Configure scale and GPUs

Outside of your training function, create a :class:~ray.train.ScalingConfig object to configure:

:class:num_workers <ray.train.ScalingConfig> - The number of distributed training worker processes.
:class:use_gpu <ray.train.ScalingConfig> - Whether each worker should use a GPU (or CPU).

.. testcode::

from ray.train import ScalingConfig
scaling_config = ScalingConfig(num_workers=2, use_gpu=True)

For more details, see :ref:train_scaling_config.

Configure persistent storage

Create a :class:~ray.train.RunConfig object to specify the path where results (including checkpoints and artifacts) will be saved.

.. testcode::

from ray.train import RunConfig

# Local path (/some/local/path/unique_run_name)
run_config = RunConfig(storage_path="/some/local/path", name="unique_run_name")

# Shared cloud storage URI (s3://bucket/unique_run_name)
run_config = RunConfig(storage_path="s3://bucket", name="unique_run_name")

# Shared NFS path (/mnt/nfs/unique_run_name)
run_config = RunConfig(storage_path="/mnt/nfs", name="unique_run_name")

.. warning::

Specifying a *shared storage location* (such as cloud storage or NFS) is
*optional* for single-node clusters, but it is **required for multi-node clusters.**
Using a local path will :ref:`raise an error <multinode-local-storage-warning>`
during checkpointing for multi-node clusters.

For more details, see :ref:persistent-storage-guide.

Launch a training job

Tying this all together, you can now launch a distributed training job with a :class:~ray.train.torch.TorchTrainer.

.. testcode:: :hide:

from ray.train import ScalingConfig

train_func = lambda: None
scaling_config = ScalingConfig(num_workers=1)
run_config = None

.. testcode::

from ray.train.torch import TorchTrainer

trainer = TorchTrainer(
    train_func, scaling_config=scaling_config, run_config=run_config
)
result = trainer.fit()

Access training results

After training completes, a :class:~ray.train.Result object is returned which contains information about the training run, including the metrics and checkpoints reported during training.

.. testcode::

result.metrics     # The metrics reported during training.
result.checkpoint  # The latest checkpoint reported during training.
result.path        # The path where logs are stored.
result.error       # The exception that was raised, if training failed.

For more usage examples, see :ref:train-inspect-results.