wehub-resource-sync
51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
from abc import ABCMeta, abstractmethod
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from mlflow.utils.annotations import developer_stable
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@developer_stable
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class AbstractBackend:
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"""
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Abstract plugin class defining the interface needed to execute MLflow projects. You can define
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subclasses of ``AbstractBackend`` and expose them as third-party plugins to enable running
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MLflow projects against custom execution backends (e.g. to run projects against your team's
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in-house cluster or job scheduler). See `MLflow Plugins <../../plugins.html>`_ for more
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information.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def run(
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self,
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project_uri,
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entry_point,
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params,
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version,
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backend_config,
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tracking_uri,
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experiment_id,
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):
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"""
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Submit an entrypoint. It must return a SubmittedRun object to track the execution
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Args:
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project_uri: URI of the project to execute, e.g. a local filesystem path
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or a Git repository URI like https://github.com/mlflow/mlflow-example
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entry_point: Entry point to run within the project.
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params: Dict of parameters to pass to the entry point
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version: For git-based projects, either a commit hash or a branch name.
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backend_config: A dictionary, or a path to a JSON file (must end in '.json'), which
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will be passed as config to the backend. The exact content which
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should be provided is different for each execution backend and is
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documented at https://www.mlflow.org/docs/latest/projects.html.
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tracking_uri: URI of tracking server against which to log run information related
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to project execution.
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experiment_id: ID of experiment under which to launch the run.
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Returns:
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A :py:class:`mlflow.projects.SubmittedRun`. This function is expected to run
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the project asynchronously, i.e. it should trigger project execution and then
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immediately return a `SubmittedRun` to track execution status.
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"""
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