===============
Getting Started
===============

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Key Steps
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1. Initialize the code workspace to prepare for distributed training
2. Build container image
3. Tag and Push the image to ``ocir``
4. Define the cluster requirement using :doc:`YAML Spec<../yaml_schema>`
5. Start distributed training using ads opctl run –f <yaml file>
6. Monitor the job using ads jobs watch <main job run id>


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Prepare container image for distributed workload
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**Prerequisite**: 

1. Internet Connection
2. ADS cli is :doc:`installed<../../../cli/quickstart>`
3. Docker engine


.. code-block:: shell
  
  ads opctl distributed-training init --framework <framework choice>

To run a distributed workload on ``OCI Data Science Jobs``, you need prepare a container image with the source code that you want to run and the framework (Dask|Horovod|PyTorch) setup. ``OCI Data Science`` provides you with the Dockerfiles and bootstrapping scripts to build framework specific container images. This step creates a folder in the current working directory called ``oci_distributed_training``. This folder contains all the artifacts required to setup and bootstrap the framework code. Refer to ``README.md`` file to see more details on how to build and push the container image to the ocir



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Check Config File generated by the Main and the Worker Nodes
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**Prerequisite**: 

1. A cluster that is in In-Progress, Succeeded or Failed (Supported only in some cases)
2. Job ``OCID``  and the ``work dir`` of the cluster or a yaml file which contains all the details displayed during cluster creation. 

.. code-block:: shell

  ads opctl distributed-training show-config -f <cluster yaml file>

The main node generates ``MAIN_config.json`` and worker nodes generate ``WORKER_<job run ocid>_config.json``. You may want to check the configuration details for find the IP address of the Main node. This can be useful to bring up dashboard for dask or debugging.