wehub-resource-sync
87 lines
2.6 KiB
ReStructuredText
87 lines
2.6 KiB
ReStructuredText
Configure scale and GPUs
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------------------------
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Outside of your training function, create a :class:`~ray.train.ScalingConfig` object to configure:
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1. :class:`num_workers <ray.train.ScalingConfig>` - The number of distributed training worker processes.
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2. :class:`use_gpu <ray.train.ScalingConfig>` - Whether each worker should use a GPU (or CPU).
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.. testcode::
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from ray.train import ScalingConfig
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scaling_config = ScalingConfig(num_workers=2, use_gpu=True)
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For more details, see :ref:`train_scaling_config`.
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Configure persistent storage
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----------------------------
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Create a :class:`~ray.train.RunConfig` object to specify the path where results
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(including checkpoints and artifacts) will be saved.
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.. testcode::
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from ray.train import RunConfig
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# Local path (/some/local/path/unique_run_name)
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run_config = RunConfig(storage_path="/some/local/path", name="unique_run_name")
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# Shared cloud storage URI (s3://bucket/unique_run_name)
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run_config = RunConfig(storage_path="s3://bucket", name="unique_run_name")
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# Shared NFS path (/mnt/nfs/unique_run_name)
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run_config = RunConfig(storage_path="/mnt/nfs", name="unique_run_name")
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.. warning::
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Specifying a *shared storage location* (such as cloud storage or NFS) is
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*optional* for single-node clusters, but it is **required for multi-node clusters.**
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Using a local path will :ref:`raise an error <multinode-local-storage-warning>`
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during checkpointing for multi-node clusters.
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For more details, see :ref:`persistent-storage-guide`.
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Launch a training job
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---------------------
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Tying this all together, you can now launch a distributed training job
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with a :class:`~ray.train.torch.TorchTrainer`.
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.. testcode::
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:hide:
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from ray.train import ScalingConfig
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train_func = lambda: None
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scaling_config = ScalingConfig(num_workers=1)
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run_config = None
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.. testcode::
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from ray.train.torch import TorchTrainer
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trainer = TorchTrainer(
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train_func, scaling_config=scaling_config, run_config=run_config
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)
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result = trainer.fit()
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Access training results
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-----------------------
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After training completes, a :class:`~ray.train.Result` object is returned which contains
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information about the training run, including the metrics and checkpoints reported during training.
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.. testcode::
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result.metrics # The metrics reported during training.
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result.checkpoint # The latest checkpoint reported during training.
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result.path # The path where logs are stored.
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result.error # The exception that was raised, if training failed.
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For more usage examples, see :ref:`train-inspect-results`.
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