ray/doc/site/_posts/2017-11-30-ray-0.3-release.markdown

---
layout: post
title: "Ray: 0.3 Release"
excerpt: "This post announces the release of Ray 0.3."
date: 2017-11-30 14:00:00
---

We are pleased to announce the Ray 0.3 release. This release introduces
[**distributed actor handles**][1] and [**Ray.tune**][2] --- a new
hyperparameter search library. It also includes a number of bug fixes and
stability enhancements.

To upgrade to the latest version, run

```
pip install -U ray
```

## Hyperparameter Search Tool

This release adds [**Ray.tune**][2], a distributed hyperparameter evaluation
tool for long-running tasks such as reinforcement learning and deep learning
training. It currently includes the following features:

- Pluggable **early stopping algorithms** including the Median Stopping Rule and
  [Hyperband][5].
- Integration with **visualization tools** such as [TensorBoard][6],
  [rllab's VisKit][7], and a [parallel coordinates visualization][8].
- Flexible **trial variant generation**, including grid search, random search,
  and conditional parameter distributions.
- **Resource-aware scheduling**, including support for concurrent runs of
  algorithms that require GPUs or are themselves parallel and distributed.

Ray.tune provides a Python API for use with deep learning and other compute
intensive training tasks. Here is a toy example illustrating usage.

```python
from ray.tune import register_trainable, grid_search, run_experiments

def my_func(config, reporter):
    import time, numpy as np
    i = 0
    while True:
        reporter(timesteps_total=i, mean_accuracy=i ** config['alpha'])

        i += config['beta']
        time.sleep(0.01)

register_trainable('my_func', my_func)

run_experiments({
    'my_experiment': {
        'run': 'my_func',
        'resources': {'cpu': 1, 'gpu': 0},
        'stop': {'mean_accuracy': 100},
        'config': {
            'alpha': grid_search([0.2, 0.4, 0.6]),
            'beta': grid_search([1, 2]),
        },
    }
})
```

In-progress results can be visualized live using tools such as Tensorboard and
rllab's VisKit (or you can read the JSON format logs directly from the driver
node):

View the [documentation][2] and the [code][9].

### Ray.tune integration with RLlib

You can try out RLlib with Ray.tune with the following example.

```bash
cd ray/python/ray/rllib
python train.py -f tuned_examples/cartpole-grid-search-example.yaml
```

The `tuned_examples` directory also contains pre-tuned hyperparameter
configurations for common benchmark tasks such as Pong and Humanoid. View the
[RLlib documentation][3].

### Initial Support for PyTorch in RLlib

A modern reinforcement learning library should work with multiple deep learning
frameworks. As a step toward this goal, 0.3 adds support for PyTorch models for
A3C in RLlib. You can try this out with the following A3C config.

```bash
cd ray/python/ray/rllib
./train.py --run=A3C \
           --env=PongDeterministic-v4 \
           --config='{"use_pytorch": true, "num_workers": 8, "use_lstm": false, "model": {"grayscale": true, "zero_mean": false, "dim": 80, "channel_major": true}}'
```

## Distributed Actor Handles

Ray 0.3 adds support for [distributed actor handles][1], that is, the ability to
have multiple callers invoke methods on the same actor. The actor's creator may
pass the actor handle as an argument to other tasks or to other actor methods.
Here's an example in which the driver creates an actor to log messages and
passes an actor handle to other tasks:

```python
import ray

ray.init()

@ray.remote
class Logger(object):
    def __init__(self):
        self.logs = []
    def log(self, log_msg):
        self.logs.append(log_msg)
    def read_logs(self):
        return self.logs

@ray.remote
def task(logger, task_index):
    # Do some work.
    logged = logger.log.remote('Task {} is done'.format(task_index))

# Create an actor and get a handle to it.
logger = Logger.remote()
# Pass the actor handle to some tasks.
tasks = [task.remote(logger, i) for i in range(10)]
# Wait for the tasks to finish.
ray.get(tasks)
# Get the logs from the tasks.
print(ray.get(logger.read_logs.remote()))
```

This feature is still considered experimental, but we've already found
distributed actor handles useful for implementing [**parameter server**][10] and
[**streaming MapReduce**][11] applications.

[1]: http://docs.ray.io/en/master/actors.html#passing-around-actor-handles-experimental
[2]: http://docs.ray.io/en/master/tune.html
[3]: http://docs.ray.io/en/master/rllib.html
[4]: https://research.google.com/pubs/pub46180.html
[5]: https://arxiv.org/abs/1603.06560
[6]: https://www.tensorflow.org/get_started/summaries_and_tensorboard
[7]: https://media.readthedocs.org/pdf/rllab/latest/rllab.pdf
[8]: https://en.wikipedia.org/wiki/Parallel_coordinates
[9]: https://github.com/ray-project/ray/tree/master/python/ray/tune
[10]: http://docs.ray.io/en/master/example-parameter-server.html
[11]: http://docs.ray.io/en/master/example-streaming.html