Ray 0.7.4 Release Notes

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. 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:
```
@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.
There is now preliminary support for projects, see the the project documentation. Projects allow you to
package your code and easily share it with others, ensuring a reproducible cluster setup. To get started, you
can run
```
# 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. 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.

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

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

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

We thank the following contributors for their amazing contributions: