Distributed PyTorch Lightning Training on Ray

This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed computing framework.

These PyTorch Lightning Plugins on Ray enable quick and easy parallel training while still leveraging all the benefits of PyTorch Lightning and using your desired training protocol, either PyTorch Distributed Data Parallel or Horovod.

Once you add your plugin to the PyTorch Lightning Trainer, you can parallelize training to all the cores in your laptop, or across a massive multi-node, multi-GPU cluster with no additional code changes.

This library also comes with an integration with Ray Tune for distributed hyperparameter tuning experiments.

Installation

You can install Ray Lightning via pip:

pip install ray_lightning

Or to install master:

pip install git+https://github.com/ray-project/ray_lightning#ray_lightning

PyTorch Lightning Compatibility

Here are the supported PyTorch Lightning versions:

Ray Lightning

PyTorch Lightning

0.1

1.4

0.2

1.5

master

1.5

PyTorch Distributed Data Parallel Plugin on Ray

The RayPlugin provides Distributed Data Parallel training on a Ray cluster. PyTorch DDP is used as the distributed training protocol, and Ray is used to launch and manage the training worker processes.

Here is a simplified example:

import pytorch_lightning as pl
from ray_lightning import RayPlugin

# Create your PyTorch Lightning model here.
ptl_model = MNISTClassifier(...)
plugin = RayPlugin(num_workers=4, num_cpus_per_worker=1, use_gpu=True)

# Don't set ``gpus`` in the ``Trainer``.
# The actual number of GPUs is determined by ``num_workers``.
trainer = pl.Trainer(..., plugins=[plugin])
trainer.fit(ptl_model)

Because Ray is used to launch processes, instead of the same script being called multiple times, you CAN use this plugin even in cases when you cannot use the standard DDPPlugin such as

  • Jupyter Notebooks, Google Colab, Kaggle

  • Calling fit or test multiple times in the same script

Multi-node Distributed Training

Using the same examples above, you can run distributed training on a multi-node cluster with just a couple simple steps.

First, use Ray’s Cluster launcher to start a Ray cluster:

ray up my_cluster_config.yaml

Then, run your Ray script using one of the following options:

  1. on the head node of the cluster (python train_script.py)

  2. via ray job submit (docs) from your laptop (ray job submit -- python train.py)

Multi-node Training from your Laptop

Ray provides capabilities to run multi-node and GPU training all from your laptop through Ray Client

Ray’s Cluster launcher to setup the cluster. Then, add this line to the beginning of your script to connect to the cluster:

import ray
# replace with the appropriate host and port
ray.init("ray://<head_node_host>:10001")

Now you can run your training script on the laptop, but have it execute as if your laptop has all the resources of the cluster essentially providing you with an infinite laptop.

Note: When using with Ray Client, you must disable checkpointing and logging for your Trainer by setting checkpoint_callback and logger to False.

Horovod Plugin on Ray

Or if you prefer to use Horovod as the distributed training protocol, use the HorovodRayPlugin instead.

import pytorch_lightning as pl
from ray_lightning import HorovodRayPlugin

# Create your PyTorch Lightning model here.
ptl_model = MNISTClassifier(...)

# 2 workers, 1 CPU and 1 GPU each.
plugin = HorovodRayPlugin(num_workers=2, use_gpu=True)

# Don't set ``gpus`` in the ``Trainer``.
# The actual number of GPUs is determined by ``num_workers``.
trainer = pl.Trainer(..., plugins=[plugin])
trainer.fit(ptl_model)

Model Parallel Sharded Training on Ray

The RayShardedPlugin integrates with FairScale to provide sharded DDP training on a Ray cluster. With sharded training, leverage the scalability of data parallel training while drastically reducing memory usage when training large models.

import pytorch_lightning as pl
from ray_lightning import RayShardedPlugin

# Create your PyTorch Lightning model here.
ptl_model = MNISTClassifier(...)
plugin = RayShardedPlugin(num_workers=4, num_cpus_per_worker=1, use_gpu=True)

# Don't set ``gpus`` in the ``Trainer``.
# The actual number of GPUs is determined by ``num_workers``.
trainer = pl.Trainer(..., plugins=[plugin])
trainer.fit(ptl_model)

See the Pytorch Lightning docs for more information on sharded training.

Hyperparameter Tuning with Ray Tune

ray_lightning also integrates with Ray Tune to provide distributed hyperparameter tuning for your distributed model training. You can run multiple PyTorch Lightning training runs in parallel, each with a different hyperparameter configuration, and each training run parallelized by itself. All you have to do is move your training code to a function, pass the function to tune.run, and make sure to add the appropriate callback (Either TuneReportCallback or TuneReportCheckpointCallback) to your PyTorch Lightning Trainer.

Example using ray_lightning with Tune:

from ray import tune

from ray_lightning import RayPlugin
from ray_lightning.examples.ray_ddp_example import MNISTClassifier
from ray_lightning.tune import TuneReportCallback, get_tune_resources

import pytorch_lightning as pl


def train_mnist(config):
    
    # Create your PTL model.
    model = MNISTClassifier(config)

    # Create the Tune Reporting Callback
    metrics = {"loss": "ptl/val_loss", "acc": "ptl/val_accuracy"}
    callbacks = [TuneReportCallback(metrics, on="validation_end")]
    
    trainer = pl.Trainer(
        max_epochs=4,
        callbacks=callbacks,
        plugins=[RayPlugin(num_workers=4, use_gpu=False)])
    trainer.fit(model)
    
config = {
    "layer_1": tune.choice([32, 64, 128]),
    "layer_2": tune.choice([64, 128, 256]),
    "lr": tune.loguniform(1e-4, 1e-1),
    "batch_size": tune.choice([32, 64, 128]),
}

# Make sure to pass in ``resources_per_trial`` using the ``get_tune_resources`` utility.
analysis = tune.run(
        train_mnist,
        metric="loss",
        mode="min",
        config=config,
        num_samples=2,
        resources_per_trial=get_tune_resources(num_workers=4),
        name="tune_mnist")
        
print("Best hyperparameters found were: ", analysis.best_config)

Note: Ray Tune requires 1 additional CPU per trial to use for the Trainable driver. So the actual number of resources each trial requires is num_workers * num_cpus_per_worker + 1.

FAQ

I see that RayPlugin is based off of Pytorch Lightning’s DDPSpawnPlugin. However, doesn’t the PTL team discourage the use of spawn?

As discussed here, using a spawn approach instead of launch is not all that detrimental. The original factors for discouraging spawn were:

  1. not being able to use ‘spawn’ in a Jupyter or Colab notebook, and

  2. not being able to use multiple workers for data loading.

Neither of these should be an issue with the RayPlugin due to Ray’s serialization mechanisms. The only thing to keep in mind is that when using this plugin, your model does have to be serializable/pickleable.

API Reference

Tune Integration