wehub-resource-sync
100 lines
4.1 KiB
Python
100 lines
4.1 KiB
Python
import logging
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import warnings
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from mlflow.tracking.context.abstract_context import RunContextProvider
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from mlflow.tracking.context.databricks_cluster_context import DatabricksClusterRunContext
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from mlflow.tracking.context.databricks_command_context import DatabricksCommandRunContext
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from mlflow.tracking.context.databricks_job_context import DatabricksJobRunContext
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from mlflow.tracking.context.databricks_notebook_context import DatabricksNotebookRunContext
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from mlflow.tracking.context.databricks_repo_context import DatabricksRepoRunContext
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from mlflow.tracking.context.default_context import DefaultRunContext
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from mlflow.tracking.context.git_context import GitRunContext
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from mlflow.tracking.context.jupyter_notebook_context import JupyterNotebookRunContext
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from mlflow.tracking.context.system_environment_context import SystemEnvironmentContext
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from mlflow.utils.plugins import get_entry_points
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_logger = logging.getLogger(__name__)
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class RunContextProviderRegistry:
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"""Registry for run context provider implementations
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This class allows the registration of a run context provider which can be used to infer meta
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information about the context of an MLflow experiment run. Implementations declared though the
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entrypoints `mlflow.run_context_provider` group can be automatically registered through the
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`register_entrypoints` method.
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Registered run context providers can return tags that override those implemented in the core
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library, however the order in which plugins are resolved is undefined.
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"""
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def __init__(self):
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self._registry = []
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def register(self, run_context_provider_cls):
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self._registry.append(run_context_provider_cls())
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def register_entrypoints(self):
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"""Register tracking stores provided by other packages"""
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for entrypoint in get_entry_points("mlflow.run_context_provider"):
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try:
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self.register(entrypoint.load())
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except (AttributeError, ImportError) as exc:
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warnings.warn(
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'Failure attempting to register context provider "{}": {}'.format(
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entrypoint.name, str(exc)
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),
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stacklevel=2,
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)
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def __iter__(self):
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return iter(self._registry)
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_run_context_provider_registry = RunContextProviderRegistry()
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_run_context_provider_registry.register(DefaultRunContext)
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_run_context_provider_registry.register(GitRunContext)
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_run_context_provider_registry.register(JupyterNotebookRunContext)
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_run_context_provider_registry.register(DatabricksNotebookRunContext)
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_run_context_provider_registry.register(DatabricksJobRunContext)
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_run_context_provider_registry.register(DatabricksClusterRunContext)
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_run_context_provider_registry.register(DatabricksCommandRunContext)
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_run_context_provider_registry.register(DatabricksRepoRunContext)
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_run_context_provider_registry.register(SystemEnvironmentContext)
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_run_context_provider_registry.register_entrypoints()
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def resolve_tags(tags=None, ignore: list[RunContextProvider] | None = None):
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"""Generate a set of tags for the current run context. Tags are resolved in the order,
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contexts are registered. Argument tags are applied last.
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This function iterates through all run context providers in the registry. Additional context
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providers can be registered as described in
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:py:class:`mlflow.tracking.context.RunContextProvider`.
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Args:
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tags: A dictionary of tags to override. If specified, tags passed in this argument will
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override those inferred from the context.
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ignore: A list of RunContextProvider classes to exclude from the resolution.
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Returns:
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A dictionary of resolved tags.
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"""
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ignore = ignore or []
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all_tags = {}
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for provider in _run_context_provider_registry:
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if any(isinstance(provider, ig) for ig in ignore):
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continue
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try:
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if provider.in_context():
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all_tags.update(provider.tags())
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except Exception as e:
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_logger.warning("Encountered unexpected error during resolving tags: %s", e)
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if tags is not None:
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all_tags.update(tags)
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return all_tags
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