mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 07:02:22 +08:00
Richard Liaw
037aa2b961
* Refactor documentation and directory structurre * update loss * ,ore examples * fix comments * more code * svgs * formatting * more_docs * more writing * comments ready * move * whitespace * examples * fix * bold * pytorch * batch * fix * fix test * Apply suggestions from code review * quarantinegp * tests/ * fix missing
66 lines
2.0 KiB
ReStructuredText
66 lines
2.0 KiB
ReStructuredText
RaySGD: Distributed Deep Learning
|
|
=================================
|
|
|
|
.. tip:: Get in touch with us if you're using or considering using `RaySGD <https://forms.gle/26EMwdahdgm7Lscy9>`_!
|
|
.. warning:: This is still an experimental API and is subject to change in the near future.
|
|
|
|
|
|
.. image:: raysgdlogo.png
|
|
:scale: 20%
|
|
:align: center
|
|
|
|
RaySGD is a lightweight library for distributed deep learning, providing thin wrappers around PyTorch and TensorFlow native modules for data parallel training.
|
|
|
|
The main features are:
|
|
|
|
- **Ease of use**: Scale Pytorch's native ``DistributedDataParallel`` and TensorFlow's ``tf.distribute.MirroredStrategy`` without needing to monitor individual nodes.
|
|
- **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.
|
|
- **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.
|
|
|
|
|
|
Getting Started
|
|
---------------
|
|
|
|
You can start a ``PyTorchTrainer`` with the following:
|
|
|
|
.. code-block:: python
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch import distributed
|
|
|
|
from ray.experimental.sgd import PyTorchTrainer
|
|
from ray.experimental.sgd.examples.train_example import LinearDataset
|
|
|
|
|
|
def model_creator(config):
|
|
return nn.Linear(1, 1)
|
|
|
|
|
|
def optimizer_creator(model, config):
|
|
"""Returns optimizer."""
|
|
return torch.optim.SGD(model.parameters(), lr=1e-2)
|
|
|
|
|
|
def data_creator(batch_size, config):
|
|
"""Returns training dataloader, validation dataloader."""
|
|
return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
|
|
|
|
ray.init()
|
|
|
|
trainer1 = PyTorchTrainer(
|
|
model_creator,
|
|
data_creator,
|
|
optimizer_creator,
|
|
loss_creator,
|
|
num_replicas=2,
|
|
use_gpu=True,
|
|
batch_size=512,
|
|
backend="nccl")
|
|
|
|
stats = trainer1.train()
|
|
print(stats)
|
|
trainer1.shutdown()
|
|
print("success!")
|