.. _train-eval-predict:

Train, Eval, and Predict
========================

This section describes how to:

- Run training, eval or prediction on the Cerebras system.
- Without modifying your code, run training, eval or prediction on a CPU or a GPU.

You will use the ``csrun_wse`` script to accomplish this. See below.

.. seealso::

    See :ref:`csrun-wse` for detailed documentation on how to use this Bash script.

.. _fig-csrun-wse:
.. figure:: ../../images/csrun-wse.png
    :align: center
    :width: 700 px


Examples
--------

Train
~~~~~

The following command executes the user command ``python run.py --mode=train --cs_ip=0.0.0.0``, which initiates model training on the CS system at the given ``cs_ip`` address.

.. code-block:: bash

    csrun_wse python run.py --mode=train --cs_ip=0.0.0.0

The following command executes the user command ``python run.py --mode=train --cs_ip=0.0.0.0``, which initiates training using the trained model on the CS system at the given ``cs_ip`` address. The Slurm orchestrator uses 3 nodes, 5 tasks per worker and 16 CPUs per task for this training run.

.. code-block:: bash

    csrun_wse --nodes=3 --tasks_per_worker=5 --cpus_per_task=16 python run.py --mode=train --cs_ip=0.0.0.0

The following command executes the user command ``python run.py --mode train --params configs/your-params-file.yaml --model_dir your-model-dir  --cs_ip 10.255.253.0``, which initiates training on the CS system at the IP address: 10.255.253.0. If the Slurm variables are not passed to the ``csrun_wse`` script, then the values provided by system administrator in the ``csrun_cpu`` script are used. See :ref:`config-csrun-cpu`.

.. code-block:: bash

    csrun_wse python run.py --mode=train \
		--params configs/your-params-file.yaml \
		--model_dir your-model-dir \
		--cs_ip=10.255.253.0


Eval
~~~~

.. code-block:: bash

    csrun_wse python run.py --mode=eval --cs_ip=0.0.0.0

- Executes the command ``python run.py --mode=eval --cs_ip=0.0.0.0``, which initiates model evaluation on the CS system at the given ``cs_ip`` address.

Predict
~~~~~~~

.. code-block:: bash

    csrun_wse python run.py --mode=predict --cs_ip=0.0.0.0

- Executes the command ``python run.py --mode=predict --cs_ip=0.0.0.0``, which initiates prediction using the trained model on the CS system at the given ``cs_ip`` address.

.. note::

	Also note another advantage after you ported your TensorFlow code to Cerebras: You can run training, eval or prediction on either the CS system or on CPU- and GPU-based systems without recoding your model.


Train on CPU, GPU
-----------------

On a CPU within the Cerebras cluster
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To train on a CPU, do not use Slurm to invoke the standard Singularity container. Instead, simply launch the training command with ``cs_cpu``. See the following example:

.. code-block:: bash

     csrun_cpu python run.py


.. seealso::

    See :ref:`csrun-cpu` for detailed documentation on how to use this Bash script.


On a CPU or GPU outside the Cerebras cluster
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To run on CPU or GPU outside of the CS system environment, you will not use the Cerebras container. However, you must use the version of ``CerebrasEstimator`` provided in the `Cerebras Model Zoo repository <https://github.com/Cerebras/modelzoo>`__. Follow these steps:

- Clone the Cerebras Cerebras Model Zoo repository.
- Import the versions of ``CerebrasEstimator`` and ``RunConfig`` from the repo.
- Use these versions both within the Cerebras container and outside of the Cerebras environment to easily switch between the CS system and GPU.