.. _cs-input-analyzer:

The ``cs_input_analyzer`` Script
================================

We recommend that you run the ``cs_input_analyzer`` Bash script before running your model on the Cerebras system.  This script analyzes your model's input pipeline to determine optimal distributed CPU compute resources you need.

.. admonition:: CS Input Analyzer Script vs Input Function Analyzer Report

	This ``cs_input_analyzer`` Bash script is different from the :ref:`input-function-report`. The Input Function Report is generated automatically during compile time and contains a report on your TensorFlow model's input function.

The ``cs_input_analyzer`` Bash script first compiles your model on the CPU and then analyzes your model's input pipeline. The script then generates a report containing the recommended Slurm configurations that you should use with the :ref:`csrun-wse` script to train your model on the Cerebras system. The report also contains an estimate of the input performance.


.. important::

	Follow the :ref:`cs-command-line-pattern` to use the ``cs_input_analyzer`` Bash script correctly.


.. _fig-cs-analyze-input:
.. figure:: /images/cs-analyze-input.png
    :align: center
    :width: 400 px


For example:

The following command:

.. code-block:: bash

	cs_input_analyzer python run.py --mode=train --compile_only

will run compile and then return an input performance estimate.

and the following command:

.. code-block:: bash

	cs_input_analyzer --available-nodes=3 python run.py --mode=train --validate_only

will run validation and then return an input performance estimate assuming that three nodes are available for training.

.. important::

	All command examples showing command line options for validate, compile or train and so on, for example, ``python run.py --validate_only``, are for the run script ``run.py`` written by Cerebras. If you have your own run script, make sure you pass the command line options defined in your run script.

Usage
-----

.. code-block:: bash

    $ ./cs_input_analyzer --help
    Usage: cs_input_analyzer [--help] [--available-nodes] [--mount-dirs] command_for_validate_compile
    ...
    ...
    ...


Description
~~~~~~~~~~~

Analyzes your input pipeline after completing a compile or validation.
A report containing recommended Slurm configurations for use when running
on the CS system, along with the estimated input performance, is logged at
the end. See the description for the ``command_for_validate_compile``
argument for how to specify full-compile or	validate-only options.

To adhere to the recommended Slurm settings printed in the report, you must
specify them when calling the :ref:`csrun-wse` script.

.. admonition:: Improve performance

	The quality of the performance estimates and the generated Slurm
	settings improves if:

		1. A full-compile is performed instead of validate-only, and
		2. The ``cs_ip`` argument is set in CSRunConfig (generally this can be set in the run.py also).

.. important::

	To ensure consistent results, this script allocates a whole node while executing.

Arguments
~~~~~~~~~

- ``command_for_validate_compile``: A Python command to initiate a full-compile or validation-only. For example:

	.. code-block:: bash

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

	.. code-block:: bash

		python run.py --mode=train --validate_only

- ``--available-nodes``: *Optional*. Set this to the number of nodes available when executing on the CS system.

	.. note::

		This value is only used for estimating the performance and generating
		recommended	Slurm configuration. Default is 1.

- ``--mount-dirs``: *Optional*. A string of comma-separated paths to mount in addition to	the standard paths listed in the ``csrun_cpu`` script. Default is an empty string (only the paths listed in ``csrun_cpu`` are mounted).

        .. seealso::
            For how to list the paths in ``csrun_cpu``, see :ref:`config-csrun-cpu`.

Example
~~~~~~~

The following command:

.. code-block:: bash

	cs_input_analyzer --available-nodes=3 python run.py --mode=train --validate_only

Executes the command ``python run.py --mode=validate_only --cs_ip=0.0.0.0``,
which sets the ``cs_ip`` in the CSRunConfig and runs the validation inside the
Cerebras container on a CPU node. The input pipeline performance is then
analyzed using the validation information to print a performance report.

	.. note::

		Providing the ``cs_ip`` enhances the quality of the estimates.

The following command:

.. code-block:: bash

	cs_input_analyzer --mount-dirs="/data/ml,/lab/ml" python run.py --mode=train --compile_only --cs_ip=0.0.0.0

Mounts ``/data/ml`` and ``/lab/ml`` in addition to the default mount
directories, and executes the command ``python run.py --mode=compile_only``,
which runs a full compilation inside the Cerebras container on a CPU node. The
input pipeline performance is then analyzed using the compile information to
print a performance report and recommended Slurm settings.

.. note::

	Unless the input pipeline is a bottleneck, the input pipeline performance in the report will likely be higher than the actual training performance.


.. seealso::
    - :ref:`validate-and-compile-on-cpu`.
    - :ref:`cs-tf-workflow`.