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Update data_science_job.rst
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docs/source/user_guide/jobs/data_science_job.rst

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See also: :doc:`policies` and `About Data Science Policies <https://docs.oracle.com/en-us/iaas/data-science/using/policies.htm>`_.
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In ADS, a job is defined by :doc:`infrastructure` and :doc:`runtime`.
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The Data Science Job infrastructure is configured through a :py:class:`~ads.jobs.builders.infrastructure.dsc_job.DataScienceJob` instance.
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The runtime can be an instance of :py:class:`~ads.jobs.builders.runtimes.python_runtime.PythonRuntime`,
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:py:class:`~ads.jobs.builders.runtimes.python_runtime.GitPythonRuntime`,
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:py:class:`~ads.jobs.builders.runtimes.python_runtime.NotebookRuntime`,
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:py:class:`~ads.jobs.builders.runtimes.python_runtime.ScriptRuntime`, or
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:py:class:`~ads.jobs.builders.runtimes.python_runtime.ContainerRuntime`
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The Data Science Job infrastructure is configured through a :py:class:`~ads.jobs.DataScienceJob` instance.
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The runtime can be an instance of :py:class:`~ads.jobs.PythonRuntime`,
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:py:class:`~ads.jobs.GitPythonRuntime`,
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:py:class:`~ads.jobs.NotebookRuntime`,
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:py:class:`~ads.jobs.ScriptRuntime`, or
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:py:class:`~ads.jobs.ContainerRuntime`
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Create and Run a Job
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For more details, see :doc:`infrastructure` configurations and see :doc:`runtime` configurations.
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In :py:class:`~ads.jobs.builders.runtimes.python_runtime.PythonRuntime`,
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In :py:class:`~ads.jobs.PythonRuntime`,
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the ``entrypoint`` can be a Python script, a Python function or a Jupyter notebook.
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Once the job is created, the job OCID can be accessed through ``job.id``.
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Once the job run is created, the job run OCID can be accessed through ``run.id``.
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The ``watch()`` method is useful to monitor the progress of the job run if logging is configured.
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The :py:meth:`~ads.jobs.DataScienceJobRun.watch` method is useful to monitor the progress of the job run.
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It will stream the logs to terminal and return once the job is finished.
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Logging configurations are required for this method to show logs.
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Here is an example of the logs:
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.. code-block:: text

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