## What do these changes do?
#2362 left a bug where it assumed that the driver task ID was nil. This fixes the bug to check the `SchedulingQueue` for any driver task IDs instead.
* [WIP] Support different backend log lib
* Refine code, unify level, address comment
* Address comment and change formatter
* Fix linux building failure.
* Fix lint
* Remove log4cplus.
* Add log init to raylet main and add test to travis.
* Address comment and refine.
* Update logging_test.cc
* + Compatibility fix under py2 on ray.tune
* + Revert changes on master branch
* + Use default JsonEncoder in ray.tune.logger
* + Add UT for infinity support
A bunch of minor rllib fixes:
pull in latest baselines atari wrapper changes (and use deepmind wrapper by default)
move reward clipping to policy evaluator
add a2c variant of a3c
reduce vision network fc layer size to 256 units
switch to 84x84 images
doc tweaks
print timesteps in tune status
This PR makes it so that when Ray is started via ray.init() (as opposed to via ray start) the Redis servers will be started in "protected mode" (which means that clients can only connect by connecting to localhost).
In practice, we actually connect redis clients by passing in the node IP address (not localhost), so I need to create a redis config file on the fly to allow both localhost and the node's actual IP address (it would have been nice to find a way to do this from the Python redis client, but I couldn't find one).
This adds some experimental (undocumented) support for launching Ray on existing nodes. You have to provide the head ip, and the list of worker ips.
There are also a couple additional utils added for rsyncing files and port-forward.
This PR introduces the following changes:
* Ray Tune -> Tune
* [breaking] Creation of `schedulers/`, moving PBT, HyperBand into a submodule
* [breaking] Search Algorithms now must take in experiment configurations via `add_configurations` rather through initialization
* Support `"run": (function | class | str)` with automatic registering of trainable
* Documentation Changes
Currently, log directory in Java is a relative path . This PR changes it to `/tmp/raylogs` (with the same format as Python, e.g., `local_scheduler-2018-51-17_17-8-6-05164.err`). It also cleans up some relative code.
The goal of this PR is to allow custom policies to perform model-based rollouts. In the multi-agent setting, this requires access to not only policies of other agents, but also their current observations.
Also, you might want to return the model-based trajectories as part of the rollout for efficiency.
compute_actions() now takes a new keyword arg episodes
pull out internal episode class into a top-level file
add function to return extra trajectories from an episode that will be appended to the sample batch
documentation
ray exec CLUSTER CMD [--screen] [--start] [--stop]
ray attach CLUSTER [--start]
Example:
ray exec sgd.yaml 'source activate tensorflow_p27 && cd ~/ray/python/ray/rllib && ./train.py --run=PPO --env=CartPole-v0' --screen --start --stop
This will in one command create a cluster and run the command on it in a screen session. The screen can later be attached to via ray attach. After the command finishes, the cluster workers will be terminated and the head node stopped.
* Cache a Task's object dependencies
* Cache the parent task IDs for lineage cache entries
* Cache the parent task IDs in lineage cache entries
* revert
* Fix test
* remove unused line
* Fix test
* Support building Java and Python version at the same time.
* Remove duplicated definition.
* Refine the building process of local_scheduler
* Refine
* Add comment for languages
* Modify instruction and add python,jave building to CI.
* change according to comment
* Move all ObjectManager members to bottom of class def
* Better Pull requests
- suppress duplicate Pulls
- retry the Pull at the next client after a timeout
- cancel a Pull if the object no longer appears on any clients
* increase object manager Pull timeout
* Make the component failure test harder.
* note
* Notify SubscribeObjectLocations caller of empty list
* Address melih's comments
* Fix wait...
* Make component failure test easier for legacy ray
* lint
* Log a warning on remote object manager failures
* Mark a task that was failed to be forwarded as pending
* Raylet component failure test and make it harder
* Turn on component failure test for xray
* Remove return status from ReleaseSender
* lint
to support TF version < 1.5
to support rmsprop optimizer in Impala
Before TF1.5, tf.reduce_sum() and tf.reduce_max() has an argument keep_dims which has been renamed as keepdims in later versions.
In the original paper of Impala, they use rmsprop algorithm to optimize the model. We'd better also support it so that users can reproduce their experiments. Without any tuning, say that using the same hyper-parameters as AdamOptimizer, it reaches "episode_reward_mean": 19.083333333333332 in Pong after consume 3,610,350 samples.
* use different random number generator to be compatible with older valgrind versions
* seed from time
* style
* fix
* remove more random devices
* also remove random_device from global scheduler
* rename mutex
* linting
## What do these changes do?
This implements basic task reconstruction in raylet. There are two parts to this PR:
1. Reconstruction suppression through the `TaskReconstructionLog`. This prevents two raylets from reconstructing the same task if they decide simultaneously (via the logic in #2497) that reconstruction is necessary.
2. Task resubmission once a raylet becomes responsible for reconstructing a task.
Reconstruction is quite slow in this PR, especially for long chains of dependent tasks. This is mainly due to the lease table mechanism, where nodes may wait too long before trying to reconstruct a task. There are two ways to improve this:
1. Expire entries in the lease table using Redis `PEXPIRE`. This is a WIP and I may include it in this PR.
2. Introduce a "fast path" for reconstructing dependencies of a re-executed task. Normally, we wait for an initial timeout before checking whether a task requires reconstruction. However, if a task requires reconstruction, then it's likely that its dependencies also require reconstruction. In this case, we could skip the initial timeout before checking the GCS to see whether reconstruction is necessary (e.g., if the object has been evicted).
Since handling failures of other raylets is probably not yet complete in master, this only turns back on Python tests for reconstructing evicted objects.
This PR adds a driver table for the new GCS, which enables cleanup functionality associated with monitoring driver death.
Some testing in `monitor_test.py` is restored, but redis sharding for xray is needed to enable remaining tests.
