Integrations
zenml.integrations
special
The ZenML integrations module contains sub-modules for each integration that we
support. This includes orchestrators like Apache Airflow, visualization tools
like the facets library, as well as deep learning libraries like PyTorch.
airflow
special
The Airflow integration sub-module powers an alternative to the local
orchestrator. You can enable it by registering the Airflow orchestrator with
the CLI tool, then bootstrap using the zenml orchestrator up command.
AirflowIntegration (Integration)
Definition of Airflow Integration for ZenML.
Source code in zenml/integrations/airflow/__init__.py
class AirflowIntegration(Integration):
"""Definition of Airflow Integration for ZenML."""
NAME = AIRFLOW
REQUIREMENTS = ["apache-airflow==2.2.0"]
@classmethod
def activate(cls):
"""Activates all classes required for the airflow integration."""
from zenml.integrations.airflow import orchestrators # noqa
activate()
classmethod
Activates all classes required for the airflow integration.
Source code in zenml/integrations/airflow/__init__.py
@classmethod
def activate(cls):
"""Activates all classes required for the airflow integration."""
from zenml.integrations.airflow import orchestrators # noqa
orchestrators
special
airflow_component
Definition for Airflow component for TFX.
AirflowComponent (PythonOperator)
Airflow-specific TFX Component. This class wrap a component run into its own PythonOperator in Airflow.
Source code in zenml/integrations/airflow/orchestrators/airflow_component.py
class AirflowComponent(python.PythonOperator):
"""Airflow-specific TFX Component.
This class wrap a component run into its own PythonOperator in Airflow.
"""
def __init__(
self,
*,
parent_dag: airflow.DAG,
pipeline_node: pipeline_pb2.PipelineNode,
mlmd_connection: metadata.Metadata,
pipeline_info: pipeline_pb2.PipelineInfo,
pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec,
executor_spec: Optional[message.Message] = None,
custom_driver_spec: Optional[message.Message] = None
) -> None:
"""Constructs an Airflow implementation of TFX component.
Args:
parent_dag: The airflow DAG that this component is contained in.
pipeline_node: The specification of the node to launch.
mlmd_connection: ML metadata connection info.
pipeline_info: The information of the pipeline that this node
runs in.
pipeline_runtime_spec: The runtime information of the pipeline
that this node runs in.
executor_spec: Specification for the executor of the node.
custom_driver_spec: Specification for custom driver.
"""
launcher_callable = functools.partial(
_airflow_component_launcher,
pipeline_node=pipeline_node,
mlmd_connection=mlmd_connection,
pipeline_info=pipeline_info,
pipeline_runtime_spec=pipeline_runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
)
super().__init__(
task_id=pipeline_node.node_info.id,
provide_context=True,
python_callable=launcher_callable,
dag=parent_dag,
)
__init__(self, *, parent_dag, pipeline_node, mlmd_connection, pipeline_info, pipeline_runtime_spec, executor_spec=None, custom_driver_spec=None)
special
Constructs an Airflow implementation of TFX component.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
parent_dag |
DAG |
The airflow DAG that this component is contained in. |
required |
pipeline_node |
PipelineNode |
The specification of the node to launch. |
required |
mlmd_connection |
Metadata |
ML metadata connection info. |
required |
pipeline_info |
PipelineInfo |
The information of the pipeline that this node runs in. |
required |
pipeline_runtime_spec |
PipelineRuntimeSpec |
The runtime information of the pipeline that this node runs in. |
required |
executor_spec |
Optional[google.protobuf.message.Message] |
Specification for the executor of the node. |
None |
custom_driver_spec |
Optional[google.protobuf.message.Message] |
Specification for custom driver. |
None |
Source code in zenml/integrations/airflow/orchestrators/airflow_component.py
def __init__(
self,
*,
parent_dag: airflow.DAG,
pipeline_node: pipeline_pb2.PipelineNode,
mlmd_connection: metadata.Metadata,
pipeline_info: pipeline_pb2.PipelineInfo,
pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec,
executor_spec: Optional[message.Message] = None,
custom_driver_spec: Optional[message.Message] = None
) -> None:
"""Constructs an Airflow implementation of TFX component.
Args:
parent_dag: The airflow DAG that this component is contained in.
pipeline_node: The specification of the node to launch.
mlmd_connection: ML metadata connection info.
pipeline_info: The information of the pipeline that this node
runs in.
pipeline_runtime_spec: The runtime information of the pipeline
that this node runs in.
executor_spec: Specification for the executor of the node.
custom_driver_spec: Specification for custom driver.
"""
launcher_callable = functools.partial(
_airflow_component_launcher,
pipeline_node=pipeline_node,
mlmd_connection=mlmd_connection,
pipeline_info=pipeline_info,
pipeline_runtime_spec=pipeline_runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
)
super().__init__(
task_id=pipeline_node.node_info.id,
provide_context=True,
python_callable=launcher_callable,
dag=parent_dag,
)
airflow_dag_runner
Definition of Airflow TFX runner. This is an unmodified copy from the TFX source code (outside of superficial, stylistic changes)
AirflowDagRunner (TfxRunner)
Tfx runner on Airflow.
Source code in zenml/integrations/airflow/orchestrators/airflow_dag_runner.py
class AirflowDagRunner(tfx_runner.TfxRunner):
"""Tfx runner on Airflow."""
def __init__(
self,
config: Optional[Union[Dict[str, Any], AirflowPipelineConfig]] = None,
):
"""Creates an instance of AirflowDagRunner.
Args:
config: Optional Airflow pipeline config for customizing the
launching of each component.
"""
if isinstance(config, dict):
warnings.warn(
"Pass config as a dict type is going to deprecated in 0.1.16. "
"Use AirflowPipelineConfig type instead.",
PendingDeprecationWarning,
)
config = AirflowPipelineConfig(airflow_dag_config=config)
super().__init__(config)
def run(
self, pipeline: tfx_pipeline.Pipeline, run_name: str = ""
) -> "airflow.DAG":
"""Deploys given logical pipeline on Airflow.
Args:
pipeline: Logical pipeline containing pipeline args and comps.
run_name: Optional name for the run.
Returns:
An Airflow DAG.
"""
# Only import these when needed.
import airflow # noqa
from zenml.integrations.airflow.orchestrators import airflow_component
# Merge airflow-specific configs with pipeline args
airflow_dag = airflow.DAG(
dag_id=pipeline.pipeline_info.pipeline_name,
**(
typing.cast(
AirflowPipelineConfig, self._config
).airflow_dag_config
),
is_paused_upon_creation=False,
catchup=False, # no backfill
)
if "tmp_dir" not in pipeline.additional_pipeline_args:
tmp_dir = os.path.join(
pipeline.pipeline_info.pipeline_root, ".temp", ""
)
pipeline.additional_pipeline_args["tmp_dir"] = tmp_dir
for component in pipeline.components:
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies(
pipeline.pipeline_info.pipeline_root
)
self._replace_runtime_params(component)
c = compiler.Compiler()
pipeline = c.compile(pipeline)
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
pipeline,
{
"pipeline-run-id": run_name,
},
)
deployment_config = runner_utils.extract_local_deployment_config(
pipeline
)
connection_config = deployment_config.metadata_connection_config # type: ignore[attr-defined] # noqa
component_impl_map = {}
for node in pipeline.nodes:
pipeline_node = node.pipeline_node
node_id = pipeline_node.node_info.id
executor_spec = runner_utils.extract_executor_spec(
deployment_config, node_id
)
custom_driver_spec = runner_utils.extract_custom_driver_spec(
deployment_config, node_id
)
current_airflow_component = airflow_component.AirflowComponent(
parent_dag=airflow_dag,
pipeline_node=pipeline_node,
mlmd_connection=connection_config,
pipeline_info=pipeline.pipeline_info,
pipeline_runtime_spec=pipeline.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
)
component_impl_map[node_id] = current_airflow_component
for upstream_node in node.pipeline_node.upstream_nodes:
assert (
upstream_node in component_impl_map
), "Components is not in topological order"
current_airflow_component.set_upstream(
component_impl_map[upstream_node]
)
return airflow_dag
def _replace_runtime_params(
self, comp: base_node.BaseNode
) -> base_node.BaseNode:
"""Replaces runtime params for dynamic Airflow parameter execution.
Args:
comp: TFX component to be parsed.
Returns:
Returns edited component.
"""
for k, prop in comp.exec_properties.copy().items():
if isinstance(prop, RuntimeParameter):
# Airflow only supports string parameters.
if prop.ptype != str:
raise RuntimeError(
f"RuntimeParameter in Airflow does not support "
f"{prop.ptype}. The only ptype supported is string."
)
# If the default is a template, drop the template markers
# when inserting it into the .get() default argument below.
# Otherwise, provide the default as a quoted string.
default = cast(str, prop.default)
if default.startswith("{{") and default.endswith("}}"):
default = default[2:-2]
else:
default = json.dumps(default)
template_field = '{{ dag_run.conf.get("%s", %s) }}' % (
prop.name,
default,
)
comp.exec_properties[k] = template_field
return comp
__init__(self, config=None)
special
Creates an instance of AirflowDagRunner.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
config |
Union[Dict[str, Any], zenml.integrations.airflow.orchestrators.airflow_dag_runner.AirflowPipelineConfig] |
Optional Airflow pipeline config for customizing the |
None |
Source code in zenml/integrations/airflow/orchestrators/airflow_dag_runner.py
def __init__(
self,
config: Optional[Union[Dict[str, Any], AirflowPipelineConfig]] = None,
):
"""Creates an instance of AirflowDagRunner.
Args:
config: Optional Airflow pipeline config for customizing the
launching of each component.
"""
if isinstance(config, dict):
warnings.warn(
"Pass config as a dict type is going to deprecated in 0.1.16. "
"Use AirflowPipelineConfig type instead.",
PendingDeprecationWarning,
)
config = AirflowPipelineConfig(airflow_dag_config=config)
super().__init__(config)
run(self, pipeline, run_name='')
Deploys given logical pipeline on Airflow.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline |
Pipeline |
Logical pipeline containing pipeline args and comps. |
required |
run_name |
str |
Optional name for the run. |
'' |
Returns:
| Type | Description |
|---|---|
airflow.DAG |
An Airflow DAG. |
Source code in zenml/integrations/airflow/orchestrators/airflow_dag_runner.py
def run(
self, pipeline: tfx_pipeline.Pipeline, run_name: str = ""
) -> "airflow.DAG":
"""Deploys given logical pipeline on Airflow.
Args:
pipeline: Logical pipeline containing pipeline args and comps.
run_name: Optional name for the run.
Returns:
An Airflow DAG.
"""
# Only import these when needed.
import airflow # noqa
from zenml.integrations.airflow.orchestrators import airflow_component
# Merge airflow-specific configs with pipeline args
airflow_dag = airflow.DAG(
dag_id=pipeline.pipeline_info.pipeline_name,
**(
typing.cast(
AirflowPipelineConfig, self._config
).airflow_dag_config
),
is_paused_upon_creation=False,
catchup=False, # no backfill
)
if "tmp_dir" not in pipeline.additional_pipeline_args:
tmp_dir = os.path.join(
pipeline.pipeline_info.pipeline_root, ".temp", ""
)
pipeline.additional_pipeline_args["tmp_dir"] = tmp_dir
for component in pipeline.components:
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies(
pipeline.pipeline_info.pipeline_root
)
self._replace_runtime_params(component)
c = compiler.Compiler()
pipeline = c.compile(pipeline)
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
pipeline,
{
"pipeline-run-id": run_name,
},
)
deployment_config = runner_utils.extract_local_deployment_config(
pipeline
)
connection_config = deployment_config.metadata_connection_config # type: ignore[attr-defined] # noqa
component_impl_map = {}
for node in pipeline.nodes:
pipeline_node = node.pipeline_node
node_id = pipeline_node.node_info.id
executor_spec = runner_utils.extract_executor_spec(
deployment_config, node_id
)
custom_driver_spec = runner_utils.extract_custom_driver_spec(
deployment_config, node_id
)
current_airflow_component = airflow_component.AirflowComponent(
parent_dag=airflow_dag,
pipeline_node=pipeline_node,
mlmd_connection=connection_config,
pipeline_info=pipeline.pipeline_info,
pipeline_runtime_spec=pipeline.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
)
component_impl_map[node_id] = current_airflow_component
for upstream_node in node.pipeline_node.upstream_nodes:
assert (
upstream_node in component_impl_map
), "Components is not in topological order"
current_airflow_component.set_upstream(
component_impl_map[upstream_node]
)
return airflow_dag
AirflowPipelineConfig (PipelineConfig)
Pipeline config for AirflowDagRunner.
Source code in zenml/integrations/airflow/orchestrators/airflow_dag_runner.py
class AirflowPipelineConfig(pipeline_config.PipelineConfig):
"""Pipeline config for AirflowDagRunner."""
def __init__(
self, airflow_dag_config: Optional[Dict[str, Any]] = None, **kwargs: Any
):
"""Creates an instance of AirflowPipelineConfig.
Args:
airflow_dag_config: Configs of Airflow DAG model. See
https://airflow.apache.org/_api/airflow/models/dag/index.html#airflow.models.dag.DAG
for the full spec.
**kwargs: keyword args for PipelineConfig.
"""
super().__init__(**kwargs)
self.airflow_dag_config = airflow_dag_config or {}
__init__(self, airflow_dag_config=None, **kwargs)
special
Creates an instance of AirflowPipelineConfig.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
airflow_dag_config |
Optional[Dict[str, Any]] |
Configs of Airflow DAG model. See https://airflow.apache.org/_api/airflow/models/dag/index.html#airflow.models.dag.DAG for the full spec. |
None |
**kwargs |
Any |
keyword args for PipelineConfig. |
{} |
Source code in zenml/integrations/airflow/orchestrators/airflow_dag_runner.py
def __init__(
self, airflow_dag_config: Optional[Dict[str, Any]] = None, **kwargs: Any
):
"""Creates an instance of AirflowPipelineConfig.
Args:
airflow_dag_config: Configs of Airflow DAG model. See
https://airflow.apache.org/_api/airflow/models/dag/index.html#airflow.models.dag.DAG
for the full spec.
**kwargs: keyword args for PipelineConfig.
"""
super().__init__(**kwargs)
self.airflow_dag_config = airflow_dag_config or {}
airflow_orchestrator
AirflowOrchestrator (BaseOrchestrator)
pydantic-model
Orchestrator responsible for running pipelines using Airflow.
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
class AirflowOrchestrator(BaseOrchestrator):
"""Orchestrator responsible for running pipelines using Airflow."""
airflow_home: str = ""
airflow_config: Optional[Dict[str, Any]] = {}
schedule_interval_minutes: int = 1
def __init__(self, **values: Any):
"""Sets environment variables to configure airflow."""
super().__init__(**values)
self._set_env()
@root_validator
def set_airflow_home(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Sets airflow home according to orchestrator UUID."""
if "uuid" not in values:
raise ValueError("`uuid` needs to exist for AirflowOrchestrator.")
values["airflow_home"] = os.path.join(
zenml.io.utils.get_global_config_directory(),
AIRFLOW_ROOT_DIR,
str(values["uuid"]),
)
return values
@property
def dags_directory(self) -> str:
"""Returns path to the airflow dags directory."""
return os.path.join(self.airflow_home, "dags")
@property
def pid_file(self) -> str:
"""Returns path to the daemon PID file."""
return os.path.join(self.airflow_home, "airflow_daemon.pid")
@property
def log_file(self) -> str:
"""Returns path to the airflow log file."""
return os.path.join(self.airflow_home, "airflow_orchestrator.log")
@property
def password_file(self) -> str:
"""Returns path to the webserver password file."""
return os.path.join(self.airflow_home, "standalone_admin_password.txt")
@property
def is_running(self) -> bool:
"""Returns whether the airflow daemon is currently running."""
from airflow.cli.commands.standalone_command import StandaloneCommand
from airflow.jobs.triggerer_job import TriggererJob
daemon_running = daemon.check_if_daemon_is_running(self.pid_file)
command = StandaloneCommand()
webserver_port_open = command.port_open(8080)
if not daemon_running:
if webserver_port_open:
raise RuntimeError(
"The airflow daemon does not seem to be running but "
"local port 8080 is occupied. Make sure the port is "
"available and try again."
)
# exit early so we don't check non-existing airflow databases
return False
# we can't use StandaloneCommand().is_ready() here as the
# Airflow SequentialExecutor apparently does not send a heartbeat
# while running a task which would result in this returning `False`
# even if Airflow is running.
airflow_running = webserver_port_open and command.job_running(
TriggererJob
)
return airflow_running
def _set_env(self) -> None:
"""Sets environment variables to configure airflow."""
os.environ["AIRFLOW_HOME"] = self.airflow_home
os.environ["AIRFLOW__CORE__DAGS_FOLDER"] = self.dags_directory
os.environ["AIRFLOW__CORE__DAG_DISCOVERY_SAFE_MODE"] = "false"
os.environ["AIRFLOW__CORE__LOAD_EXAMPLES"] = "false"
# check the DAG folder every 10 seconds for new files
os.environ["AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL"] = "10"
def _copy_to_dag_directory_if_necessary(self, dag_filepath: str):
"""Copies the DAG module to the airflow DAGs directory if it's not
already located there.
Args:
dag_filepath: Path to the file in which the DAG is defined.
"""
dags_directory = fileio.resolve_relative_path(self.dags_directory)
if dags_directory == os.path.dirname(dag_filepath):
logger.debug("File is already in airflow DAGs directory.")
else:
logger.debug(
"Copying dag file '%s' to DAGs directory.", dag_filepath
)
destination_path = os.path.join(
dags_directory, os.path.basename(dag_filepath)
)
if fileio.file_exists(destination_path):
logger.info(
"File '%s' already exists, overwriting with new DAG file",
destination_path,
)
fileio.copy(dag_filepath, destination_path, overwrite=True)
def _log_webserver_credentials(self):
"""Logs URL and credentials to login to the airflow webserver.
Raises:
FileNotFoundError: If the password file does not exist.
"""
if fileio.file_exists(self.password_file):
with open(self.password_file) as file:
password = file.read().strip()
else:
raise FileNotFoundError(
f"Can't find password file '{self.password_file}'"
)
logger.info(
"To inspect your DAGs, login to http://0.0.0.0:8080 "
"with username: admin password: %s",
password,
)
def pre_run(self, pipeline: "BasePipeline", caller_filepath: str) -> None:
"""Checks whether airflow is running and copies the DAG file to the
airflow DAGs directory.
Args:
pipeline: Pipeline that will be run.
caller_filepath: Path to the file in which `pipeline.run()` was
called. This contains the airflow DAG that is returned by
the `run()` method.
Raises:
RuntimeError: If airflow is not running.
"""
if not self.is_running:
raise RuntimeError(
"Airflow orchestrator is currently not running. "
"Run `zenml orchestrator up` to start the "
"orchestrator of the active stack."
)
self._copy_to_dag_directory_if_necessary(dag_filepath=caller_filepath)
def up(self) -> None:
"""Ensures that Airflow is running."""
if self.is_running:
logger.info("Airflow is already running.")
self._log_webserver_credentials()
return
if not fileio.file_exists(self.dags_directory):
fileio.create_dir_recursive_if_not_exists(self.dags_directory)
from airflow.cli.commands.standalone_command import StandaloneCommand
try:
command = StandaloneCommand()
# Run the daemon with a working directory inside the current
# zenml repo so the same repo will be used to run the DAGs
daemon.run_as_daemon(
command.run,
pid_file=self.pid_file,
log_file=self.log_file,
working_directory=zenml.io.utils.get_zenml_dir(),
)
while not self.is_running:
# Wait until the daemon started all the relevant airflow processes
time.sleep(0.1)
self._log_webserver_credentials()
except Exception as e:
logger.error(e)
logger.error(
"An error occurred while starting the Airflow daemon. "
"If you want to start it manually, use the commands described "
"in the official Airflow quickstart guide for running Airflow locally."
)
self.down()
def down(self) -> None:
"""Stops the airflow daemon if necessary and tears down resources."""
if self.is_running:
daemon.stop_daemon(self.pid_file, kill_children=True)
fileio.rm_dir(self.airflow_home)
logger.info("Airflow spun down.")
def run(
self,
zenml_pipeline: "BasePipeline",
run_name: str,
**kwargs: Any,
) -> "airflow.DAG":
"""Prepares the pipeline so it can be run in Airflow.
Args:
zenml_pipeline: The pipeline to run.
run_name: Name of the pipeline run.
**kwargs: Unused argument to conform with base class signature.
"""
self.airflow_config = {
"schedule_interval": datetime.timedelta(
minutes=self.schedule_interval_minutes
),
# We set this in the past and turn catchup off and then it works
"start_date": datetime.datetime(2019, 1, 1),
}
runner = AirflowDagRunner(AirflowPipelineConfig(self.airflow_config))
tfx_pipeline = create_tfx_pipeline(zenml_pipeline)
return runner.run(tfx_pipeline, run_name=run_name)
dags_directory: str
property
readonly
Returns path to the airflow dags directory.
is_running: bool
property
readonly
Returns whether the airflow daemon is currently running.
log_file: str
property
readonly
Returns path to the airflow log file.
password_file: str
property
readonly
Returns path to the webserver password file.
pid_file: str
property
readonly
Returns path to the daemon PID file.
__init__(self, **values)
special
Sets environment variables to configure airflow.
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
def __init__(self, **values: Any):
"""Sets environment variables to configure airflow."""
super().__init__(**values)
self._set_env()
down(self)
Stops the airflow daemon if necessary and tears down resources.
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
def down(self) -> None:
"""Stops the airflow daemon if necessary and tears down resources."""
if self.is_running:
daemon.stop_daemon(self.pid_file, kill_children=True)
fileio.rm_dir(self.airflow_home)
logger.info("Airflow spun down.")
pre_run(self, pipeline, caller_filepath)
Checks whether airflow is running and copies the DAG file to the airflow DAGs directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline |
BasePipeline |
Pipeline that will be run. |
required |
caller_filepath |
str |
Path to the file in which |
required |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If airflow is not running. |
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
def pre_run(self, pipeline: "BasePipeline", caller_filepath: str) -> None:
"""Checks whether airflow is running and copies the DAG file to the
airflow DAGs directory.
Args:
pipeline: Pipeline that will be run.
caller_filepath: Path to the file in which `pipeline.run()` was
called. This contains the airflow DAG that is returned by
the `run()` method.
Raises:
RuntimeError: If airflow is not running.
"""
if not self.is_running:
raise RuntimeError(
"Airflow orchestrator is currently not running. "
"Run `zenml orchestrator up` to start the "
"orchestrator of the active stack."
)
self._copy_to_dag_directory_if_necessary(dag_filepath=caller_filepath)
run(self, zenml_pipeline, run_name, **kwargs)
Prepares the pipeline so it can be run in Airflow.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
zenml_pipeline |
BasePipeline |
The pipeline to run. |
required |
run_name |
str |
Name of the pipeline run. |
required |
**kwargs |
Any |
Unused argument to conform with base class signature. |
{} |
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
def run(
self,
zenml_pipeline: "BasePipeline",
run_name: str,
**kwargs: Any,
) -> "airflow.DAG":
"""Prepares the pipeline so it can be run in Airflow.
Args:
zenml_pipeline: The pipeline to run.
run_name: Name of the pipeline run.
**kwargs: Unused argument to conform with base class signature.
"""
self.airflow_config = {
"schedule_interval": datetime.timedelta(
minutes=self.schedule_interval_minutes
),
# We set this in the past and turn catchup off and then it works
"start_date": datetime.datetime(2019, 1, 1),
}
runner = AirflowDagRunner(AirflowPipelineConfig(self.airflow_config))
tfx_pipeline = create_tfx_pipeline(zenml_pipeline)
return runner.run(tfx_pipeline, run_name=run_name)
set_airflow_home(values)
classmethod
Sets airflow home according to orchestrator UUID.
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
@root_validator
def set_airflow_home(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Sets airflow home according to orchestrator UUID."""
if "uuid" not in values:
raise ValueError("`uuid` needs to exist for AirflowOrchestrator.")
values["airflow_home"] = os.path.join(
zenml.io.utils.get_global_config_directory(),
AIRFLOW_ROOT_DIR,
str(values["uuid"]),
)
return values
up(self)
Ensures that Airflow is running.
Source code in zenml/integrations/airflow/orchestrators/airflow_orchestrator.py
def up(self) -> None:
"""Ensures that Airflow is running."""
if self.is_running:
logger.info("Airflow is already running.")
self._log_webserver_credentials()
return
if not fileio.file_exists(self.dags_directory):
fileio.create_dir_recursive_if_not_exists(self.dags_directory)
from airflow.cli.commands.standalone_command import StandaloneCommand
try:
command = StandaloneCommand()
# Run the daemon with a working directory inside the current
# zenml repo so the same repo will be used to run the DAGs
daemon.run_as_daemon(
command.run,
pid_file=self.pid_file,
log_file=self.log_file,
working_directory=zenml.io.utils.get_zenml_dir(),
)
while not self.is_running:
# Wait until the daemon started all the relevant airflow processes
time.sleep(0.1)
self._log_webserver_credentials()
except Exception as e:
logger.error(e)
logger.error(
"An error occurred while starting the Airflow daemon. "
"If you want to start it manually, use the commands described "
"in the official Airflow quickstart guide for running Airflow locally."
)
self.down()
dash
special
DashIntegration (Integration)
Definition of Dash integration for ZenML.
Source code in zenml/integrations/dash/__init__.py
class DashIntegration(Integration):
"""Definition of Dash integration for ZenML."""
NAME = DASH
REQUIREMENTS = [
"dash>=2.0.0",
"dash-cytoscape>=0.3.0",
"dash-bootstrap-components>=1.0.1",
]
visualizers
special
pipeline_run_lineage_visualizer
PipelineRunLineageVisualizer (BasePipelineRunVisualizer)
Implementation of a lineage diagram via the dash and dash-cyctoscape library.
Source code in zenml/integrations/dash/visualizers/pipeline_run_lineage_visualizer.py
class PipelineRunLineageVisualizer(BasePipelineRunVisualizer):
"""Implementation of a lineage diagram via the [dash](
https://plotly.com/dash/) and [dash-cyctoscape](
https://dash.plotly.com/cytoscape) library."""
ARTIFACT_PREFIX = "artifact_"
STEP_PREFIX = "step_"
STATUS_CLASS_MAPPING = {
ExecutionStatus.CACHED: "green",
ExecutionStatus.FAILED: "red",
ExecutionStatus.RUNNING: "yellow",
ExecutionStatus.COMPLETED: "blue",
}
def visualize(
self, object: PipelineRunView, *args: Any, **kwargs: Any
) -> dash.Dash:
"""Method to visualize pipeline runs via the Dash library. The layout
puts every layer of the dag in a column.
"""
app = dash.Dash(
__name__,
external_stylesheets=[
dbc.themes.BOOTSTRAP,
dbc.icons.BOOTSTRAP,
],
)
nodes, edges, first_step_id = [], [], None
first_step_id = None
for step in object.steps:
step_output_artifacts = list(step.outputs.values())
execution_id = (
step_output_artifacts[0].producer_step.id
if step_output_artifacts
else step.id
)
step_id = self.STEP_PREFIX + str(step.id)
if first_step_id is None:
first_step_id = step_id
nodes.append(
{
"data": {
"id": step_id,
"execution_id": execution_id,
"label": f"{execution_id} / {step.name}",
"name": step.name, # redundant for consistency
"type": "step",
"parameters": step.parameters,
"inputs": {k: v.uri for k, v in step.inputs.items()},
"outputs": {k: v.uri for k, v in step.outputs.items()},
},
"classes": self.STATUS_CLASS_MAPPING[step.status],
}
)
for artifact_name, artifact in step.outputs.items():
nodes.append(
{
"data": {
"id": self.ARTIFACT_PREFIX + str(artifact.id),
"execution_id": artifact.id,
"label": f"{artifact.id} / {artifact_name} ("
f"{artifact.data_type})",
"type": "artifact",
"name": artifact_name,
"is_cached": artifact.is_cached,
"artifact_type": artifact.type,
"artifact_data_type": artifact.data_type,
"parent_step_id": artifact.parent_step_id,
"producer_step_id": artifact.producer_step.id,
"uri": artifact.uri,
},
"classes": f"rectangle "
f"{self.STATUS_CLASS_MAPPING[step.status]}",
}
)
edges.append(
{
"data": {
"source": self.STEP_PREFIX + str(step.id),
"target": self.ARTIFACT_PREFIX + str(artifact.id),
},
"classes": f"edge-arrow "
f"{self.STATUS_CLASS_MAPPING[step.status]}"
+ (" dashed" if artifact.is_cached else " solid"),
}
)
for artifact_name, artifact in step.inputs.items():
edges.append(
{
"data": {
"source": self.ARTIFACT_PREFIX + str(artifact.id),
"target": self.STEP_PREFIX + str(step.id),
},
"classes": "edge-arrow "
+ (
f"{self.STATUS_CLASS_MAPPING[ExecutionStatus.CACHED]} dashed"
if artifact.is_cached
else f"{self.STATUS_CLASS_MAPPING[step.status]} solid"
),
}
)
app.layout = dbc.Row(
[
dbc.Container(f"Run: {object.name}", class_name="h1"),
dbc.Row(
[
dbc.Col(
[
dbc.Row(
[
html.Span(
[
html.Span(
[
html.I(
className="bi bi-circle-fill me-1"
),
"Step",
],
className="me-2",
),
html.Span(
[
html.I(
className="bi bi-square-fill me-1"
),
"Artifact",
],
className="me-4",
),
dbc.Badge(
"Completed",
color=COLOR_BLUE,
className="me-1",
),
dbc.Badge(
"Cached",
color=COLOR_GREEN,
className="me-1",
),
dbc.Badge(
"Running",
color=COLOR_YELLOW,
className="me-1",
),
dbc.Badge(
"Failed",
color=COLOR_RED,
className="me-1",
),
]
),
]
),
dbc.Row(
[
cyto.Cytoscape(
id="cytoscape",
layout={
"name": "breadthfirst",
"roots": f'[id = "{first_step_id}"]',
},
elements=edges + nodes,
stylesheet=STYLESHEET,
style={
"width": "100%",
"height": "800px",
},
zoom=1,
)
]
),
dbc.Row(
[
dbc.Button(
"Reset",
id="bt-reset",
color="primary",
className="me-1",
)
]
),
]
),
dbc.Col(
[
dcc.Markdown(id="markdown-selected-node-data"),
]
),
]
),
],
className="p-5",
)
@app.callback( # type: ignore[misc]
Output("markdown-selected-node-data", "children"),
Input("cytoscape", "selectedNodeData"),
)
def display_data(data_list: List[Dict[str, Any]]) -> str:
"""Callback for the text area below the graph"""
if data_list is None:
return "Click on a node in the diagram."
text = ""
for data in data_list:
text += f'## {data["execution_id"]} / {data["name"]}' + "\n\n"
if data["type"] == "artifact":
for item in [
"artifact_data_type",
"is_cached",
"producer_step_id",
"parent_step_id",
"uri",
]:
text += f"**{item}**: {data[item]}" + "\n\n"
elif data["type"] == "step":
text += "### Inputs:" + "\n\n"
for k, v in data["inputs"].items():
text += f"**{k}**: {v}" + "\n\n"
text += "### Outputs:" + "\n\n"
for k, v in data["outputs"].items():
text += f"**{k}**: {v}" + "\n\n"
text += "### Params:"
for k, v in data["parameters"].items():
text += f"**{k}**: {v}" + "\n\n"
return text
@app.callback( # type: ignore[misc]
[Output("cytoscape", "zoom"), Output("cytoscape", "elements")],
[Input("bt-reset", "n_clicks")],
)
def reset_layout(
n_clicks: int,
) -> List[Union[int, List[Dict[str, Collection[str]]]]]:
"""Resets the layout"""
logger.debug(n_clicks, "clicked in reset button.")
return [1, edges + nodes]
app.run_server()
return app
visualize(self, object, *args, **kwargs)
Method to visualize pipeline runs via the Dash library. The layout puts every layer of the dag in a column.
Source code in zenml/integrations/dash/visualizers/pipeline_run_lineage_visualizer.py
def visualize(
self, object: PipelineRunView, *args: Any, **kwargs: Any
) -> dash.Dash:
"""Method to visualize pipeline runs via the Dash library. The layout
puts every layer of the dag in a column.
"""
app = dash.Dash(
__name__,
external_stylesheets=[
dbc.themes.BOOTSTRAP,
dbc.icons.BOOTSTRAP,
],
)
nodes, edges, first_step_id = [], [], None
first_step_id = None
for step in object.steps:
step_output_artifacts = list(step.outputs.values())
execution_id = (
step_output_artifacts[0].producer_step.id
if step_output_artifacts
else step.id
)
step_id = self.STEP_PREFIX + str(step.id)
if first_step_id is None:
first_step_id = step_id
nodes.append(
{
"data": {
"id": step_id,
"execution_id": execution_id,
"label": f"{execution_id} / {step.name}",
"name": step.name, # redundant for consistency
"type": "step",
"parameters": step.parameters,
"inputs": {k: v.uri for k, v in step.inputs.items()},
"outputs": {k: v.uri for k, v in step.outputs.items()},
},
"classes": self.STATUS_CLASS_MAPPING[step.status],
}
)
for artifact_name, artifact in step.outputs.items():
nodes.append(
{
"data": {
"id": self.ARTIFACT_PREFIX + str(artifact.id),
"execution_id": artifact.id,
"label": f"{artifact.id} / {artifact_name} ("
f"{artifact.data_type})",
"type": "artifact",
"name": artifact_name,
"is_cached": artifact.is_cached,
"artifact_type": artifact.type,
"artifact_data_type": artifact.data_type,
"parent_step_id": artifact.parent_step_id,
"producer_step_id": artifact.producer_step.id,
"uri": artifact.uri,
},
"classes": f"rectangle "
f"{self.STATUS_CLASS_MAPPING[step.status]}",
}
)
edges.append(
{
"data": {
"source": self.STEP_PREFIX + str(step.id),
"target": self.ARTIFACT_PREFIX + str(artifact.id),
},
"classes": f"edge-arrow "
f"{self.STATUS_CLASS_MAPPING[step.status]}"
+ (" dashed" if artifact.is_cached else " solid"),
}
)
for artifact_name, artifact in step.inputs.items():
edges.append(
{
"data": {
"source": self.ARTIFACT_PREFIX + str(artifact.id),
"target": self.STEP_PREFIX + str(step.id),
},
"classes": "edge-arrow "
+ (
f"{self.STATUS_CLASS_MAPPING[ExecutionStatus.CACHED]} dashed"
if artifact.is_cached
else f"{self.STATUS_CLASS_MAPPING[step.status]} solid"
),
}
)
app.layout = dbc.Row(
[
dbc.Container(f"Run: {object.name}", class_name="h1"),
dbc.Row(
[
dbc.Col(
[
dbc.Row(
[
html.Span(
[
html.Span(
[
html.I(
className="bi bi-circle-fill me-1"
),
"Step",
],
className="me-2",
),
html.Span(
[
html.I(
className="bi bi-square-fill me-1"
),
"Artifact",
],
className="me-4",
),
dbc.Badge(
"Completed",
color=COLOR_BLUE,
className="me-1",
),
dbc.Badge(
"Cached",
color=COLOR_GREEN,
className="me-1",
),
dbc.Badge(
"Running",
color=COLOR_YELLOW,
className="me-1",
),
dbc.Badge(
"Failed",
color=COLOR_RED,
className="me-1",
),
]
),
]
),
dbc.Row(
[
cyto.Cytoscape(
id="cytoscape",
layout={
"name": "breadthfirst",
"roots": f'[id = "{first_step_id}"]',
},
elements=edges + nodes,
stylesheet=STYLESHEET,
style={
"width": "100%",
"height": "800px",
},
zoom=1,
)
]
),
dbc.Row(
[
dbc.Button(
"Reset",
id="bt-reset",
color="primary",
className="me-1",
)
]
),
]
),
dbc.Col(
[
dcc.Markdown(id="markdown-selected-node-data"),
]
),
]
),
],
className="p-5",
)
@app.callback( # type: ignore[misc]
Output("markdown-selected-node-data", "children"),
Input("cytoscape", "selectedNodeData"),
)
def display_data(data_list: List[Dict[str, Any]]) -> str:
"""Callback for the text area below the graph"""
if data_list is None:
return "Click on a node in the diagram."
text = ""
for data in data_list:
text += f'## {data["execution_id"]} / {data["name"]}' + "\n\n"
if data["type"] == "artifact":
for item in [
"artifact_data_type",
"is_cached",
"producer_step_id",
"parent_step_id",
"uri",
]:
text += f"**{item}**: {data[item]}" + "\n\n"
elif data["type"] == "step":
text += "### Inputs:" + "\n\n"
for k, v in data["inputs"].items():
text += f"**{k}**: {v}" + "\n\n"
text += "### Outputs:" + "\n\n"
for k, v in data["outputs"].items():
text += f"**{k}**: {v}" + "\n\n"
text += "### Params:"
for k, v in data["parameters"].items():
text += f"**{k}**: {v}" + "\n\n"
return text
@app.callback( # type: ignore[misc]
[Output("cytoscape", "zoom"), Output("cytoscape", "elements")],
[Input("bt-reset", "n_clicks")],
)
def reset_layout(
n_clicks: int,
) -> List[Union[int, List[Dict[str, Collection[str]]]]]:
"""Resets the layout"""
logger.debug(n_clicks, "clicked in reset button.")
return [1, edges + nodes]
app.run_server()
return app
evidently
special
The Evidently integration provides a way to monitor your models in production. It includes a way to detect data drift and different kinds of model performance issues.
The results of Evidently calculations can either be exported as an interactive dashboard (visualized as an html file or in your Jupyter notebook), or as a JSON file.
EvidentlyIntegration (Integration)
Definition of Evidently integration for ZenML.
Source code in zenml/integrations/evidently/__init__.py
class EvidentlyIntegration(Integration):
"""Definition of [Evidently](https://github.com/evidentlyai/evidently) integration
for ZenML."""
NAME = EVIDENTLY
REQUIREMENTS = ["evidently>=v0.1.40.dev0"]
steps
special
evidently_profile
EvidentlyProfileConfig (BaseDriftDetectionConfig)
pydantic-model
Config class for Evidently profile steps.
column_mapping: properties of the dataframe's columns used !!! profile_section "a string that identifies the profile section to be used." The following are valid options supported by Evidently: - "datadrift" - "categoricaltargetdrift" - "numericaltargetdrift" - "classificationmodelperformance" - "regressionmodelperformance" - "probabilisticmodelperformance"
Source code in zenml/integrations/evidently/steps/evidently_profile.py
class EvidentlyProfileConfig(BaseDriftDetectionConfig):
"""Config class for Evidently profile steps.
column_mapping: properties of the dataframe's columns used
profile_section: a string that identifies the profile section to be used.
The following are valid options supported by Evidently:
- "datadrift"
- "categoricaltargetdrift"
- "numericaltargetdrift"
- "classificationmodelperformance"
- "regressionmodelperformance"
- "probabilisticmodelperformance"
"""
def get_profile_sections_and_tabs(
self,
) -> Tuple[List[ProfileSection], List[Tab]]:
try:
return (
[
profile_mapper[profile]()
for profile in self.profile_sections
],
[
dashboard_mapper[profile]()
for profile in self.profile_sections
],
)
except KeyError:
nl = "\n"
raise ValueError(
f"Invalid profile section: {self.profile_sections} \n\n"
f"Valid and supported options are: {nl}- "
f'{f"{nl}- ".join(list(profile_mapper.keys()))}'
)
column_mapping: Optional[ColumnMapping]
profile_sections: Sequence[str]
EvidentlyProfileStep (BaseDriftDetectionStep)
Simple step implementation which implements Evidently's functionality for creating a profile.
Source code in zenml/integrations/evidently/steps/evidently_profile.py
class EvidentlyProfileStep(BaseDriftDetectionStep):
"""Simple step implementation which implements Evidently's functionality for
creating a profile."""
OUTPUT_SPEC = {
"profile": DataAnalysisArtifact,
"dashboard": DataAnalysisArtifact,
}
def entrypoint( # type: ignore[override]
self,
reference_dataset: DataArtifact,
comparison_dataset: DataArtifact,
config: EvidentlyProfileConfig,
context: StepContext,
) -> Output( # type:ignore[valid-type]
profile=dict, dashboard=str
):
"""Main entrypoint for the Evidently categorical target drift detection
step.
Args:
reference_dataset: a Pandas dataframe
comparison_dataset: a Pandas dataframe of new data you wish to
compare against the reference data
config: the configuration for the step
context: the context of the step
Returns:
profile: dictionary report extracted from an Evidently Profile
generated for the data drift
dashboard: HTML report extracted from an Evidently Dashboard
generated for the data drift
"""
sections, tabs = config.get_profile_sections_and_tabs()
data_drift_dashboard = Dashboard(tabs=tabs)
data_drift_dashboard.calculate(
reference_dataset,
comparison_dataset,
column_mapping=config.column_mapping or None,
)
data_drift_profile = Profile(sections=sections)
data_drift_profile.calculate(
reference_dataset,
comparison_dataset,
column_mapping=config.column_mapping or None,
)
return [data_drift_profile.object(), data_drift_dashboard.html()]
CONFIG_CLASS (BaseDriftDetectionConfig)
pydantic-model
Config class for Evidently profile steps.
column_mapping: properties of the dataframe's columns used !!! profile_section "a string that identifies the profile section to be used." The following are valid options supported by Evidently: - "datadrift" - "categoricaltargetdrift" - "numericaltargetdrift" - "classificationmodelperformance" - "regressionmodelperformance" - "probabilisticmodelperformance"
Source code in zenml/integrations/evidently/steps/evidently_profile.py
class EvidentlyProfileConfig(BaseDriftDetectionConfig):
"""Config class for Evidently profile steps.
column_mapping: properties of the dataframe's columns used
profile_section: a string that identifies the profile section to be used.
The following are valid options supported by Evidently:
- "datadrift"
- "categoricaltargetdrift"
- "numericaltargetdrift"
- "classificationmodelperformance"
- "regressionmodelperformance"
- "probabilisticmodelperformance"
"""
def get_profile_sections_and_tabs(
self,
) -> Tuple[List[ProfileSection], List[Tab]]:
try:
return (
[
profile_mapper[profile]()
for profile in self.profile_sections
],
[
dashboard_mapper[profile]()
for profile in self.profile_sections
],
)
except KeyError:
nl = "\n"
raise ValueError(
f"Invalid profile section: {self.profile_sections} \n\n"
f"Valid and supported options are: {nl}- "
f'{f"{nl}- ".join(list(profile_mapper.keys()))}'
)
column_mapping: Optional[ColumnMapping]
profile_sections: Sequence[str]
entrypoint(self, reference_dataset, comparison_dataset, config, context)
Main entrypoint for the Evidently categorical target drift detection step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
reference_dataset |
DataArtifact |
a Pandas dataframe |
required |
comparison_dataset |
DataArtifact |
a Pandas dataframe of new data you wish to compare against the reference data |
required |
config |
EvidentlyProfileConfig |
the configuration for the step |
required |
context |
StepContext |
the context of the step |
required |
Returns:
| Type | Description |
|---|---|
profile |
dictionary report extracted from an Evidently Profile generated for the data drift dashboard: HTML report extracted from an Evidently Dashboard generated for the data drift |
Source code in zenml/integrations/evidently/steps/evidently_profile.py
def entrypoint( # type: ignore[override]
self,
reference_dataset: DataArtifact,
comparison_dataset: DataArtifact,
config: EvidentlyProfileConfig,
context: StepContext,
) -> Output( # type:ignore[valid-type]
profile=dict, dashboard=str
):
"""Main entrypoint for the Evidently categorical target drift detection
step.
Args:
reference_dataset: a Pandas dataframe
comparison_dataset: a Pandas dataframe of new data you wish to
compare against the reference data
config: the configuration for the step
context: the context of the step
Returns:
profile: dictionary report extracted from an Evidently Profile
generated for the data drift
dashboard: HTML report extracted from an Evidently Dashboard
generated for the data drift
"""
sections, tabs = config.get_profile_sections_and_tabs()
data_drift_dashboard = Dashboard(tabs=tabs)
data_drift_dashboard.calculate(
reference_dataset,
comparison_dataset,
column_mapping=config.column_mapping or None,
)
data_drift_profile = Profile(sections=sections)
data_drift_profile.calculate(
reference_dataset,
comparison_dataset,
column_mapping=config.column_mapping or None,
)
return [data_drift_profile.object(), data_drift_dashboard.html()]
visualizers
special
evidently_visualizer
EvidentlyVisualizer (BaseStepVisualizer)
The implementation of an Evidently Visualizer.
Source code in zenml/integrations/evidently/visualizers/evidently_visualizer.py
class EvidentlyVisualizer(BaseStepVisualizer):
"""The implementation of an Evidently Visualizer."""
@abstractmethod
def visualize(self, object: StepView, *args: Any, **kwargs: Any) -> None:
"""Method to visualize components
Args:
object: StepView fetched from run.get_step().
"""
for artifact_view in object.outputs.values():
# filter out anything but data analysis artifacts
if (
artifact_view.type == DataAnalysisArtifact.__name__
and artifact_view.data_type == "builtins.str"
):
artifact = artifact_view.read()
self.generate_facet(artifact)
def generate_facet(self, html_: str) -> None:
"""Generate a Facet Overview
Args:
h: HTML represented as a string.
"""
if self.running_in_notebook():
from IPython.core.display import HTML, display
display(HTML(html_))
else:
logger.warn(
"The magic functions are only usable in a Jupyter notebook."
)
with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
zenml.io.utils.write_file_contents_as_string(f.name, html_)
url = f"file:///{f.name}"
logger.info("Opening %s in a new browser.." % f.name)
webbrowser.open(url, new=2)
generate_facet(self, html_)
Generate a Facet Overview
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
h |
HTML represented as a string. |
required |
Source code in zenml/integrations/evidently/visualizers/evidently_visualizer.py
def generate_facet(self, html_: str) -> None:
"""Generate a Facet Overview
Args:
h: HTML represented as a string.
"""
if self.running_in_notebook():
from IPython.core.display import HTML, display
display(HTML(html_))
else:
logger.warn(
"The magic functions are only usable in a Jupyter notebook."
)
with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
zenml.io.utils.write_file_contents_as_string(f.name, html_)
url = f"file:///{f.name}"
logger.info("Opening %s in a new browser.." % f.name)
webbrowser.open(url, new=2)
visualize(self, object, *args, **kwargs)
Method to visualize components
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
object |
StepView |
StepView fetched from run.get_step(). |
required |
Source code in zenml/integrations/evidently/visualizers/evidently_visualizer.py
@abstractmethod
def visualize(self, object: StepView, *args: Any, **kwargs: Any) -> None:
"""Method to visualize components
Args:
object: StepView fetched from run.get_step().
"""
for artifact_view in object.outputs.values():
# filter out anything but data analysis artifacts
if (
artifact_view.type == DataAnalysisArtifact.__name__
and artifact_view.data_type == "builtins.str"
):
artifact = artifact_view.read()
self.generate_facet(artifact)
facets
special
The Facets integration provides a simple way to visualize post-execution objects
like PipelineView, PipelineRunView and StepView. These objects can be
extended using the BaseVisualization class. This integration requires
facets-overview be installed in your Python environment.
FacetsIntegration (Integration)
Definition of Facet integration for ZenML.
Source code in zenml/integrations/facets/__init__.py
class FacetsIntegration(Integration):
"""Definition of [Facet](https://pair-code.github.io/facets/) integration
for ZenML."""
NAME = FACETS
REQUIREMENTS = ["facets-overview>=1.0.0", "IPython"]
visualizers
special
facet_statistics_visualizer
FacetStatisticsVisualizer (BaseStepVisualizer)
The base implementation of a ZenML Visualizer.
Source code in zenml/integrations/facets/visualizers/facet_statistics_visualizer.py
class FacetStatisticsVisualizer(BaseStepVisualizer):
"""The base implementation of a ZenML Visualizer."""
@abstractmethod
def visualize(
self, object: StepView, magic: bool = False, *args: Any, **kwargs: Any
) -> None:
"""Method to visualize components
Args:
object: StepView fetched from run.get_step().
magic: Whether to render in a Jupyter notebook or not.
"""
datasets = []
for output_name, artifact_view in object.outputs.items():
df = artifact_view.read()
if type(df) is not pd.DataFrame:
logger.warning(
"`%s` is not a pd.DataFrame. You can only visualize "
"statistics of steps that output pandas dataframes. "
"Skipping this output.." % output_name
)
else:
datasets.append({"name": output_name, "table": df})
h = self.generate_html(datasets)
self.generate_facet(h, magic)
def generate_html(self, datasets: List[Dict[Text, pd.DataFrame]]) -> str:
"""Generates html for facet.
Args:
datasets: List of dicts of dataframes to be visualized as stats.
Returns:
HTML template with proto string embedded.
"""
proto = GenericFeatureStatisticsGenerator().ProtoFromDataFrames(
datasets
)
protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8")
template = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
"stats.html",
)
html_template = zenml.io.utils.read_file_contents_as_string(template)
html_ = html_template.replace("protostr", protostr)
return html_
def generate_facet(self, html_: str, magic: bool = False) -> None:
"""Generate a Facet Overview
Args:
h: HTML represented as a string.
magic: Whether to magically materialize facet in a notebook.
"""
if magic:
if not self.running_in_notebook():
raise EnvironmentError(
"The magic functions are only usable in a Jupyter notebook."
)
display(HTML(html_))
else:
with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
zenml.io.utils.write_file_contents_as_string(f.name, html_)
url = f"file:///{f.name}"
logger.info("Opening %s in a new browser.." % f.name)
webbrowser.open(url, new=2)
generate_facet(self, html_, magic=False)
Generate a Facet Overview
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
h |
HTML represented as a string. |
required | |
magic |
bool |
Whether to magically materialize facet in a notebook. |
False |
Source code in zenml/integrations/facets/visualizers/facet_statistics_visualizer.py
def generate_facet(self, html_: str, magic: bool = False) -> None:
"""Generate a Facet Overview
Args:
h: HTML represented as a string.
magic: Whether to magically materialize facet in a notebook.
"""
if magic:
if not self.running_in_notebook():
raise EnvironmentError(
"The magic functions are only usable in a Jupyter notebook."
)
display(HTML(html_))
else:
with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
zenml.io.utils.write_file_contents_as_string(f.name, html_)
url = f"file:///{f.name}"
logger.info("Opening %s in a new browser.." % f.name)
webbrowser.open(url, new=2)
generate_html(self, datasets)
Generates html for facet.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
datasets |
List[Dict[str, pandas.core.frame.DataFrame]] |
List of dicts of dataframes to be visualized as stats. |
required |
Returns:
| Type | Description |
|---|---|
str |
HTML template with proto string embedded. |
Source code in zenml/integrations/facets/visualizers/facet_statistics_visualizer.py
def generate_html(self, datasets: List[Dict[Text, pd.DataFrame]]) -> str:
"""Generates html for facet.
Args:
datasets: List of dicts of dataframes to be visualized as stats.
Returns:
HTML template with proto string embedded.
"""
proto = GenericFeatureStatisticsGenerator().ProtoFromDataFrames(
datasets
)
protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8")
template = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
"stats.html",
)
html_template = zenml.io.utils.read_file_contents_as_string(template)
html_ = html_template.replace("protostr", protostr)
return html_
visualize(self, object, magic=False, *args, **kwargs)
Method to visualize components
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
object |
StepView |
StepView fetched from run.get_step(). |
required |
magic |
bool |
Whether to render in a Jupyter notebook or not. |
False |
Source code in zenml/integrations/facets/visualizers/facet_statistics_visualizer.py
@abstractmethod
def visualize(
self, object: StepView, magic: bool = False, *args: Any, **kwargs: Any
) -> None:
"""Method to visualize components
Args:
object: StepView fetched from run.get_step().
magic: Whether to render in a Jupyter notebook or not.
"""
datasets = []
for output_name, artifact_view in object.outputs.items():
df = artifact_view.read()
if type(df) is not pd.DataFrame:
logger.warning(
"`%s` is not a pd.DataFrame. You can only visualize "
"statistics of steps that output pandas dataframes. "
"Skipping this output.." % output_name
)
else:
datasets.append({"name": output_name, "table": df})
h = self.generate_html(datasets)
self.generate_facet(h, magic)
gcp
special
The GCP integration submodule provides a way to run ZenML pipelines in a cloud
environment. Specifically, it allows the use of cloud artifact stores, metadata
stores, and an io module to handle file operations on Google Cloud Storage (GCS).
GcpIntegration (Integration)
Definition of Google Cloud Platform integration for ZenML.
Source code in zenml/integrations/gcp/__init__.py
class GcpIntegration(Integration):
"""Definition of Google Cloud Platform integration for ZenML."""
NAME = GCP
REQUIREMENTS = ["gcsfs"]
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.gcp import artifact_stores # noqa
from zenml.integrations.gcp import io # noqa
activate()
classmethod
Activates the integration.
Source code in zenml/integrations/gcp/__init__.py
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.gcp import artifact_stores # noqa
from zenml.integrations.gcp import io # noqa
artifact_stores
special
gcp_artifact_store
GCPArtifactStore (BaseArtifactStore)
pydantic-model
Artifact Store for Google Cloud Storage based artifacts.
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
class GCPArtifactStore(BaseArtifactStore):
"""Artifact Store for Google Cloud Storage based artifacts."""
@validator("path")
def must_be_gcs_path(cls, v: str) -> str:
"""Validates that the path is a valid gcs path."""
if not v.startswith("gs://"):
raise ValueError(
"Must be a valid gcs path, i.e., starting with `gs://`"
)
return v
must_be_gcs_path(v)
classmethod
Validates that the path is a valid gcs path.
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
@validator("path")
def must_be_gcs_path(cls, v: str) -> str:
"""Validates that the path is a valid gcs path."""
if not v.startswith("gs://"):
raise ValueError(
"Must be a valid gcs path, i.e., starting with `gs://`"
)
return v
io
special
gcs_plugin
Plugin which is created to add Google Cloud Store support to ZenML. It inherits from the base Filesystem created by TFX and overwrites the corresponding functions thanks to gcsfs.
ZenGCS (Filesystem)
Filesystem that delegates to Google Cloud Store using gcsfs.
Note: To allow TFX to check for various error conditions, we need to
raise their custom NotFoundError instead of the builtin python
FileNotFoundError.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
class ZenGCS(Filesystem):
"""Filesystem that delegates to Google Cloud Store using gcsfs.
**Note**: To allow TFX to check for various error conditions, we need to
raise their custom `NotFoundError` instead of the builtin python
FileNotFoundError."""
SUPPORTED_SCHEMES = ["gs://"]
fs: gcsfs.GCSFileSystem = None
@classmethod
def _ensure_filesystem_set(cls) -> None:
"""Ensures that the filesystem is set."""
if ZenGCS.fs is None:
ZenGCS.fs = gcsfs.GCSFileSystem()
@staticmethod
def open(path: PathType, mode: str = "r") -> Any:
"""Open a file at the given path.
Args:
path: Path of the file to open.
mode: Mode in which to open the file. Currently only
'rb' and 'wb' to read and write binary files are supported.
"""
ZenGCS._ensure_filesystem_set()
try:
return ZenGCS.fs.open(path=path, mode=mode)
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def copy(src: PathType, dst: PathType, overwrite: bool = False) -> None:
"""Copy a file.
Args:
src: The path to copy from.
dst: The path to copy to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileNotFoundError: If the source file does not exist.
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
ZenGCS._ensure_filesystem_set()
if not overwrite and ZenGCS.fs.exists(dst):
raise FileExistsError(
f"Unable to copy to destination '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to copy anyway."
)
# TODO [ENG-151]: Check if it works with overwrite=True or if we need to
# manually remove it first
try:
ZenGCS.fs.copy(path1=src, path2=dst)
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def exists(path: PathType) -> bool:
"""Check whether a path exists."""
ZenGCS._ensure_filesystem_set()
return ZenGCS.fs.exists(path=path) # type: ignore[no-any-return]
@staticmethod
def glob(pattern: PathType) -> List[PathType]:
"""Return all paths that match the given glob pattern.
The glob pattern may include:
- '*' to match any number of characters
- '?' to match a single character
- '[...]' to match one of the characters inside the brackets
- '**' as the full name of a path component to match to search
in subdirectories of any depth (e.g. '/some_dir/**/some_file)
Args:
pattern: The glob pattern to match, see details above.
Returns:
A list of paths that match the given glob pattern.
"""
ZenGCS._ensure_filesystem_set()
return ZenGCS.fs.glob(path=pattern) # type: ignore[no-any-return]
@staticmethod
def isdir(path: PathType) -> bool:
"""Check whether a path is a directory."""
ZenGCS._ensure_filesystem_set()
return ZenGCS.fs.isdir(path=path) # type: ignore[no-any-return]
@staticmethod
def listdir(path: PathType) -> List[PathType]:
"""Return a list of files in a directory."""
ZenGCS._ensure_filesystem_set()
try:
return ZenGCS.fs.listdir(path=path) # type: ignore[no-any-return]
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def makedirs(path: PathType) -> None:
"""Create a directory at the given path. If needed also
create missing parent directories."""
ZenGCS._ensure_filesystem_set()
ZenGCS.fs.makedirs(path=path, exist_ok=True)
@staticmethod
def mkdir(path: PathType) -> None:
"""Create a directory at the given path."""
ZenGCS._ensure_filesystem_set()
ZenGCS.fs.makedir(path=path)
@staticmethod
def remove(path: PathType) -> None:
"""Remove the file at the given path."""
ZenGCS._ensure_filesystem_set()
try:
ZenGCS.fs.rm_file(path=path)
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def rename(src: PathType, dst: PathType, overwrite: bool = False) -> None:
"""Rename source file to destination file.
Args:
src: The path of the file to rename.
dst: The path to rename the source file to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileNotFoundError: If the source file does not exist.
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
ZenGCS._ensure_filesystem_set()
if not overwrite and ZenGCS.fs.exists(dst):
raise FileExistsError(
f"Unable to rename file to '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to rename anyway."
)
# TODO [ENG-152]: Check if it works with overwrite=True or if we need
# to manually remove it first
try:
ZenGCS.fs.rename(path1=src, path2=dst)
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def rmtree(path: PathType) -> None:
"""Remove the given directory."""
ZenGCS._ensure_filesystem_set()
try:
ZenGCS.fs.delete(path=path, recursive=True)
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def stat(path: PathType) -> Dict[str, Any]:
"""Return stat info for the given path."""
ZenGCS._ensure_filesystem_set()
try:
return ZenGCS.fs.stat(path=path) # type: ignore[no-any-return]
except FileNotFoundError as e:
raise NotFoundError() from e
@staticmethod
def walk(
top: PathType,
topdown: bool = True,
onerror: Optional[Callable[..., None]] = None,
) -> Iterable[Tuple[PathType, List[PathType], List[PathType]]]:
"""Return an iterator that walks the contents of the given directory.
Args:
top: Path of directory to walk.
topdown: Unused argument to conform to interface.
onerror: Unused argument to conform to interface.
Returns:
An Iterable of Tuples, each of which contain the path of the current
directory path, a list of directories inside the current directory
and a list of files inside the current directory.
"""
ZenGCS._ensure_filesystem_set()
# TODO [ENG-153]: Additional params
return ZenGCS.fs.walk(path=top) # type: ignore[no-any-return]
copy(src, dst, overwrite=False)
staticmethod
Copy a file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
src |
Union[bytes, str] |
The path to copy from. |
required |
dst |
Union[bytes, str] |
The path to copy to. |
required |
overwrite |
bool |
If a file already exists at the destination, this
method will overwrite it if overwrite= |
False |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the source file does not exist. |
FileExistsError |
If a file already exists at the destination
and overwrite is not set to |
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def copy(src: PathType, dst: PathType, overwrite: bool = False) -> None:
"""Copy a file.
Args:
src: The path to copy from.
dst: The path to copy to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileNotFoundError: If the source file does not exist.
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
ZenGCS._ensure_filesystem_set()
if not overwrite and ZenGCS.fs.exists(dst):
raise FileExistsError(
f"Unable to copy to destination '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to copy anyway."
)
# TODO [ENG-151]: Check if it works with overwrite=True or if we need to
# manually remove it first
try:
ZenGCS.fs.copy(path1=src, path2=dst)
except FileNotFoundError as e:
raise NotFoundError() from e
exists(path)
staticmethod
Check whether a path exists.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def exists(path: PathType) -> bool:
"""Check whether a path exists."""
ZenGCS._ensure_filesystem_set()
return ZenGCS.fs.exists(path=path) # type: ignore[no-any-return]
glob(pattern)
staticmethod
Return all paths that match the given glob pattern. The glob pattern may include: - '' to match any number of characters - '?' to match a single character - '[...]' to match one of the characters inside the brackets - '' as the full name of a path component to match to search in subdirectories of any depth (e.g. '/some_dir/*/some_file)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pattern |
Union[bytes, str] |
The glob pattern to match, see details above. |
required |
Returns:
| Type | Description |
|---|---|
List[Union[bytes, str]] |
A list of paths that match the given glob pattern. |
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def glob(pattern: PathType) -> List[PathType]:
"""Return all paths that match the given glob pattern.
The glob pattern may include:
- '*' to match any number of characters
- '?' to match a single character
- '[...]' to match one of the characters inside the brackets
- '**' as the full name of a path component to match to search
in subdirectories of any depth (e.g. '/some_dir/**/some_file)
Args:
pattern: The glob pattern to match, see details above.
Returns:
A list of paths that match the given glob pattern.
"""
ZenGCS._ensure_filesystem_set()
return ZenGCS.fs.glob(path=pattern) # type: ignore[no-any-return]
isdir(path)
staticmethod
Check whether a path is a directory.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def isdir(path: PathType) -> bool:
"""Check whether a path is a directory."""
ZenGCS._ensure_filesystem_set()
return ZenGCS.fs.isdir(path=path) # type: ignore[no-any-return]
listdir(path)
staticmethod
Return a list of files in a directory.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def listdir(path: PathType) -> List[PathType]:
"""Return a list of files in a directory."""
ZenGCS._ensure_filesystem_set()
try:
return ZenGCS.fs.listdir(path=path) # type: ignore[no-any-return]
except FileNotFoundError as e:
raise NotFoundError() from e
makedirs(path)
staticmethod
Create a directory at the given path. If needed also create missing parent directories.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def makedirs(path: PathType) -> None:
"""Create a directory at the given path. If needed also
create missing parent directories."""
ZenGCS._ensure_filesystem_set()
ZenGCS.fs.makedirs(path=path, exist_ok=True)
mkdir(path)
staticmethod
Create a directory at the given path.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def mkdir(path: PathType) -> None:
"""Create a directory at the given path."""
ZenGCS._ensure_filesystem_set()
ZenGCS.fs.makedir(path=path)
open(path, mode='r')
staticmethod
Open a file at the given path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
Path of the file to open. |
required |
mode |
str |
Mode in which to open the file. Currently only 'rb' and 'wb' to read and write binary files are supported. |
'r' |
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def open(path: PathType, mode: str = "r") -> Any:
"""Open a file at the given path.
Args:
path: Path of the file to open.
mode: Mode in which to open the file. Currently only
'rb' and 'wb' to read and write binary files are supported.
"""
ZenGCS._ensure_filesystem_set()
try:
return ZenGCS.fs.open(path=path, mode=mode)
except FileNotFoundError as e:
raise NotFoundError() from e
remove(path)
staticmethod
Remove the file at the given path.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def remove(path: PathType) -> None:
"""Remove the file at the given path."""
ZenGCS._ensure_filesystem_set()
try:
ZenGCS.fs.rm_file(path=path)
except FileNotFoundError as e:
raise NotFoundError() from e
rename(src, dst, overwrite=False)
staticmethod
Rename source file to destination file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
src |
Union[bytes, str] |
The path of the file to rename. |
required |
dst |
Union[bytes, str] |
The path to rename the source file to. |
required |
overwrite |
bool |
If a file already exists at the destination, this
method will overwrite it if overwrite= |
False |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the source file does not exist. |
FileExistsError |
If a file already exists at the destination
and overwrite is not set to |
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def rename(src: PathType, dst: PathType, overwrite: bool = False) -> None:
"""Rename source file to destination file.
Args:
src: The path of the file to rename.
dst: The path to rename the source file to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileNotFoundError: If the source file does not exist.
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
ZenGCS._ensure_filesystem_set()
if not overwrite and ZenGCS.fs.exists(dst):
raise FileExistsError(
f"Unable to rename file to '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to rename anyway."
)
# TODO [ENG-152]: Check if it works with overwrite=True or if we need
# to manually remove it first
try:
ZenGCS.fs.rename(path1=src, path2=dst)
except FileNotFoundError as e:
raise NotFoundError() from e
rmtree(path)
staticmethod
Remove the given directory.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def rmtree(path: PathType) -> None:
"""Remove the given directory."""
ZenGCS._ensure_filesystem_set()
try:
ZenGCS.fs.delete(path=path, recursive=True)
except FileNotFoundError as e:
raise NotFoundError() from e
stat(path)
staticmethod
Return stat info for the given path.
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def stat(path: PathType) -> Dict[str, Any]:
"""Return stat info for the given path."""
ZenGCS._ensure_filesystem_set()
try:
return ZenGCS.fs.stat(path=path) # type: ignore[no-any-return]
except FileNotFoundError as e:
raise NotFoundError() from e
walk(top, topdown=True, onerror=None)
staticmethod
Return an iterator that walks the contents of the given directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
top |
Union[bytes, str] |
Path of directory to walk. |
required |
topdown |
bool |
Unused argument to conform to interface. |
True |
onerror |
Optional[Callable[..., NoneType]] |
Unused argument to conform to interface. |
None |
Returns:
| Type | Description |
|---|---|
Iterable[Tuple[Union[bytes, str], List[Union[bytes, str]], List[Union[bytes, str]]]] |
An Iterable of Tuples, each of which contain the path of the current directory path, a list of directories inside the current directory and a list of files inside the current directory. |
Source code in zenml/integrations/gcp/io/gcs_plugin.py
@staticmethod
def walk(
top: PathType,
topdown: bool = True,
onerror: Optional[Callable[..., None]] = None,
) -> Iterable[Tuple[PathType, List[PathType], List[PathType]]]:
"""Return an iterator that walks the contents of the given directory.
Args:
top: Path of directory to walk.
topdown: Unused argument to conform to interface.
onerror: Unused argument to conform to interface.
Returns:
An Iterable of Tuples, each of which contain the path of the current
directory path, a list of directories inside the current directory
and a list of files inside the current directory.
"""
ZenGCS._ensure_filesystem_set()
# TODO [ENG-153]: Additional params
return ZenGCS.fs.walk(path=top) # type: ignore[no-any-return]
graphviz
special
GraphvizIntegration (Integration)
Definition of Graphviz integration for ZenML.
Source code in zenml/integrations/graphviz/__init__.py
class GraphvizIntegration(Integration):
"""Definition of Graphviz integration for ZenML."""
NAME = GRAPHVIZ
REQUIREMENTS = ["graphviz>=0.17"]
SYSTEM_REQUIREMENTS = {"graphviz": "dot"}
visualizers
special
pipeline_run_dag_visualizer
PipelineRunDagVisualizer (BasePipelineRunVisualizer)
Visualize the lineage of runs in a pipeline.
Source code in zenml/integrations/graphviz/visualizers/pipeline_run_dag_visualizer.py
class PipelineRunDagVisualizer(BasePipelineRunVisualizer):
"""Visualize the lineage of runs in a pipeline."""
ARTIFACT_DEFAULT_COLOR = "blue"
ARTIFACT_CACHED_COLOR = "green"
ARTIFACT_SHAPE = "box"
ARTIFACT_PREFIX = "artifact_"
STEP_COLOR = "#431D93"
STEP_SHAPE = "ellipse"
STEP_PREFIX = "step_"
FONT = "Roboto"
@abstractmethod
def visualize(
self, object: PipelineRunView, *args: Any, **kwargs: Any
) -> graphviz.Digraph:
"""Creates a pipeline lineage diagram using graphviz."""
logger.warning(
"This integration is not completed yet. Results might be unexpected."
)
dot = graphviz.Digraph(comment=object.name)
# link the steps together
for step in object.steps:
# add each step as a node
dot.node(
self.STEP_PREFIX + str(step.id),
step.name,
shape=self.STEP_SHAPE,
)
# for each parent of a step, add an edge
for artifact_name, artifact in step.outputs.items():
dot.node(
self.ARTIFACT_PREFIX + str(artifact.id),
f"{artifact_name} \n" f"({artifact._data_type})",
shape=self.ARTIFACT_SHAPE,
)
dot.edge(
self.STEP_PREFIX + str(step.id),
self.ARTIFACT_PREFIX + str(artifact.id),
)
for artifact_name, artifact in step.inputs.items():
dot.edge(
self.ARTIFACT_PREFIX + str(artifact.id),
self.STEP_PREFIX + str(step.id),
)
with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
dot.render(filename=f.name, format="png", view=True, cleanup=True)
return dot
visualize(self, object, *args, **kwargs)
Creates a pipeline lineage diagram using graphviz.
Source code in zenml/integrations/graphviz/visualizers/pipeline_run_dag_visualizer.py
@abstractmethod
def visualize(
self, object: PipelineRunView, *args: Any, **kwargs: Any
) -> graphviz.Digraph:
"""Creates a pipeline lineage diagram using graphviz."""
logger.warning(
"This integration is not completed yet. Results might be unexpected."
)
dot = graphviz.Digraph(comment=object.name)
# link the steps together
for step in object.steps:
# add each step as a node
dot.node(
self.STEP_PREFIX + str(step.id),
step.name,
shape=self.STEP_SHAPE,
)
# for each parent of a step, add an edge
for artifact_name, artifact in step.outputs.items():
dot.node(
self.ARTIFACT_PREFIX + str(artifact.id),
f"{artifact_name} \n" f"({artifact._data_type})",
shape=self.ARTIFACT_SHAPE,
)
dot.edge(
self.STEP_PREFIX + str(step.id),
self.ARTIFACT_PREFIX + str(artifact.id),
)
for artifact_name, artifact in step.inputs.items():
dot.edge(
self.ARTIFACT_PREFIX + str(artifact.id),
self.STEP_PREFIX + str(step.id),
)
with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
dot.render(filename=f.name, format="png", view=True, cleanup=True)
return dot
integration
Integration
Base class for integration in ZenML
Source code in zenml/integrations/integration.py
class Integration(metaclass=IntegrationMeta):
"""Base class for integration in ZenML"""
NAME = "base_integration"
REQUIREMENTS: List[str] = []
SYSTEM_REQUIREMENTS: Dict[str, str] = {}
@classmethod
def check_installation(cls) -> bool:
"""Method to check whether the required packages are installed"""
try:
for requirement, command in cls.SYSTEM_REQUIREMENTS.items():
result = shutil.which(command)
if result is None:
logger.debug(
"Unable to find the required packages for %s on your "
"system. Please install the packages on your system "
"and try again.",
requirement,
)
return False
for r in cls.REQUIREMENTS:
pkg_resources.get_distribution(r)
logger.debug(
f"Integration {cls.NAME} is installed correctly with "
f"requirements {cls.REQUIREMENTS}."
)
return True
except pkg_resources.DistributionNotFound as e:
logger.debug(
f"Unable to find required package '{e.req}' for "
f"integration {cls.NAME}."
)
return False
except pkg_resources.VersionConflict as e:
logger.debug(
f"VersionConflict error when loading installation {cls.NAME}: "
f"{str(e)}"
)
return False
@staticmethod
def activate() -> None:
"""Abstract method to activate the integration"""
activate()
staticmethod
Abstract method to activate the integration
Source code in zenml/integrations/integration.py
@staticmethod
def activate() -> None:
"""Abstract method to activate the integration"""
check_installation()
classmethod
Method to check whether the required packages are installed
Source code in zenml/integrations/integration.py
@classmethod
def check_installation(cls) -> bool:
"""Method to check whether the required packages are installed"""
try:
for requirement, command in cls.SYSTEM_REQUIREMENTS.items():
result = shutil.which(command)
if result is None:
logger.debug(
"Unable to find the required packages for %s on your "
"system. Please install the packages on your system "
"and try again.",
requirement,
)
return False
for r in cls.REQUIREMENTS:
pkg_resources.get_distribution(r)
logger.debug(
f"Integration {cls.NAME} is installed correctly with "
f"requirements {cls.REQUIREMENTS}."
)
return True
except pkg_resources.DistributionNotFound as e:
logger.debug(
f"Unable to find required package '{e.req}' for "
f"integration {cls.NAME}."
)
return False
except pkg_resources.VersionConflict as e:
logger.debug(
f"VersionConflict error when loading installation {cls.NAME}: "
f"{str(e)}"
)
return False
IntegrationMeta (type)
Metaclass responsible for registering different Integration subclasses
Source code in zenml/integrations/integration.py
class IntegrationMeta(type):
"""Metaclass responsible for registering different Integration
subclasses"""
def __new__(
mcs, name: str, bases: Tuple[Type[Any], ...], dct: Dict[str, Any]
) -> "IntegrationMeta":
"""Hook into creation of an Integration class."""
cls = cast(Type["Integration"], super().__new__(mcs, name, bases, dct))
if name != "Integration":
integration_registry.register_integration(cls.NAME, cls)
return cls
__new__(mcs, name, bases, dct)
special
staticmethod
Hook into creation of an Integration class.
Source code in zenml/integrations/integration.py
def __new__(
mcs, name: str, bases: Tuple[Type[Any], ...], dct: Dict[str, Any]
) -> "IntegrationMeta":
"""Hook into creation of an Integration class."""
cls = cast(Type["Integration"], super().__new__(mcs, name, bases, dct))
if name != "Integration":
integration_registry.register_integration(cls.NAME, cls)
return cls
kubeflow
special
The Kubeflow integration sub-module powers an alternative to the local orchestrator. You can enable it by registering the Kubeflow orchestrator with the CLI tool.
KubeflowIntegration (Integration)
Definition of Kubeflow Integration for ZenML.
Source code in zenml/integrations/kubeflow/__init__.py
class KubeflowIntegration(Integration):
"""Definition of Kubeflow Integration for ZenML."""
NAME = KUBEFLOW
REQUIREMENTS = ["kfp==1.8.9"]
@classmethod
def activate(cls) -> None:
"""Activates all classes required for the airflow integration."""
from zenml.integrations.kubeflow import metadata # noqa
from zenml.integrations.kubeflow import orchestrators # noqa
activate()
classmethod
Activates all classes required for the airflow integration.
Source code in zenml/integrations/kubeflow/__init__.py
@classmethod
def activate(cls) -> None:
"""Activates all classes required for the airflow integration."""
from zenml.integrations.kubeflow import metadata # noqa
from zenml.integrations.kubeflow import orchestrators # noqa
container_entrypoint
Main entrypoint for containers with Kubeflow TFX component executors.
main()
Runs a single step defined by the command line arguments.
Source code in zenml/integrations/kubeflow/container_entrypoint.py
def main() -> None:
"""Runs a single step defined by the command line arguments."""
# Log to the container's stdout so Kubeflow Pipelines UI can display logs to
# the user.
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().setLevel(logging.INFO)
args = _parse_command_line_arguments()
tfx_pipeline = pipeline_pb2.Pipeline()
json_format.Parse(args.tfx_ir, tfx_pipeline)
_resolve_runtime_parameters(
tfx_pipeline, args.run_name, args.runtime_parameter
)
node_id = args.node_id
pipeline_node = _get_pipeline_node(tfx_pipeline, node_id)
deployment_config = runner_utils.extract_local_deployment_config(
tfx_pipeline
)
executor_spec = runner_utils.extract_executor_spec(
deployment_config, node_id
)
custom_driver_spec = runner_utils.extract_custom_driver_spec(
deployment_config, node_id
)
custom_executor_operators = {
executable_spec_pb2.ContainerExecutableSpec: kubernetes_executor_operator.KubernetesExecutorOperator
}
# make sure all integrations are activated so all materializers etc. are
# available
integration_registry.activate_integrations()
metadata_store = Repository().get_active_stack().metadata_store
if isinstance(metadata_store, KubeflowMetadataStore):
# set up the metadata connection so it connects to the internal kubeflow
# mysql database
connection_config = _get_grpc_metadata_connection_config()
else:
connection_config = deployment_config.metadata_connection_config # type: ignore[attr-defined] # noqa
metadata_connection = metadata.Metadata(connection_config)
# import the user main module to register all the materializers
importlib.import_module(args.main_module)
if hasattr(executor_spec, "class_path"):
executor_module_parts = getattr(executor_spec, "class_path").split(".")
executor_class_target_module_name = ".".join(executor_module_parts[:-1])
_create_executor_class(
step_source_module_name=args.step_module,
step_function_name=args.step_function_name,
executor_class_target_module_name=executor_class_target_module_name,
input_artifact_type_mapping=json.loads(args.input_artifact_types),
)
else:
raise RuntimeError(
f"No class path found inside executor spec: {executor_spec}."
)
component_launcher = launcher.Launcher(
pipeline_node=pipeline_node,
mlmd_connection=metadata_connection,
pipeline_info=tfx_pipeline.pipeline_info,
pipeline_runtime_spec=tfx_pipeline.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
custom_executor_operators=custom_executor_operators,
)
execution_info = execute_step(component_launcher)
if execution_info:
_dump_ui_metadata(pipeline_node, execution_info, args.metadata_ui_path)
docker_utils
build_docker_image(build_context_path, image_name, dockerfile_path=None, dockerignore_path=None, requirements=None, use_local_requirements=False, base_image=None)
Builds a docker image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
build_context_path |
str |
Path to a directory that will be sent to the docker daemon as build context. |
required |
image_name |
str |
The name to use for the created docker image. |
required |
dockerfile_path |
Optional[str] |
Optional path to a dockerfile. If no value is given, a temporary dockerfile will be created. |
None |
dockerignore_path |
Optional[str] |
Optional path to a dockerignore file. If no value is
given, the .dockerignore in the root of the build context will be
used if it exists. Otherwise, all files inside |
None |
requirements |
Optional[List[str]] |
Optional list of pip requirements to install. This
will only be used if no value is given for |
None |
use_local_requirements |
bool |
If |
False |
base_image |
Optional[str] |
The image to use as base for the docker image. |
None |
Source code in zenml/integrations/kubeflow/docker_utils.py
def build_docker_image(
build_context_path: str,
image_name: str,
dockerfile_path: Optional[str] = None,
dockerignore_path: Optional[str] = None,
requirements: Optional[List[str]] = None,
use_local_requirements: bool = False,
base_image: Optional[str] = None,
) -> None:
"""Builds a docker image.
Args:
build_context_path: Path to a directory that will be sent to the
docker daemon as build context.
image_name: The name to use for the created docker image.
dockerfile_path: Optional path to a dockerfile. If no value is given,
a temporary dockerfile will be created.
dockerignore_path: Optional path to a dockerignore file. If no value is
given, the .dockerignore in the root of the build context will be
used if it exists. Otherwise, all files inside `build_context_path`
are included in the build context.
requirements: Optional list of pip requirements to install. This
will only be used if no value is given for `dockerfile_path`.
use_local_requirements: If `True` and no values are given for
`dockerfile_path` and `requirements`, then the packages installed
in the environment of the current python processed will be
installed in the docker image.
base_image: The image to use as base for the docker image.
"""
if not requirements and use_local_requirements:
local_requirements = get_current_environment_requirements()
requirements = [
f"{package}=={version}"
for package, version in local_requirements.items()
if package != "zenml" # exclude ZenML
]
logger.info(
"Using requirements from local environment to build "
"docker image: %s",
requirements,
)
if dockerfile_path:
dockerfile_contents = zenml.io.utils.read_file_contents_as_string(
dockerfile_path
)
else:
dockerfile_contents = generate_dockerfile_contents(
requirements=requirements,
base_image=base_image or DEFAULT_BASE_IMAGE,
)
build_context = create_custom_build_context(
build_context_path=build_context_path,
dockerfile_contents=dockerfile_contents,
dockerignore_path=dockerignore_path,
)
# If a custom base image is provided, make sure to always pull the
# latest version of that image. If no base image is provided, we use
# the static default ZenML image so there is no need to constantly pull
always_pull_base_image = bool(base_image)
logger.info(
"Building docker image '%s', this might take a while...", image_name
)
docker_client = DockerClient.from_env()
# We use the client api directly here so we can stream the logs
output_stream = docker_client.images.client.api.build(
fileobj=build_context,
custom_context=True,
tag=image_name,
pull=always_pull_base_image,
rm=False, # don't remove intermediate containers
)
_process_stream(output_stream)
logger.info("Finished building docker image.")
create_custom_build_context(build_context_path, dockerfile_contents, dockerignore_path=None)
Creates a docker build context.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
build_context_path |
str |
Path to a directory that will be sent to the docker daemon as build context. |
required |
dockerfile_contents |
str |
File contents of the Dockerfile to use for the build. |
required |
dockerignore_path |
Optional[str] |
Optional path to a dockerignore file. If no value is
given, the .dockerignore in the root of the build context will be
used if it exists. Otherwise, all files inside |
None |
Returns:
| Type | Description |
|---|---|
Any |
Docker build context that can be passed when building a docker image. |
Source code in zenml/integrations/kubeflow/docker_utils.py
def create_custom_build_context(
build_context_path: str,
dockerfile_contents: str,
dockerignore_path: Optional[str] = None,
) -> Any:
"""Creates a docker build context.
Args:
build_context_path: Path to a directory that will be sent to the
docker daemon as build context.
dockerfile_contents: File contents of the Dockerfile to use for the
build.
dockerignore_path: Optional path to a dockerignore file. If no value is
given, the .dockerignore in the root of the build context will be
used if it exists. Otherwise, all files inside `build_context_path`
are included in the build context.
Returns:
Docker build context that can be passed when building a docker image.
"""
exclude_patterns = []
default_dockerignore_path = os.path.join(
build_context_path, ".dockerignore"
)
if dockerignore_path:
exclude_patterns = _parse_dockerignore(dockerignore_path)
elif fileio.file_exists(default_dockerignore_path):
logger.info(
"Using dockerignore found at path '%s' to create docker "
"build context.",
default_dockerignore_path,
)
exclude_patterns = _parse_dockerignore(default_dockerignore_path)
else:
logger.info(
"No explicit dockerignore specified and no file called "
".dockerignore exists at the build context root (%s)."
"Creating docker build context with all files inside the build "
"context root directory.",
build_context_path,
)
logger.debug(
"Exclude patterns for creating docker build context: %s",
exclude_patterns,
)
no_ignores_found = not exclude_patterns
files = docker_build_utils.exclude_paths(
build_context_path, patterns=exclude_patterns
)
extra_files = [("Dockerfile", dockerfile_contents)]
context = docker_build_utils.create_archive(
root=build_context_path,
files=sorted(files),
gzip=False,
extra_files=extra_files,
)
build_context_size = os.path.getsize(context.name)
if build_context_size > 50 * 1024 * 1024 and no_ignores_found:
# The build context exceeds 50MiB and we didn't find any excludes
# in dockerignore files -> remind to specify a .dockerignore file
logger.warning(
"Build context size for docker image: %s. If you believe this is "
"unreasonably large, make sure to include a .dockerignore file at "
"the root of your build context (%s) or specify a custom file "
"when defining your pipeline.",
string_utils.get_human_readable_filesize(build_context_size),
default_dockerignore_path,
)
return context
generate_dockerfile_contents(base_image, command=None, requirements=None)
Generates a Dockerfile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
base_image |
str |
The image to use as base for the dockerfile. |
required |
command |
Optional[str] |
The default command that gets executed when running a container of an image created by this dockerfile. |
None |
requirements |
Optional[List[str]] |
Optional list of pip requirements to install. |
None |
Returns:
| Type | Description |
|---|---|
str |
Content of a dockerfile. |
Source code in zenml/integrations/kubeflow/docker_utils.py
def generate_dockerfile_contents(
base_image: str,
command: Optional[str] = None,
requirements: Optional[List[str]] = None,
) -> str:
"""Generates a Dockerfile.
Args:
base_image: The image to use as base for the dockerfile.
command: The default command that gets executed when running a
container of an image created by this dockerfile.
requirements: Optional list of pip requirements to install.
Returns:
Content of a dockerfile.
"""
lines = [f"FROM {base_image}", "WORKDIR /app"]
if requirements:
lines.extend(
[
f"RUN pip install --no-cache {' '.join(requirements)}",
]
)
lines.append("COPY . .")
if command:
lines.append(f"CMD {command}")
return "\n".join(lines)
get_current_environment_requirements()
Returns a dict of package requirements for the environment that the current python process is running in.
Source code in zenml/integrations/kubeflow/docker_utils.py
def get_current_environment_requirements() -> Dict[str, str]:
"""Returns a dict of package requirements for the environment that
the current python process is running in."""
return {
distribution.key: distribution.version
for distribution in pkg_resources.working_set
}
get_image_digest(image_name)
Gets the digest of a docker image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_name |
str |
Name of the image to get the digest for. |
required |
Returns:
| Type | Description |
|---|---|
Optional[str] |
Returns the repo digest for the given image if there exists exactly one.
If there are zero or multiple repo digests, returns |
Source code in zenml/integrations/kubeflow/docker_utils.py
def get_image_digest(image_name: str) -> Optional[str]:
"""Gets the digest of a docker image.
Args:
image_name: Name of the image to get the digest for.
Returns:
Returns the repo digest for the given image if there exists exactly one.
If there are zero or multiple repo digests, returns `None`.
"""
docker_client = DockerClient.from_env()
image = docker_client.images.get(image_name)
repo_digests = image.attrs["RepoDigests"]
if len(repo_digests) == 1:
return cast(str, repo_digests[0])
else:
logger.debug(
"Found zero or more repo digests for docker image '%s': %s",
image_name,
repo_digests,
)
return None
push_docker_image(image_name)
Pushes a docker image to a container registry.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_name |
str |
The full name (including a tag) of the image to push. |
required |
Source code in zenml/integrations/kubeflow/docker_utils.py
def push_docker_image(image_name: str) -> None:
"""Pushes a docker image to a container registry.
Args:
image_name: The full name (including a tag) of the image to push.
"""
logger.info("Pushing docker image '%s'.", image_name)
docker_client = DockerClient.from_env()
output_stream = docker_client.images.push(image_name, stream=True)
_process_stream(output_stream)
logger.info("Finished pushing docker image.")
metadata
special
kubeflow_metadata_store
KubeflowMetadataStore (MySQLMetadataStore)
pydantic-model
Kubeflow MySQL backend for ZenML metadata store.
Source code in zenml/integrations/kubeflow/metadata/kubeflow_metadata_store.py
class KubeflowMetadataStore(MySQLMetadataStore):
"""Kubeflow MySQL backend for ZenML metadata store."""
host: str = "127.0.0.1"
port: int = 3306
database: str = "metadb"
username: str = "root"
password: str = ""
orchestrators
special
kubeflow_component
Kubeflow Pipelines based implementation of TFX components. These components are lightweight wrappers around the KFP DSL's ContainerOp, and ensure that the container gets called with the right set of input arguments. It also ensures that each component exports named output attributes that are consistent with those provided by the native TFX components, thus ensuring that both types of pipeline definitions are compatible. Note: This requires Kubeflow Pipelines SDK to be installed.
KubeflowComponent
Base component for all Kubeflow pipelines TFX components. Returns a wrapper around a KFP DSL ContainerOp class, and adds named output attributes that match the output names for the corresponding native TFX components.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_component.py
class KubeflowComponent:
"""Base component for all Kubeflow pipelines TFX components.
Returns a wrapper around a KFP DSL ContainerOp class, and adds named output
attributes that match the output names for the corresponding native TFX
components.
"""
def __init__(
self,
component: tfx_base_component.BaseComponent,
depends_on: Set[dsl.ContainerOp],
image: str,
tfx_ir: pipeline_pb2.Pipeline, # type: ignore[valid-type]
pod_labels_to_attach: Dict[str, str],
main_module: str,
step_module: str,
step_function_name: str,
runtime_parameters: List[data_types.RuntimeParameter],
metadata_ui_path: str = "/tmp/mlpipeline-ui-metadata.json",
):
"""Creates a new Kubeflow-based component.
This class essentially wraps a dsl.ContainerOp construct in Kubeflow
Pipelines.
Args:
component: The logical TFX component to wrap.
depends_on: The set of upstream KFP ContainerOp components that this
component will depend on.
image: The container image to use for this component.
tfx_ir: The TFX intermedia representation of the pipeline.
pod_labels_to_attach: Dict of pod labels to attach to the GKE pod.
runtime_parameters: Runtime parameters of the pipeline.
metadata_ui_path: File location for metadata-ui-metadata.json file.
"""
utils.replace_placeholder(component)
input_artifact_type_mapping = _get_input_artifact_type_mapping(
component
)
arguments = [
"--node_id",
component.id,
"--tfx_ir",
json_format.MessageToJson(tfx_ir),
"--metadata_ui_path",
metadata_ui_path,
"--main_module",
main_module,
"--step_module",
step_module,
"--step_function_name",
step_function_name,
"--input_artifact_types",
json.dumps(input_artifact_type_mapping),
"--run_name",
"{{workflow.annotations.pipelines.kubeflow.org/run_name}}",
]
for param in runtime_parameters:
arguments.append("--runtime_parameter")
arguments.append(_encode_runtime_parameter(param))
repo = Repository()
artifact_store = repo.get_active_stack().artifact_store
metadata_store = repo.get_active_stack().metadata_store
volumes: Dict[str, k8s_client.V1Volume] = {}
has_local_repos = False
if isinstance(artifact_store, LocalArtifactStore):
has_local_repos = True
host_path = k8s_client.V1HostPathVolumeSource(
path=artifact_store.path, type="Directory"
)
volumes[artifact_store.path] = k8s_client.V1Volume(
name="local-artifact-store", host_path=host_path
)
logger.debug(
"Adding host path volume for local artifact store (path: %s) "
"in kubeflow pipelines container.",
artifact_store.path,
)
if isinstance(metadata_store, SQLiteMetadataStore):
has_local_repos = True
metadata_store_dir = os.path.dirname(metadata_store.uri)
host_path = k8s_client.V1HostPathVolumeSource(
path=metadata_store_dir, type="Directory"
)
volumes[metadata_store_dir] = k8s_client.V1Volume(
name="local-metadata-store", host_path=host_path
)
logger.debug(
"Adding host path volume for local metadata store (uri: %s) "
"in kubeflow pipelines container.",
metadata_store.uri,
)
self.container_op = dsl.ContainerOp(
name=component.id,
command=CONTAINER_ENTRYPOINT_COMMAND,
image=image,
arguments=arguments,
output_artifact_paths={
"mlpipeline-ui-metadata": metadata_ui_path,
},
pvolumes=volumes,
)
if has_local_repos:
if sys.platform == "win32":
# File permissions are not checked on Windows. This if clause
# prevents mypy from complaining about unused 'type: ignore'
# statements
pass
else:
# Run KFP containers in the context of the local UID/GID
# to ensure that the artifact and metadata stores can be shared
# with the local pipeline runs.
self.container_op.container.security_context = (
k8s_client.V1SecurityContext(
run_as_user=os.getuid(),
run_as_group=os.getgid(),
)
)
logger.debug(
"Setting security context UID and GID to local user/group "
"in kubeflow pipelines container."
)
for op in depends_on:
self.container_op.after(op)
self.container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=ENV_ZENML_PREVENT_PIPELINE_EXECUTION, value="True"
)
)
for k, v in pod_labels_to_attach.items():
self.container_op.add_pod_label(k, v)
__init__(self, component, depends_on, image, tfx_ir, pod_labels_to_attach, main_module, step_module, step_function_name, runtime_parameters, metadata_ui_path='/tmp/mlpipeline-ui-metadata.json')
special
Creates a new Kubeflow-based component. This class essentially wraps a dsl.ContainerOp construct in Kubeflow Pipelines.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
component |
BaseComponent |
The logical TFX component to wrap. |
required |
depends_on |
Set[kfp.dsl._container_op.ContainerOp] |
The set of upstream KFP ContainerOp components that this component will depend on. |
required |
image |
str |
The container image to use for this component. |
required |
tfx_ir |
Pipeline |
The TFX intermedia representation of the pipeline. |
required |
pod_labels_to_attach |
Dict[str, str] |
Dict of pod labels to attach to the GKE pod. |
required |
runtime_parameters |
List[tfx.orchestration.data_types.RuntimeParameter] |
Runtime parameters of the pipeline. |
required |
metadata_ui_path |
str |
File location for metadata-ui-metadata.json file. |
'/tmp/mlpipeline-ui-metadata.json' |
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_component.py
def __init__(
self,
component: tfx_base_component.BaseComponent,
depends_on: Set[dsl.ContainerOp],
image: str,
tfx_ir: pipeline_pb2.Pipeline, # type: ignore[valid-type]
pod_labels_to_attach: Dict[str, str],
main_module: str,
step_module: str,
step_function_name: str,
runtime_parameters: List[data_types.RuntimeParameter],
metadata_ui_path: str = "/tmp/mlpipeline-ui-metadata.json",
):
"""Creates a new Kubeflow-based component.
This class essentially wraps a dsl.ContainerOp construct in Kubeflow
Pipelines.
Args:
component: The logical TFX component to wrap.
depends_on: The set of upstream KFP ContainerOp components that this
component will depend on.
image: The container image to use for this component.
tfx_ir: The TFX intermedia representation of the pipeline.
pod_labels_to_attach: Dict of pod labels to attach to the GKE pod.
runtime_parameters: Runtime parameters of the pipeline.
metadata_ui_path: File location for metadata-ui-metadata.json file.
"""
utils.replace_placeholder(component)
input_artifact_type_mapping = _get_input_artifact_type_mapping(
component
)
arguments = [
"--node_id",
component.id,
"--tfx_ir",
json_format.MessageToJson(tfx_ir),
"--metadata_ui_path",
metadata_ui_path,
"--main_module",
main_module,
"--step_module",
step_module,
"--step_function_name",
step_function_name,
"--input_artifact_types",
json.dumps(input_artifact_type_mapping),
"--run_name",
"{{workflow.annotations.pipelines.kubeflow.org/run_name}}",
]
for param in runtime_parameters:
arguments.append("--runtime_parameter")
arguments.append(_encode_runtime_parameter(param))
repo = Repository()
artifact_store = repo.get_active_stack().artifact_store
metadata_store = repo.get_active_stack().metadata_store
volumes: Dict[str, k8s_client.V1Volume] = {}
has_local_repos = False
if isinstance(artifact_store, LocalArtifactStore):
has_local_repos = True
host_path = k8s_client.V1HostPathVolumeSource(
path=artifact_store.path, type="Directory"
)
volumes[artifact_store.path] = k8s_client.V1Volume(
name="local-artifact-store", host_path=host_path
)
logger.debug(
"Adding host path volume for local artifact store (path: %s) "
"in kubeflow pipelines container.",
artifact_store.path,
)
if isinstance(metadata_store, SQLiteMetadataStore):
has_local_repos = True
metadata_store_dir = os.path.dirname(metadata_store.uri)
host_path = k8s_client.V1HostPathVolumeSource(
path=metadata_store_dir, type="Directory"
)
volumes[metadata_store_dir] = k8s_client.V1Volume(
name="local-metadata-store", host_path=host_path
)
logger.debug(
"Adding host path volume for local metadata store (uri: %s) "
"in kubeflow pipelines container.",
metadata_store.uri,
)
self.container_op = dsl.ContainerOp(
name=component.id,
command=CONTAINER_ENTRYPOINT_COMMAND,
image=image,
arguments=arguments,
output_artifact_paths={
"mlpipeline-ui-metadata": metadata_ui_path,
},
pvolumes=volumes,
)
if has_local_repos:
if sys.platform == "win32":
# File permissions are not checked on Windows. This if clause
# prevents mypy from complaining about unused 'type: ignore'
# statements
pass
else:
# Run KFP containers in the context of the local UID/GID
# to ensure that the artifact and metadata stores can be shared
# with the local pipeline runs.
self.container_op.container.security_context = (
k8s_client.V1SecurityContext(
run_as_user=os.getuid(),
run_as_group=os.getgid(),
)
)
logger.debug(
"Setting security context UID and GID to local user/group "
"in kubeflow pipelines container."
)
for op in depends_on:
self.container_op.after(op)
self.container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=ENV_ZENML_PREVENT_PIPELINE_EXECUTION, value="True"
)
)
for k, v in pod_labels_to_attach.items():
self.container_op.add_pod_label(k, v)
kubeflow_dag_runner
The below code is copied from the TFX source repo with minor changes. All credits goes to the TFX team for the core implementation
KubeflowDagRunner (TfxRunner)
Kubeflow Pipelines runner. Constructs a pipeline definition YAML file based on the TFX logical pipeline.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
class KubeflowDagRunner(tfx_runner.TfxRunner):
"""Kubeflow Pipelines runner.
Constructs a pipeline definition YAML file based on the TFX logical pipeline.
"""
def __init__(
self,
config: KubeflowDagRunnerConfig,
output_path: str,
pod_labels_to_attach: Optional[Dict[str, str]] = None,
):
"""Initializes KubeflowDagRunner for compiling a Kubeflow Pipeline.
Args:
config: A KubeflowDagRunnerConfig object to specify runtime
configuration when running the pipeline under Kubeflow.
output_path: Path where the pipeline definition file will be stored.
pod_labels_to_attach: Optional set of pod labels to attach to GKE pod
spinned up for this pipeline. Default to the 3 labels:
1. add-pod-env: true,
2. pipeline SDK type,
3. pipeline unique ID,
where 2 and 3 are instrumentation of usage tracking.
"""
super().__init__(config)
self._kubeflow_config = config
self._output_path = output_path
self._compiler = compiler.Compiler()
self._tfx_compiler = tfx_compiler.Compiler()
self._params: List[dsl.PipelineParam] = []
self._params_by_component_id: Dict[
str, List[data_types.RuntimeParameter]
] = collections.defaultdict(list)
self._deduped_parameter_names: Set[str] = set()
self._pod_labels_to_attach = (
pod_labels_to_attach or get_default_pod_labels()
)
def _parse_parameter_from_component(
self, component: tfx_base_component.BaseComponent
) -> None:
"""Extract embedded RuntimeParameter placeholders from a component.
Extract embedded RuntimeParameter placeholders from a component, then append
the corresponding dsl.PipelineParam to KubeflowDagRunner.
Args:
component: a TFX component.
"""
deduped_parameter_names_for_component = set()
for parameter in component.exec_properties.values():
if not isinstance(parameter, data_types.RuntimeParameter):
continue
# Ignore pipeline root because it will be added later.
if parameter.name == tfx_pipeline.ROOT_PARAMETER.name:
continue
if parameter.name in deduped_parameter_names_for_component:
continue
deduped_parameter_names_for_component.add(parameter.name)
self._params_by_component_id[component.id].append(parameter)
if parameter.name not in self._deduped_parameter_names:
self._deduped_parameter_names.add(parameter.name)
dsl_parameter = dsl.PipelineParam(
name=parameter.name, value=str(parameter.default)
)
self._params.append(dsl_parameter)
def _parse_parameter_from_pipeline(
self, pipeline: tfx_pipeline.Pipeline
) -> None:
"""Extract all the RuntimeParameter placeholders from the pipeline."""
for component in pipeline.components:
self._parse_parameter_from_component(component)
def _construct_pipeline_graph(
self, pipeline: tfx_pipeline.Pipeline
) -> None:
"""Constructs a Kubeflow Pipeline graph.
Args:
pipeline: The logical TFX pipeline to base the construction on.
pipeline_root: dsl.PipelineParam representing the pipeline root.
"""
component_to_kfp_op: Dict[base_node.BaseNode, dsl.ContainerOp] = {}
tfx_ir = self._generate_tfx_ir(pipeline)
# Assumption: There is a partial ordering of components in the list,
# i.e. if component A depends on component B and C, then A appears
# after B and C in the list.
for component in pipeline.components:
# Keep track of the set of upstream dsl.ContainerOps for this
# component.
depends_on = set()
for upstream_component in component.upstream_nodes:
depends_on.add(component_to_kfp_op[upstream_component])
# remove the extra pipeline node information
tfx_node_ir = self._dehydrate_tfx_ir(tfx_ir, component.id)
from zenml.utils import source_utils
main_module_file = sys.modules["__main__"].__file__
main_module = source_utils.get_module_source_from_file_path(
os.path.abspath(main_module_file)
)
step_module = component.component_type.split(".")[:-1]
if step_module[0] == "__main__":
step_module = main_module
else:
step_module = ".".join(step_module)
kfp_component = KubeflowComponent(
main_module=main_module,
step_module=step_module,
step_function_name=component.id,
component=component,
depends_on=depends_on,
image=self._kubeflow_config.image,
pod_labels_to_attach=self._pod_labels_to_attach,
tfx_ir=tfx_node_ir,
metadata_ui_path=self._kubeflow_config.metadata_ui_path,
runtime_parameters=self._params_by_component_id[component.id],
)
for operator in self._kubeflow_config.pipeline_operator_funcs:
kfp_component.container_op.apply(operator)
component_to_kfp_op[component] = kfp_component.container_op
def _del_unused_field(
self, node_id: str, message_dict: MutableMapping[str, Any]
) -> None:
"""Remove fields that are not used by the pipeline."""
for item in list(message_dict.keys()):
if item != node_id:
del message_dict[item]
def _dehydrate_tfx_ir(
self, original_pipeline: pipeline_pb2.Pipeline, node_id: str # type: ignore[valid-type] # noqa
) -> pipeline_pb2.Pipeline: # type: ignore[valid-type]
"""Dehydrate the TFX IR to remove unused fields."""
pipeline = copy.deepcopy(original_pipeline)
for node in pipeline.nodes: # type: ignore[attr-defined]
if (
node.WhichOneof("node") == "pipeline_node"
and node.pipeline_node.node_info.id == node_id
):
del pipeline.nodes[:] # type: ignore[attr-defined]
pipeline.nodes.extend([node]) # type: ignore[attr-defined]
break
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
pipeline.deployment_config.Unpack(deployment_config) # type: ignore[attr-defined] # noqa
self._del_unused_field(node_id, deployment_config.executor_specs)
self._del_unused_field(node_id, deployment_config.custom_driver_specs)
self._del_unused_field(
node_id, deployment_config.node_level_platform_configs
)
pipeline.deployment_config.Pack(deployment_config) # type: ignore[attr-defined] # noqa
return pipeline
def _generate_tfx_ir(
self, pipeline: tfx_pipeline.Pipeline
) -> Optional[pipeline_pb2.Pipeline]: # type: ignore[valid-type]
"""Generate the TFX IR from the logical TFX pipeline."""
result = self._tfx_compiler.compile(pipeline)
return result
def run(self, pipeline: tfx_pipeline.Pipeline) -> None:
"""Compiles and outputs a Kubeflow Pipeline YAML definition file.
Args:
pipeline: The logical TFX pipeline to use when building the Kubeflow
pipeline.
"""
for component in pipeline.components:
# TODO(b/187122662): Pass through pip dependencies as a first-class
# component flag.
if isinstance(component, tfx_base_component.BaseComponent):
component._resolve_pip_dependencies(
# pylint: disable=protected-access
pipeline.pipeline_info.pipeline_root
)
def _construct_pipeline() -> None:
"""Creates Kubeflow ContainerOps for each TFX component
encountered in the pipeline definition."""
self._construct_pipeline_graph(pipeline)
# Need to run this first to get self._params populated. Then KFP
# compiler can correctly match default value with PipelineParam.
self._parse_parameter_from_pipeline(pipeline)
# Create workflow spec and write out to package.
self._compiler._create_and_write_workflow(
# pylint: disable=protected-access
pipeline_func=_construct_pipeline,
pipeline_name=pipeline.pipeline_info.pipeline_name,
params_list=self._params,
package_path=self._output_path,
)
logger.info(
"Finished writing kubeflow pipeline definition file '%s'.",
self._output_path,
)
__init__(self, config, output_path, pod_labels_to_attach=None)
special
Initializes KubeflowDagRunner for compiling a Kubeflow Pipeline.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
config |
KubeflowDagRunnerConfig |
A KubeflowDagRunnerConfig object to specify runtime configuration when running the pipeline under Kubeflow. |
required |
output_path |
str |
Path where the pipeline definition file will be stored. |
required |
pod_labels_to_attach |
Optional[Dict[str, str]] |
Optional set of pod labels to attach to GKE pod spinned up for this pipeline. Default to the 3 labels: 1. add-pod-env: true, 2. pipeline SDK type, 3. pipeline unique ID, where 2 and 3 are instrumentation of usage tracking. |
None |
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
def __init__(
self,
config: KubeflowDagRunnerConfig,
output_path: str,
pod_labels_to_attach: Optional[Dict[str, str]] = None,
):
"""Initializes KubeflowDagRunner for compiling a Kubeflow Pipeline.
Args:
config: A KubeflowDagRunnerConfig object to specify runtime
configuration when running the pipeline under Kubeflow.
output_path: Path where the pipeline definition file will be stored.
pod_labels_to_attach: Optional set of pod labels to attach to GKE pod
spinned up for this pipeline. Default to the 3 labels:
1. add-pod-env: true,
2. pipeline SDK type,
3. pipeline unique ID,
where 2 and 3 are instrumentation of usage tracking.
"""
super().__init__(config)
self._kubeflow_config = config
self._output_path = output_path
self._compiler = compiler.Compiler()
self._tfx_compiler = tfx_compiler.Compiler()
self._params: List[dsl.PipelineParam] = []
self._params_by_component_id: Dict[
str, List[data_types.RuntimeParameter]
] = collections.defaultdict(list)
self._deduped_parameter_names: Set[str] = set()
self._pod_labels_to_attach = (
pod_labels_to_attach or get_default_pod_labels()
)
run(self, pipeline)
Compiles and outputs a Kubeflow Pipeline YAML definition file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline |
Pipeline |
The logical TFX pipeline to use when building the Kubeflow pipeline. |
required |
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
def run(self, pipeline: tfx_pipeline.Pipeline) -> None:
"""Compiles and outputs a Kubeflow Pipeline YAML definition file.
Args:
pipeline: The logical TFX pipeline to use when building the Kubeflow
pipeline.
"""
for component in pipeline.components:
# TODO(b/187122662): Pass through pip dependencies as a first-class
# component flag.
if isinstance(component, tfx_base_component.BaseComponent):
component._resolve_pip_dependencies(
# pylint: disable=protected-access
pipeline.pipeline_info.pipeline_root
)
def _construct_pipeline() -> None:
"""Creates Kubeflow ContainerOps for each TFX component
encountered in the pipeline definition."""
self._construct_pipeline_graph(pipeline)
# Need to run this first to get self._params populated. Then KFP
# compiler can correctly match default value with PipelineParam.
self._parse_parameter_from_pipeline(pipeline)
# Create workflow spec and write out to package.
self._compiler._create_and_write_workflow(
# pylint: disable=protected-access
pipeline_func=_construct_pipeline,
pipeline_name=pipeline.pipeline_info.pipeline_name,
params_list=self._params,
package_path=self._output_path,
)
logger.info(
"Finished writing kubeflow pipeline definition file '%s'.",
self._output_path,
)
KubeflowDagRunnerConfig (PipelineConfig)
Runtime configuration parameters specific to execution on Kubeflow.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
class KubeflowDagRunnerConfig(pipeline_config.PipelineConfig):
"""Runtime configuration parameters specific to execution on Kubeflow."""
def __init__(
self,
image: str,
pipeline_operator_funcs: Optional[List[OpFunc]] = None,
supported_launcher_classes: Optional[
List[Type[base_component_launcher.BaseComponentLauncher]]
] = None,
metadata_ui_path: str = "/tmp/mlpipeline-ui-metadata.json",
**kwargs: Any
):
"""Creates a KubeflowDagRunnerConfig object.
The user can use pipeline_operator_funcs to apply modifications to
ContainerOps used in the pipeline. For example, to ensure the pipeline
steps mount a GCP secret, and a Persistent Volume, one can create config
object like so:
from kfp import gcp, onprem
mount_secret_op = gcp.use_secret('my-secret-name)
mount_volume_op = onprem.mount_pvc(
"my-persistent-volume-claim",
"my-volume-name",
"/mnt/volume-mount-path")
config = KubeflowDagRunnerConfig(
pipeline_operator_funcs=[mount_secret_op, mount_volume_op]
)
Args:
image: The docker image to use in the pipeline.
pipeline_operator_funcs: A list of ContainerOp modifying functions that
will be applied to every container step in the pipeline.
supported_launcher_classes: A list of component launcher classes that are
supported by the current pipeline. List sequence determines the order in
which launchers are chosen for each component being run.
metadata_ui_path: File location for metadata-ui-metadata.json file.
**kwargs: keyword args for PipelineConfig.
"""
supported_launcher_classes = supported_launcher_classes or [
in_process_component_launcher.InProcessComponentLauncher,
kubernetes_component_launcher.KubernetesComponentLauncher,
]
super().__init__(
supported_launcher_classes=supported_launcher_classes, **kwargs
)
self.pipeline_operator_funcs = (
pipeline_operator_funcs or get_default_pipeline_operator_funcs()
)
self.image = image
self.metadata_ui_path = metadata_ui_path
__init__(self, image, pipeline_operator_funcs=None, supported_launcher_classes=None, metadata_ui_path='/tmp/mlpipeline-ui-metadata.json', **kwargs)
special
Creates a KubeflowDagRunnerConfig object. The user can use pipeline_operator_funcs to apply modifications to ContainerOps used in the pipeline. For example, to ensure the pipeline steps mount a GCP secret, and a Persistent Volume, one can create config object like so: from kfp import gcp, onprem mount_secret_op = gcp.use_secret('my-secret-name) mount_volume_op = onprem.mount_pvc( "my-persistent-volume-claim", "my-volume-name", "/mnt/volume-mount-path") config = KubeflowDagRunnerConfig( pipeline_operator_funcs=[mount_secret_op, mount_volume_op] )
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The docker image to use in the pipeline. |
required |
pipeline_operator_funcs |
Optional[List[Callable[[kfp.dsl._container_op.ContainerOp], Union[kfp.dsl._container_op.ContainerOp, NoneType]]]] |
A list of ContainerOp modifying functions that will be applied to every container step in the pipeline. |
None |
supported_launcher_classes |
Optional[List[Type[tfx.orchestration.launcher.base_component_launcher.BaseComponentLauncher]]] |
A list of component launcher classes that are supported by the current pipeline. List sequence determines the order in which launchers are chosen for each component being run. |
None |
metadata_ui_path |
str |
File location for metadata-ui-metadata.json file. |
'/tmp/mlpipeline-ui-metadata.json' |
**kwargs |
Any |
keyword args for PipelineConfig. |
{} |
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
def __init__(
self,
image: str,
pipeline_operator_funcs: Optional[List[OpFunc]] = None,
supported_launcher_classes: Optional[
List[Type[base_component_launcher.BaseComponentLauncher]]
] = None,
metadata_ui_path: str = "/tmp/mlpipeline-ui-metadata.json",
**kwargs: Any
):
"""Creates a KubeflowDagRunnerConfig object.
The user can use pipeline_operator_funcs to apply modifications to
ContainerOps used in the pipeline. For example, to ensure the pipeline
steps mount a GCP secret, and a Persistent Volume, one can create config
object like so:
from kfp import gcp, onprem
mount_secret_op = gcp.use_secret('my-secret-name)
mount_volume_op = onprem.mount_pvc(
"my-persistent-volume-claim",
"my-volume-name",
"/mnt/volume-mount-path")
config = KubeflowDagRunnerConfig(
pipeline_operator_funcs=[mount_secret_op, mount_volume_op]
)
Args:
image: The docker image to use in the pipeline.
pipeline_operator_funcs: A list of ContainerOp modifying functions that
will be applied to every container step in the pipeline.
supported_launcher_classes: A list of component launcher classes that are
supported by the current pipeline. List sequence determines the order in
which launchers are chosen for each component being run.
metadata_ui_path: File location for metadata-ui-metadata.json file.
**kwargs: keyword args for PipelineConfig.
"""
supported_launcher_classes = supported_launcher_classes or [
in_process_component_launcher.InProcessComponentLauncher,
kubernetes_component_launcher.KubernetesComponentLauncher,
]
super().__init__(
supported_launcher_classes=supported_launcher_classes, **kwargs
)
self.pipeline_operator_funcs = (
pipeline_operator_funcs or get_default_pipeline_operator_funcs()
)
self.image = image
self.metadata_ui_path = metadata_ui_path
get_default_pipeline_operator_funcs(use_gcp_sa=False)
Returns a default list of pipeline operator functions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
use_gcp_sa |
bool |
If true, mount a GCP service account secret to each pod, with the name _KUBEFLOW_GCP_SECRET_NAME. |
False |
Returns:
| Type | Description |
|---|---|
List[Callable[[kfp.dsl._container_op.ContainerOp], Optional[kfp.dsl._container_op.ContainerOp]]] |
A list of functions with type OpFunc. |
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
def get_default_pipeline_operator_funcs(
use_gcp_sa: bool = False,
) -> List[OpFunc]:
"""Returns a default list of pipeline operator functions.
Args:
use_gcp_sa: If true, mount a GCP service account secret to each pod, with
the name _KUBEFLOW_GCP_SECRET_NAME.
Returns:
A list of functions with type OpFunc.
"""
# Enables authentication for GCP services if needed.
gcp_secret_op = gcp.use_gcp_secret(_KUBEFLOW_GCP_SECRET_NAME)
# Mounts configmap containing Metadata gRPC server configuration.
mount_config_map_op = _mount_config_map_op("metadata-grpc-configmap")
if use_gcp_sa:
return [gcp_secret_op, mount_config_map_op]
else:
return [mount_config_map_op]
get_default_pod_labels()
Returns the default pod label dict for Kubeflow.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_dag_runner.py
def get_default_pod_labels() -> Dict[str, str]:
"""Returns the default pod label dict for Kubeflow."""
# KFP default transformers add pod env:
# https://github.com/kubeflow/pipelines/blob/0.1.32/sdk/python/kfp/compiler/_default_transformers.py
result = {"add-pod-env": "true", telemetry_utils.LABEL_KFP_SDK_ENV: "tfx"}
return result
kubeflow_orchestrator
KubeflowOrchestrator (BaseOrchestrator)
pydantic-model
Orchestrator responsible for running pipelines using Kubeflow.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
class KubeflowOrchestrator(BaseOrchestrator):
"""Orchestrator responsible for running pipelines using Kubeflow."""
custom_docker_base_image_name: Optional[str] = None
kubeflow_pipelines_ui_port: int = DEFAULT_KFP_UI_PORT
kubernetes_context: Optional[str] = None
def get_docker_image_name(self, pipeline_name: str) -> str:
"""Returns the full docker image name including registry and tag."""
base_image_name = f"zenml-kubeflow:{pipeline_name}"
container_registry = Repository().get_active_stack().container_registry
if container_registry:
registry_uri = container_registry.uri.rstrip("/")
return f"{registry_uri}/{base_image_name}"
else:
return base_image_name
@property
def root_directory(self) -> str:
"""Returns path to the root directory for all files concerning
this orchestrator."""
return os.path.join(
zenml.io.utils.get_global_config_directory(),
"kubeflow",
str(self.uuid),
)
@property
def pipeline_directory(self) -> str:
"""Returns path to a directory in which the kubeflow pipeline files
are stored."""
return os.path.join(self.root_directory, "pipelines")
def pre_run(self, pipeline: "BasePipeline", caller_filepath: str) -> None:
"""Builds a docker image for the current environment and uploads it to
a container registry if configured.
"""
from zenml.integrations.kubeflow.docker_utils import (
build_docker_image,
push_docker_image,
)
image_name = self.get_docker_image_name(pipeline.name)
repository_root = Repository().path
requirements = (
["kubernetes"]
+ self._get_stack_requirements()
+ self._get_pipeline_requirements(pipeline)
)
logger.debug("Kubeflow docker container requirements: %s", requirements)
build_docker_image(
build_context_path=repository_root,
image_name=image_name,
dockerignore_path=pipeline.dockerignore_file,
requirements=requirements,
base_image=self.custom_docker_base_image_name,
)
if Repository().get_active_stack().container_registry:
push_docker_image(image_name)
def run(
self,
zenml_pipeline: "BasePipeline",
run_name: str,
**kwargs: Any,
) -> None:
"""Runs the pipeline on Kubeflow.
Args:
zenml_pipeline: The pipeline to run.
run_name: Name of the pipeline run.
**kwargs: Unused kwargs to conform with base signature
"""
from zenml.integrations.kubeflow.docker_utils import get_image_digest
image_name = self.get_docker_image_name(zenml_pipeline.name)
image_name = get_image_digest(image_name) or image_name
fileio.make_dirs(self.pipeline_directory)
pipeline_file_path = os.path.join(
self.pipeline_directory, f"{zenml_pipeline.name}.yaml"
)
runner_config = KubeflowDagRunnerConfig(image=image_name)
runner = KubeflowDagRunner(
config=runner_config, output_path=pipeline_file_path
)
tfx_pipeline = create_tfx_pipeline(zenml_pipeline)
runner.run(tfx_pipeline)
run_name = run_name or datetime.now().strftime("%d_%h_%y-%H_%M_%S_%f")
self._upload_and_run_pipeline(
pipeline_file_path=pipeline_file_path,
run_name=run_name,
enable_cache=zenml_pipeline.enable_cache,
)
def _upload_and_run_pipeline(
self, pipeline_file_path: str, run_name: str, enable_cache: bool
) -> None:
"""Tries to upload and run a KFP pipeline.
Args:
pipeline_file_path: Path to the pipeline definition file.
run_name: A name for the pipeline run that will be started.
enable_cache: Whether caching is enabled for this pipeline run.
"""
try:
if self.kubernetes_context:
logger.info(
"Running in kubernetes context '%s'.",
self.kubernetes_context,
)
# load kubernetes config to authorize the KFP client
config.load_kube_config(context=self.kubernetes_context)
# upload the pipeline to Kubeflow and start it
client = kfp.Client()
result = client.create_run_from_pipeline_package(
pipeline_file_path,
arguments={},
run_name=run_name,
enable_caching=enable_cache,
)
logger.info("Started pipeline run with ID '%s'.", result.run_id)
except urllib3.exceptions.HTTPError as error:
logger.warning(
"Failed to upload Kubeflow pipeline: %s. "
"Please make sure your kube config is configured and the "
"current context is set correctly.",
error,
)
def _get_stack_requirements(self) -> List[str]:
"""Gets list of requirements for the current active stack."""
stack = Repository().get_active_stack()
requirements = []
artifact_store_module = stack.artifact_store.__module__
requirements += get_requirements_for_module(artifact_store_module)
metadata_store_module = stack.metadata_store.__module__
requirements += get_requirements_for_module(metadata_store_module)
return requirements
def _get_pipeline_requirements(self, pipeline: "BasePipeline") -> List[str]:
"""Gets list of requirements for a pipeline."""
if pipeline.requirements_file and fileio.file_exists(
pipeline.requirements_file
):
logger.debug(
"Using requirements from file %s.", pipeline.requirements_file
)
with fileio.open(pipeline.requirements_file, "r") as f:
return [
requirement.strip() for requirement in f.read().split("\n")
]
else:
return []
@property
def _pid_file_path(self) -> str:
"""Returns path to the daemon PID file."""
return os.path.join(self.root_directory, "kubeflow_daemon.pid")
@property
def log_file(self) -> str:
"""Path of the daemon log file."""
return os.path.join(self.root_directory, "kubeflow_daemon.log")
@property
def _k3d_cluster_name(self) -> str:
"""Returns the K3D cluster name."""
# K3D only allows cluster names with up to 32 characters, use the
# first 8 chars of the orchestrator UUID as identifier
return f"zenml-kubeflow-{str(self.uuid)[:8]}"
def _get_k3d_registry_name(self, port: int) -> str:
"""Returns the K3D registry name."""
return f"k3d-zenml-kubeflow-registry.localhost:{port}"
@property
def _k3d_registry_config_path(self) -> str:
"""Returns the path to the K3D registry config yaml."""
return os.path.join(self.root_directory, "k3d_registry.yaml")
@property
def is_running(self) -> bool:
"""Returns whether the orchestrator is running."""
if not local_deployment_utils.check_prerequisites():
# if any prerequisites are missing there is certainly no
# local deployment running
return False
return local_deployment_utils.k3d_cluster_exists(
cluster_name=self._k3d_cluster_name
)
def list_manual_setup_steps(
self, container_registry_name: str, container_registry_path: str
) -> None:
"""Logs manual steps needed to setup the Kubeflow local orchestrator."""
global_config_dir_path = zenml.io.utils.get_global_config_directory()
kubeflow_commands = [
f"> k3d cluster create CLUSTER_NAME --registry-create {container_registry_name} --registry-config {container_registry_path} --volume {global_config_dir_path}:{global_config_dir_path}\n",
f"> kubectl --context CLUSTER_NAME apply -k github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref={KFP_VERSION}&timeout=1m",
"> kubectl --context CLUSTER_NAME wait --timeout=60s --for condition=established crd/applications.app.k8s.io",
f"> kubectl --context CLUSTER_NAME apply -k github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref={KFP_VERSION}&timeout=1m",
f"> kubectl --namespace kubeflow port-forward svc/ml-pipeline-ui {self.kubeflow_pipelines_ui_port}:80",
]
logger.error("Unable to spin up local Kubeflow Pipelines deployment.")
logger.info(
"If you wish to spin up this Kubeflow local orchestrator manually, "
"please enter the following commands (substituting where appropriate):\n"
)
logger.info("\n".join(kubeflow_commands))
def up(self) -> None:
"""Spins up a local Kubeflow Pipelines deployment."""
if self.is_running:
logger.info(
"Found already existing local Kubeflow Pipelines deployment. "
"If there are any issues with the existing deployment, please "
"run 'zenml orchestrator down' to delete it."
)
return
if not local_deployment_utils.check_prerequisites():
logger.error(
"Unable to spin up local Kubeflow Pipelines deployment: "
"Please install 'k3d' and 'kubectl' and try again."
)
return
container_registry = Repository().get_active_stack().container_registry
if not container_registry:
logger.error(
"Unable to spin up local Kubeflow Pipelines deployment: "
"Missing container registry in current stack."
)
return
logger.info("Spinning up local Kubeflow Pipelines deployment...")
fileio.make_dirs(self.root_directory)
container_registry_port = int(container_registry.uri.split(":")[-1])
container_registry_name = self._get_k3d_registry_name(
port=container_registry_port
)
local_deployment_utils.write_local_registry_yaml(
yaml_path=self._k3d_registry_config_path,
registry_name=container_registry_name,
registry_uri=container_registry.uri,
)
try:
local_deployment_utils.create_k3d_cluster(
cluster_name=self._k3d_cluster_name,
registry_name=container_registry_name,
registry_config_path=self._k3d_registry_config_path,
)
kubernetes_context = f"k3d-{self._k3d_cluster_name}"
local_deployment_utils.deploy_kubeflow_pipelines(
kubernetes_context=kubernetes_context
)
port = self.kubeflow_pipelines_ui_port
if (
port == DEFAULT_KFP_UI_PORT
and not networking_utils.port_available(port)
):
# if the user didn't specify a specific port and the default
# port is occupied, fallback to a random open port
port = networking_utils.find_available_port()
local_deployment_utils.start_kfp_ui_daemon(
pid_file_path=self._pid_file_path,
log_file_path=self.log_file,
port=port,
)
except Exception as e:
logger.error(e)
self.list_manual_setup_steps(
container_registry_name, self._k3d_registry_config_path
)
self.down()
def down(self) -> None:
"""Tears down a local Kubeflow Pipelines deployment."""
if self.is_running:
local_deployment_utils.delete_k3d_cluster(
cluster_name=self._k3d_cluster_name
)
if fileio.file_exists(self._pid_file_path):
if sys.platform == "win32":
# Daemon functionality is not supported on Windows, so the PID
# file won't exist. This if clause exists just for mypy to not
# complain about missing functions
pass
else:
from zenml.utils import daemon
daemon.stop_daemon(self._pid_file_path, kill_children=True)
fileio.remove(self._pid_file_path)
if fileio.file_exists(self.log_file):
fileio.remove(self.log_file)
logger.info("Local kubeflow pipelines deployment spun down.")
is_running: bool
property
readonly
Returns whether the orchestrator is running.
log_file: str
property
readonly
Path of the daemon log file.
pipeline_directory: str
property
readonly
Returns path to a directory in which the kubeflow pipeline files are stored.
root_directory: str
property
readonly
Returns path to the root directory for all files concerning this orchestrator.
down(self)
Tears down a local Kubeflow Pipelines deployment.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
def down(self) -> None:
"""Tears down a local Kubeflow Pipelines deployment."""
if self.is_running:
local_deployment_utils.delete_k3d_cluster(
cluster_name=self._k3d_cluster_name
)
if fileio.file_exists(self._pid_file_path):
if sys.platform == "win32":
# Daemon functionality is not supported on Windows, so the PID
# file won't exist. This if clause exists just for mypy to not
# complain about missing functions
pass
else:
from zenml.utils import daemon
daemon.stop_daemon(self._pid_file_path, kill_children=True)
fileio.remove(self._pid_file_path)
if fileio.file_exists(self.log_file):
fileio.remove(self.log_file)
logger.info("Local kubeflow pipelines deployment spun down.")
get_docker_image_name(self, pipeline_name)
Returns the full docker image name including registry and tag.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
def get_docker_image_name(self, pipeline_name: str) -> str:
"""Returns the full docker image name including registry and tag."""
base_image_name = f"zenml-kubeflow:{pipeline_name}"
container_registry = Repository().get_active_stack().container_registry
if container_registry:
registry_uri = container_registry.uri.rstrip("/")
return f"{registry_uri}/{base_image_name}"
else:
return base_image_name
list_manual_setup_steps(self, container_registry_name, container_registry_path)
Logs manual steps needed to setup the Kubeflow local orchestrator.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
def list_manual_setup_steps(
self, container_registry_name: str, container_registry_path: str
) -> None:
"""Logs manual steps needed to setup the Kubeflow local orchestrator."""
global_config_dir_path = zenml.io.utils.get_global_config_directory()
kubeflow_commands = [
f"> k3d cluster create CLUSTER_NAME --registry-create {container_registry_name} --registry-config {container_registry_path} --volume {global_config_dir_path}:{global_config_dir_path}\n",
f"> kubectl --context CLUSTER_NAME apply -k github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref={KFP_VERSION}&timeout=1m",
"> kubectl --context CLUSTER_NAME wait --timeout=60s --for condition=established crd/applications.app.k8s.io",
f"> kubectl --context CLUSTER_NAME apply -k github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref={KFP_VERSION}&timeout=1m",
f"> kubectl --namespace kubeflow port-forward svc/ml-pipeline-ui {self.kubeflow_pipelines_ui_port}:80",
]
logger.error("Unable to spin up local Kubeflow Pipelines deployment.")
logger.info(
"If you wish to spin up this Kubeflow local orchestrator manually, "
"please enter the following commands (substituting where appropriate):\n"
)
logger.info("\n".join(kubeflow_commands))
pre_run(self, pipeline, caller_filepath)
Builds a docker image for the current environment and uploads it to a container registry if configured.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
def pre_run(self, pipeline: "BasePipeline", caller_filepath: str) -> None:
"""Builds a docker image for the current environment and uploads it to
a container registry if configured.
"""
from zenml.integrations.kubeflow.docker_utils import (
build_docker_image,
push_docker_image,
)
image_name = self.get_docker_image_name(pipeline.name)
repository_root = Repository().path
requirements = (
["kubernetes"]
+ self._get_stack_requirements()
+ self._get_pipeline_requirements(pipeline)
)
logger.debug("Kubeflow docker container requirements: %s", requirements)
build_docker_image(
build_context_path=repository_root,
image_name=image_name,
dockerignore_path=pipeline.dockerignore_file,
requirements=requirements,
base_image=self.custom_docker_base_image_name,
)
if Repository().get_active_stack().container_registry:
push_docker_image(image_name)
run(self, zenml_pipeline, run_name, **kwargs)
Runs the pipeline on Kubeflow.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
zenml_pipeline |
BasePipeline |
The pipeline to run. |
required |
run_name |
str |
Name of the pipeline run. |
required |
**kwargs |
Any |
Unused kwargs to conform with base signature |
{} |
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
def run(
self,
zenml_pipeline: "BasePipeline",
run_name: str,
**kwargs: Any,
) -> None:
"""Runs the pipeline on Kubeflow.
Args:
zenml_pipeline: The pipeline to run.
run_name: Name of the pipeline run.
**kwargs: Unused kwargs to conform with base signature
"""
from zenml.integrations.kubeflow.docker_utils import get_image_digest
image_name = self.get_docker_image_name(zenml_pipeline.name)
image_name = get_image_digest(image_name) or image_name
fileio.make_dirs(self.pipeline_directory)
pipeline_file_path = os.path.join(
self.pipeline_directory, f"{zenml_pipeline.name}.yaml"
)
runner_config = KubeflowDagRunnerConfig(image=image_name)
runner = KubeflowDagRunner(
config=runner_config, output_path=pipeline_file_path
)
tfx_pipeline = create_tfx_pipeline(zenml_pipeline)
runner.run(tfx_pipeline)
run_name = run_name or datetime.now().strftime("%d_%h_%y-%H_%M_%S_%f")
self._upload_and_run_pipeline(
pipeline_file_path=pipeline_file_path,
run_name=run_name,
enable_cache=zenml_pipeline.enable_cache,
)
up(self)
Spins up a local Kubeflow Pipelines deployment.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_orchestrator.py
def up(self) -> None:
"""Spins up a local Kubeflow Pipelines deployment."""
if self.is_running:
logger.info(
"Found already existing local Kubeflow Pipelines deployment. "
"If there are any issues with the existing deployment, please "
"run 'zenml orchestrator down' to delete it."
)
return
if not local_deployment_utils.check_prerequisites():
logger.error(
"Unable to spin up local Kubeflow Pipelines deployment: "
"Please install 'k3d' and 'kubectl' and try again."
)
return
container_registry = Repository().get_active_stack().container_registry
if not container_registry:
logger.error(
"Unable to spin up local Kubeflow Pipelines deployment: "
"Missing container registry in current stack."
)
return
logger.info("Spinning up local Kubeflow Pipelines deployment...")
fileio.make_dirs(self.root_directory)
container_registry_port = int(container_registry.uri.split(":")[-1])
container_registry_name = self._get_k3d_registry_name(
port=container_registry_port
)
local_deployment_utils.write_local_registry_yaml(
yaml_path=self._k3d_registry_config_path,
registry_name=container_registry_name,
registry_uri=container_registry.uri,
)
try:
local_deployment_utils.create_k3d_cluster(
cluster_name=self._k3d_cluster_name,
registry_name=container_registry_name,
registry_config_path=self._k3d_registry_config_path,
)
kubernetes_context = f"k3d-{self._k3d_cluster_name}"
local_deployment_utils.deploy_kubeflow_pipelines(
kubernetes_context=kubernetes_context
)
port = self.kubeflow_pipelines_ui_port
if (
port == DEFAULT_KFP_UI_PORT
and not networking_utils.port_available(port)
):
# if the user didn't specify a specific port and the default
# port is occupied, fallback to a random open port
port = networking_utils.find_available_port()
local_deployment_utils.start_kfp_ui_daemon(
pid_file_path=self._pid_file_path,
log_file_path=self.log_file,
port=port,
)
except Exception as e:
logger.error(e)
self.list_manual_setup_steps(
container_registry_name, self._k3d_registry_config_path
)
self.down()
kubeflow_utils
Common utility for Kubeflow-based orchestrator.
replace_placeholder(component)
Replaces the RuntimeParameter placeholders with kfp.dsl.PipelineParam.
Source code in zenml/integrations/kubeflow/orchestrators/kubeflow_utils.py
def replace_placeholder(component: base_node.BaseNode) -> None:
"""Replaces the RuntimeParameter placeholders with kfp.dsl.PipelineParam."""
keys = list(component.exec_properties.keys())
for key in keys:
exec_property = component.exec_properties[key]
if not isinstance(exec_property, data_types.RuntimeParameter):
continue
component.exec_properties[key] = str(
dsl.PipelineParam(name=exec_property.name)
)
local_deployment_utils
check_prerequisites()
Checks whether all prerequisites for a local kubeflow pipelines deployment are installed.
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def check_prerequisites() -> bool:
"""Checks whether all prerequisites for a local kubeflow pipelines
deployment are installed."""
k3d_installed = shutil.which("k3d") is not None
kubectl_installed = shutil.which("kubectl") is not None
logger.debug(
"Local kubeflow deployment prerequisites: K3D - %s, Kubectl - %s",
k3d_installed,
kubectl_installed,
)
return k3d_installed and kubectl_installed
create_k3d_cluster(cluster_name, registry_name, registry_config_path)
Creates a K3D cluster.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
cluster_name |
str |
Name of the cluster to create. |
required |
registry_name |
str |
Name of the registry to create for this cluster. |
required |
registry_config_path |
str |
Path to the registry config file. |
required |
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def create_k3d_cluster(
cluster_name: str, registry_name: str, registry_config_path: str
) -> None:
"""Creates a K3D cluster.
Args:
cluster_name: Name of the cluster to create.
registry_name: Name of the registry to create for this cluster.
registry_config_path: Path to the registry config file.
"""
logger.info("Creating local K3D cluster '%s'.", cluster_name)
global_config_dir_path = zenml.io.utils.get_global_config_directory()
subprocess.check_call(
[
"k3d",
"cluster",
"create",
cluster_name,
"--registry-create",
registry_name,
"--registry-config",
registry_config_path,
"--volume",
f"{global_config_dir_path}:{global_config_dir_path}",
]
)
logger.info("Finished K3D cluster creation.")
delete_k3d_cluster(cluster_name)
Deletes a K3D cluster with the given name.
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def delete_k3d_cluster(cluster_name: str) -> None:
"""Deletes a K3D cluster with the given name."""
subprocess.check_call(["k3d", "cluster", "delete", cluster_name])
logger.info("Deleted local k3d cluster '%s'.", cluster_name)
deploy_kubeflow_pipelines(kubernetes_context)
Deploys Kubeflow Pipelines.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
kubernetes_context |
str |
The kubernetes context on which Kubeflow Pipelines should be deployed. |
required |
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def deploy_kubeflow_pipelines(kubernetes_context: str) -> None:
"""Deploys Kubeflow Pipelines.
Args:
kubernetes_context: The kubernetes context on which Kubeflow Pipelines
should be deployed.
"""
logger.info("Deploying Kubeflow Pipelines.")
subprocess.check_call(
[
"kubectl",
"--context",
kubernetes_context,
"apply",
"-k",
f"github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref={KFP_VERSION}&timeout=1m",
]
)
subprocess.check_call(
[
"kubectl",
"--context",
kubernetes_context,
"wait",
"--timeout=60s",
"--for",
"condition=established",
"crd/applications.app.k8s.io",
]
)
subprocess.check_call(
[
"kubectl",
"--context",
kubernetes_context,
"apply",
"-k",
f"github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref={KFP_VERSION}&timeout=1m",
]
)
logger.info(
"Waiting for all Kubeflow Pipelines pods to be ready (this might "
"take a few minutes)."
)
while True:
logger.info("Current pod status:")
subprocess.check_call(
[
"kubectl",
"--context",
kubernetes_context,
"--namespace",
"kubeflow",
"get",
"pods",
]
)
if kubeflow_pipelines_ready(kubernetes_context=kubernetes_context):
break
logger.info("One or more pods not ready yet, waiting for 30 seconds...")
time.sleep(30)
logger.info("Finished Kubeflow Pipelines setup.")
k3d_cluster_exists(cluster_name)
Checks whether there exists a K3D cluster with the given name.
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def k3d_cluster_exists(cluster_name: str) -> bool:
"""Checks whether there exists a K3D cluster with the given name."""
output = subprocess.check_output(
["k3d", "cluster", "list", "--output", "json"]
)
clusters = json.loads(output)
for cluster in clusters:
if cluster["name"] == cluster_name:
return True
return False
kubeflow_pipelines_ready(kubernetes_context)
Returns whether all Kubeflow Pipelines pods are ready.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
kubernetes_context |
str |
The kubernetes context in which the pods should be checked. |
required |
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def kubeflow_pipelines_ready(kubernetes_context: str) -> bool:
"""Returns whether all Kubeflow Pipelines pods are ready.
Args:
kubernetes_context: The kubernetes context in which the pods
should be checked.
"""
try:
subprocess.check_call(
[
"kubectl",
"--context",
kubernetes_context,
"--namespace",
"kubeflow",
"wait",
"--for",
"condition=ready",
"--timeout=0s",
"pods",
"--all",
],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
return True
except subprocess.CalledProcessError:
return False
start_kfp_ui_daemon(pid_file_path, log_file_path, port)
Starts a daemon process that forwards ports so the Kubeflow Pipelines UI is accessible in the browser.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pid_file_path |
str |
Path where the file with the daemons process ID should be written. |
required |
log_file_path |
str |
Path to a file where the daemon logs should be written. |
required |
port |
int |
Port on which the UI should be accessible. |
required |
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def start_kfp_ui_daemon(
pid_file_path: str, log_file_path: str, port: int
) -> None:
"""Starts a daemon process that forwards ports so the Kubeflow Pipelines
UI is accessible in the browser.
Args:
pid_file_path: Path where the file with the daemons process ID should
be written.
log_file_path: Path to a file where the daemon logs should be written.
port: Port on which the UI should be accessible.
"""
command = [
"kubectl",
"--namespace",
"kubeflow",
"port-forward",
"svc/ml-pipeline-ui",
f"{port}:80",
]
if not networking_utils.port_available(port):
modified_command = command.copy()
modified_command[-1] = "PORT:80"
logger.warning(
"Unable to port-forward Kubeflow Pipelines UI to local port %d "
"because the port is occupied. In order to access the Kubeflow "
"Pipelines UI at http://localhost:PORT/, please run '%s' in a "
"separate command line shell (replace PORT with a free port of "
"your choice).",
port,
" ".join(modified_command),
)
elif sys.platform == "win32":
logger.warning(
"Daemon functionality not supported on Windows. "
"In order to access the Kubeflow Pipelines UI at "
"http://localhost:%d/, please run '%s' in a separate command "
"line shell.",
port,
" ".join(command),
)
else:
from zenml.utils import daemon
def _daemon_function() -> None:
"""Port-forwards the Kubeflow Pipelines UI pod."""
subprocess.check_call(command)
daemon.run_as_daemon(
_daemon_function, pid_file=pid_file_path, log_file=log_file_path
)
logger.info(
"Started Kubeflow Pipelines UI daemon (check the daemon logs at %s "
"in case you're not able to view the UI). The Kubeflow Pipelines "
"UI should now be accessible at http://localhost:%d/.",
log_file_path,
port,
)
write_local_registry_yaml(yaml_path, registry_name, registry_uri)
Writes a K3D registry config file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
yaml_path |
str |
Path where the config file should be written to. |
required |
registry_name |
str |
Name of the registry. |
required |
registry_uri |
str |
URI of the registry. |
required |
Source code in zenml/integrations/kubeflow/orchestrators/local_deployment_utils.py
def write_local_registry_yaml(
yaml_path: str, registry_name: str, registry_uri: str
) -> None:
"""Writes a K3D registry config file.
Args:
yaml_path: Path where the config file should be written to.
registry_name: Name of the registry.
registry_uri: URI of the registry.
"""
yaml_content = {
"mirrors": {registry_uri: {"endpoint": [f"http://{registry_name}"]}}
}
yaml_utils.write_yaml(yaml_path, yaml_content)
mlflow
special
The mlflow integrations currently enables you to use mlflow tracking as a convenient way to visualize your experiment runs within the mlflow ui
MlflowIntegration (Integration)
Definition of Plotly integration for ZenML.
Source code in zenml/integrations/mlflow/__init__.py
class MlflowIntegration(Integration):
"""Definition of Plotly integration for ZenML."""
NAME = MLFLOW
REQUIREMENTS = ["mlflow>=1.2.0"]
mlflow_utils
enable_mlflow(_pipeline, experiment_name=None)
Outer decorator function for the creation of a ZenML pipeline with mlflow tracking enabled.
In order for a pipeline to run within the context of mlflow, the mlflow experiment should be associated with the pipeline directly. Each separate pipeline run needs to be associated directly with a pipeline run. For this, the init and run method need to be extended accordingly.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
_pipeline |
Type[zenml.pipelines.base_pipeline.BasePipeline] |
The decorated pipeline |
required |
experiment_name |
Optional[str] |
Experiment name to use for mlflow |
None |
Returns:
| Type | Description |
|---|---|
Type[zenml.pipelines.base_pipeline.BasePipeline] |
the inner decorator which has a pipeline with the two methods extended |
Source code in zenml/integrations/mlflow/mlflow_utils.py
def enable_mlflow(
_pipeline: Type[BasePipeline], experiment_name: Optional[str] = None
) -> Type[BasePipeline]:
"""Outer decorator function for the creation of a ZenML pipeline with mlflow
tracking enabled.
In order for a pipeline to run within the context of mlflow, the mlflow
experiment should be associated with the pipeline directly. Each separate
pipeline run needs to be associated directly with a pipeline run. For this,
the __init__ and run method need to be extended accordingly.
Args:
_pipeline: The decorated pipeline
experiment_name: Experiment name to use for mlflow
Returns:
the inner decorator which has a pipeline with the two methods extended
"""
def inner_decorator(pipeline: Type[BasePipeline]) -> Type[BasePipeline]:
"""Inner decorator function for the creation of a ZenML Pipeline with
mlflow
The __init__ and run method are both extended.
Args:
pipeline: BasePipeline which will be extended
Returns:
the class of a newly generated ZenML Pipeline with mlflow
"""
return type( # noqa
pipeline.__name__,
(pipeline,),
{
"__init__": enable_mlflow_init(
pipeline.__init__, experiment_name
),
"run": enable_mlflow_run(pipeline.run),
},
)
return inner_decorator(_pipeline)
enable_mlflow_init(original_init, experiment=None)
Outer decorator function for extending the init method for pipelines that should be run using mlflow
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
original_init |
Callable[[zenml.pipelines.base_pipeline.BasePipeline, zenml.steps.base_step.BaseStep, Any], NoneType] |
The init method that should be extended |
required |
experiment |
Optional[str] |
The users chosen experiment name to use for mlflow |
None |
Returns:
| Type | Description |
|---|---|
Callable[..., NoneType] |
the inner decorator which extends the init method |
Source code in zenml/integrations/mlflow/mlflow_utils.py
def enable_mlflow_init(
original_init: Callable[[BasePipeline, BaseStep, Any], None],
experiment: Optional[str] = None,
) -> Callable[..., None]:
"""Outer decorator function for extending the __init__ method for pipelines
that should be run using mlflow
Args:
original_init: The __init__ method that should be extended
experiment: The users chosen experiment name to use for mlflow
Returns:
the inner decorator which extends the __init__ method
"""
def inner_decorator(
self: BasePipeline, *args: BaseStep, **kwargs: Any
) -> None:
"""Inner decorator overwriting the pipeline __init__
Makes sure mlflow is properly set up and all mlflow logging takes place
within one mlflow experiment that is associated with the pipeline
"""
original_init(self, *args, **kwargs)
setup_mlflow(
backend_store_uri=local_mlflow_backend(),
experiment_name=experiment if experiment else self.name,
)
return inner_decorator
enable_mlflow_run(run)
Outer decorator function for extending the run method for pipelines that should be run using mlflow
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
run |
Callable[..., Any] |
The run method that should be extended |
required |
Returns:
| Type | Description |
|---|---|
Callable[..., Any] |
the inner decorator which extends the run method |
Source code in zenml/integrations/mlflow/mlflow_utils.py
def enable_mlflow_run(run: Callable[..., Any]) -> Callable[..., Any]:
"""Outer decorator function for extending the run method for pipelines
that should be run using mlflow
Args:
run: The run method that should be extended
Returns:
the inner decorator which extends the run method
"""
def inner_decorator(
self: BasePipeline, run_name: Optional[str] = None
) -> Any:
"""Inner decorator used to extend the run method of a pipeline.
This ensures each pipeline run is run within a different mlflow context.
Args:
self: self of the original pipeline class
run_name: Optional name for the run.
"""
with mlflow.start_run(run_name=run_name):
run(self, run_name)
return inner_decorator
local_mlflow_backend()
Returns the local mlflow backend inside the global zenml directory
Source code in zenml/integrations/mlflow/mlflow_utils.py
def local_mlflow_backend() -> str:
"""Returns the local mlflow backend inside the global zenml directory"""
local_mlflow_backend_uri = os.path.join(
get_global_config_directory(), "local_stores", "mlruns"
)
if not os.path.exists(local_mlflow_backend_uri):
os.makedirs(local_mlflow_backend_uri)
# TODO [medium]: safely access (possibly non-existent) artifact stores
return "file:" + local_mlflow_backend_uri
setup_mlflow(backend_store_uri=None, experiment_name='default')
Setup all mlflow related configurations. This includes specifying which mlflow tracking uri should b e used and which experiment the tracking will be associated with.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
backend_store_uri |
Optional[str] |
The mlflow backend to log to |
None |
experiment_name |
str |
The experiment name under which all runs will be tracked |
'default' |
Source code in zenml/integrations/mlflow/mlflow_utils.py
def setup_mlflow(
backend_store_uri: Optional[str] = None, experiment_name: str = "default"
) -> None:
"""Setup all mlflow related configurations. This includes specifying which
mlflow tracking uri should b e used and which experiment the tracking
will be associated with.
Args:
backend_store_uri: The mlflow backend to log to
experiment_name: The experiment name under which all runs will be
tracked
"""
# TODO [ENG-316]: Implement a way to get the mlflow token and set
# it as env variable at MLFLOW_TRACKING_TOKEN
if not backend_store_uri:
backend_store_uri = local_mlflow_backend()
set_tracking_uri(backend_store_uri)
# Set which experiment is used within mlflow
set_experiment(experiment_name)
plotly
special
PlotlyIntegration (Integration)
Definition of Plotly integration for ZenML.
Source code in zenml/integrations/plotly/__init__.py
class PlotlyIntegration(Integration):
"""Definition of Plotly integration for ZenML."""
NAME = PLOTLY
REQUIREMENTS = ["plotly>=5.4.0"]
visualizers
special
pipeline_lineage_visualizer
PipelineLineageVisualizer (BasePipelineVisualizer)
Visualize the lineage of runs in a pipeline using plotly.
Source code in zenml/integrations/plotly/visualizers/pipeline_lineage_visualizer.py
class PipelineLineageVisualizer(BasePipelineVisualizer):
"""Visualize the lineage of runs in a pipeline using plotly."""
@abstractmethod
def visualize(
self, object: PipelineView, *args: Any, **kwargs: Any
) -> Figure:
"""Creates a pipeline lineage diagram using plotly."""
logger.warning(
"This integration is not completed yet. Results might be unexpected."
)
category_dict = {}
dimensions = ["run"]
for run in object.runs:
category_dict[run.name] = {"run": run.name}
for step in run.steps:
category_dict[run.name].update(
{
step.name: str(step.id),
}
)
if step.name not in dimensions:
dimensions.append(f"{step.name}")
category_df = pd.DataFrame.from_dict(category_dict, orient="index")
category_df = category_df.reset_index()
fig = px.parallel_categories(
category_df,
dimensions,
color=None,
labels="status",
)
fig.show()
return fig
visualize(self, object, *args, **kwargs)
Creates a pipeline lineage diagram using plotly.
Source code in zenml/integrations/plotly/visualizers/pipeline_lineage_visualizer.py
@abstractmethod
def visualize(
self, object: PipelineView, *args: Any, **kwargs: Any
) -> Figure:
"""Creates a pipeline lineage diagram using plotly."""
logger.warning(
"This integration is not completed yet. Results might be unexpected."
)
category_dict = {}
dimensions = ["run"]
for run in object.runs:
category_dict[run.name] = {"run": run.name}
for step in run.steps:
category_dict[run.name].update(
{
step.name: str(step.id),
}
)
if step.name not in dimensions:
dimensions.append(f"{step.name}")
category_df = pd.DataFrame.from_dict(category_dict, orient="index")
category_df = category_df.reset_index()
fig = px.parallel_categories(
category_df,
dimensions,
color=None,
labels="status",
)
fig.show()
return fig
pytorch
special
PytorchIntegration (Integration)
Definition of PyTorch integration for ZenML.
Source code in zenml/integrations/pytorch/__init__.py
class PytorchIntegration(Integration):
"""Definition of PyTorch integration for ZenML."""
NAME = PYTORCH
REQUIREMENTS = ["torch"]
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.pytorch import materializers # noqa
activate()
classmethod
Activates the integration.
Source code in zenml/integrations/pytorch/__init__.py
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.pytorch import materializers # noqa
materializers
special
pytorch_materializer
PyTorchMaterializer (BaseMaterializer)
Materializer to read/write Pytorch models.
Source code in zenml/integrations/pytorch/materializers/pytorch_materializer.py
class PyTorchMaterializer(BaseMaterializer):
"""Materializer to read/write Pytorch models."""
ASSOCIATED_TYPES = [Module, TorchDict]
ASSOCIATED_ARTIFACT_TYPES = [ModelArtifact]
def handle_input(self, data_type: Type[Any]) -> Union[Module, TorchDict]:
"""Reads and returns a PyTorch model.
Returns:
A loaded pytorch model.
"""
super().handle_input(data_type)
return torch.load(os.path.join(self.artifact.uri, DEFAULT_FILENAME)) # type: ignore[no-untyped-call] # noqa
def handle_return(self, model: Union[Module, TorchDict]) -> None:
"""Writes a PyTorch model.
Args:
model: A torch.nn.Module or a dict to pass into model.save
"""
super().handle_return(model)
torch.save(model, os.path.join(self.artifact.uri, DEFAULT_FILENAME))
handle_input(self, data_type)
Reads and returns a PyTorch model.
Returns:
| Type | Description |
|---|---|
Union[torch.nn.modules.module.Module, zenml.integrations.pytorch.materializers.pytorch_types.TorchDict] |
A loaded pytorch model. |
Source code in zenml/integrations/pytorch/materializers/pytorch_materializer.py
def handle_input(self, data_type: Type[Any]) -> Union[Module, TorchDict]:
"""Reads and returns a PyTorch model.
Returns:
A loaded pytorch model.
"""
super().handle_input(data_type)
return torch.load(os.path.join(self.artifact.uri, DEFAULT_FILENAME)) # type: ignore[no-untyped-call] # noqa
handle_return(self, model)
Writes a PyTorch model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
model |
Union[torch.nn.modules.module.Module, zenml.integrations.pytorch.materializers.pytorch_types.TorchDict] |
A torch.nn.Module or a dict to pass into model.save |
required |
Source code in zenml/integrations/pytorch/materializers/pytorch_materializer.py
def handle_return(self, model: Union[Module, TorchDict]) -> None:
"""Writes a PyTorch model.
Args:
model: A torch.nn.Module or a dict to pass into model.save
"""
super().handle_return(model)
torch.save(model, os.path.join(self.artifact.uri, DEFAULT_FILENAME))
pytorch_types
TorchDict (dict, Generic)
A type of dict that represents saving a model.
Source code in zenml/integrations/pytorch/materializers/pytorch_types.py
class TorchDict(Dict[str, Any]):
"""A type of dict that represents saving a model."""
pytorch_lightning
special
PytorchLightningIntegration (Integration)
Definition of PyTorch Lightning integration for ZenML.
Source code in zenml/integrations/pytorch_lightning/__init__.py
class PytorchLightningIntegration(Integration):
"""Definition of PyTorch Lightning integration for ZenML."""
NAME = PYTORCH_L
REQUIREMENTS = ["pytorch_lightning"]
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.pytorch_lightning import materializers # noqa
activate()
classmethod
Activates the integration.
Source code in zenml/integrations/pytorch_lightning/__init__.py
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.pytorch_lightning import materializers # noqa
materializers
special
pytorch_lightning_materializer
PyTorchLightningMaterializer (BaseMaterializer)
Materializer to read/write Pytorch models.
Source code in zenml/integrations/pytorch_lightning/materializers/pytorch_lightning_materializer.py
class PyTorchLightningMaterializer(BaseMaterializer):
"""Materializer to read/write Pytorch models."""
ASSOCIATED_TYPES = [Trainer]
ASSOCIATED_ARTIFACT_TYPES = [ModelArtifact]
def handle_input(self, data_type: Type[Any]) -> Trainer:
"""Reads and returns a PyTorch Lightning trainer.
Returns:
A PyTorch Lightning trainer object.
"""
super().handle_input(data_type)
return Trainer(
resume_from_checkpoint=os.path.join(
self.artifact.uri, CHECKPOINT_NAME
)
)
def handle_return(self, trainer: Trainer) -> None:
"""Writes a PyTorch Lightning trainer.
Args:
trainer: A PyTorch Lightning trainer object.
"""
super().handle_return(trainer)
trainer.save_checkpoint(
os.path.join(self.artifact.uri, CHECKPOINT_NAME)
)
handle_input(self, data_type)
Reads and returns a PyTorch Lightning trainer.
Returns:
| Type | Description |
|---|---|
Trainer |
A PyTorch Lightning trainer object. |
Source code in zenml/integrations/pytorch_lightning/materializers/pytorch_lightning_materializer.py
def handle_input(self, data_type: Type[Any]) -> Trainer:
"""Reads and returns a PyTorch Lightning trainer.
Returns:
A PyTorch Lightning trainer object.
"""
super().handle_input(data_type)
return Trainer(
resume_from_checkpoint=os.path.join(
self.artifact.uri, CHECKPOINT_NAME
)
)
handle_return(self, trainer)
Writes a PyTorch Lightning trainer.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
trainer |
Trainer |
A PyTorch Lightning trainer object. |
required |
Source code in zenml/integrations/pytorch_lightning/materializers/pytorch_lightning_materializer.py
def handle_return(self, trainer: Trainer) -> None:
"""Writes a PyTorch Lightning trainer.
Args:
trainer: A PyTorch Lightning trainer object.
"""
super().handle_return(trainer)
trainer.save_checkpoint(
os.path.join(self.artifact.uri, CHECKPOINT_NAME)
)
registry
IntegrationRegistry
Registry to keep track of ZenML Integrations
Source code in zenml/integrations/registry.py
class IntegrationRegistry(object):
"""Registry to keep track of ZenML Integrations"""
def __init__(self) -> None:
"""Initializing the integration registry"""
self._integrations: Dict[str, Type["Integration"]] = {}
@property
def integrations(self) -> Dict[str, Type["Integration"]]:
"""Method to get integrations dictionary.
Returns:
A dict of integration key to type of `Integration`.
"""
return self._integrations
@integrations.setter
def integrations(self, i: Any) -> None:
"""Setter method for the integrations property"""
raise IntegrationError(
"Please do not manually change the integrations within the "
"registry. If you would like to register a new integration "
"manually, please use "
"`integration_registry.register_integration()`."
)
def register_integration(
self, key: str, type_: Type["Integration"]
) -> None:
"""Method to register an integration with a given name"""
self._integrations[key] = type_
def activate_integrations(self) -> None:
"""Method to activate the integrations with are registered in the
registry"""
for name, integration in self._integrations.items():
if integration.check_installation():
integration.activate()
logger.debug(f"Integration `{name}` is activated.")
else:
logger.debug(f"Integration `{name}` could not be activated.")
@property
def list_integration_names(self) -> List[str]:
"""Get a list of all possible integrations"""
return [name for name in self._integrations]
def select_integration_requirements(
self, integration_name: Optional[str] = None
) -> List[str]:
"""Select the requirements for a given integration
or all integrations"""
if integration_name:
if integration_name in self.list_integration_names:
return self._integrations[integration_name].REQUIREMENTS
else:
raise KeyError(
f"Version {integration_name} does not exist. "
f"Currently the following integrations are implemented. "
f"{self.list_integration_names}"
)
else:
return [
requirement
for name in self.list_integration_names
for requirement in self._integrations[name].REQUIREMENTS
]
def is_installed(self, integration_name: Optional[str] = None) -> bool:
"""Checks if all requirements for an integration are installed"""
if integration_name in self.list_integration_names:
return self._integrations[integration_name].check_installation()
elif not integration_name:
all_installed = [
self._integrations[item].check_installation()
for item in self.list_integration_names
]
return any(all_installed)
else:
raise KeyError(
f"Integration '{integration_name}' not found. "
f"Currently the following integrations are available: "
f"{self.list_integration_names}"
)
integrations: Dict[str, Type[Integration]]
property
writable
Method to get integrations dictionary.
Returns:
| Type | Description |
|---|---|
Dict[str, Type[Integration]] |
A dict of integration key to type of |
list_integration_names: List[str]
property
readonly
Get a list of all possible integrations
__init__(self)
special
Initializing the integration registry
Source code in zenml/integrations/registry.py
def __init__(self) -> None:
"""Initializing the integration registry"""
self._integrations: Dict[str, Type["Integration"]] = {}
activate_integrations(self)
Method to activate the integrations with are registered in the registry
Source code in zenml/integrations/registry.py
def activate_integrations(self) -> None:
"""Method to activate the integrations with are registered in the
registry"""
for name, integration in self._integrations.items():
if integration.check_installation():
integration.activate()
logger.debug(f"Integration `{name}` is activated.")
else:
logger.debug(f"Integration `{name}` could not be activated.")
is_installed(self, integration_name=None)
Checks if all requirements for an integration are installed
Source code in zenml/integrations/registry.py
def is_installed(self, integration_name: Optional[str] = None) -> bool:
"""Checks if all requirements for an integration are installed"""
if integration_name in self.list_integration_names:
return self._integrations[integration_name].check_installation()
elif not integration_name:
all_installed = [
self._integrations[item].check_installation()
for item in self.list_integration_names
]
return any(all_installed)
else:
raise KeyError(
f"Integration '{integration_name}' not found. "
f"Currently the following integrations are available: "
f"{self.list_integration_names}"
)
register_integration(self, key, type_)
Method to register an integration with a given name
Source code in zenml/integrations/registry.py
def register_integration(
self, key: str, type_: Type["Integration"]
) -> None:
"""Method to register an integration with a given name"""
self._integrations[key] = type_
select_integration_requirements(self, integration_name=None)
Select the requirements for a given integration or all integrations
Source code in zenml/integrations/registry.py
def select_integration_requirements(
self, integration_name: Optional[str] = None
) -> List[str]:
"""Select the requirements for a given integration
or all integrations"""
if integration_name:
if integration_name in self.list_integration_names:
return self._integrations[integration_name].REQUIREMENTS
else:
raise KeyError(
f"Version {integration_name} does not exist. "
f"Currently the following integrations are implemented. "
f"{self.list_integration_names}"
)
else:
return [
requirement
for name in self.list_integration_names
for requirement in self._integrations[name].REQUIREMENTS
]
sklearn
special
SklearnIntegration (Integration)
Definition of sklearn integration for ZenML.
Source code in zenml/integrations/sklearn/__init__.py
class SklearnIntegration(Integration):
"""Definition of sklearn integration for ZenML."""
NAME = SKLEARN
REQUIREMENTS = ["scikit-learn"]
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.sklearn import materializers # noqa
activate()
classmethod
Activates the integration.
Source code in zenml/integrations/sklearn/__init__.py
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.sklearn import materializers # noqa
helpers
special
digits
get_digits()
Returns the digits dataset in the form of a tuple of numpy arrays.
Source code in zenml/integrations/sklearn/helpers/digits.py
def get_digits() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Returns the digits dataset in the form of a tuple of numpy
arrays."""
digits = load_digits()
# flatten the images
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
# Split data into 50% train and 50% test subsets
X_train, X_test, y_train, y_test = train_test_split(
data, digits.target, test_size=0.5, shuffle=False
)
return X_train, X_test, y_train, y_test
get_digits_model()
Creates a support vector classifier for digits dataset.
Source code in zenml/integrations/sklearn/helpers/digits.py
def get_digits_model() -> ClassifierMixin:
"""Creates a support vector classifier for digits dataset."""
return SVC(gamma=0.001)
materializers
special
sklearn_materializer
SklearnMaterializer (BaseMaterializer)
Materializer to read data to and from sklearn.
Source code in zenml/integrations/sklearn/materializers/sklearn_materializer.py
class SklearnMaterializer(BaseMaterializer):
"""Materializer to read data to and from sklearn."""
ASSOCIATED_TYPES = [
BaseEstimator,
ClassifierMixin,
ClusterMixin,
BiclusterMixin,
OutlierMixin,
RegressorMixin,
MetaEstimatorMixin,
MultiOutputMixin,
DensityMixin,
TransformerMixin,
]
ASSOCIATED_ARTIFACT_TYPES = [ModelArtifact]
def handle_input(
self, data_type: Type[Any]
) -> Union[
BaseEstimator,
ClassifierMixin,
ClusterMixin,
BiclusterMixin,
OutlierMixin,
RegressorMixin,
MetaEstimatorMixin,
MultiOutputMixin,
DensityMixin,
TransformerMixin,
]:
"""Reads a base sklearn model from a pickle file."""
super().handle_input(data_type)
filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
with fileio.open(filepath, "rb") as fid:
clf = pickle.load(fid)
return clf
def handle_return(
self,
clf: Union[
BaseEstimator,
ClassifierMixin,
ClusterMixin,
BiclusterMixin,
OutlierMixin,
RegressorMixin,
MetaEstimatorMixin,
MultiOutputMixin,
DensityMixin,
TransformerMixin,
],
) -> None:
"""Creates a pickle for a sklearn model.
Args:
clf: A sklearn model.
"""
super().handle_return(clf)
filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
with fileio.open(filepath, "wb") as fid:
pickle.dump(clf, fid)
handle_input(self, data_type)
Reads a base sklearn model from a pickle file.
Source code in zenml/integrations/sklearn/materializers/sklearn_materializer.py
def handle_input(
self, data_type: Type[Any]
) -> Union[
BaseEstimator,
ClassifierMixin,
ClusterMixin,
BiclusterMixin,
OutlierMixin,
RegressorMixin,
MetaEstimatorMixin,
MultiOutputMixin,
DensityMixin,
TransformerMixin,
]:
"""Reads a base sklearn model from a pickle file."""
super().handle_input(data_type)
filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
with fileio.open(filepath, "rb") as fid:
clf = pickle.load(fid)
return clf
handle_return(self, clf)
Creates a pickle for a sklearn model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
clf |
Union[sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.ClusterMixin, sklearn.base.BiclusterMixin, sklearn.base.OutlierMixin, sklearn.base.RegressorMixin, sklearn.base.MetaEstimatorMixin, sklearn.base.MultiOutputMixin, sklearn.base.DensityMixin, sklearn.base.TransformerMixin] |
A sklearn model. |
required |
Source code in zenml/integrations/sklearn/materializers/sklearn_materializer.py
def handle_return(
self,
clf: Union[
BaseEstimator,
ClassifierMixin,
ClusterMixin,
BiclusterMixin,
OutlierMixin,
RegressorMixin,
MetaEstimatorMixin,
MultiOutputMixin,
DensityMixin,
TransformerMixin,
],
) -> None:
"""Creates a pickle for a sklearn model.
Args:
clf: A sklearn model.
"""
super().handle_return(clf)
filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
with fileio.open(filepath, "wb") as fid:
pickle.dump(clf, fid)
steps
special
sklearn_evaluator
SklearnEvaluator (BaseEvaluatorStep)
A simple step implementation which utilizes sklearn to evaluate the performance of a given model on a given test dataset
Source code in zenml/integrations/sklearn/steps/sklearn_evaluator.py
class SklearnEvaluator(BaseEvaluatorStep):
"""A simple step implementation which utilizes sklearn to evaluate the
performance of a given model on a given test dataset"""
def entrypoint( # type: ignore[override]
self,
dataset: pd.DataFrame,
model: tf.keras.Model,
config: SklearnEvaluatorConfig,
) -> dict: # type: ignore[type-arg]
"""Method which is responsible for the computation of the evaluation
Args:
dataset: a pandas Dataframe which represents the test dataset
model: a trained tensorflow Keras model
config: the configuration for the step
Returns:
a dictionary which has the evaluation report
"""
labels = dataset.pop(config.label_class_column)
predictions = model.predict(dataset)
predicted_classes = [1 if v > 0.5 else 0 for v in predictions]
report = classification_report(
labels, predicted_classes, output_dict=True
)
return report # type: ignore[no-any-return]
CONFIG_CLASS (BaseEvaluatorConfig)
pydantic-model
Config class for the sklearn evaluator
Source code in zenml/integrations/sklearn/steps/sklearn_evaluator.py
class SklearnEvaluatorConfig(BaseEvaluatorConfig):
"""Config class for the sklearn evaluator"""
label_class_column: str
entrypoint(self, dataset, model, config)
Method which is responsible for the computation of the evaluation
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset |
DataFrame |
a pandas Dataframe which represents the test dataset |
required |
model |
Model |
a trained tensorflow Keras model |
required |
config |
SklearnEvaluatorConfig |
the configuration for the step |
required |
Returns:
| Type | Description |
|---|---|
dict |
a dictionary which has the evaluation report |
Source code in zenml/integrations/sklearn/steps/sklearn_evaluator.py
def entrypoint( # type: ignore[override]
self,
dataset: pd.DataFrame,
model: tf.keras.Model,
config: SklearnEvaluatorConfig,
) -> dict: # type: ignore[type-arg]
"""Method which is responsible for the computation of the evaluation
Args:
dataset: a pandas Dataframe which represents the test dataset
model: a trained tensorflow Keras model
config: the configuration for the step
Returns:
a dictionary which has the evaluation report
"""
labels = dataset.pop(config.label_class_column)
predictions = model.predict(dataset)
predicted_classes = [1 if v > 0.5 else 0 for v in predictions]
report = classification_report(
labels, predicted_classes, output_dict=True
)
return report # type: ignore[no-any-return]
SklearnEvaluatorConfig (BaseEvaluatorConfig)
pydantic-model
Config class for the sklearn evaluator
Source code in zenml/integrations/sklearn/steps/sklearn_evaluator.py
class SklearnEvaluatorConfig(BaseEvaluatorConfig):
"""Config class for the sklearn evaluator"""
label_class_column: str
sklearn_splitter
SklearnSplitter (BaseSplitStep)
A simple step implementation which utilizes sklearn to split a given dataset into train, test and validation splits
Source code in zenml/integrations/sklearn/steps/sklearn_splitter.py
class SklearnSplitter(BaseSplitStep):
"""A simple step implementation which utilizes sklearn to split a given
dataset into train, test and validation splits"""
def entrypoint( # type: ignore[override]
self,
dataset: pd.DataFrame,
config: SklearnSplitterConfig,
) -> Output( # type:ignore[valid-type]
train=pd.DataFrame, test=pd.DataFrame, validation=pd.DataFrame
):
"""Method which is responsible for the splitting logic
Args:
dataset: a pandas Dataframe which entire dataset
config: the configuration for the step
Returns:
three dataframes representing the splits
"""
if (
any(
[
split not in config.ratios
for split in ["train", "test", "validation"]
]
)
or len(config.ratios) != 3
):
raise KeyError(
f"Make sure that you only use 'train', 'test' and "
f"'validation' as keys in the ratios dict. Current keys: "
f"{config.ratios.keys()}"
)
if sum(config.ratios.values()) != 1:
raise ValueError(
f"Make sure that the ratios sum up to 1. Current "
f"ratios: {config.ratios}"
)
train_dataset, test_dataset = train_test_split(
dataset, test_size=config.ratios["test"]
)
train_dataset, val_dataset = train_test_split(
train_dataset,
test_size=(
config.ratios["validation"]
/ (config.ratios["validation"] + config.ratios["train"])
),
)
return train_dataset, test_dataset, val_dataset
CONFIG_CLASS (BaseSplitStepConfig)
pydantic-model
Config class for the sklearn splitter
Source code in zenml/integrations/sklearn/steps/sklearn_splitter.py
class SklearnSplitterConfig(BaseSplitStepConfig):
"""Config class for the sklearn splitter"""
ratios: Dict[str, float]
entrypoint(self, dataset, config)
Method which is responsible for the splitting logic
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset |
DataFrame |
a pandas Dataframe which entire dataset |
required |
config |
SklearnSplitterConfig |
the configuration for the step |
required |
Returns:
| Type | Description |
|---|---|
<zenml.steps.step_output.Output object at 0x7f9b6cd434f0> |
three dataframes representing the splits |
Source code in zenml/integrations/sklearn/steps/sklearn_splitter.py
def entrypoint( # type: ignore[override]
self,
dataset: pd.DataFrame,
config: SklearnSplitterConfig,
) -> Output( # type:ignore[valid-type]
train=pd.DataFrame, test=pd.DataFrame, validation=pd.DataFrame
):
"""Method which is responsible for the splitting logic
Args:
dataset: a pandas Dataframe which entire dataset
config: the configuration for the step
Returns:
three dataframes representing the splits
"""
if (
any(
[
split not in config.ratios
for split in ["train", "test", "validation"]
]
)
or len(config.ratios) != 3
):
raise KeyError(
f"Make sure that you only use 'train', 'test' and "
f"'validation' as keys in the ratios dict. Current keys: "
f"{config.ratios.keys()}"
)
if sum(config.ratios.values()) != 1:
raise ValueError(
f"Make sure that the ratios sum up to 1. Current "
f"ratios: {config.ratios}"
)
train_dataset, test_dataset = train_test_split(
dataset, test_size=config.ratios["test"]
)
train_dataset, val_dataset = train_test_split(
train_dataset,
test_size=(
config.ratios["validation"]
/ (config.ratios["validation"] + config.ratios["train"])
),
)
return train_dataset, test_dataset, val_dataset
SklearnSplitterConfig (BaseSplitStepConfig)
pydantic-model
Config class for the sklearn splitter
Source code in zenml/integrations/sklearn/steps/sklearn_splitter.py
class SklearnSplitterConfig(BaseSplitStepConfig):
"""Config class for the sklearn splitter"""
ratios: Dict[str, float]
sklearn_standard_scaler
SklearnStandardScaler (BasePreprocessorStep)
Simple step implementation which utilizes the StandardScaler from sklearn to transform the numeric columns of a pd.DataFrame
Source code in zenml/integrations/sklearn/steps/sklearn_standard_scaler.py
class SklearnStandardScaler(BasePreprocessorStep):
"""Simple step implementation which utilizes the StandardScaler from sklearn
to transform the numeric columns of a pd.DataFrame"""
def entrypoint( # type: ignore[override]
self,
train_dataset: pd.DataFrame,
test_dataset: pd.DataFrame,
validation_dataset: pd.DataFrame,
statistics: pd.DataFrame,
schema: pd.DataFrame,
config: SklearnStandardScalerConfig,
) -> Output( # type:ignore[valid-type]
train_transformed=pd.DataFrame,
test_transformed=pd.DataFrame,
validation_transformed=pd.DataFrame,
):
"""Main entrypoint function for the StandardScaler
Args:
train_dataset: pd.DataFrame, the training dataset
test_dataset: pd.DataFrame, the test dataset
validation_dataset: pd.DataFrame, the validation dataset
statistics: pd.DataFrame, the statistics over the train dataset
schema: pd.DataFrame, the detected schema of the dataset
config: the configuration for the step
Returns:
the transformed train, test and validation datasets as
pd.DataFrames
"""
schema_dict = {k: v[0] for k, v in schema.to_dict().items()}
# Exclude columns
feature_set = set(train_dataset.columns) - set(config.exclude_columns)
for feature, feature_type in schema_dict.items():
if feature_type != "int64" and feature_type != "float64":
feature_set.remove(feature)
logger.warning(
f"{feature} column is a not numeric, thus it is excluded "
f"from the standard scaling."
)
transform_feature_set = feature_set - set(config.ignore_columns)
# Transform the datasets
scaler = StandardScaler()
scaler.mean_ = statistics["mean"][transform_feature_set]
scaler.scale_ = statistics["std"][transform_feature_set]
train_dataset[list(transform_feature_set)] = scaler.transform(
train_dataset[transform_feature_set]
)
test_dataset[list(transform_feature_set)] = scaler.transform(
test_dataset[transform_feature_set]
)
validation_dataset[list(transform_feature_set)] = scaler.transform(
validation_dataset[transform_feature_set]
)
return train_dataset, test_dataset, validation_dataset
CONFIG_CLASS (BasePreprocessorConfig)
pydantic-model
Config class for the sklearn standard scaler
ignore_columns: a list of column names which should not be scaled exclude_columns: a list of column names to be excluded from the dataset
Source code in zenml/integrations/sklearn/steps/sklearn_standard_scaler.py
class SklearnStandardScalerConfig(BasePreprocessorConfig):
"""Config class for the sklearn standard scaler
ignore_columns: a list of column names which should not be scaled
exclude_columns: a list of column names to be excluded from the dataset
"""
ignore_columns: List[str] = []
exclude_columns: List[str] = []
entrypoint(self, train_dataset, test_dataset, validation_dataset, statistics, schema, config)
Main entrypoint function for the StandardScaler
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
train_dataset |
DataFrame |
pd.DataFrame, the training dataset |
required |
test_dataset |
DataFrame |
pd.DataFrame, the test dataset |
required |
validation_dataset |
DataFrame |
pd.DataFrame, the validation dataset |
required |
statistics |
DataFrame |
pd.DataFrame, the statistics over the train dataset |
required |
schema |
DataFrame |
pd.DataFrame, the detected schema of the dataset |
required |
config |
SklearnStandardScalerConfig |
the configuration for the step |
required |
Returns:
| Type | Description |
|---|---|
<zenml.steps.step_output.Output object at 0x7f9b6cd5a280> |
the transformed train, test and validation datasets as pd.DataFrames |
Source code in zenml/integrations/sklearn/steps/sklearn_standard_scaler.py
def entrypoint( # type: ignore[override]
self,
train_dataset: pd.DataFrame,
test_dataset: pd.DataFrame,
validation_dataset: pd.DataFrame,
statistics: pd.DataFrame,
schema: pd.DataFrame,
config: SklearnStandardScalerConfig,
) -> Output( # type:ignore[valid-type]
train_transformed=pd.DataFrame,
test_transformed=pd.DataFrame,
validation_transformed=pd.DataFrame,
):
"""Main entrypoint function for the StandardScaler
Args:
train_dataset: pd.DataFrame, the training dataset
test_dataset: pd.DataFrame, the test dataset
validation_dataset: pd.DataFrame, the validation dataset
statistics: pd.DataFrame, the statistics over the train dataset
schema: pd.DataFrame, the detected schema of the dataset
config: the configuration for the step
Returns:
the transformed train, test and validation datasets as
pd.DataFrames
"""
schema_dict = {k: v[0] for k, v in schema.to_dict().items()}
# Exclude columns
feature_set = set(train_dataset.columns) - set(config.exclude_columns)
for feature, feature_type in schema_dict.items():
if feature_type != "int64" and feature_type != "float64":
feature_set.remove(feature)
logger.warning(
f"{feature} column is a not numeric, thus it is excluded "
f"from the standard scaling."
)
transform_feature_set = feature_set - set(config.ignore_columns)
# Transform the datasets
scaler = StandardScaler()
scaler.mean_ = statistics["mean"][transform_feature_set]
scaler.scale_ = statistics["std"][transform_feature_set]
train_dataset[list(transform_feature_set)] = scaler.transform(
train_dataset[transform_feature_set]
)
test_dataset[list(transform_feature_set)] = scaler.transform(
test_dataset[transform_feature_set]
)
validation_dataset[list(transform_feature_set)] = scaler.transform(
validation_dataset[transform_feature_set]
)
return train_dataset, test_dataset, validation_dataset
SklearnStandardScalerConfig (BasePreprocessorConfig)
pydantic-model
Config class for the sklearn standard scaler
ignore_columns: a list of column names which should not be scaled exclude_columns: a list of column names to be excluded from the dataset
Source code in zenml/integrations/sklearn/steps/sklearn_standard_scaler.py
class SklearnStandardScalerConfig(BasePreprocessorConfig):
"""Config class for the sklearn standard scaler
ignore_columns: a list of column names which should not be scaled
exclude_columns: a list of column names to be excluded from the dataset
"""
ignore_columns: List[str] = []
exclude_columns: List[str] = []
tensorflow
special
TensorflowIntegration (Integration)
Definition of Tensorflow integration for ZenML.
Source code in zenml/integrations/tensorflow/__init__.py
class TensorflowIntegration(Integration):
"""Definition of Tensorflow integration for ZenML."""
NAME = TENSORFLOW
REQUIREMENTS = ["tensorflow"]
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.tensorflow import materializers # noqa
activate()
classmethod
Activates the integration.
Source code in zenml/integrations/tensorflow/__init__.py
@classmethod
def activate(cls) -> None:
"""Activates the integration."""
from zenml.integrations.tensorflow import materializers # noqa
materializers
special
keras_materializer
KerasMaterializer (BaseMaterializer)
Materializer to read/write Keras models.
Source code in zenml/integrations/tensorflow/materializers/keras_materializer.py
class KerasMaterializer(BaseMaterializer):
"""Materializer to read/write Keras models."""
ASSOCIATED_TYPES = [keras.Model]
ASSOCIATED_ARTIFACT_TYPES = [ModelArtifact]
def handle_input(self, data_type: Type[Any]) -> keras.Model:
"""Reads and returns a Keras model.
Returns:
A tf.keras.Model model.
"""
super().handle_input(data_type)
return keras.models.load_model(self.artifact.uri)
def handle_return(self, model: keras.Model) -> None:
"""Writes a keras model.
Args:
model: A tf.keras.Model model.
"""
super().handle_return(model)
model.save(self.artifact.uri)
handle_input(self, data_type)
Reads and returns a Keras model.
Returns:
| Type | Description |
|---|---|
Model |
A tf.keras.Model model. |
Source code in zenml/integrations/tensorflow/materializers/keras_materializer.py
def handle_input(self, data_type: Type[Any]) -> keras.Model:
"""Reads and returns a Keras model.
Returns:
A tf.keras.Model model.
"""
super().handle_input(data_type)
return keras.models.load_model(self.artifact.uri)
handle_return(self, model)
Writes a keras model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
model |
Model |
A tf.keras.Model model. |
required |
Source code in zenml/integrations/tensorflow/materializers/keras_materializer.py
def handle_return(self, model: keras.Model) -> None:
"""Writes a keras model.
Args:
model: A tf.keras.Model model.
"""
super().handle_return(model)
model.save(self.artifact.uri)
tf_dataset_materializer
TensorflowDatasetMaterializer (BaseMaterializer)
Materializer to read data to and from tf.data.Dataset.
Source code in zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py
class TensorflowDatasetMaterializer(BaseMaterializer):
"""Materializer to read data to and from tf.data.Dataset."""
ASSOCIATED_TYPES = [tf.data.Dataset]
ASSOCIATED_ARTIFACT_TYPES = [DataArtifact]
def handle_input(self, data_type: Type[Any]) -> Any:
"""Reads data into tf.data.Dataset"""
super().handle_input(data_type)
path = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
return tf.data.experimental.load(path)
def handle_return(self, dataset: tf.data.Dataset) -> None:
"""Persists a tf.data.Dataset object."""
super().handle_return(dataset)
path = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
tf.data.experimental.save(
dataset, path, compression=None, shard_func=None
)
handle_input(self, data_type)
Reads data into tf.data.Dataset
Source code in zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py
def handle_input(self, data_type: Type[Any]) -> Any:
"""Reads data into tf.data.Dataset"""
super().handle_input(data_type)
path = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
return tf.data.experimental.load(path)
handle_return(self, dataset)
Persists a tf.data.Dataset object.
Source code in zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py
def handle_return(self, dataset: tf.data.Dataset) -> None:
"""Persists a tf.data.Dataset object."""
super().handle_return(dataset)
path = os.path.join(self.artifact.uri, DEFAULT_FILENAME)
tf.data.experimental.save(
dataset, path, compression=None, shard_func=None
)
steps
special
tensorflow_trainer
TensorflowBinaryClassifier (BaseTrainerStep)
Simple step implementation which creates a simple tensorflow feedforward neural network and trains it on a given pd.DataFrame dataset
Source code in zenml/integrations/tensorflow/steps/tensorflow_trainer.py
class TensorflowBinaryClassifier(BaseTrainerStep):
"""Simple step implementation which creates a simple tensorflow feedforward
neural network and trains it on a given pd.DataFrame dataset
"""
def entrypoint( # type: ignore[override]
self,
train_dataset: pd.DataFrame,
validation_dataset: pd.DataFrame,
config: TensorflowBinaryClassifierConfig,
) -> tf.keras.Model:
"""Main entrypoint for the tensorflow trainer
Args:
train_dataset: pd.DataFrame, the training dataset
validation_dataset: pd.DataFrame, the validation dataset
config: the configuration of the step
Returns:
the trained tf.keras.Model
"""
model = tf.keras.Sequential()
model.add(tf.keras.layers.InputLayer(input_shape=config.input_shape))
model.add(tf.keras.layers.Flatten())
last_layer = config.layers.pop()
for i, layer in enumerate(config.layers):
model.add(tf.keras.layers.Dense(layer, activation="relu"))
model.add(tf.keras.layers.Dense(last_layer, activation="sigmoid"))
model.compile(
optimizer=tf.keras.optimizers.Adam(config.learning_rate),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=config.metrics,
)
train_target = train_dataset.pop(config.target_column)
validation_target = validation_dataset.pop(config.target_column)
model.fit(
x=train_dataset,
y=train_target,
validation_data=(validation_dataset, validation_target),
batch_size=config.batch_size,
epochs=config.epochs,
)
model.summary()
return model
CONFIG_CLASS (BaseTrainerConfig)
pydantic-model
Config class for the tensorflow trainer
target_column: the name of the label column layers: the number of units in the fully connected layers input_shape: the shape of the input learning_rate: the learning rate metrics: the list of metrics to be computed epochs: the number of epochs batch_size: the size of the batch
Source code in zenml/integrations/tensorflow/steps/tensorflow_trainer.py
class TensorflowBinaryClassifierConfig(BaseTrainerConfig):
"""Config class for the tensorflow trainer
target_column: the name of the label column
layers: the number of units in the fully connected layers
input_shape: the shape of the input
learning_rate: the learning rate
metrics: the list of metrics to be computed
epochs: the number of epochs
batch_size: the size of the batch
"""
target_column: str
layers: List[int] = [256, 64, 1]
input_shape: Tuple[int] = (8,)
learning_rate: float = 0.001
metrics: List[str] = ["accuracy"]
epochs: int = 50
batch_size: int = 8
entrypoint(self, train_dataset, validation_dataset, config)
Main entrypoint for the tensorflow trainer
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
train_dataset |
DataFrame |
pd.DataFrame, the training dataset |
required |
validation_dataset |
DataFrame |
pd.DataFrame, the validation dataset |
required |
config |
TensorflowBinaryClassifierConfig |
the configuration of the step |
required |
Returns:
| Type | Description |
|---|---|
Model |
the trained tf.keras.Model |
Source code in zenml/integrations/tensorflow/steps/tensorflow_trainer.py
def entrypoint( # type: ignore[override]
self,
train_dataset: pd.DataFrame,
validation_dataset: pd.DataFrame,
config: TensorflowBinaryClassifierConfig,
) -> tf.keras.Model:
"""Main entrypoint for the tensorflow trainer
Args:
train_dataset: pd.DataFrame, the training dataset
validation_dataset: pd.DataFrame, the validation dataset
config: the configuration of the step
Returns:
the trained tf.keras.Model
"""
model = tf.keras.Sequential()
model.add(tf.keras.layers.InputLayer(input_shape=config.input_shape))
model.add(tf.keras.layers.Flatten())
last_layer = config.layers.pop()
for i, layer in enumerate(config.layers):
model.add(tf.keras.layers.Dense(layer, activation="relu"))
model.add(tf.keras.layers.Dense(last_layer, activation="sigmoid"))
model.compile(
optimizer=tf.keras.optimizers.Adam(config.learning_rate),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=config.metrics,
)
train_target = train_dataset.pop(config.target_column)
validation_target = validation_dataset.pop(config.target_column)
model.fit(
x=train_dataset,
y=train_target,
validation_data=(validation_dataset, validation_target),
batch_size=config.batch_size,
epochs=config.epochs,
)
model.summary()
return model
TensorflowBinaryClassifierConfig (BaseTrainerConfig)
pydantic-model
Config class for the tensorflow trainer
target_column: the name of the label column layers: the number of units in the fully connected layers input_shape: the shape of the input learning_rate: the learning rate metrics: the list of metrics to be computed epochs: the number of epochs batch_size: the size of the batch
Source code in zenml/integrations/tensorflow/steps/tensorflow_trainer.py
class TensorflowBinaryClassifierConfig(BaseTrainerConfig):
"""Config class for the tensorflow trainer
target_column: the name of the label column
layers: the number of units in the fully connected layers
input_shape: the shape of the input
learning_rate: the learning rate
metrics: the list of metrics to be computed
epochs: the number of epochs
batch_size: the size of the batch
"""
target_column: str
layers: List[int] = [256, 64, 1]
input_shape: Tuple[int] = (8,)
learning_rate: float = 0.001
metrics: List[str] = ["accuracy"]
epochs: int = 50
batch_size: int = 8
utils
get_integration_for_module(module_name)
Gets the integration class for a module inside an integration.
If the module given by module_name is not part of a ZenML integration,
this method will return None. If it is part of a ZenML integration,
it will return the integration class found inside the integration
init file.
Source code in zenml/integrations/utils.py
def get_integration_for_module(module_name: str) -> Optional[Type[Integration]]:
"""Gets the integration class for a module inside an integration.
If the module given by `module_name` is not part of a ZenML integration,
this method will return `None`. If it is part of a ZenML integration,
it will return the integration class found inside the integration
__init__ file.
"""
integration_prefix = "zenml.integrations."
if not module_name.startswith(integration_prefix):
return None
integration_module_name = ".".join(module_name.split(".", 3)[:3])
try:
integration_module = sys.modules[integration_module_name]
except KeyError:
integration_module = importlib.import_module(integration_module_name)
for name, member in inspect.getmembers(integration_module):
if (
member is not Integration
and isinstance(member, IntegrationMeta)
and issubclass(member, Integration)
):
return cast(Type[Integration], member)
return None
get_requirements_for_module(module_name)
Gets requirements for a module inside an integration.
If the module given by module_name is not part of a ZenML integration,
this method will return an empty list. If it is part of a ZenML integration,
it will return the list of requirements specified inside the integration
class found inside the integration init file.
Source code in zenml/integrations/utils.py
def get_requirements_for_module(module_name: str) -> List[str]:
"""Gets requirements for a module inside an integration.
If the module given by `module_name` is not part of a ZenML integration,
this method will return an empty list. If it is part of a ZenML integration,
it will return the list of requirements specified inside the integration
class found inside the integration __init__ file.
"""
integration = get_integration_for_module(module_name)
return integration.REQUIREMENTS if integration else []