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Eric Liang
67 lines
2.0 KiB
ReStructuredText
67 lines
2.0 KiB
ReStructuredText
RaySGD: Distributed Training Wrappers
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=====================================
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.. _`issue on GitHub`: https://github.com/ray-project/ray/issues
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RaySGD is a lightweight library for distributed deep learning, providing thin wrappers around PyTorch and TensorFlow native modules for data parallel training.
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The main features are:
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- **Ease of use**: Scale Pytorch's native ``DistributedDataParallel`` and TensorFlow's ``tf.distribute.MirroredStrategy`` without needing to monitor individual nodes.
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- **Composability**: RaySGD is built on top of the Ray Actor API, enabling seamless integration with existing Ray applications such as RLlib, Tune, and Ray.Serve.
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- **Scale up and down**: Start on single CPU. Scale up to multi-node, multi-CPU, or multi-GPU clusters by changing 2 lines of code.
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.. note::
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This API is new and may be revised in future Ray releases. If you encounter
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any bugs, please file an `issue on GitHub`_.
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Getting Started
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---------------
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You can start a ``PyTorchTrainer`` with the following:
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.. code-block:: python
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import numpy as np
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import torch
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import torch.nn as nn
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from torch import distributed
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from ray.util.sgd import PyTorchTrainer
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from ray.util.sgd.examples.train_example import LinearDataset
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def model_creator(config):
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return nn.Linear(1, 1)
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def optimizer_creator(model, config):
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"""Returns optimizer."""
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return torch.optim.SGD(model.parameters(), lr=1e-2)
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def data_creator(batch_size, config):
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"""Returns training dataloader, validation dataloader."""
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return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
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ray.init()
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trainer1 = PyTorchTrainer(
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model_creator,
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data_creator,
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optimizer_creator,
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loss_creator=nn.MSELoss,
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num_replicas=2,
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use_gpu=True,
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batch_size=512,
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backend="nccl")
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stats = trainer1.train()
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print(stats)
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trainer1.shutdown()
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print("success!")
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.. tip:: Get in touch with us if you're using or considering using `RaySGD <https://forms.gle/26EMwdahdgm7Lscy9>`_!
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