mantik.runs.local¶
Attributes¶
Classes¶
The purpose of this class is so that the two functions inside it |
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Like the tee linux command that prints to stdout |
Functions¶
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Module Contents¶
- class mantik.runs.local.LocalRunManager[source]¶
The purpose of this class is so that the two functions inside it can be more easily monkey-patched together, feel free to improve this by monkeypatching the functions without the need of an extra class to clean up the code
- static start_local_run(mlflow_experiment_id: str, data: mantik.runs.schemas.RunConfiguration, project_id: uuid.UUID, mantik_token: str, uri: str, env_manager: str = 'local') LocalRunOutput [source]¶
- class mantik.runs.local.Tee(*streams)[source]¶
Bases:
io.StringIO
Like the tee linux command that prints to stdout and save to a/multiple file the output of a command. This class takes n streams and populates them with the same data. A stream could be sys.stdout or sys.stderr, to keep printing to them.
Initialize self. See help(type(self)) for accurate signature.
- mantik.runs.local.fetch_code_and_experiment(project_id: uuid.UUID, code_repository_id: uuid.UUID, experiment_repository_id: uuid.UUID, mantik_token: str) Tuple[mantik.utils.mantik_api.code_repository.CodeRepository, mantik.utils.mantik_api.experiment_repository.ExperimentRepository] [source]¶
- mantik.runs.local.get_env_vars_for_run(run_config: mantik.runs.schemas.RunConfiguration, mantik_token: str, project_id: str, data_target_dir: str | None = None) dict [source]¶
- mantik.runs.local.run(data: mantik.runs.schemas.RunConfiguration, project_id: uuid.UUID, mantik_token: str, data_target_dir: str | None = None, run_manager: LocalRunManager = LocalRunManager(), env_manager: str | None = 'local')[source]¶
- mantik.runs.local.save_run_data(data: mantik.runs.schemas.RunConfiguration, project_id: uuid.UUID, mantik_token: str) uuid.UUID [source]¶
- mantik.runs.local.start_local_run(project_id: uuid.UUID, name: str, experiment_repository_id: uuid.UUID, code_repository_id: uuid.UUID, branch: str | None, commit: str | None, data_repository_id: uuid.UUID | None, mlflow_mlproject_file_path: str, entry_point: str, data_branch: str | None, data_commit: str | None, data_target_dir: str | None, env_manager: str | None, mlflow_parameters: dict)[source]¶
- Parameters:
project_id (ID of the project to which the run should be linked)
name (Name of the Run)
experiment_repository_id (ID of the experiment repository) – to which the run should be linked
code_repository_id (ID of the code repository) – where the mlproject is located
branch (Name of the code repository's branch)
commit (Name of the code repository's commit (has precedence over branch))
data_repository_id (ID of the data repository) – where the data is located, this is optional
mlflow_mlproject_file_path (Path in your code directory) – to the MLproject file
entry_point (entry point name)
mlflow_parameters (Mlflow parameters present in your entry point)
data_branch (Data branch to checkout. Defaults to newest.)
data_commit (Data commit to checkout. Takes precedence over data_branch.)
env_manager (Which environment manager to use to handle installing run)
Local (dependencies. Could be)
Virtualenv (Conda or)
data_target_dir (Relative path to directory) – where the data will be stored (from code root)
- Return type:
Response from the mantik API that contains the run id