* Add script for running stress tests.
* Add an actor tree test where actors die with some probability
* Improve test.
* Small fix
* Update tests.
* Minor change
## What do these changes do?
This passes in the right obs space to the lstm model wrapper, so that it doesn't attempt to un-flatten the already processed dict observation.
## Related issue number
Closes https://github.com/ray-project/ray/issues/3367
This PR introduces single-node fault tolerance for Tune.
## Previous behavior:
- Actors will be restarted without checking if resources are available. This can lead to problems if we lose resources.
## New behavior:
- RUNNING trials will be resumed on another node on a best effort basis (meaning they will run if resources available).
- If the cluster is saturated, RUNNING trials on that failed node will become PENDING and queued.
- During recovery, TrialSchedulers and SearchAlgorithms should receive notification of this (via `trial_runner.stop_trial`) so that they don’t wait/block for a trial that isn’t running.
Remaining questions:
- Should `last_result` be consistent during restore?
Yes; but not for earlier trials (trials that are yet to be checkpointed).
- Waiting for some PRs to merge first (#3239)
Closes#2851.
* Broadcast actor death, clean up dummy objects
* Reduce logging and clean up state when failing a task
* lint
* Make actor failure test nicer, reduce node timeout
* Suppress duplicate pre-emptive object pushes.
* Add test.
* Fix linting
* Remove timer and inline recent_pushes_ into local_objects_.
* Improve test.
* Fix
* Fix linting
* Enable retrying pull from same object manager. Randomize object manager.
* Speed up test
* Linting
* Add test.
* Minor
* Lengthen pull timeout and reissue pull every time a new object becomes available.
* Increase pull timeout in test.
* Wait for nodes to start in object manager test.
* Wait longer for nodes to start up in test.
* Small fixes.
* _submit -> _remote
* Change assert to warning.
* Make scheduling queues RemoveTasks return task states as well.
* Add test
* Don't unsubscribe for infeasible tasks when spilling over.
* Linting
* Address comments.
IMPALA support for multiagent was broken since IMPALA has a requirement that batch sizes be of a certain length. However multi-agent envs can create variable-length batches.
Fix this by adding zero-padding as needed (similar to the RNN case).
* example
* add env
* test pg
* change to test
* add atexit test
* Update rllib-env.rst
* comment
* revert unnecessary file
* fix title when actor is idle
* Update python/ray/actor.py
Co-Authored-By: ericl <ekhliang@gmail.com>
## What do these changes do?
This PR exposes the CL option for using a config parameter. This is important for certain tests (i.e., FT tests that removing nodes) to run quickly.
Note that this is bad practice and should be replaced with GFLAGS or some equivalent as soon as possible.
#3239 depends on this.
TODO:
- [x] Add documentation to method arguments before merging.
- [x] Add test to verify this works?
## Related issue number
* Increase timeout to 10s
* Skip eviction reconstruction tests
* Add stress test for many actors to one
* Fix test by shortening it.
* lower number of processes in stress test
* Skip slow test
This includes most of the TF code used for the OSDI experiment. Perf sanity check on p3.16xl instances: Overall scaling looks ok, with the multi-node results within 5% of OSDI final numbers. This seems reasonable given that hugepages are not enabled here, and the param server shards are placed randomly.
$ RAY_USE_XRAY=1 ./test_sgd.py --gpu --batch-size=64 --num-workers=N \
--devices-per-worker=M --strategy=<simple|ps> \
--warmup --object-store-memory=10000000000
Images per second total
gpus total | simple | ps
========================================
1 | 218
2 (1 worker) | 388
4 (1 worker) | 759
4 (2 workers) | 176 | 623
8 (1 worker) | 985
8 (2 workers) | 349 | 1031
16 (2 nodes, 2 workers) | 600 | 1661
16 (2 nodes, 4 workers) | 468 | 1712 <--- OSDI perf was 1817
This tests the case in which a worker is blocked in a call to ray.get or ray.wait, and then the worker dies. Then later, the object that the worker was waiting for becomes available. We need to make sure not to try to send a message to the dead worker and then die. Related to #2790.
