Reproducibility

.. tab-item:: PyTorch

    To limit sources of nondeterministic behavior, add
    :func:`ray.train.torch.enable_reproducibility` to the top of your training
    function.

    .. code-block:: diff

         def train_func():
        +    train.torch.enable_reproducibility()

             model = NeuralNetwork()
             model = train.torch.prepare_model(model)

             ...

    .. warning:: :func:`ray.train.torch.enable_reproducibility` can't guarantee
        completely reproducible results across executions. To learn more, read
        the `PyTorch notes on randomness <https://pytorch.org/docs/stable/notes/randomness.html>`_.

def training_func(config):
    dataloader = ray.train.get_dataset()\
        .get_shard(torch.rank())\
        .iter_torch_batches(batch_size=config["batch_size"])

    for i in config["epochs"]:
        ray.train.report(...)  # use same intermediate reporting API

# Declare the specification for training.
trainer = Trainer(backend="torch", num_workers=12, use_gpu=True)
dataset = ray.dataset.window()

# Convert this to a trainable.
trainable = trainer.to_tune_trainable(training_func, dataset=dataset)

tuner = tune.Tuner(trainable,
    param_space={"lr": tune.uniform(), "batch_size": tune.randint(1, 2, 3)},
    tune_config=tune.TuneConfig(num_samples=12))
results = tuner.fit()