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clearml/automation/controller.py
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@ -255,19 +255,19 @@ class PipelineController(object):
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param docker: Select the docker image to be executed in by the remote session
:param docker_args: Add docker arguments, pass a single string
:param docker_bash_setup_script: Add bash script to be executed
@ -495,45 +495,46 @@ class PipelineController(object):
- Task ID ``parameter_override={'Args/input_file': '${stage3.id}' }``
:param recursively_parse_parameters: If True, recursively parse parameters from parameter_override in lists, dicts, or tuples.
Example:
- ``parameter_override={'Args/input_file': ['${<step_name>.artifacts.<artifact_name>.url}', 'file2.txt']}`` will be correctly parsed.
- ``parameter_override={'Args/input_file': ('${<step_name_1>.parameters.Args/input_file}', '${<step_name_2>.parameters.Args/input_file}')}`` will be correctly parsed.
- ``parameter_override={'Args/input_file': ['${<step_name>.artifacts.<artifact_name>.url}', 'file2.txt']}`` will be correctly parsed.
- ``parameter_override={'Args/input_file': ('${<step_name_1>.parameters.Args/input_file}', '${<step_name_2>.parameters.Args/input_file}')}`` will be correctly parsed.
:param configuration_overrides: Optional, override Task configuration objects.
Expected dictionary of configuration object name and configuration object content.
Examples:
``{'General': dict(key='value')}``
``{'General': 'configuration file content'}``
``{'OmegaConf': YAML.dumps(full_hydra_dict)}``
- ``{'General': dict(key='value')}``
- ``{'General': 'configuration file content'}``
- ``{'OmegaConf': YAML.dumps(full_hydra_dict)}``
:param task_overrides: Optional task section overriding dictionary.
The dict values can reference a previously executed step using the following form ``'${step_name}'``. Examples:
- get the latest commit from a specific branch ``task_overrides={'script.version_num': '', 'script.branch': 'main'}``
- match git repository branch to a previous step ``task_overrides={'script.branch': '${stage1.script.branch}', 'script.version_num': ''}``
- change container image ``task_overrides={'container.image': 'nvidia/cuda:11.6.0-devel-ubuntu20.04', 'container.arguments': '--ipc=host'}``
- match container image to a previous step ``task_overrides={'container.image': '${stage1.container.image}'}``
- reset requirements (the agent will use the "requirements.txt" inside the repo) ``task_overrides={'script.requirements.pip': ""}``
- Get the latest commit from a specific branch ``task_overrides={'script.version_num': '', 'script.branch': 'main'}``
- Match git repository branch to a previous step ``task_overrides={'script.branch': '${stage1.script.branch}', 'script.version_num': ''}``
- Change container image ``task_overrides={'container.image': 'nvidia/cuda:11.6.0-devel-ubuntu20.04', 'container.arguments': '--ipc=host'}``
- Match container image to a previous step ``task_overrides={'container.image': '${stage1.container.image}'}``
- Reset requirements (the agent will use the "requirements.txt" inside the repo) ``task_overrides={'script.requirements.pip': ""}``
:param execution_queue: Optional, the queue to use for executing this specific step.
If not provided, the task will be sent to the default execution queue, as defined on the class
:param monitor_metrics: Optional, log the step's metrics on the pipeline Task.
Format is a list of pairs metric (title, series) to log:
[(step_metric_title, step_metric_series), ]
Example: [('test', 'accuracy'), ]
Format is a list of pairs metric (title, series) to log: ``[(step_metric_title, step_metric_series), ]``.
For example: ``[('test', 'accuracy'), ]``.
Or a list of tuple pairs, to specify a different target metric for to use on the pipeline Task:
[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]
Example: [[('test', 'accuracy'), ('model', 'accuracy')], ]
``[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]``.
For example: ``[[('test', 'accuracy'), ('model', 'accuracy')], ]``
:param monitor_artifacts: Optional, log the step's artifacts on the pipeline Task.
Provided a list of artifact names existing on the step's Task, they will also appear on the Pipeline itself.
Example: [('processed_data', 'final_processed_data'), ]
Example: ``[('processed_data', 'final_processed_data'), ]``.
Alternatively user can also provide a list of artifacts to monitor
(target artifact name will be the same as original artifact name)
Example: ['processed_data', ]
(target artifact name will be the same as original artifact name).
Example: ``['processed_data', ]``
:param monitor_models: Optional, log the step's output models on the pipeline Task.
Provided a list of model names existing on the step's Task, they will also appear on the Pipeline itself.
Example: [('model_weights', 'final_model_weights'), ]
Example: ``[('model_weights', 'final_model_weights'), ]``.
Alternatively user can also provide a list of models to monitor
(target models name will be the same as original model)
Example: ['model_weights', ]
To select the latest (lexicographic) model use "model_*", or the last created model with just "*"
Example: ['model_weights_*', ]
(target models name will be the same as original model).
Example: ``['model_weights', ]``.
To select the latest (lexicographic) model use "model_*", or the last created model with just "*".
Example: ``['model_weights_*', ]``
:param time_limit: Default None, no time limit.
Step execution time limit, if exceeded the Task is aborted and the pipeline is stopped and marked failed.
:param base_task_project: If base_task_id is not given,
@ -549,7 +550,7 @@ class PipelineController(object):
:param pre_execute_callback: Callback function, called when the step (Task) is created
and before it is sent for execution. Allows a user to modify the Task before launch.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
`parameters` are the configuration arguments passed to the ClearmlJob.
``parameters`` are the configuration arguments passed to the ClearmlJob.
If the callback returned value is `False`,
the Node is skipped and so is any node in the DAG that relies on this node.
@ -589,19 +590,19 @@ class PipelineController(object):
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param status_change_callback: Callback function, called when the status of a step (Task) changes.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
@ -618,19 +619,20 @@ class PipelineController(object):
:param output_uri: The storage / output url for this step. This is the default location for output
models and other artifacts. Check Task.init reference docs for more info (output_uri is a parameter).
:param continue_behaviour: Controls whether the pipeline will continue running after a
step failed/was aborted. Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
If False, the pipeline will stop
- skip_children_on_fail - If True, the children of this step will be skipped if it failed.
If False, the children will run even if this step failed.
Any parameters passed from the failed step to its children will default to None
- skip_children_on_abort - If True, the children of this step will be skipped if it was aborted.
If False, the children will run even if this step was aborted.
Any parameters passed from the failed step to its children will default to None
If the keys are not present in the dictionary, their values will default to True
:param continue_behaviour: Controls whether the pipeline will continue running after a step failed/was aborted.
Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
If False, the pipeline will stop
- skip_children_on_fail - If True, the children of this step will be skipped if it failed.
If False, the children will run even if this step failed.
Any parameters passed from the failed step to its children will default to None
- skip_children_on_abort - If True, the children of this step will be skipped if it was aborted.
If False, the children will run even if this step was aborted.
Any parameters passed from the failed step to its children will default to None
- If the keys are not present in the dictionary, their values will default to True
:return: True if successful
"""
@ -814,26 +816,25 @@ class PipelineController(object):
:param execution_queue: Optional, the queue to use for executing this specific step.
If not provided, the task will be sent to the default execution queue, as defined on the class
:param monitor_metrics: Optional, log the step's metrics on the pipeline Task.
Format is a list of pairs metric (title, series) to log:
[(step_metric_title, step_metric_series), ]
Example: [('test', 'accuracy'), ]
Format is a list of pairs metric (title, series) to log: ``[(step_metric_title, step_metric_series), ]``.
For example: ``[('test', 'accuracy'), ]``.
Or a list of tuple pairs, to specify a different target metric for to use on the pipeline Task:
[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]
Example: [[('test', 'accuracy'), ('model', 'accuracy')], ]
``[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]``.
For example: ``[[('test', 'accuracy'), ('model', 'accuracy')], ]``
:param monitor_artifacts: Optional, log the step's artifacts on the pipeline Task.
Provided a list of artifact names existing on the step's Task, they will also appear on the Pipeline itself.
Example: [('processed_data', 'final_processed_data'), ]
Example: ``[('processed_data', 'final_processed_data'), ]``.
Alternatively user can also provide a list of artifacts to monitor
(target artifact name will be the same as original artifact name)
Example: ['processed_data', ]
(target artifact name will be the same as original artifact name).
Example: ``['processed_data', ]``
:param monitor_models: Optional, log the step's output models on the pipeline Task.
Provided a list of model names existing on the step's Task, they will also appear on the Pipeline itself.
Example: [('model_weights', 'final_model_weights'), ]
Example: ``[('model_weights', 'final_model_weights'), ]``.
Alternatively user can also provide a list of models to monitor
(target models name will be the same as original model)
Example: ['model_weights', ]
To select the latest (lexicographic) model use "model_*", or the last created model with just "*"
Example: ['model_weights_*', ]
(target models name will be the same as original model).
Example: ``['model_weights', ]``.
To select the latest (lexicographic) model use "model_*", or the last created model with just "*".
Example: ``['model_weights_*', ]``
:param time_limit: Default None, no time limit.
Step execution time limit, if exceeded the Task is aborted and the pipeline is stopped and marked failed.
:param continue_on_fail: (Deprecated, use `continue_behaviour` instead).
@ -843,7 +844,7 @@ class PipelineController(object):
:param pre_execute_callback: Callback function, called when the step (Task) is created
and before it is sent for execution. Allows a user to modify the Task before launch.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
`parameters` are the configuration arguments passed to the ClearmlJob.
``parameters`` are the configuration arguments passed to the ClearmlJob.
If the callback returned value is `False`,
the Node is skipped and so is any node in the DAG that relies on this node.
@ -880,19 +881,19 @@ class PipelineController(object):
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param status_change_callback: Callback function, called when the status of a step (Task) changes.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
@ -914,19 +915,20 @@ class PipelineController(object):
models and other artifacts. Check Task.init reference docs for more info (output_uri is a parameter).
:param draft: (default False). If True, the Task will be created as a draft task.
:param working_dir: Working directory to launch the script from.
:param continue_behaviour: Controls whether the pipeline will continue running after a
step failed/was aborted. Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
If False, the pipeline will stop
- skip_children_on_fail - If True, the children of this step will be skipped if it failed.
If False, the children will run even if this step failed.
Any parameters passed from the failed step to its children will default to None
- skip_children_on_abort - If True, the children of this step will be skipped if it was aborted.
If False, the children will run even if this step was aborted.
Any parameters passed from the failed step to its children will default to None
If the keys are not present in the dictionary, their values will default to True
:param continue_behaviour: Controls whether the pipeline will continue running after a step failed/was aborted.
Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
If False, the pipeline will stop
- skip_children_on_fail - If True, the children of this step will be skipped if it failed.
If False, the children will run even if this step failed. Any parameters passed from the failed step to its
children will default to None
- skip_children_on_abort - If True, the children of this step will be skipped if it was aborted.
If False, the children will run even if this step was aborted.
Any parameters passed from the failed step to its children will default to None
- If the keys are not present in the dictionary, their values will default to True
:return: True if successful
"""
@ -1119,10 +1121,10 @@ class PipelineController(object):
Specify one of the following:
- A dictionary/list - A dictionary containing the configuration. ClearML stores the configuration in
the **ClearML Server** (backend), in a HOCON format (JSON-like format) which is editable.
the **ClearML Server** (backend), in a HOCON format (JSON-like format) which is editable.
- A ``pathlib2.Path`` string - A path to the configuration file. ClearML stores the content of the file.
A local path must be relative path. When executing a pipeline remotely in a worker, the contents brought
from the **ClearML Server** (backend) overwrites the contents of the file.
A local path must be relative path. When executing a pipeline remotely in a worker, the contents brought
from the **ClearML Server** (backend) overwrites the contents of the file.
:param str name: Configuration section name. default: 'General'
Allowing users to store multiple configuration dicts/files
@ -1238,8 +1240,8 @@ class PipelineController(object):
:return: The status of the upload.
- ``True`` - Upload succeeded.
- ``False`` - Upload failed.
- ``True`` - Upload succeeded.
- ``False`` - Upload failed.
:raise: If the artifact object type is not supported, raise a ``ValueError``.
"""
@ -1441,6 +1443,7 @@ class PipelineController(object):
# type: () -> dict
"""
Return the pipeline parameters dictionary
:return: Dictionary str -> str
"""
return self._pipeline_args
@ -2371,7 +2374,7 @@ class PipelineController(object):
:param pre_execute_callback: Callback function, called when the step (Task) is created,
and before it is sent for execution. Allows a user to modify the Task before launch.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
`parameters` are the configuration arguments passed to the ClearmlJob.
``parameters`` are the configuration arguments passed to the ClearmlJob.
If the callback returned value is `False`,
the Node is skipped and so is any node in the DAG that relies on this node.
@ -2408,19 +2411,19 @@ class PipelineController(object):
:param retry_on_failure: Integer (number of retries) or Callback function that returns True to allow a retry
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed
The function must return a `bool`: True if the node should be retried and False otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed
The function must return a `bool`: True if the node should be retried and False otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param status_change_callback: Callback function, called when the status of a step (Task) changes.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
@ -2442,8 +2445,8 @@ class PipelineController(object):
models and other artifacts. Check Task.init reference docs for more info (output_uri is a parameter).
:param draft: (default False). If True, the Task will be created as a draft task.
:param working_dir: Working directory to launch the step from.
:param continue_behaviour: Controls whether the pipeline will continue running after a
step failed/was aborted. Different behaviours can be set using a dictionary of boolean options. Supported options are:
:param continue_behaviour: Controls whether the pipeline will continue running after a step failed/was aborted.
Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
@ -3706,19 +3709,19 @@ class PipelineDecorator(PipelineController):
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param docker: Select the docker image to be executed in by the remote session
:param docker_args: Add docker arguments, pass a single string
:param docker_bash_setup_script: Add bash script to be executed
@ -4218,51 +4221,51 @@ class PipelineDecorator(PipelineController):
could call the additional functions.
Example, assuming we have two functions parse_data(), and load_data(): [parse_data, load_data]
:param monitor_metrics: Optional, Automatically log the step's reported metrics also on the pipeline Task.
The expected format is a list of pairs metric (title, series) to log:
[(step_metric_title, step_metric_series), ]
Example: [('test', 'accuracy'), ]
The expected format is a list of pairs metric (title, series) to log: ``[(step_metric_title, step_metric_series), ]``.
For example: ``[('test', 'accuracy'), ]``.
Or a list of tuple pairs, to specify a different target metric to use on the pipeline Task:
[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]
Example: [[('test', 'accuracy'), ('model', 'accuracy')], ]
``[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]``.
For example: ``[[('test', 'accuracy'), ('model', 'accuracy')], ]``
:param monitor_artifacts: Optional, Automatically log the step's artifacts on the pipeline Task.
Provided a list of artifact names created by the step function, these artifacts will be logged
automatically also on the Pipeline Task itself.
Example: ['processed_data', ]
(target artifact name on the Pipeline Task will hav ethe same name as the original artifact)
Alternatively, provide a list of pairs (source_artifact_name, target_artifact_name):
Example: ``['processed_data', ]``
(target artifact name on the Pipeline Task will hav ethe same name as the original artifact).
Alternatively, provide a list of pairs ``(source_artifact_name, target_artifact_name)``:
where the first string is the artifact name as it appears on the component Task,
and the second is the target artifact name to put on the Pipeline Task
Example: [('processed_data', 'final_processed_data'), ]
and the second is the target artifact name to put on the Pipeline Task.
Example: ``[('processed_data', 'final_processed_data'), ]``
:param monitor_models: Optional, Automatically log the step's output models on the pipeline Task.
Provided a list of model names created by the step's Task, they will also appear on the Pipeline itself.
Example: ['model_weights', ]
To select the latest (lexicographic) model use "model_*", or the last created model with just "*"
Example: ['model_weights_*', ]
Alternatively, provide a list of pairs (source_model_name, target_model_name):
Example: ``['model_weights', ]``.
To select the latest (lexicographic) model use "model_*", or the last created model with just "*".
Example: ``['model_weights_*', ]``.
Alternatively, provide a list of pairs ``(source_model_name, target_model_name)``:
where the first string is the model name as it appears on the component Task,
and the second is the target model name to put on the Pipeline Task
Example: [('model_weights', 'final_model_weights'), ]
and the second is the target model name to put on the Pipeline Task.
Example: ``[('model_weights', 'final_model_weights'), ]``
:param retry_on_failure: Integer (number of retries) or Callback function that returns True to allow a retry
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed
The function must return a `bool`: True if the node should be retried and False otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed
The function must return a `bool`: True if the node should be retried and False otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param pre_execute_callback: Callback function, called when the step (Task) is created,
and before it is sent for execution. Allows a user to modify the Task before launch.
and before it is sent for execution. Allows a user to modify the Task before launch.
Use `node.job` to access the ClearmlJob object, or `node.job.task` to directly access the Task object.
`parameters` are the configuration arguments passed to the ClearmlJob.
``parameters`` are the configuration arguments passed to the ClearmlJob.
If the callback returned value is `False`,
the Node is skipped and so is any node in the DAG that relies on this node.
@ -4310,19 +4313,20 @@ class PipelineDecorator(PipelineController):
models and other artifacts. Check Task.init reference docs for more info (output_uri is a parameter).
:param draft: (default False). If True, the Task will be created as a draft task.
:param working_dir: Working directory to launch the step from.
:param continue_behaviour: Controls whether the pipeline will continue running after a
step failed/was aborted. Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
If False, the pipeline will stop
- skip_children_on_fail - If True, the children of this step will be skipped if it failed.
If False, the children will run even if this step failed.
Any parameters passed from the failed step to its children will default to None
- skip_children_on_abort - If True, the children of this step will be skipped if it was aborted.
If False, the children will run even if this step was aborted.
Any parameters passed from the failed step to its children will default to None
If the keys are not present in the dictionary, their values will default to True
:param continue_behaviour: Controls whether the pipeline will continue running after a step failed/was aborted.
Different behaviours can be set using a dictionary of boolean options. Supported options are:
- continue_on_fail - If True, the pipeline will continue even if the step failed.
If False, the pipeline will stop
- continue_on_abort - If True, the pipeline will continue even if the step was aborted.
If False, the pipeline will stop
- skip_children_on_fail - If True, the children of this step will be skipped if it failed.
If False, the children will run even if this step failed.
Any parameters passed from the failed step to its children will default to None
- skip_children_on_abort - If True, the children of this step will be skipped if it was aborted.
If False, the children will run even if this step was aborted.
Any parameters passed from the failed step to its children will default to None
- If the keys are not present in the dictionary, their values will default to True
:return: function wrapper
"""
@ -4675,19 +4679,19 @@ class PipelineDecorator(PipelineController):
- Integer: In case of node failure, retry the node the number of times indicated by this parameter.
- Callable: A function called on node failure. Takes as parameters:
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
the PipelineController instance, the PipelineController.Node that failed and an int
representing the number of previous retries for the node that failed.
The function must return ``True`` if the node should be retried and ``False`` otherwise.
If True, the node will be re-queued and the number of retries left will be decremented by 1.
By default, if this callback is not specified, the function will be retried the number of
times indicated by `retry_on_failure`.
.. code-block:: py
.. code-block:: py
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
def example_retry_on_failure_callback(pipeline, node, retries):
print(node.name, ' failed')
# allow up to 5 retries (total of 6 runs)
return retries < 5
:param docker: Select the docker image to be executed in by the remote session
:param docker_args: Add docker arguments, pass a single string
:param docker_bash_setup_script: Add bash script to be executed
@ -4962,9 +4966,10 @@ class PipelineDecorator(PipelineController):
"""
Set debugging mode, run all functions locally as functions (serially)
Run the full pipeline DAG locally, where steps are executed as functions
Notice:
running the DAG locally assumes the local code execution (i.e. it will not clone & apply git diff)
Pipeline steps are executed as functions (no Task will be created), fo ease debugging J
.. note::
Running the DAG locally assumes local code execution (i.e. it will not clone & apply git diff).
Pipeline steps are executed as functions (no Task will be created).
"""
cls._debug_execute_step_process = True
cls._debug_execute_step_function = True

6
clearml/task.py
View File

@ -2025,10 +2025,10 @@ class Task(_Task):
Specify one of the following:
- A dictionary/list - A dictionary containing the configuration. ClearML stores the configuration in
the **ClearML Server** (backend), in a HOCON format (JSON-like format) which is editable.
the **ClearML Server** (backend), in a HOCON format (JSON-like format) which is editable.
- A ``pathlib2.Path`` string - A path to the configuration file. ClearML stores the content of the file.
A local path must be relative path. When executing a Task remotely in a worker, the contents brought
from the **ClearML Server** (backend) overwrites the contents of the file.
A local path must be relative path. When executing a Task remotely in a worker, the contents brought
from the **ClearML Server** (backend) overwrites the contents of the file.
:param str name: Configuration section name. default: 'General'
Allowing users to store multiple configuration dicts/files