Skip to content

Digitalocean

zenml.integrations.digitalocean

Initialization of the DigitalOcean integration.

The DigitalOcean integration provides ZenML stack component flavors for DigitalOcean services: Spaces (S3-compatible object storage) as an artifact store, and the DigitalOcean Container Registry (DOCR) as a container registry.

Orchestration, sandboxes, and step execution on DigitalOcean run on DigitalOcean Kubernetes (DOKS) through the existing kubernetes flavors, so this integration deliberately does not ship its own orchestrator/sandbox/step-operator flavors.

Attributes

DIGITALOCEAN = 'digitalocean' module-attribute

DIGITALOCEAN_CONTAINER_REGISTRY_FLAVOR = 'digitalocean' module-attribute

DIGITALOCEAN_SPACES_ARTIFACT_STORE_FLAVOR = 'digitalocean' module-attribute

Classes

DigitalOceanIntegration

Bases: Integration

Definition of the DigitalOcean integration for ZenML.

Methods:
flavors() -> List[Type[Flavor]] classmethod

Declare the stack component flavors for the DigitalOcean integration.

Returns:

Type Description
List[Type[Flavor]]

List of stack component flavors for this integration.
Source code in src/zenml/integrations/digitalocean/__init__.py 
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the DigitalOcean integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.digitalocean.flavors import (
        DigitalOceanContainerRegistryFlavor,
        DigitalOceanSpacesArtifactStoreFlavor,
    )

    return [
        DigitalOceanSpacesArtifactStoreFlavor,
        DigitalOceanContainerRegistryFlavor,
    ]

Flavor

Class for ZenML Flavors.

Attributes
config_class: Type[StackComponentConfig] abstractmethod property

Returns StackComponentConfig config class.

Returns:

Type Description
Type[StackComponentConfig]

The config class.
config_schema: Dict[str, Any] property

The config schema for a flavor.

Returns:

Type Description
Dict[str, Any]

The config schema.
display_name: Optional[str] property

The display name of the flavor.

By default, converts the technical name to a human-readable format. For example, "vm_kubernetes" becomes "VM Kubernetes". Flavors can override this to provide custom display names.

Returns:

Type Description
Optional[str]

The display name of the flavor.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[StackComponent] abstractmethod property

Implementation class for this flavor.

Returns:

Type Description
Type[StackComponent]

The implementation class for this flavor.
logo_url: Optional[str] property

A url to represent the flavor in the dashboard.

Returns:

Type Description
Optional[str]

The flavor logo.
name: str abstractmethod property

The flavor name.

Returns:

Type Description
str

The flavor name.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] 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.
type: StackComponentType abstractmethod property

The stack component type.

Returns:

Type Description
StackComponentType

The stack component type.
Methods:
from_model(flavor_model: FlavorResponse) -> Flavor classmethod

Loads a flavor from a model.

Parameters:

Name Type Description Default
flavor_model FlavorResponse

The model to load from.

 required

Raises:

Type Description
CustomFlavorImportError

If the custom flavor can't be imported.
ImportError

If the flavor can't be imported.

Returns:

Type Description
Flavor

The loaded flavor.
Source code in src/zenml/stack/flavor.py 
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
@classmethod
def from_model(cls, flavor_model: FlavorResponse) -> "Flavor":
    """Loads a flavor from a model.

    Args:
        flavor_model: The model to load from.

    Raises:
        CustomFlavorImportError: If the custom flavor can't be imported.
        ImportError: If the flavor can't be imported.

    Returns:
        The loaded flavor.
    """
    try:
        _, flavor = validate_flavor_source(
            source=flavor_model.source,
            component_type=flavor_model.type,
            validate_component_classes=False,
        )
    except (TypeError, ValueError) as err:
        if flavor_model.is_custom:
            flavor_module, _, _ = flavor_model.source.rpartition(".")
            expected_file_path = os.path.join(
                source_utils.get_source_root(),
                flavor_module.replace(".", os.path.sep),
            )
            raise CustomFlavorImportError(
                f"Couldn't import custom flavor {flavor_model.name}: "
                f"{err}. Make sure the custom flavor class "
                f"`{flavor_model.source}` is importable. If it is part of "
                "a library, make sure it is installed. If "
                "it is a local code file, make sure it exists at "
                f"`{expected_file_path}.py`."
            ) from err
        else:
            raise ImportError(
                f"Couldn't import flavor {flavor_model.name}: {err}"
            ) from err
    return flavor
generate_default_docs_url() -> str

Generate the doc urls for all inbuilt and integration flavors.

Note that this method is not going to be useful for custom flavors, which do not have any docs in the main zenml docs.

Returns:

Type Description
str

The complete url to the zenml documentation
Source code in src/zenml/stack/flavor.py 
223
224
225
226
227
228
229
230
231
232
233
234
235
236
def generate_default_docs_url(self) -> str:
    """Generate the doc urls for all inbuilt and integration flavors.

    Note that this method is not going to be useful for custom flavors,
    which do not have any docs in the main zenml docs.

    Returns:
        The complete url to the zenml documentation
    """
    component_type = self.type.plural.replace("_", "-")
    name = self.name.replace("_", "-")

    base = "https://docs.zenml.io"
    return f"{base}/stack-components/{component_type}/{name}"
generate_default_sdk_docs_url() -> str

Generate SDK docs url for a flavor.

Returns:

Type Description
str

The complete url to the zenml SDK docs
Source code in src/zenml/stack/flavor.py 
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
def generate_default_sdk_docs_url(self) -> str:
    """Generate SDK docs url for a flavor.

    Returns:
        The complete url to the zenml SDK docs
    """
    from zenml import __version__

    base = f"https://sdkdocs.zenml.io/{__version__}"

    component_type = self.type.plural

    if "zenml.integrations" in self.__module__:
        # Get integration name out of module path which will look something
        #  like this "zenml.integrations.<integration>....
        integration = self.__module__.split(
            "zenml.integrations.", maxsplit=1
        )[1].split(".")[0]

        # Get the config class name to point to the specific class
        config_class_name = self.config_class.__name__

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}"
            f"#zenml.integrations.{integration}.flavors.{config_class_name}"
        )

    else:
        return (
            f"{base}/core_code_docs/core-{component_type}/"
            f"#{self.__module__}"
        )
to_model(integration: Optional[str] = None, is_custom: bool = True) -> FlavorRequest

Converts a flavor to a model.

Parameters:

Name Type Description Default
integration Optional[str]

The integration to use for the model.

 None
is_custom bool

Whether the flavor is a custom flavor.

 True

Returns:

Type Description
FlavorRequest

The model.
Source code in src/zenml/stack/flavor.py 
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
def to_model(
    self,
    integration: Optional[str] = None,
    is_custom: bool = True,
) -> FlavorRequest:
    """Converts a flavor to a model.

    Args:
        integration: The integration to use for the model.
        is_custom: Whether the flavor is a custom flavor.

    Returns:
        The model.
    """
    connector_requirements = self.service_connector_requirements
    connector_type = (
        connector_requirements.connector_type
        if connector_requirements
        else None
    )
    resource_type = (
        connector_requirements.resource_type
        if connector_requirements
        else None
    )
    resource_id_attr = (
        connector_requirements.resource_id_attr
        if connector_requirements
        else None
    )

    model = FlavorRequest(
        name=self.name,
        display_name=self.display_name,
        type=self.type,
        source=source_utils.resolve(self.__class__).import_path,
        config_schema=self.config_schema,
        connector_type=connector_type,
        connector_resource_type=resource_type,
        connector_resource_id_attr=resource_id_attr,
        integration=integration,
        logo_url=self.logo_url,
        docs_url=self.docs_url,
        sdk_docs_url=self.sdk_docs_url,
        is_custom=is_custom,
    )
    return model

Integration

Base class for integration in ZenML.

Methods:
activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py 
136
137
138
@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""
check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type Description
bool

True if all required packages are installed, False otherwise.
Source code in src/zenml/integrations/integration.py 
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
@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for requirement in cls.get_requirements():
        parsed_requirement = Requirement(requirement)

        if not requirement_installed(parsed_requirement):
            logger.debug(
                "Requirement '%s' for integration '%s' is not installed "
                "or installed with the wrong version.",
                requirement,
                cls.NAME,
            )
            return False

        dependencies = get_dependencies(parsed_requirement)

        for dependency in dependencies:
            if not requirement_installed(dependency):
                logger.debug(
                    "Requirement '%s' for integration '%s' is not "
                    "installed or installed with the wrong version.",
                    dependency,
                    cls.NAME,
                )
                return False

    logger.debug(
        f"Integration '{cls.NAME}' is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True
flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type Description
List[Type[Flavor]]

A list of new stack component flavors.
Source code in src/zenml/integrations/integration.py 
140
141
142
143
144
145
146
147
@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []
get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None
python_version Optional[str]

The Python version to use for the requirements.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS
get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret

Modules

artifact_stores

Initialization of the DigitalOcean Spaces Artifact Store.

Classes
DigitalOceanSpacesArtifactStore(*args: Any, **kwargs: Any)

Bases: S3ArtifactStore

Artifact Store backed by a DigitalOcean Spaces bucket via the S3 API.

Source code in src/zenml/integrations/s3/artifact_stores/s3_artifact_store.py 
169
170
171
172
173
174
175
176
177
178
179
180
181
def __init__(
    self,
    *args: Any,
    **kwargs: Any,
) -> None:
    """Initializes the artifact store.

    Args:
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.
    """
    super().__init__(*args, **kwargs)
    self._boto3_bucket_holder = None
Attributes
config: DigitalOceanSpacesArtifactStoreConfig property

Get the typed config of this artifact store.

Returns:

Type Description
DigitalOceanSpacesArtifactStoreConfig

The config of this artifact store.
Modules
digitalocean_artifact_store

Implementation of the DigitalOcean Spaces Artifact Store.

DigitalOcean Spaces exposes an S3-compatible API that behaves identically to S3 for the read/write operations ZenML needs, so this class subclasses :class:S3ArtifactStore. The Spaces endpoint URL is derived from the configured region at runtime unless an explicit endpoint_url is provided through client_kwargs. Keeping the derived endpoint out of the persisted config avoids stale endpoint values when the region changes.

Classes
DigitalOceanSpacesArtifactStore(*args: Any, **kwargs: Any)

Bases: S3ArtifactStore

Artifact Store backed by a DigitalOcean Spaces bucket via the S3 API.

Source code in src/zenml/integrations/s3/artifact_stores/s3_artifact_store.py 
169
170
171
172
173
174
175
176
177
178
179
180
181
def __init__(
    self,
    *args: Any,
    **kwargs: Any,
) -> None:
    """Initializes the artifact store.

    Args:
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.
    """
    super().__init__(*args, **kwargs)
    self._boto3_bucket_holder = None
Attributes
config: DigitalOceanSpacesArtifactStoreConfig property

Get the typed config of this artifact store.

Returns:

Type Description
DigitalOceanSpacesArtifactStoreConfig

The config of this artifact store.

flavors

DigitalOcean integration flavors.

Classes
DigitalOceanContainerRegistryConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseContainerRegistryConfig

Configuration for the DigitalOcean Container Registry.

Source code in src/zenml/stack/stack_component.py 
 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
def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
Methods:
validate_uri(uri: str) -> str classmethod

Validates that the URI is a DigitalOcean Container Registry URI.

Parameters:

Name Type Description Default
uri str

URI to validate.

 required

Returns:

Type Description
str

The validated URI.

Raises:

Type Description
ValueError

If the URI is not a valid DOCR URI.
Source code in src/zenml/integrations/digitalocean/flavors/digitalocean_container_registry_flavor.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
@field_validator("uri")
@classmethod
def validate_uri(cls, uri: str) -> str:
    """Validates that the URI is a DigitalOcean Container Registry URI.

    Args:
        uri: URI to validate.

    Returns:
        The validated URI.

    Raises:
        ValueError: If the URI is not a valid DOCR URI.
    """
    # The inherited base validator has already stripped any trailing
    # slash. Require the exact DOCR host followed by a registry name: a
    # bare `startswith` would accept lookalike hosts such as
    # `registry.digitalocean.com.evil.example`, and a bare host without a
    # registry name is not a valid image push target.
    prefix = f"{DOCR_URI_PREFIX}/"
    if not uri.startswith(prefix) or not uri[len(prefix) :]:
        raise ValueError(
            f"Property `uri` for the DigitalOcean container registry must "
            f"be the DOCR host followed by your registry name. An example "
            f"of a valid URI is `{DOCR_URI_PREFIX}/my-registry`."
        )
    return uri
DigitalOceanContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

DigitalOcean Container Registry (DOCR) flavor.

Attributes
config_class: Type[DigitalOceanContainerRegistryConfig] property

Config class for this flavor.

Returns:

Type Description
Type[DigitalOceanContainerRegistryConfig]

The config class.
display_name: str property

Display name of the flavor.

Returns:

Type Description
str

The display name of the flavor.
docs_url: Optional[str] property

A URL to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
logo_url: str property

A URL to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A URL to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] 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. Any connector that provides a Docker registry resource can be used.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

Requirements for compatible service connectors.
DigitalOceanSpacesArtifactStoreConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: S3ArtifactStoreConfig

Configuration for the DigitalOcean Spaces artifact store.

Inherits every option of the S3 artifact store (Spaces is S3-compatible) and adds a region field. The bucket URI continues to use the s3:// scheme because the underlying filesystem (s3fs) addresses Spaces buckets through the S3 API.

Example 
zenml artifact-store register do_spaces           --flavor=digitalocean --path=s3://my-space --region=fra1
Source code in src/zenml/stack/stack_component.py 
 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
def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
DigitalOceanSpacesArtifactStoreFlavor

Bases: S3ArtifactStoreFlavor

Flavor of the DigitalOcean Spaces artifact store.

Attributes
config_class: Type[DigitalOceanSpacesArtifactStoreConfig] property

The config class of the flavor.

Returns:

Type Description
Type[DigitalOceanSpacesArtifactStoreConfig]

The config class of the flavor.
display_name: str property

Display name of the flavor.

Returns:

Type Description
str

The display name of the flavor.
implementation_class: Type[DigitalOceanSpacesArtifactStore] property

Implementation class for this flavor.

Returns:

Type Description
Type[DigitalOceanSpacesArtifactStore]

The implementation class for this flavor.
logo_url: str property

A URL to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

A dedicated DigitalOcean service connector is a planned follow-up; until it exists, the Spaces artifact store authenticates with the key / secret Spaces access keys directly, so no connector is advertised.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

None, until a DigitalOcean service connector is available.
Modules
digitalocean_artifact_store_flavor

DigitalOcean Spaces artifact store flavor.

DigitalOcean Spaces is S3-compatible, so this flavor subclasses the S3 implementation rather than duplicating it. The config layer only validates user-provided values (the Spaces region); the region-derived endpoint URL is applied in the artifact store implementation so it is not persisted as stack component configuration.

Classes
DigitalOceanSpacesArtifactStoreConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: S3ArtifactStoreConfig

Configuration for the DigitalOcean Spaces artifact store.

Inherits every option of the S3 artifact store (Spaces is S3-compatible) and adds a region field. The bucket URI continues to use the s3:// scheme because the underlying filesystem (s3fs) addresses Spaces buckets through the S3 API.

Example 
zenml artifact-store register do_spaces           --flavor=digitalocean --path=s3://my-space --region=fra1
Source code in src/zenml/stack/stack_component.py 
 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
def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
DigitalOceanSpacesArtifactStoreFlavor

Bases: S3ArtifactStoreFlavor

Flavor of the DigitalOcean Spaces artifact store.

Attributes
config_class: Type[DigitalOceanSpacesArtifactStoreConfig] property

The config class of the flavor.

Returns:

Type Description
Type[DigitalOceanSpacesArtifactStoreConfig]

The config class of the flavor.
display_name: str property

Display name of the flavor.

Returns:

Type Description
str

The display name of the flavor.
implementation_class: Type[DigitalOceanSpacesArtifactStore] property

Implementation class for this flavor.

Returns:

Type Description
Type[DigitalOceanSpacesArtifactStore]

The implementation class for this flavor.
logo_url: str property

A URL to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

A dedicated DigitalOcean service connector is a planned follow-up; until it exists, the Spaces artifact store authenticates with the key / secret Spaces access keys directly, so no connector is advertised.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

None, until a DigitalOcean service connector is available.
digitalocean_container_registry_flavor

DigitalOcean Container Registry (DOCR) flavor.

Classes
DigitalOceanContainerRegistryConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseContainerRegistryConfig

Configuration for the DigitalOcean Container Registry.

Source code in src/zenml/stack/stack_component.py 
 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
def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
Methods:
validate_uri(uri: str) -> str classmethod

Validates that the URI is a DigitalOcean Container Registry URI.

Parameters:

Name Type Description Default
uri str

URI to validate.

 required

Returns:

Type Description
str

The validated URI.

Raises:

Type Description
ValueError

If the URI is not a valid DOCR URI.
Source code in src/zenml/integrations/digitalocean/flavors/digitalocean_container_registry_flavor.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
@field_validator("uri")
@classmethod
def validate_uri(cls, uri: str) -> str:
    """Validates that the URI is a DigitalOcean Container Registry URI.

    Args:
        uri: URI to validate.

    Returns:
        The validated URI.

    Raises:
        ValueError: If the URI is not a valid DOCR URI.
    """
    # The inherited base validator has already stripped any trailing
    # slash. Require the exact DOCR host followed by a registry name: a
    # bare `startswith` would accept lookalike hosts such as
    # `registry.digitalocean.com.evil.example`, and a bare host without a
    # registry name is not a valid image push target.
    prefix = f"{DOCR_URI_PREFIX}/"
    if not uri.startswith(prefix) or not uri[len(prefix) :]:
        raise ValueError(
            f"Property `uri` for the DigitalOcean container registry must "
            f"be the DOCR host followed by your registry name. An example "
            f"of a valid URI is `{DOCR_URI_PREFIX}/my-registry`."
        )
    return uri
DigitalOceanContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

DigitalOcean Container Registry (DOCR) flavor.

Attributes
config_class: Type[DigitalOceanContainerRegistryConfig] property

Config class for this flavor.

Returns:

Type Description
Type[DigitalOceanContainerRegistryConfig]

The config class.
display_name: str property

Display name of the flavor.

Returns:

Type Description
str

The display name of the flavor.
docs_url: Optional[str] property

A URL to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
logo_url: str property

A URL to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A URL to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] 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. Any connector that provides a Docker registry resource can be used.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

Requirements for compatible service connectors.