* Fix QMix, SAC, and MADDPA too.
* Unpin gym and deprecate pendulum v0
Many tests in rllib depended on pendulum v0,
however in gym 0.21, pendulum v0 was deprecated
in favor of pendulum v1. This may change reward
thresholds, so will have to potentially rerun
all of the pendulum v1 benchmarks, or use another
environment in favor. The same applies to frozen
lake v0 and frozen lake v1
Lastly, all of the RLlib tests and have
been moved to python 3.7
* Add gym installation based on python version.
Pin python<= 3.6 to gym 0.19 due to install
issues with atari roms in gym 0.20
* Reformatting
* Fixing tests
* Move atari-py install conditional to req.txt
* migrate to new ale install method
* Fix QMix, SAC, and MADDPA too.
* Unpin gym and deprecate pendulum v0
Many tests in rllib depended on pendulum v0,
however in gym 0.21, pendulum v0 was deprecated
in favor of pendulum v1. This may change reward
thresholds, so will have to potentially rerun
all of the pendulum v1 benchmarks, or use another
environment in favor. The same applies to frozen
lake v0 and frozen lake v1
Lastly, all of the RLlib tests and have
been moved to python 3.7
* Add gym installation based on python version.
Pin python<= 3.6 to gym 0.19 due to install
issues with atari roms in gym 0.20
Move atari-py install conditional to req.txt
migrate to new ale install method
Make parametric_actions_cartpole return float32 actions/obs
Adding type conversions if obs/actions don't match space
Add utils to make elements match gym space dtypes
Co-authored-by: Jun Gong <jungong@anyscale.com>
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* Unpin gym and deprecate pendulum v0
Many tests in rllib depended on pendulum v0,
however in gym 0.21, pendulum v0 was deprecated
in favor of pendulum v1. This may change reward
thresholds, so will have to potentially rerun
all of the pendulum v1 benchmarks, or use another
environment in favor. The same applies to frozen
lake v0 and frozen lake v1
Lastly, all of the RLlib tests and Tune tests have
been moved to python 3.7
* fix tune test_sampler::testSampleBoundsAx
* fix re-install ray for py3.7 tests
Co-authored-by: avnishn <avnishn@uw.edu>
* Added fixes to CartPole example
* Apply suggestions from code review
Co-authored-by: will <will@anyscale.com>
Co-authored-by: Sven Mika <sven@anyscale.io>
* Updated PettingZoo+RLlib tutorial
Updated the tutorial and added link to the blog post by the PettingZoo team.
* Ran linting
* Converted link to tinyurl for linting
* fixed line lengths
* Decrease num_workers to 1
* Added comments
* Decreased num_workers
* Decreased timesteps
* Increased num_workers
* Update links and remove pettingzoo_env.py
* remove pettingzoo.py script from tests
Co-authored-by: sven1977 <svenmika1977@gmail.com>
## Why are these changes needed?
- Since broadcasting is moving to grpc, introducing the option to increase the client side thread number
- For hybrid schedule, ignore the threshold if gcs based actor scheduler is enabled
With these fixing, actor creation rate > 600actor/s vs ~ 140 actor/s
## Related issue number
This PR puts the final touches on apple silicon support. There are 3 main caveats to supporting M1 macs right now (described in the docs):
Requires using forge.
Requires special installation instructions to get grpc working (this is an underlying grpc issue, so ideally it will be fixed upstream).
We're only publishing release wheels, not nightlies right now.
This also includes a grpc import check to ensure that we provide an actionable error message if the user tries the regular pip install ray process to properly install grpcio.
postprocess_trajectory is referred to incorrectly in the rllib-environments documentation. When defining a custom policy, a user never directly modifies Policy.postprocess_trajectory, they define postprocess_fn, which is in turn called by postprocess_trajectory.
* round robin on failure to launch
* still round-robin spot instances
* prioritize first AZ
* no more round-robining
* doc updates
* Order subnets by AZ
* add spot instance advisor link
* ensure we try all AZs
* fix typos
## Why are these changes needed?
This PR aims to port concurrency groups functionality with asyncio for Python.
### API
```python
@ray.remote(concurrency_groups={"io": 2, "compute": 4})
class AsyncActor:
def __init__(self):
pass
@ray.method(concurrency_group="io")
async def f1(self):
pass
@ray.method(concurrency_group="io")
def f2(self):
pass
@ray.method(concurrency_group="compute")
def f3(self):
pass
@ray.method(concurrency_group="compute")
def f4(self):
pass
def f5(self):
pass
```
The annotation above the actor class `AsyncActor` defines this actor will have 2 concurrency groups and defines their max concurrencies, and it has a default concurrency group. Every concurrency group has an async eventloop and a pythread to execute the methods which is defined on them.
Method `f1` will be invoked in the `io` concurrency group. `f2` in `io`, `f3` in `compute` and etc.
TO BE NOTICED, `f5` and `__init__` will be invoked in the default concurrency.
The following method `f2` will be invoked in the concurrency group `compute` since the dynamic specifying has a higher priority.
```python
a.f2.options(concurrency_group="compute").remote()
```
### Implementation
The straightforward implementation details are:
- Before we only have 1 eventloop binding 1 pythread for an asyncio actor. Now we create 1 eventloop binding 1 pythread for every concurrency group of the asyncio actor.
- Before we have 1 fiber state for every caller in the asyncio actor. Now we create a FiberStateManager for every caller in the asyncio actor. And the FiberStateManager manages the fiber states for concurrency groups.
## Related issue number
#16047
