> ## Documentation Index
> Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Reproducibility

> Reproducibility is an essential component of training ML models.

The [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class features a way to enable determinism across runs, if so desired.

On this page, you will learn how to configure the Trainer to ensure reproducibility.

## Prerequisites

Make sure to have read through [Trainer Overview](../../../model-zoo/trainer-overview) and [Trainer Configuration Overview](../../../model-zoo/trainer-configuration-overview) which provide the basic overview of how to run Model Zoo models. In this document, you will be using the tools and configurations outlined in those pages.

Trainer Seed
The [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) supports configuring reproducibility by piggybacking off of [torch seed](https://pytorch.org/docs/stable/generated/torch.manual_seed.html) settings. While it is possible to manually set the torch seed outside of the Trainer class, it is strongly recommended to use the `seed` argument of the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class to handle that for you. The following example shows how you can set the seed to 1234:

<CodeGroup>
  ```yaml YAML theme={null}

  trainer:
    init:
    ...
    seed:  1234
    ...

  ```

  ```python Python theme={null}
  from cerebras.modelzoo import Trainer

  trainer = Trainer(
      ...,
      seed=1234,
  )
  ...

  ```
</CodeGroup>

<Note>
  If the seed is not provided or is **None** (the default value), determinism across runs is not ensured.
</Note>

<Note>
  [Torch modules](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) initialize their weights upon instantiation. Setting the seed after a Module has already been instantiated may not necessarily ensure determinism. To avoid this pitfall, instead of passing an already-constructed model instance to the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class, you should pass a callable that returns a torch Module. The Trainer will set the seed before invoking the callback, thus ensuring reproducibility. This is in line with deferred weight initialization, as described in [Defer Weight Initialization](../../../model-zoo/components/trainer-components/defer-weight-initialization).
</Note>

For a given run, the seed settings may affect any of the following:

* The order of input data;

* The global seed captured in the graph which may affect the values generated by random ops in the model;

* The compile hash. For example, a model that has a random op, such as Dropout, may have a different compile hash for different seed settings. To avoid unnecessary recompiles, make sure to set the trainer seed.

## Conclusion

Ensuring reproducibility in ML model training is crucial for consistency and reliability of results. By leveraging the `seed` argument in the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class, you can achieve deterministic behavior across runs. This guide has provided step-by-step instructions on configuring the Trainer for reproducibility using both YAML and Python.

## Further Reading

To learn more about how you can extend the capabilities of the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class, you can check out:

* [Defer Weight Initialization](../../../model-zoo/components/trainer-components/defer-weight-initialization)

* [Train a model with weight sparsity](../../../model-zoo/tutorials/train-a-model-with-weight-sparsity)

* [Checkpointing](../../../model-zoo/components/trainer-components/checkpointing)

* [Customizing the Trainer with Callbacks](../../../model-zoo/components/trainer-components/customizing-the-trainer-with-callbacks)

* [Logging](../../../model-zoo/components/trainer-components/logging)