 187 188 189 |
|
ArtifactSaveType
Bases: StrEnum
All possible method types of how artifact versions can be saved.
Source code in src/zenml/enums.py
45 46 47 48 49 50 51 52 53 |
|
ArtifactType
Bases: StrEnum
All possible types an artifact can have.
Source code in src/zenml/enums.py
22 23 24 25 26 27 28 29 30 31 |
|
AuthScheme
Bases: StrEnum
The authentication scheme.
Source code in src/zenml/enums.py
215 216 217 218 219 220 221 |
|
CliCategories
Bases: StrEnum
All possible categories for CLI commands.
Note: The order of the categories is important. The same order is used to sort the commands in the CLI help output.
Source code in src/zenml/enums.py
167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
|
ColorVariants
Bases: StrEnum
All possible color variants for frontend.
Source code in src/zenml/enums.py
329 330 331 332 333 334 335 336 337 338 339 340 341 342 |
|
ContainerRegistryFlavor
Bases: StrEnum
Flavors of container registries.
Source code in src/zenml/enums.py
157 158 159 160 161 162 163 164 |
|
DatabaseBackupStrategy
Bases: StrEnum
All available database backup strategies.
Source code in src/zenml/enums.py
375 376 377 378 379 380 381 382 383 384 385 |
|
EnvironmentType
Bases: StrEnum
Enum for environment types.
Source code in src/zenml/enums.py
298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 |
|
ExecutionStatus
Bases: StrEnum
Enum that represents the current status of a step or pipeline run.
Source code in src/zenml/enums.py
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
|
is_finished
property
Whether the execution status refers to a finished execution.
Returns:
Type | Description |
---|---|
bool
|
Whether the execution status refers to a finished execution. |
GenericFilterOps
Bases: StrEnum
Ops for all filters for string values on list methods.
Source code in src/zenml/enums.py
249 250 251 252 253 254 255 256 257 258 259 260 261 262 |
|
LoggingLevels
Bases: Enum
Enum for logging levels.
Source code in src/zenml/enums.py
96 97 98 99 100 101 102 103 104 |
|
LogicalOperators
Bases: StrEnum
Logical Ops to use to combine filters on list methods.
Source code in src/zenml/enums.py
272 273 274 275 276 |
|
MetadataResourceTypes
Bases: StrEnum
All possible resource types for adding metadata.
Source code in src/zenml/enums.py
365 366 367 368 369 370 371 372 |
|
ModelStages
Bases: StrEnum
All possible stages of a Model Version.
Source code in src/zenml/enums.py
319 320 321 322 323 324 325 326 |
|
OAuthDeviceStatus
Bases: StrEnum
The OAuth device status.
Source code in src/zenml/enums.py
233 234 235 236 237 238 239 |
|
OAuthGrantTypes
Bases: StrEnum
The OAuth grant types.
Source code in src/zenml/enums.py
224 225 226 227 228 229 230 |
|
OnboardingStep
Bases: StrEnum
All onboarding steps.
Source code in src/zenml/enums.py
404 405 406 407 408 409 410 411 412 413 414 415 416 417 |
|
OperatingSystemType
Bases: StrEnum
Enum for OS types.
Source code in src/zenml/enums.py
279 280 281 282 283 284 |
|
PluginSubType
Bases: StrEnum
All possible types of Plugins.
Source code in src/zenml/enums.py
395 396 397 398 399 400 401 |
|
PluginType
Bases: StrEnum
All possible types of Plugins.
Source code in src/zenml/enums.py
388 389 390 391 392 |
|
ResponseUpdateStrategy
Bases: StrEnum
All available strategies to handle updated properties in the response.
Source code in src/zenml/enums.py
357 358 359 360 361 362 |
|
SecretValidationLevel
Bases: StrEnum
Secret validation levels.
Source code in src/zenml/enums.py
192 193 194 195 196 197 |
|
SecretsStoreType
Bases: StrEnum
Secrets Store Backend Types.
Source code in src/zenml/enums.py
145 146 147 148 149 150 151 152 153 154 |
|
ServerProviderType
Bases: StrEnum
ZenML server providers.
Source code in src/zenml/enums.py
200 201 202 203 204 |
|
ServiceState
Bases: StrEnum
Possible states for the service and service endpoint.
Source code in src/zenml/enums.py
428 429 430 431 432 433 434 435 436 |
|
SorterOps
Bases: StrEnum
Ops for all filters for string values on list methods.
Source code in src/zenml/enums.py
265 266 267 268 269 |
|
SourceContextTypes
Bases: StrEnum
Enum for event source types.
Source code in src/zenml/enums.py
287 288 289 290 291 292 293 294 295 |
|
StackComponentType
Bases: StrEnum
All possible types a StackComponent
can have.
Source code in src/zenml/enums.py
107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
|
plural
property
Returns the plural of the enum value.
Returns:
Type | Description |
---|---|
str
|
The plural of the enum value. |
StackDeploymentProvider
Bases: StrEnum
All possible stack deployment providers.
Source code in src/zenml/enums.py
420 421 422 423 424 425 |
|
StepRunInputArtifactType
Bases: StrEnum
All possible types of a step run input artifact.
Source code in src/zenml/enums.py
34 35 36 37 38 39 40 41 42 |
|
StoreType
Bases: StrEnum
Zen Store Backend Types.
Source code in src/zenml/enums.py
138 139 140 141 142 |
|
TaggableResourceTypes
Bases: StrEnum
All possible resource types for tagging.
Source code in src/zenml/enums.py
345 346 347 348 349 350 351 352 353 354 |
|
VisualizationType
Bases: StrEnum
All currently available visualization types.
Source code in src/zenml/enums.py
56 57 58 59 60 61 62 63 |
|
ZenMLServiceType
Bases: StrEnum
All possible types a service can have.
Source code in src/zenml/enums.py
66 67 68 69 70 |
|
Environment
Environment implementation.
Environment
Provides environment information.
Individual environment components can be registered separately to extend
the global Environment object with additional information (see
BaseEnvironmentComponent
).
Source code in src/zenml/environment.py
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 |
|
__init__()
Initializes an Environment instance.
Note: Environment is a singleton class, which means this method will
only get called once. All following Environment()
calls will return
the previously initialized instance.
Source code in src/zenml/environment.py
121 122 123 124 125 126 127 |
|
get_system_info()
staticmethod
Information about the operating system.
Returns:
Type | Description |
---|---|
Dict[str, str]
|
A dictionary containing information about the operating system. |
Source code in src/zenml/environment.py
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
|
in_bitbucket_ci()
staticmethod
If the current Python process is running in Bitbucket CI.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
CI, |
Source code in src/zenml/environment.py
326 327 328 329 330 331 332 333 334 |
|
in_ci()
staticmethod
If the current Python process is running in any CI.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
CI, |
Source code in src/zenml/environment.py
336 337 338 339 340 341 342 343 344 |
|
in_circle_ci()
staticmethod
If the current Python process is running in Circle CI.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
CI, |
Source code in src/zenml/environment.py
316 317 318 319 320 321 322 323 324 |
|
in_container()
staticmethod
If the current python process is running in a container.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
container, |
Source code in src/zenml/environment.py
172 173 174 175 176 177 178 179 180 181 |
|
in_docker()
staticmethod
If the current python process is running in a docker container.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
container, |
Source code in src/zenml/environment.py
183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
|
in_github_actions()
staticmethod
If the current Python process is running in GitHub Actions.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
Actions, |
Source code in src/zenml/environment.py
296 297 298 299 300 301 302 303 304 |
|
in_github_codespaces()
staticmethod
If the current Python process is running in GitHub Codespaces.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
|
Source code in src/zenml/environment.py
259 260 261 262 263 264 265 266 267 268 269 270 271 |
|
in_gitlab_ci()
staticmethod
If the current Python process is running in GitLab CI.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
CI, |
Source code in src/zenml/environment.py
306 307 308 309 310 311 312 313 314 |
|
in_google_colab()
staticmethod
If the current Python process is running in a Google Colab.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
|
Source code in src/zenml/environment.py
219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
|
in_kubernetes()
staticmethod
If the current python process is running in a kubernetes pod.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
pod, |
Source code in src/zenml/environment.py
201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
|
in_lightning_ai_studio()
staticmethod
If the current Python process is running in Lightning.ai studios.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
|
Source code in src/zenml/environment.py
357 358 359 360 361 362 363 364 365 366 367 368 |
|
in_notebook()
staticmethod
If the current Python process is running in a notebook.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
|
Source code in src/zenml/environment.py
235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 |
|
in_paperspace_gradient()
staticmethod
If the current Python process is running in Paperspace Gradient.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
Gradient, |
Source code in src/zenml/environment.py
286 287 288 289 290 291 292 293 294 |
|
in_vscode_remote_container()
staticmethod
If the current Python process is running in a VS Code Remote Container.
Returns:
Type | Description |
---|---|
bool
|
|
bool
|
|
Source code in src/zenml/environment.py
273 274 275 276 277 278 279 280 281 282 283 284 |
|
in_wsl()
staticmethod
If the current process is running in Windows Subsystem for Linux.
source: https://www.scivision.dev/python-detect-wsl/
Returns:
Type | Description |
---|---|
bool
|
|
Source code in src/zenml/environment.py
346 347 348 349 350 351 352 353 354 355 |
|
python_version()
staticmethod
Returns the python version of the running interpreter.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
the python version |
Source code in src/zenml/environment.py
163 164 165 166 167 168 169 170 |
|
get_environment()
Returns a string representing the execution environment of the pipeline.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
the execution environment |
Source code in src/zenml/environment.py
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
|
get_run_environment_dict()
Returns a dictionary of the current run environment.
Everything that is returned here will be saved in the DB as
pipeline_run.client_environment
and
pipeline_run.orchestrator_environment
for client and orchestrator
respectively.
Returns:
Type | Description |
---|---|
Dict[str, str]
|
A dictionary of the current run environment. |
Source code in src/zenml/environment.py
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
|
get_system_details()
Returns OS, python and ZenML information.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
OS, python and ZenML information |
Source code in src/zenml/environment.py
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
|
Event Hub
ZenML Event Hub module.
The Event Hub is responsible for receiving all Events and dispatching them to the relevant Subscribers/Triggers.
Event Sources
Exceptions
ZenML specific exception definitions.
ArtifactInterfaceError
Bases: ZenMLBaseException
Raises exception when interacting with the Artifact interface in an unsupported way.
Source code in src/zenml/exceptions.py
123 124 |
|
ArtifactStoreInterfaceError
Bases: ZenMLBaseException
Raises exception when interacting with the Artifact Store interface in an unsupported way.
Source code in src/zenml/exceptions.py
135 136 |
|
AuthorizationException
Bases: ZenMLBaseException
Raised when an authorization error occurred while trying to access a ZenML resource .
Source code in src/zenml/exceptions.py
46 47 |
|
BackupSecretsStoreNotConfiguredError
Bases: NotImplementedError
Raised when a backup secrets store is not configured.
Source code in src/zenml/exceptions.py
291 292 |
|
CredentialsNotValid
Bases: AuthorizationException
Raised when the credentials provided are invalid.
This is a subclass of AuthorizationException and should only be raised when the authentication credentials are invalid (e.g. expired API token, invalid username/password, invalid signature). If caught by the ZenML client, it will trigger an invalidation of the currently cached API token and a re-authentication flow.
Source code in src/zenml/exceptions.py
50 51 52 53 54 55 56 57 58 |
|
CustomFlavorImportError
Bases: ImportError
Raised when failing to import a custom flavor.
Source code in src/zenml/exceptions.py
295 296 |
|
DoesNotExistException
Bases: ZenMLBaseException
Raises exception when the entity does not exist in the system but an action is being done that requires it to be present.
Source code in src/zenml/exceptions.py
61 62 63 64 65 66 67 68 69 70 |
|
__init__(message)
Initializes the exception.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
str
|
Message with details of exception. |
required |
Source code in src/zenml/exceptions.py
64 65 66 67 68 69 70 |
|
DuplicateRunNameError
Bases: RuntimeError
Raises exception when a run with the same name already exists.
Source code in src/zenml/exceptions.py
147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
|
__init__(message='Unable to run a pipeline with a run name that already exists.')
Initializes the exception.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
str
|
Message with details of exception. |
'Unable to run a pipeline with a run name that already exists.'
|
Source code in src/zenml/exceptions.py
150 151 152 153 154 155 156 157 158 159 160 |
|
DuplicatedConfigurationError
Bases: ZenMLBaseException
Raised when a configuration parameter is set twice.
Source code in src/zenml/exceptions.py
195 196 |
|
EntityCreationError
Bases: ZenMLBaseException
, RuntimeError
Raised when failing to create an entity.
Source code in src/zenml/exceptions.py
171 172 |
|
EntityExistsError
Bases: ZenMLBaseException
Raised when trying to register an entity that already exists.
Source code in src/zenml/exceptions.py
167 168 |
|
GitException
Bases: ZenMLBaseException
Raises exception when a problem occurs in git resolution.
Source code in src/zenml/exceptions.py
90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
|
__init__(message='There is a problem with git resolution. Please make sure that all relevant files are committed.')
Initializes the exception.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
str
|
Message with details of exception. |
'There is a problem with git resolution. Please make sure that all relevant files are committed.'
|
Source code in src/zenml/exceptions.py
93 94 95 96 97 98 99 100 101 102 103 104 |
|
GitNotFoundError
Bases: ImportError
Raised when ZenML CLI is used to interact with examples on a machine with no git installation.
Source code in src/zenml/exceptions.py
191 192 |
|
HydrationError
Bases: ZenMLBaseException
Raised when the model hydration failed.
Source code in src/zenml/exceptions.py
219 220 |
|
IllegalOperationError
Bases: ZenMLBaseException
Raised when an illegal operation is attempted.
Source code in src/zenml/exceptions.py
199 200 |
|
InitializationException
Bases: ZenMLBaseException
Raised when an error occurred during initialization of a ZenML repository.
Source code in src/zenml/exceptions.py
42 43 |
|
InputResolutionError
Bases: ZenMLBaseException
Raised when step input resolving failed.
Source code in src/zenml/exceptions.py
211 212 |
|
IntegrationError
Bases: ZenMLBaseException
Raises exceptions when a requested integration can not be activated.
Source code in src/zenml/exceptions.py
143 144 |
|
MaterializerInterfaceError
Bases: ZenMLBaseException
Raises exception when interacting with the Materializer interface in an unsupported way.
Source code in src/zenml/exceptions.py
111 112 |
|
MethodNotAllowedError
Bases: ZenMLBaseException
Raised when the server does not allow a request method.
Source code in src/zenml/exceptions.py
203 204 |
|
OAuthError
Bases: ValueError
OAuth2 error.
Source code in src/zenml/exceptions.py
243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 |
|
__init__(error, status_code=400, error_description=None, error_uri=None)
Initializes the OAuthError.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
status_code
|
int
|
HTTP status code. |
400
|
error
|
str
|
Error code. |
required |
error_description
|
Optional[str]
|
Error description. |
None
|
error_uri
|
Optional[str]
|
Error URI. |
None
|
Source code in src/zenml/exceptions.py
246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 |
|
__str__()
String function.
Returns:
Type | Description |
---|---|
str
|
the error message |
Source code in src/zenml/exceptions.py
278 279 280 281 282 283 284 |
|
to_dict()
Returns the OAuthError as a dictionary.
Returns:
Type | Description |
---|---|
Dict[str, Optional[str]]
|
The OAuthError as a dictionary. |
Source code in src/zenml/exceptions.py
266 267 268 269 270 271 272 273 274 275 276 |
|
PipelineConfigurationError
Bases: ZenMLBaseException
Raises exceptions when a pipeline configuration contains invalid values.
Source code in src/zenml/exceptions.py
139 140 |
|
PipelineInterfaceError
Bases: ZenMLBaseException
Raises exception when interacting with the Pipeline interface in an unsupported way.
Source code in src/zenml/exceptions.py
119 120 |
|
PipelineNotSucceededException
Bases: ZenMLBaseException
Raises exception when trying to fetch artifacts from a not succeeded pipeline.
Source code in src/zenml/exceptions.py
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
|
__init__(name='', message='{} is not yet completed successfully.')
Initializes the exception.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the pipeline. |
''
|
message
|
str
|
Message with details of exception. |
'{} is not yet completed successfully.'
|
Source code in src/zenml/exceptions.py
76 77 78 79 80 81 82 83 84 85 86 87 |
|
ProvisioningError
Bases: ZenMLBaseException
Raised when an error occurs when provisioning resources for a StackComponent.
Source code in src/zenml/exceptions.py
187 188 |
|
SecretsStoreNotConfiguredError
Bases: NotImplementedError
Raised when a secrets store is not configured.
Source code in src/zenml/exceptions.py
287 288 |
|
SettingsResolvingError
Bases: ZenMLBaseException
Raised when resolving settings failed.
Source code in src/zenml/exceptions.py
207 208 |
|
StackComponentDeploymentError
Bases: ZenMLBaseException
Raises exception when deploying a stack component fails.
Source code in src/zenml/exceptions.py
131 132 |
|
StackComponentInterfaceError
Bases: ZenMLBaseException
Raises exception when interacting with the stack components in an unsupported way.
Source code in src/zenml/exceptions.py
127 128 |
|
StackComponentValidationError
Bases: ZenMLBaseException
Raised when a stack component configuration is not valid.
Source code in src/zenml/exceptions.py
183 184 |
|
StackValidationError
Bases: ZenMLBaseException
Raised when a stack configuration is not valid.
Source code in src/zenml/exceptions.py
179 180 |
|
StepContextError
Bases: ZenMLBaseException
Raises exception when interacting with a StepContext in an unsupported way.
Source code in src/zenml/exceptions.py
115 116 |
|
StepInterfaceError
Bases: ZenMLBaseException
Raises exception when interacting with the Step interface in an unsupported way.
Source code in src/zenml/exceptions.py
107 108 |
|
SubscriptionUpgradeRequiredError
Bases: ZenMLBaseException
Raised when user tries to perform an action outside their current subscription tier.
Source code in src/zenml/exceptions.py
215 216 |
|
ValidationError
Bases: ZenMLBaseException
Raised when the Model passed to the ZenStore.
Source code in src/zenml/exceptions.py
163 164 |
|
WebhookInactiveError
Bases: ZenMLBaseException
Raised when source is inactive.
Source code in src/zenml/exceptions.py
175 176 |
|
ZenKeyError
Bases: KeyError
Specialized key error which allows error messages with line breaks.
Source code in src/zenml/exceptions.py
223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
|
__init__(message)
Initialization.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
str
|
str, the error message |
required |
Source code in src/zenml/exceptions.py
226 227 228 229 230 231 232 |
|
__str__()
String function.
Returns:
Type | Description |
---|---|
str
|
the error message |
Source code in src/zenml/exceptions.py
234 235 236 237 238 239 240 |
|
ZenMLBaseException
Bases: Exception
Base exception for all ZenML Exceptions.
Source code in src/zenml/exceptions.py
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
|
__init__(message=None, url=None)
The BaseException used to format messages displayed to the user.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
Optional[str]
|
Message with details of exception. This message
will be appended with another message directing user to
|
None
|
url
|
Optional[str]
|
URL to point to in exception message. If |
None
|
Source code in src/zenml/exceptions.py
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
|
Experiment Trackers
Experiment trackers let you track your ML experiments.
They log the parameters used and allow you to compare between runs. In the ZenML world, every pipeline run is considered an experiment, and ZenML facilitates the storage of experiment results through ExperimentTracker stack components. This establishes a clear link between pipeline runs and experiments.
BaseExperimentTracker
Bases: StackComponent
, ABC
Base class for all ZenML experiment trackers.
Source code in src/zenml/experiment_trackers/base_experiment_tracker.py
28 29 30 31 32 33 34 35 36 37 38 |
|
config
property
Returns the config of the experiment tracker.
Returns:
Type | Description |
---|---|
BaseExperimentTrackerConfig
|
The config of the experiment tracker. |
Feature Stores
A feature store enables an offline and online serving of feature data.
Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization.
As a data scientist working on training your model, your requirements for how you access your batch / 'offline' data will almost certainly be different from how you access that data as part of a real-time or online inference setting. Feast solves the problem of developing train-serve skew where those two sources of data diverge from each other.
BaseFeatureStore
Bases: StackComponent
, ABC
Base class for all ZenML feature stores.
Source code in src/zenml/feature_stores/base_feature_store.py
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
|
config
property
Returns the BaseFeatureStoreConfig
config.
Returns:
Type | Description |
---|---|
BaseFeatureStoreConfig
|
The configuration. |
get_historical_features(entity_df, features, full_feature_names=False)
abstractmethod
Returns the historical features for training or batch scoring.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
entity_df
|
Any
|
The entity DataFrame or entity name. |
required |
features
|
List[str]
|
The features to retrieve. |
required |
full_feature_names
|
bool
|
Whether to return the full feature names. |
False
|
Returns:
Type | Description |
---|---|
Any
|
The historical features. |
Source code in src/zenml/feature_stores/base_feature_store.py
40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
|
get_online_features(entity_rows, features, full_feature_names=False)
abstractmethod
Returns the latest online feature data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
entity_rows
|
List[Dict[str, Any]]
|
The entity rows to retrieve. |
required |
features
|
List[str]
|
The features to retrieve. |
required |
full_feature_names
|
bool
|
Whether to return the full feature names. |
False
|
Returns:
Type | Description |
---|---|
Dict[str, Any]
|
The latest online feature data as a dictionary. |
Source code in src/zenml/feature_stores/base_feature_store.py
58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
|
Hooks
The hooks package exposes some standard hooks that can be used in ZenML.
Hooks are functions that run after a step has exited.
alerter_failure_hook(exception)
Standard failure hook that executes after step fails.
This hook uses any BaseAlerter
that is configured within the active stack to post a message.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
exception
|
BaseException
|
Original exception that lead to step failing. |
required |
Source code in src/zenml/hooks/alerter_hooks.py
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
|
alerter_success_hook()
Standard success hook that executes after step finishes successfully.
This hook uses any BaseAlerter
that is configured within the active stack to post a message.
Source code in src/zenml/hooks/alerter_hooks.py
63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
|
resolve_and_validate_hook(hook)
Resolves and validates a hook callback.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
hook
|
HookSpecification
|
Hook function or source. |
required |
Returns:
Type | Description |
---|---|
Source
|
Hook source. |
Raises:
Type | Description |
---|---|
ValueError
|
If |
Source code in src/zenml/hooks/hook_validators.py
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
|
Image Builders
Image builders allow you to build container images.
BaseImageBuilder
Bases: StackComponent
, ABC
Base class for all ZenML image builders.
Source code in src/zenml/image_builders/base_image_builder.py
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
|
build_context_class
property
Build context class to use.
The default build context class creates a build context that works for the Docker daemon. Override this method if your image builder requires a custom context.
Returns:
Type | Description |
---|---|
Type[BuildContext]
|
The build context class. |
config
property
The stack component configuration.
Returns:
Type | Description |
---|---|
BaseImageBuilderConfig
|
The configuration. |
is_building_locally
abstractmethod
property
Whether the image builder builds the images on the client machine.
Returns:
Type | Description |
---|---|
bool
|
True if the image builder builds locally, False otherwise. |
build(image_name, build_context, docker_build_options, container_registry=None)
abstractmethod
Builds a Docker image.
If a container registry is passed, the image will be pushed to that registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
image_name
|
str
|
Name of the image to build. |
required |
build_context
|
BuildContext
|
The build context to use for the image. |
required |
docker_build_options
|
Dict[str, Any]
|
Docker build options. |
required |
container_registry
|
Optional[BaseContainerRegistry]
|
Optional container registry to push to. |
None
|
Returns:
Type | Description |
---|---|
str
|
The Docker image repo digest or name. |
Source code in src/zenml/image_builders/base_image_builder.py
77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
|
BaseImageBuilderConfig
Bases: StackComponentConfig
Base config for image builders.
Source code in src/zenml/image_builders/base_image_builder.py
37 38 |
|
BaseImageBuilderFlavor
Bases: Flavor
, ABC
Base class for all ZenML image builder flavors.
Source code in src/zenml/image_builders/base_image_builder.py
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
|
config_class
property
implementation_class
property
type
property
BuildContext
Bases: Archivable
Image build context.
This class is responsible for creating an archive of the files needed to build a container image.
Source code in src/zenml/image_builders/build_context.py
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
|
dockerignore_file
property
The dockerignore file to use.
Returns:
Type | Description |
---|---|
Optional[str]
|
Path to the dockerignore file to use. |
__init__(root=None, dockerignore_file=None)
Initializes a build context.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root
|
Optional[str]
|
Optional root directory for the build context. |
None
|
dockerignore_file
|
Optional[str]
|
Optional path to a dockerignore file. If not
given, a file called |
None
|
Source code in src/zenml/image_builders/build_context.py
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
|
get_files()
Gets all regular files that should be included in the archive.
Returns:
Type | Description |
---|---|
Dict[str, str]
|
A dict {path_in_archive: path_on_filesystem} for all regular files |
Dict[str, str]
|
in the archive. |
Source code in src/zenml/image_builders/build_context.py
101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
|
write_archive(output_file, archive_type=ArchiveType.TAR_GZ)
Writes an archive of the build context to the given file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
output_file
|
IO[bytes]
|
The file to write the archive to. |
required |
archive_type
|
ArchiveType
|
The type of archive to create. |
TAR_GZ
|
Source code in src/zenml/image_builders/build_context.py
71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
|
LocalImageBuilder
Bases: BaseImageBuilder
Local image builder implementation.
Source code in src/zenml/image_builders/local_image_builder.py
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|
config
property
The stack component configuration.
Returns:
Type | Description |
---|---|
LocalImageBuilderConfig
|
The configuration. |
is_building_locally
property
Whether the image builder builds the images on the client machine.
Returns:
Type | Description |
---|---|
bool
|
True if the image builder builds locally, False otherwise. |
build(image_name, build_context, docker_build_options=None, container_registry=None)
Builds and optionally pushes an image using the local Docker client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
image_name
|
str
|
Name of the image to build and push. |
required |
build_context
|
BuildContext
|
The build context to use for the image. |
required |
docker_build_options
|
Optional[Dict[str, Any]]
|
Docker build options. |
None
|
container_registry
|
Optional[BaseContainerRegistry]
|
Optional container registry to push to. |
None
|
Returns:
Type | Description |
---|---|
str
|
The Docker image repo digest. |
Source code in src/zenml/image_builders/local_image_builder.py
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|
LocalImageBuilderConfig
Bases: BaseImageBuilderConfig
Local image builder configuration.
Source code in src/zenml/image_builders/local_image_builder.py
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|
LocalImageBuilderFlavor
Bases: BaseImageBuilderFlavor
Local image builder flavor.
Source code in src/zenml/image_builders/local_image_builder.py
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|
config_class
property
docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
implementation_class
property
logo_url
property
A url to represent the flavor in the dashboard.
Returns:
Type | Description |
---|---|
str
|
The flavor logo. |
name
property
The flavor name.
Returns:
Type | Description |
---|---|
str
|
The flavor name. |
sdk_docs_url
property
A url to point at docs explaining this flavor.
Returns:
Type | Description |
---|---|
Optional[str]
|
A flavor docs url. |
Io
The io
module handles file operations for the ZenML package.
It offers a standard interface for reading, writing and manipulating files and
directories. It is heavily influenced and inspired by the io
module of tfx
.
Logger
Logger implementation.
CustomFormatter
Bases: Formatter
Formats logs according to custom specifications.
Source code in src/zenml/logger.py
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|
format(record)
Converts a log record to a (colored) string.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
record
|
LogRecord
|
LogRecord generated by the code. |
required |
Returns:
Type | Description |
---|---|
str
|
A string formatted according to specifications. |
Source code in src/zenml/logger.py
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|
get_console_handler()
Get console handler for logging.
Returns:
Type | Description |
---|---|
Any
|
A console handler. |
Source code in src/zenml/logger.py
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|
get_formatter()
Get a configured logging formatter.
Returns:
Type | Description |
---|---|
Formatter
|
The formatter. |
Source code in src/zenml/logger.py
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|
get_logger(logger_name)
Main function to get logger name,.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
logger_name
|
str
|
Name of logger to initialize. |
required |
Returns:
Type | Description |
---|---|
Logger
|
A logger object. |
Source code in src/zenml/logger.py
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|
get_logging_level()
Get logging level from the env variable.
Returns:
Type | Description |
---|---|
LoggingLevels
|
The logging level. |
Raises:
Type | Description |
---|---|
KeyError
|
If the logging level is not found. |
Source code in src/zenml/logger.py
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|
init_logging()
Initialize logging with default levels.
Source code in src/zenml/logger.py
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|
set_root_verbosity()
Set the root verbosity.
Source code in src/zenml/logger.py
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|
Logging
Login
Materializers
Initialization of ZenML materializers.
Materializers are used to convert a ZenML artifact into a specific format. They
are most often used to handle the input or output of ZenML steps, and can be
extended by building on the BaseMaterializer
class.
BuiltInContainerMaterializer
Bases: BaseMaterializer
Handle built-in container types (dict, list, set, tuple).
Source code in src/zenml/materializers/built_in_materializer.py
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|
__init__(uri, artifact_store=None)
Define self.data_path
and self.metadata_path
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uri
|
str
|
The URI where the artifact data is stored. |
required |
artifact_store
|
Optional[BaseArtifactStore]
|
The artifact store where the artifact data is stored. |
None
|
Source code in src/zenml/materializers/built_in_materializer.py
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|
extract_metadata(data)
Extract metadata from the given built-in container object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Any
|
The built-in container object to extract metadata from. |
required |
Returns:
Type | Description |
---|---|
Dict[str, MetadataType]
|
The extracted metadata as a dictionary. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
load(data_type)
Reads a materialized built-in container object.
If the data was serialized to JSON, deserialize it.
Otherwise, reconstruct all elements according to the metadata file:
1. Resolve the data type using find_type_by_str()
,
2. Get the materializer via the default_materializer_registry
,
3. Initialize the materializer with the desired path,
4. Use load()
of that materializer to load the element.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Type[Any]
|
The type of the data to read. |
required |
Returns:
Type | Description |
---|---|
Any
|
The data read. |
Raises:
Type | Description |
---|---|
RuntimeError
|
If the data was not found. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
save(data)
Materialize a built-in container object.
If the object can be serialized to JSON, serialize it.
Otherwise, use the default_materializer_registry
to find the correct
materializer for each element and materialize each element into a
subdirectory.
Tuples and sets are cast to list before materialization.
For non-serializable dicts, materialize keys/values as separate lists.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Any
|
The built-in container object to materialize. |
required |
Raises:
Type | Description |
---|---|
Exception
|
If any exception occurs, it is raised after cleanup. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
save_visualizations(data)
Save visualizations for the given data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Any
|
The data to save visualizations for. |
required |
Returns:
Type | Description |
---|---|
Dict[str, VisualizationType]
|
A dictionary of visualization URIs and their types. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
BuiltInMaterializer
Bases: BaseMaterializer
Handle JSON-serializable basic types (bool
, float
, int
, str
).
Source code in src/zenml/materializers/built_in_materializer.py
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|
__init__(uri, artifact_store=None)
Define self.data_path
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uri
|
str
|
The URI where the artifact data is stored. |
required |
artifact_store
|
Optional[BaseArtifactStore]
|
The artifact store where the artifact data is stored. |
None
|
Source code in src/zenml/materializers/built_in_materializer.py
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|
extract_metadata(data)
Extract metadata from the given built-in container object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[bool, float, int, str]
|
The built-in container object to extract metadata from. |
required |
Returns:
Type | Description |
---|---|
Dict[str, MetadataType]
|
The extracted metadata as a dictionary. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
load(data_type)
Reads basic primitive types from JSON.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Union[Type[bool], Type[float], Type[int], Type[str]]
|
The type of the data to read. |
required |
Returns:
Type | Description |
---|---|
Any
|
The data read. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
save(data)
Serialize a basic type to JSON.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Union[bool, float, int, str]
|
The data to store. |
required |
Source code in src/zenml/materializers/built_in_materializer.py
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|
BytesMaterializer
Bases: BaseMaterializer
Handle bytes
data type, which is not JSON serializable.
Source code in src/zenml/materializers/built_in_materializer.py
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|
__init__(uri, artifact_store=None)
Define self.data_path
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uri
|
str
|
The URI where the artifact data is stored. |
required |
artifact_store
|
Optional[BaseArtifactStore]
|
The artifact store where the artifact data is stored. |
None
|
Source code in src/zenml/materializers/built_in_materializer.py
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|
load(data_type)
Reads a bytes object from file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Type[Any]
|
The type of the data to read. |
required |
Returns:
Type | Description |
---|---|
Any
|
The data read. |
Source code in src/zenml/materializers/built_in_materializer.py
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|
save(data)
Save a bytes object to file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Any
|
The data to store. |
required |
Source code in src/zenml/materializers/built_in_materializer.py
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|
CloudpickleMaterializer
Bases: BaseMaterializer
Materializer using cloudpickle.
This materializer can materialize (almost) any object, but does so in a non-reproducble way since artifacts cannot be loaded from other Python versions. It is recommended to use this materializer only as a last resort.
That is also why it has SKIP_REGISTRATION
set to True and is currently
only used as a fallback materializer inside the materializer registry.
Source code in src/zenml/materializers/cloudpickle_materializer.py
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|
load(data_type)
Reads an artifact from a cloudpickle file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Type[Any]
|
The data type of the artifact. |
required |
Returns:
Type | Description |
---|---|
Any
|
The loaded artifact data. |
Source code in src/zenml/materializers/cloudpickle_materializer.py
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|
save(data)
Saves an artifact to a cloudpickle file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
Any
|
The data to save. |
required |
Source code in src/zenml/materializers/cloudpickle_materializer.py
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|
PydanticMaterializer
Bases: BaseMaterializer
Handle Pydantic BaseModel objects.
Source code in src/zenml/materializers/pydantic_materializer.py
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|
extract_metadata(data)
Extract metadata from the given BaseModel object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
BaseModel
|
The BaseModel object to extract metadata from. |
required |
Returns:
Type | Description |
---|---|
Dict[str, MetadataType]
|
The extracted metadata as a dictionary. |
Source code in src/zenml/materializers/pydantic_materializer.py
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|
load(data_type)
Reads BaseModel from JSON.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Type[BaseModel]
|
The type of the data to read. |
required |
Returns:
Type | Description |
---|---|
Any
|
The data read. |
Source code in src/zenml/materializers/pydantic_materializer.py
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|
save(data)
Serialize a BaseModel to JSON.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
BaseModel
|
The data to store. |
required |
Source code in src/zenml/materializers/pydantic_materializer.py
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|
ServiceMaterializer
Bases: BaseMaterializer
Materializer to read/write service instances.
Source code in src/zenml/materializers/service_materializer.py
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|
extract_metadata(service)
Extract metadata from the given service.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service
|
BaseService
|
The service to extract metadata from. |
required |
Returns:
Type | Description |
---|---|
Dict[str, MetadataType]
|
The extracted metadata as a dictionary. |
Source code in src/zenml/materializers/service_materializer.py
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|
load(data_type)
Creates and returns a service.
This service is instantiated from the serialized service configuration and last known status information saved as artifact.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Type[Any]
|
The type of the data to read. |
required |
Returns:
Type | Description |
---|---|
BaseService
|
A ZenML service instance. |
Source code in src/zenml/materializers/service_materializer.py
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|
save(service)
Writes a ZenML service.
The configuration and last known status of the input service instance are serialized and saved as an artifact.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service
|
BaseService
|
A ZenML service instance. |
required |
Source code in src/zenml/materializers/service_materializer.py
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|
StructuredStringMaterializer
Bases: BaseMaterializer
Materializer for HTML or Markdown strings.
Source code in src/zenml/materializers/structured_string_materializer.py
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|
load(data_type)
Loads the data from the HTML or Markdown file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_type
|
Type[STRUCTURED_STRINGS]
|
The type of the data to read. |
required |
Returns:
Type | Description |
---|---|
STRUCTURED_STRINGS
|
The loaded data. |
Source code in src/zenml/materializers/structured_string_materializer.py
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|
save(data)
Save data as an HTML or Markdown file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
STRUCTURED_STRINGS
|
The data to save as an HTML or Markdown file. |
required |
Source code in src/zenml/materializers/structured_string_materializer.py
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|
save_visualizations(data)
Save visualizations for the given data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
STRUCTURED_STRINGS
|
The data to save visualizations for. |
required |
Returns:
Type | Description |
---|---|
Dict[str, VisualizationType]
|
A dictionary of visualization URIs and their types. |
Source code in src/zenml/materializers/structured_string_materializer.py
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|
UUIDMaterializer
Bases: BaseMaterializer
Materializer to handle UUID objects.
Source code in src/zenml/materializers/uuid_materializer.py
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|
__init__(uri, artifact_store=None)
Define self.data_path
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uri
|
str
|
The URI where the artifact data is stored. |
required |
artifact_store
|
Optional[BaseArtifactStore]
|
The artifact store where the artifact data is stored. |
None
|
Source code in src/zenml/materializers/uuid_materializer.py
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|
extract_metadata(data)
Extract metadata from the UUID.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
UUID
|
The UUID to extract metadata from. |
required |
Returns:
Type | Description |
---|---|
Dict[str, MetadataType]
|
A dictionary of metadata extracted from the UUID. |
Source code in src/zenml/materializers/uuid_materializer.py
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|
load(_)
Read UUID from artifact store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_
|
Type[UUID]
|
The type of the data to be loaded. |
required |
Returns:
Type | Description |
---|---|
UUID
|
The loaded UUID. |
Source code in src/zenml/materializers/uuid_materializer.py
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|
save(data)
Write UUID to artifact store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data
|
UUID
|
The UUID to be saved. |
required |
Source code in src/zenml/materializers/uuid_materializer.py
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|
Metadata
Initialization of ZenML metadata.
ZenML metadata is any additional, dynamic information that is associated with your pipeline runs and artifacts at runtime.
Model Deployers
Model deployers are stack components responsible for online model serving.
Online serving is the process of hosting and loading machine-learning models as part of a managed web service and providing access to the models through an API endpoint like HTTP or GRPC. Once deployed, you can send inference requests to the model through the web service's API and receive fast, low-latency responses.
Add a model deployer to your ZenML stack to be able to implement continuous model deployment pipelines that train models and continuously deploy them to a model prediction web service.
When present in a stack, the model deployer also acts as a registry for models that are served with ZenML. You can use the model deployer to list all models that are currently deployed for online inference or filtered according to a particular pipeline run or step, or to suspend, resume or delete an external model server managed through ZenML.
BaseModelDeployer
Bases: StackComponent
, ABC
Base class for all ZenML model deployers.
The model deployer serves three major purposes:
-
It contains all the stack related configuration attributes required to interact with the remote model serving tool, service or platform (e.g. hostnames, URLs, references to credentials, other client related configuration parameters).
-
It implements the continuous deployment logic necessary to deploy models in a way that updates an existing model server that is already serving a previous version of the same model instead of creating a new model server for every new model version (see the
deploy_model
abstract method). This functionality can be consumed directly from ZenML pipeline steps, but it can also be used outside the pipeline to deploy ad hoc models. It is also usually coupled with a standard model deployer step, implemented by each integration, that hides the details of the deployment process away from the user. -
It acts as a ZenML BaseService registry, where every BaseService instance is used as an internal representation of a remote model server (see the
find_model_server
abstract method). To achieve this, it must be able to re-create the configuration of a BaseService from information that is persisted externally, alongside or even part of the remote model server configuration itself. For example, for model servers that are implemented as Kubernetes resources, the BaseService instances can be serialized and saved as Kubernetes resource annotations. This allows the model deployer to keep track of all externally running model servers and to re-create their corresponding BaseService instance representations at any given time. The model deployer also defines methods that implement basic life-cycle management on remote model servers outside the coverage of a pipeline (seestop_model_server
,start_model_server
anddelete_model_server
).
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
config
property
Returns the BaseModelDeployerConfig
config.
Returns:
Type | Description |
---|---|
BaseModelDeployerConfig
|
The configuration. |
delete_model_server(uuid, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False)
Abstract method to delete a model server.
This operation is irreversible. A deleted model server must no longer
show up in the list of model servers returned by find_model_server
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uuid
|
UUID
|
UUID of the model server to stop. |
required |
timeout
|
int
|
timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
force
|
bool
|
if True, force the service to stop. |
False
|
Raises:
Type | Description |
---|---|
RuntimeError
|
if the model server is not found. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
deploy_model(config, service_type, replace=False, continuous_deployment_mode=False, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT)
Deploy a model.
the deploy_model method is the main entry point for deploying models using the model deployer. It is used to deploy a model to a model server instance that is running on a remote serving platform or service. The method is responsible for detecting if there is an existing model server instance running serving one or more previous versions of the same model and deploying the model to the serving platform or updating the existing model server instance to include the new model version. The method returns a Service object that is a representation of the external model server instance. The Service object must implement basic operational state tracking and lifecycle management operations for the model server (e.g. start, stop, etc.).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
config
|
ServiceConfig
|
Custom Service configuration parameters for the model deployer. Can include the pipeline name, the run id, the step name, the model name, the model uri, the model type etc. |
required |
replace
|
bool
|
If True, it will replace any existing model server instances that serve the same model. If False, it does not replace any existing model server instance. |
False
|
continuous_deployment_mode
|
bool
|
If True, it will replace any existing model server instances that serve the same model, regardless of the configuration. If False, it will only replace existing model server instances that serve the same model if the configuration is exactly the same. |
False
|
timeout
|
int
|
The maximum time in seconds to wait for the model server to start serving the model. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
service_type
|
ServiceType
|
The type of the service to deploy. If not provided, the default service type of the model deployer will be used. |
required |
Raises:
Type | Description |
---|---|
RuntimeError
|
if the model deployment fails. |
Returns:
Type | Description |
---|---|
BaseService
|
The deployment Service object. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
find_model_server(config=None, running=None, service_uuid=None, pipeline_name=None, pipeline_step_name=None, service_name=None, model_name=None, model_version=None, service_type=None, type=None, flavor=None, pipeline_run_id=None)
Abstract method to find one or more a model servers that match the given criteria.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
running
|
Optional[bool]
|
If true, only running services will be returned. |
None
|
service_uuid
|
Optional[UUID]
|
The UUID of the service that was originally used to deploy the model. |
None
|
pipeline_step_name
|
Optional[str]
|
The name of the pipeline step that was originally used to deploy the model. |
None
|
pipeline_name
|
Optional[str]
|
The name of the pipeline that was originally used to deploy the model from the model registry. |
None
|
model_name
|
Optional[str]
|
The name of the model that was originally used to deploy the model from the model registry. |
None
|
model_version
|
Optional[str]
|
The version of the model that was originally used to deploy the model from the model registry. |
None
|
service_type
|
Optional[ServiceType]
|
The type of the service to find. |
None
|
type
|
Optional[str]
|
The type of the service to find. |
None
|
flavor
|
Optional[str]
|
The flavor of the service to find. |
None
|
pipeline_run_id
|
Optional[str]
|
The UUID of the pipeline run that was originally used to deploy the model. |
None
|
config
|
Optional[Dict[str, Any]]
|
Custom Service configuration parameters for the model deployer. Can include the pipeline name, the run id, the step name, the model name, the model uri, the model type etc. |
None
|
service_name
|
Optional[str]
|
The name of the service to find. |
None
|
Returns:
Type | Description |
---|---|
List[BaseService]
|
One or more Service objects representing model servers that match |
List[BaseService]
|
the input search criteria. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
get_active_model_deployer()
classmethod
Get the model deployer registered in the active stack.
Returns:
Type | Description |
---|---|
BaseModelDeployer
|
The model deployer registered in the active stack. |
Raises:
Type | Description |
---|---|
TypeError
|
if a model deployer is not part of the active stack. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
get_model_server_info(service)
abstractmethod
staticmethod
Give implementation specific way to extract relevant model server properties for the user.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service
|
BaseService
|
Integration-specific service instance |
required |
Returns:
Type | Description |
---|---|
Dict[str, Optional[str]]
|
A dictionary containing the relevant model server properties. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
get_model_server_logs(uuid, follow=False, tail=None)
Get the logs of a model server.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uuid
|
UUID
|
UUID of the model server to get the logs of. |
required |
follow
|
bool
|
if True, the logs will be streamed as they are written |
False
|
tail
|
Optional[int]
|
only retrieve the last NUM lines of log output. |
None
|
Returns:
Type | Description |
---|---|
None
|
A generator that yields the logs of the model server. |
Raises:
Type | Description |
---|---|
RuntimeError
|
if the model server is not found. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
load_service(service_id)
Load a service from a URI.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_id
|
UUID
|
The ID of the service to load. |
required |
Returns:
Type | Description |
---|---|
BaseService
|
The loaded service. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
perform_delete_model(service, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False)
abstractmethod
Abstract method to delete a model server.
This operation is irreversible. A deleted model server must no longer
show up in the list of model servers returned by find_model_server
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service
|
BaseService
|
The service to delete. |
required |
timeout
|
int
|
timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
force
|
bool
|
if True, force the service to stop. |
False
|
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
perform_deploy_model(id, config, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT)
abstractmethod
Abstract method to deploy a model.
Concrete model deployer subclasses must implement the following functionality in this method: - Detect if there is an existing model server instance running serving one or more previous versions of the same model - Deploy the model to the serving platform or update the existing model server instance to include the new model version - Return a Service object that is a representation of the external model server instance. The Service must implement basic operational state tracking and lifecycle management operations for the model server (e.g. start, stop, etc.)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
id
|
UUID
|
UUID of the service that was originally used to deploy the model. |
required |
config
|
ServiceConfig
|
Custom Service configuration parameters for the model deployer. Can include the pipeline name, the run id, the step name, the model name, the model uri, the model type etc. |
required |
timeout
|
int
|
The maximum time in seconds to wait for the model server to start serving the model. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
Returns:
Type | Description |
---|---|
BaseService
|
The deployment Service object. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
perform_start_model(service, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT)
abstractmethod
Abstract method to start a model server.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service
|
BaseService
|
The service to start. |
required |
timeout
|
int
|
timeout in seconds to wait for the service to start. If set to 0, the method will return immediately after provisioning the service, without waiting for it to become active. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
perform_stop_model(service, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False)
abstractmethod
Abstract method to stop a model server.
This operation should be reversible. A stopped model server should still
show up in the list of model servers returned by find_model_server
and
it should be possible to start it again by calling start_model_server
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service
|
BaseService
|
The service to stop. |
required |
timeout
|
int
|
timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
force
|
bool
|
if True, force the service to stop. |
False
|
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
start_model_server(uuid, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT)
Abstract method to start a model server.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uuid
|
UUID
|
UUID of the model server to start. |
required |
timeout
|
int
|
timeout in seconds to wait for the service to start. If set to 0, the method will return immediately after provisioning the service, without waiting for it to become active. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
Raises:
Type | Description |
---|---|
RuntimeError
|
if the model server is not found. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
stop_model_server(uuid, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False)
Abstract method to stop a model server.
This operation should be reversible. A stopped model server should still
show up in the list of model servers returned by find_model_server
and
it should be possible to start it again by calling start_model_server
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
uuid
|
UUID
|
UUID of the model server to stop. |
required |
timeout
|
int
|
timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop. |
DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
|
force
|
bool
|
if True, force the service to stop. |
False
|
Raises:
Type | Description |
---|---|
RuntimeError
|
if the model server is not found. |
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
BaseModelDeployerFlavor
Bases: Flavor
Base class for model deployer flavors.
Source code in src/zenml/model_deployers/base_model_deployer.py
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|
config_class
property
Returns BaseModelDeployerConfig
config class.
Returns:
Type | Description |
---|---|
Type[BaseModelDeployerConfig]
|
The config class. |
implementation_class
abstractmethod
property
The class that implements the model deployer.
type
property
Model Registries
Initialization of the MLflow Service.
Model registries are centralized repositories that facilitate the collaboration and management of machine learning models. They provide functionalities such as version control, metadata tracking, and storage of model artifacts, enabling data scientists to efficiently share and keep track of their models within a team or organization.
BaseModelRegistry
Bases: StackComponent
, ABC
Base class for all ZenML model registries.
Source code in src/zenml/model_registries/base_model_registry.py
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|
config
property
Returns the config of the model registries.
Returns:
Type | Description |
---|---|
BaseModelRegistryConfig
|
The config of the model registries. |
delete_model(name)
abstractmethod
Deletes a registered model from the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
Raises:
Type | Description |
---|---|
KeyError
|
If the model does not exist. |
RuntimeError
|
If deletion fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
delete_model_version(name, version)
abstractmethod
Deletes a model version from the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
version
|
str
|
The version of the model version to delete. |
required |
Raises:
Type | Description |
---|---|
KeyError
|
If the model version does not exist. |
RuntimeError
|
If deletion fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
get_latest_model_version(name, stage=None)
Gets the latest model version for a registered model.
This method is used to get the latest model version for a registered model. If no stage is provided, the latest model version across all stages is returned. If a stage is provided, the latest model version for that stage is returned.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
stage
|
Optional[ModelVersionStage]
|
The stage of the model version. |
None
|
Returns:
Type | Description |
---|---|
Optional[RegistryModelVersion]
|
The latest model version. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
get_model(name)
abstractmethod
Gets a registered model from the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
Returns:
Type | Description |
---|---|
RegisteredModel
|
The registered model. |
Raises:
Type | Description |
---|---|
EntityExistsError
|
If the model does not exist. |
RuntimeError
|
If retrieval fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
get_model_uri_artifact_store(model_version)
abstractmethod
Gets the URI artifact store for a model version.
This method retrieves the URI of the artifact store for a specific model version. Its purpose is to ensure that the URI is in the correct format for the specific artifact store being used. This is essential for the model serving component, which relies on the URI to serve the model version. In some cases, the URI may be stored in a different format by certain model registry integrations. This method allows us to obtain the URI in the correct format, regardless of the integration being used.
Note: In some cases the URI artifact store may not be available to the user, the method should save the target model in one of the other artifact stores supported by ZenML and return the URI of that artifact store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_version
|
RegistryModelVersion
|
The model version for which to get the URI artifact store. |
required |
Returns:
Type | Description |
---|---|
str
|
The URI artifact store for the model version. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
get_model_version(name, version)
abstractmethod
Gets a model version for a registered model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
version
|
str
|
The version of the model version to get. |
required |
Returns:
Type | Description |
---|---|
RegistryModelVersion
|
The model version. |
Raises:
Type | Description |
---|---|
KeyError
|
If the model version does not exist. |
RuntimeError
|
If retrieval fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
list_model_versions(name=None, model_source_uri=None, metadata=None, stage=None, count=None, created_after=None, created_before=None, order_by_date=None, **kwargs)
abstractmethod
Lists all model versions for a registered model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
Optional[str]
|
The name of the registered model. |
None
|
model_source_uri
|
Optional[str]
|
The model source URI of the registered model. |
None
|
metadata
|
Optional[ModelRegistryModelMetadata]
|
Metadata associated with this model version. |
None
|
stage
|
Optional[ModelVersionStage]
|
The stage of the model version. |
None
|
count
|
Optional[int]
|
The number of model versions to return. |
None
|
created_after
|
Optional[datetime]
|
The timestamp after which to list model versions. |
None
|
created_before
|
Optional[datetime]
|
The timestamp before which to list model versions. |
None
|
order_by_date
|
Optional[str]
|
Whether to sort by creation time, this can be "asc" or "desc". |
None
|
kwargs
|
Any
|
Additional keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
Optional[List[RegistryModelVersion]]
|
A list of model versions. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
list_models(name=None, metadata=None)
abstractmethod
Lists all registered models in the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
Optional[str]
|
The name of the registered model. |
None
|
metadata
|
Optional[Dict[str, str]]
|
The metadata associated with the registered model. |
None
|
Returns:
Type | Description |
---|---|
List[RegisteredModel]
|
A list of registered models. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
load_model_version(name, version, **kwargs)
abstractmethod
Loads a model version from the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
version
|
str
|
The version of the model version to load. |
required |
**kwargs
|
Any
|
Additional keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
Any
|
The loaded model version. |
Raises:
Type | Description |
---|---|
KeyError
|
If the model version does not exist. |
RuntimeError
|
If loading fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
register_model(name, description=None, metadata=None)
abstractmethod
Registers a model in the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
description
|
Optional[str]
|
The description of the registered model. |
None
|
metadata
|
Optional[Dict[str, str]]
|
The metadata associated with the registered model. |
None
|
Returns:
Type | Description |
---|---|
RegisteredModel
|
The registered model. |
Raises:
Type | Description |
---|---|
EntityExistsError
|
If a model with the same name already exists. |
RuntimeError
|
If registration fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
register_model_version(name, version=None, model_source_uri=None, description=None, metadata=None, **kwargs)
abstractmethod
Registers a model version in the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
model_source_uri
|
Optional[str]
|
The source URI of the model. |
None
|
version
|
Optional[str]
|
The version of the model version. |
None
|
description
|
Optional[str]
|
The description of the model version. |
None
|
metadata
|
Optional[ModelRegistryModelMetadata]
|
The metadata associated with the model version. |
None
|
**kwargs
|
Any
|
Additional keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
RegistryModelVersion
|
The registered model version. |
Raises:
Type | Description |
---|---|
RuntimeError
|
If registration fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
update_model(name, description=None, metadata=None, remove_metadata=None)
abstractmethod
Updates a registered model in the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
description
|
Optional[str]
|
The description of the registered model. |
None
|
metadata
|
Optional[Dict[str, str]]
|
The metadata associated with the registered model. |
None
|
remove_metadata
|
Optional[List[str]]
|
The metadata to remove from the registered model. |
None
|
Raises:
Type | Description |
---|---|
KeyError
|
If the model does not exist. |
RuntimeError
|
If update fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
update_model_version(name, version, description=None, metadata=None, remove_metadata=None, stage=None)
abstractmethod
Updates a model version in the model registry.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
The name of the registered model. |
required |
version
|
str
|
The version of the model version to update. |
required |
description
|
Optional[str]
|
The description of the model version. |
None
|
metadata
|
Optional[ModelRegistryModelMetadata]
|
Metadata associated with this model version. |
None
|
remove_metadata
|
Optional[List[str]]
|
The metadata to remove from the model version. |
None
|
stage
|
Optional[ModelVersionStage]
|
The stage of the model version. |
None
|
Returns:
Type | Description |
---|---|
RegistryModelVersion
|
The updated model version. |
Raises:
Type | Description |
---|---|
KeyError
|
If the model version does not exist. |
RuntimeError
|
If update fails. |
Source code in src/zenml/model_registries/base_model_registry.py
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|
BaseModelRegistryConfig
Bases: StackComponentConfig
Base config for model registries.
Source code in src/zenml/model_registries/base_model_registry.py
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|
BaseModelRegistryFlavor
Bases: Flavor
Base class for all ZenML model registry flavors.
Source code in src/zenml/model_registries/base_model_registry.py
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|
config_class
property
Config class for this flavor.
Returns:
Type | Description |
---|---|
Type[BaseModelRegistryConfig]
|
The config class for this flavor. |
implementation_class
abstractmethod
property
Returns the implementation class for this flavor.
Returns:
Type | Description |
---|---|
Type[StackComponent]
|
The implementation class for this flavor. |
type
property
Type of the flavor.
Returns:
Name | Type | Description |
---|---|---|
StackComponentType |
StackComponentType
|
The type of the flavor. |
Model
Models
Pydantic models for the various concepts in ZenML.
APIKey
Bases: BaseModel
Encoded model for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
decode_api_key(encoded_key)
classmethod
Decodes an API key from a base64 string.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
encoded_key
|
str
|
The encoded API key. |
required |
Returns:
Type | Description |
---|---|
APIKey
|
The decoded API key. |
Raises:
Type | Description |
---|---|
ValueError
|
If the key is not valid. |
Source code in src/zenml/models/v2/core/api_key.py
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|
encode()
Encodes the API key in a base64 string that includes the key ID and prefix.
Returns:
Type | Description |
---|---|
str
|
The encoded API key. |
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyFilter
Bases: BaseFilter
Filter model for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
apply_filter(query, table)
Override to apply the service account scope as an additional filter.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query
|
AnyQuery
|
The query to which to apply the filter. |
required |
table
|
Type[AnySchema]
|
The query table. |
required |
Returns:
Type | Description |
---|---|
AnyQuery
|
The query with filter applied. |
Source code in src/zenml/models/v2/core/api_key.py
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set_service_account(service_account_id)
Set the service account by which to scope this query.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
service_account_id
|
UUID
|
The service account ID. |
required |
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyInternalResponse
Bases: APIKeyResponse
Response model for API keys used internally.
Source code in src/zenml/models/v2/core/api_key.py
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verify_key(key)
Verifies a given key against the stored (hashed) key(s).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
key
|
str
|
Input key to be verified. |
required |
Returns:
Type | Description |
---|---|
bool
|
True if the keys match. |
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyInternalUpdate
Bases: APIKeyUpdate
Update model for API keys used internally.
Source code in src/zenml/models/v2/core/api_key.py
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APIKeyRequest
Bases: BaseRequest
Request model for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyResponse
Bases: BaseIdentifiedResponse[APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources]
Response model for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
active
property
The active
property.
Returns:
Type | Description |
---|---|
bool
|
the value of the property. |
description
property
The description
property.
Returns:
Type | Description |
---|---|
str
|
the value of the property. |
key
property
The key
property.
Returns:
Type | Description |
---|---|
Optional[str]
|
the value of the property. |
last_login
property
The last_login
property.
Returns:
Type | Description |
---|---|
Optional[datetime]
|
the value of the property. |
last_rotated
property
The last_rotated
property.
Returns:
Type | Description |
---|---|
Optional[datetime]
|
the value of the property. |
retain_period_minutes
property
The retain_period_minutes
property.
Returns:
Type | Description |
---|---|
int
|
the value of the property. |
service_account
property
The service_account
property.
Returns:
Type | Description |
---|---|
ServiceAccountResponse
|
the value of the property. |
get_hydrated_version()
Get the hydrated version of this API key.
Returns:
Type | Description |
---|---|
APIKeyResponse
|
an instance of the same entity with the metadata field attached. |
Source code in src/zenml/models/v2/core/api_key.py
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|
set_key(key)
Sets the API key and encodes it.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
key
|
str
|
The API key value to be set. |
required |
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyResponseBody
Bases: BaseDatedResponseBody
Response body for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyResponseMetadata
Bases: BaseResponseMetadata
Response metadata for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyRotateRequest
Bases: BaseRequest
Request model for API key rotation.
Source code in src/zenml/models/v2/core/api_key.py
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|
APIKeyUpdate
Bases: BaseUpdate
Update model for API keys.
Source code in src/zenml/models/v2/core/api_key.py
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|
ActionFilter
Bases: ProjectScopedFilter
Model to enable advanced filtering of all actions.
Source code in src/zenml/models/v2/core/action.py
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|
ActionFlavorResponse
Bases: BasePluginFlavorResponse[ActionFlavorResponseBody, ActionFlavorResponseMetadata, ActionFlavorResponseResources]
Response model for Action Flavors.
Source code in src/zenml/models/v2/core/action_flavor.py
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|
config_schema
property
The source_config_schema
property.
Returns:
Type | Description |
---|---|
Dict[str, Any]
|
the value of the property. |
ActionFlavorResponseBody
Bases: BasePluginResponseBody
Response body for action flavors.
Source code in src/zenml/models/v2/core/action_flavor.py
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|
ActionFlavorResponseMetadata
Bases: BasePluginResponseMetadata
Response metadata for action flavors.
Source code in src/zenml/models/v2/core/action_flavor.py
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|
ActionFlavorResponseResources
Bases: BasePluginResponseResources
Response resources for action flavors.
Source code in src/zenml/models/v2/core/action_flavor.py
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|
ActionRequest
Bases: ProjectScopedRequest
Model for creating a new action.
Source code in src/zenml/models/v2/core/action.py
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|
ActionResponse
Bases: ProjectScopedResponse[ActionResponseBody, ActionResponseMetadata, ActionResponseResources]
Response model for actions.
Source code in src/zenml/models/v2/core/action.py
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|
auth_window
property
The auth_window
property.
Returns:
Type | Description |
---|---|
int
|
the value of the property. |
configuration
property
The configuration
property.
Returns:
Type | Description |
---|---|
Dict[str, Any]
|
the value of the property. |
description
property
The description
property.
Returns:
Type | Description |
---|---|
str
|
the value of the property. |
flavor
property
The flavor
property.
Returns:
Type | Description |
---|---|
str
|
the value of the property. |
plugin_subtype
property
service_account
property
get_hydrated_version()
Get the hydrated version of this action.
Returns:
Type | Description |
---|---|
ActionResponse
|
An instance of the same entity with the metadata field attached. |
Source code in src/zenml/models/v2/core/action.py
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|
set_configuration(configuration)
Set the configuration
property.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
configuration
|
Dict[str, Any]
|
The value to set. |
required |
Source code in src/zenml/models/v2/core/action.py
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|
ActionResponseBody
Bases: ProjectScopedResponseBody
Response body for actions.
Source code in src/zenml/models/v2/core/action.py
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|
ActionResponseMetadata
Bases: ProjectScopedResponseMetadata
Response metadata for actions.
Source code in src/zenml/models/v2/core/action.py
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|
ActionResponseResources
Bases: ProjectScopedResponseResources
Class for all resource models associated with the action entity.
Source code in src/zenml/models/v2/core/action.py
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|
ActionUpdate
Bases: BaseUpdate
Update model for actions.
Source code in src/zenml/models/v2/core/action.py
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|
from_response(response)
classmethod
Create an update model from a response model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
response
|
ActionResponse
|
The response model to create the update model from. |
required |
Returns:
Type | Description |
---|---|
ActionUpdate
|
The update model. |
Source code in src/zenml/models/v2/core/action.py
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|
ArtifactFilter
Bases: ProjectScopedFilter
, TaggableFilter
Model to enable advanced filtering of artifacts.
Source code in src/zenml/models/v2/core/artifact.py
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|
apply_sorting(query, table)
Apply sorting to the query for Artifacts.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query
|
AnyQuery
|
The query to which to apply the sorting. |
required |
table
|
Type[AnySchema]
|
The query table. |
required |
Returns:
Type | Description |
---|---|
AnyQuery
|
The query with sorting applied. |
Source code in src/zenml/models/v2/core/artifact.py
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|
ArtifactRequest
Bases: ProjectScopedRequest
Artifact request model.
Source code in src/zenml/models/v2/core/artifact.py
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|
ArtifactResponse
Bases: ProjectScopedResponse[ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources]
Artifact response model.
Source code in src/zenml/models/v2/core/artifact.py
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|
has_custom_name
property
The has_custom_name
property.
Returns:
Type | Description |
---|---|
bool
|
the value of the property. |
latest_version_id
property
The latest_version_id
property.
Returns:
Type | Description |
---|---|
Optional[UUID]
|
the value of the property. |
latest_version_name
property
The latest_version_name
property.
Returns:
Type | Description |
---|---|
Optional[str]
|
the value of the property. |
tags
property
versions
property
Get a list of all versions of this artifact.
Returns:
Type | Description |
---|---|
Dict[str, ArtifactVersionResponse]
|
A list of all versions of this artifact. |
get_hydrated_version()
Get the hydrated version of this artifact.
Returns:
Type | Description |
---|---|
ArtifactResponse
|
an instance of the same entity with the metadata field attached. |
Source code in src/zenml/models/v2/core/artifact.py
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|
ArtifactResponseBody
Bases: ProjectScopedResponseBody
Response body for artifacts.
Source code in src/zenml/models/v2/core/artifact.py
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|
ArtifactResponseMetadata
Bases: ProjectScopedResponseMetadata
Response metadata for artifacts.
Source code in src/zenml/models/v2/core/artifact.py
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|
ArtifactUpdate
Bases: BaseUpdate
Artifact update model.
Source code in src/zenml/models/v2/core/artifact.py
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|
ArtifactVersionFilter
Bases: ProjectScopedFilter
, TaggableFilter
, RunMetadataFilterMixin
Model to enable advanced filtering of artifact versions.
Source code in src/zenml/models/v2/core/artifact_version.py
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|
get_custom_filters(table)
Get custom filters.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
table
|
Type[AnySchema]
|
The query table. |
required |
Returns:
Type | Description |
---|---|
List[Union[ColumnElement[bool]]]
|
A list of custom filters. |
Source code in src/zenml/models/v2/core/artifact_version.py
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|
ArtifactVersionRequest
Bases: ProjectScopedRequest
Request model for artifact versions.
Source code in src/zenml/models/v2/core/artifact_version.py
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|
str_field_max_length_check(value)
classmethod
Checks if the length of the value exceeds the maximum str length.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
value
|
Any
|
the value set in the field |
required |
Returns:
Type | Description |
---|---|
Any
|
the value itself. |
Raises:
Type | Description |
---|---|
AssertionError
|
if the length of the field is longer than the maximum threshold. |
Source code in src/zenml/models/v2/core/artifact_version.py
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|
ArtifactVersionResponse
Bases: ProjectScopedResponse[ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources]
Response model for artifact versions.
Source code in src/zenml/models/v2/core/artifact_version.py
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|
artifact
property
artifact_store_id
property
The artifact_store_id
property.
Returns:
Type | Description |
---|---|
Optional[UUID]
|
the value of the property. |
data_type
property
materializer
property
name
property
The name
property.
Returns:
Type | Description |
---|---|
str
|
the value of the property. |
producer_pipeline_run_id
property
The producer_pipeline_run_id
property.
Returns:
Type | Description |
---|---|
Optional[UUID]
|
the value of the property. |
producer_step_run_id
property
The producer_step_run_id
property.
Returns:
Type | Description |
---|---|
Optional[UUID]
|
the value of the property. |
run
property
Get the pipeline run that produced this artifact.
Returns:
Type | Description |
---|---|
PipelineRunResponse
|
The pipeline run that produced this artifact. |
run_metadata
property
The metadata
property.
Returns:
Type | Description |
---|---|
Dict[str, MetadataType]
|
the value of the property. |
save_type
property
step
property
Get the step that produced this artifact.
Returns:
Type | Description |
---|---|
StepRunResponse
|
The step that produced this artifact. |
tags
property
type
property
uri
property
The uri
property.
Returns:
Type | Description |
---|---|
str
|
the value of the property. |
version
property
The version
property.
Returns:
Type | Description |
---|---|
Union[str, int]
|
the value of the property. |
visualizations
property
The visualizations
property.
Returns:
Type | Description |
---|---|
Optional[List[ArtifactVisualizationResponse]]
|
the value of the property. |
download_files(path, overwrite=False)
Downloads data for an artifact with no materializing.
Any artifacts will be saved as a zip file to the given path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
The path to save the binary data to. |
required |
overwrite
|
bool
|
Whether to overwrite the file if it already exists. |
False
|
Raises:
Type | Description |
---|---|
ValueError
|
If the path does not end with '.zip'. |
Source code in src/zenml/models/v2/core/artifact_version.py
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|