=================
Highlights
----------
- There were many **documentation improvements** (5391, 5389, 5175). As we continue to improve the documentation we value your feedback through the “Doc suggestion?” link at the top of the [documentation](https://ray.readthedocs.io/en/latest/). Notable improvements:
+ We’ve added guides for best practices using TensorFlow and PyTorch.
+ We’ve revamped the Walkthrough page for Ray users, providing a better experience for beginners.
+ We’ve revamped guides for using Actors and inspecting internal state.
- Ray supports **memory limits** now to ensure memory-intensive applications run predictably and reliably. You
can activate them through the `ray.remote` decorator:
python
ray.remote(
memory=2000 * 1024 * 1024,
object_store_memory=200 * 1024 * 1024)
class SomeActor(object):
def __init__(self, a, b):
pass
You can set limits for the heap and the object store, see the [documentation](https://ray.readthedocs.io/en/latest/memory-management.html).
- There is now preliminary support for **projects**, see the the [project documentation](https://ray.readthedocs.io/en/latest/projects.html). Projects allow you to
package your code and easily share it with others, ensuring a reproducible cluster setup. To get started, you
can run
shell
Create a new project.
ray project create <project-name>
Launch a session for the project in the current directory.
ray session start
Open a console for the given session.
ray session attach
Stop the given session and all of its worker nodes.
ray session stop
Check out the [examples](https://github.com/ray-project/ray/tree/f1dcce5a472fba1c77c4aa023589689efbfeb4f6/python/ray/projects/examples). This is an actively developed new feature so we appreciate your feedback!
**Breaking change:** The `redis_address` parameter was renamed to `address` (5412, 5602) and the former will be removed in the future.
Core
-----
- Move Java bindings on top of the core worker 5370
- Improve log file discoverability 5580
- Clean up and improve error messages 5368, 5351
RLlib
-----
- Support custom action space distributions 5164
- Add TensorFlow eager support 5436
- Add autoregressive KL 5469
- Autoregressive Action Distributions 5304
- Implement MADDPG agent 5348
- Port Soft Actor-Critic on Model v2 API 5328
- More examples: Add CARLA community example 5333 and rock paper scissors multi-agent example 5336
- Moved RLlib to top level directory 5324
Tune
-----
- Experimental Implementation of the BOHB algorithm 5382
- Breaking change: Nested dictionary results are now flattened for CSV writing: `{“a”: {“b”: 1}} => {“a/b”: 1}` 5346
- Add Logger for MLFlow 5438
- TensorBoard support for TensorFlow 2.0 5547
- Added examples for XGBoost and LightGBM 5500
- HyperOptSearch now has warmstarting 5372
Other Libraries
---------------
- SGD: Tune interface for Pytorch MultiNode SGD 5350
- Serving: The old version of ray.serve was deprecated 5541
- Autoscaler: Fix ssh control path limit 5476
- Dev experience: Ray CI tracker online at https://ray-travis-tracker.herokuapp.com/
Various fixes: Fix log monitor issues 4382 5221 5569, the top-level ray directory was cleaned up 5404
Thanks
-------
We thank the following contributors for their amazing contributions:
jon-chuang, lufol, adamochayon, idthanm, RehanSD, ericl, michaelzhiluo, nflu, pengzhenghao, hartikainen, wsjeon, raulchen, TomVeniat, layssi, jovany-wang, llan-ml, ConeyLiu, mitchellstern, gregSchwartz18, jiangzihao2009, jichan3751, mhgump, zhijunfu, micafan, simon-mo, richardliaw, stephanie-wang, edoakes, akharitonov, mawright, robertnishihara, lisadunlap, flying-mojo, pcmoritz, jredondopizarro, gehring, holli, kfstorm