0.3 release blog post. (#1274)

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+---
+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://ray.readthedocs.io/en/latest/actors.html#passing-around-actor-handles-experimental
+[2]: http://ray.readthedocs.io/en/latest/tune.html
+[3]: http://ray.readthedocs.io/en/latest/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://ray.readthedocs.io/en/latest/example-parameter-server.html
+[11]: http://ray.readthedocs.io/en/latest/example-streaming.html