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* wip * add symlink * update * remove from init * no require tune * try fix * change * * import * fix docs * address comment
81 lines
2.5 KiB
ReStructuredText
81 lines
2.5 KiB
ReStructuredText
.. _sgd-index:
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=====================================
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RaySGD: Distributed Training Wrappers
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=====================================
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.. warning:: This is an older version of Ray SGD. A newer, more light-weight version of Ray SGD is in alpha as of Ray 1.7.
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See the documentation :ref:`here <sgd-v2-docs>`.
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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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Getting Started
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---------------
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You can start a ``TorchTrainer`` with the following:
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.. code-block:: python
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import ray
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from ray.util.sgd import TorchTrainer
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from ray.util.sgd.torch import TrainingOperator
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from ray.util.sgd.torch.examples.train_example import LinearDataset
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import torch
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from torch.utils.data import DataLoader
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class CustomTrainingOperator(TrainingOperator):
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def setup(self, config):
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# Load data.
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train_loader = DataLoader(LinearDataset(2, 5), config["batch_size"])
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val_loader = DataLoader(LinearDataset(2, 5), config["batch_size"])
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# Create model.
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model = torch.nn.Linear(1, 1)
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# Create optimizer.
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optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
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# Create loss.
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loss = torch.nn.MSELoss()
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# Register model, optimizer, and loss.
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self.model, self.optimizer, self.criterion = self.register(
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models=model,
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optimizers=optimizer,
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criterion=loss)
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# Register data loaders.
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self.register_data(train_loader=train_loader, validation_loader=val_loader)
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ray.init()
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trainer1 = TorchTrainer(
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training_operator_cls=CustomTrainingOperator,
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num_workers=2,
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use_gpu=False,
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config={"batch_size": 64})
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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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.. toctree::
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:hidden:
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raysgd_pytorch.rst
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raysgd_tensorflow.rst
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raysgd_tune.rst
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raysgd_ref.rst
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