Fix CQL getting stuck when deprecated timesteps_per_iteration is used (use min_train_timesteps_per_reporting instead).
CQL does not perform sampling timesteps and the deprecated timesteps_per_iteration is automatically translated into the new min_sample_timesteps_per_reporting, but should be translated (only for CQL and other purely offline RL algos) into min_train_timesteps_per_reporting.
If timesteps_per_iteration, CQL lever leaves the first iteration as it thinks it's not done yet (sample timesteps always remain at 0).
Clean up the ci/ directory. This means getting rid of the travis/ path completely and moving the files into sensible subdirectories.
Details:
- Moves everything under ci/travis into subdirectories, e.g. ci/build, ci/lint, etc.
- Minor adjustments to some scripts (variable renames)
- Removes the outdated (unused) asan tests
This fixes the previous problems from team column revert.
This has 2 additional changes;
alert handler receives the team argument, which was the root cause of breakage; https://github.com/ray-project/ray/pull/21289
Previously, tests without a team column were raising an exception, but I made the condition weaker (warning logs). I will eventually change it to raise an exception, but for smoother transition, we will log warning instead for a short time
Please review **e2e.py and test_suite belonging to your team**!
This is the first part of https://docs.google.com/document/d/16IrwerYi2oJugnRf5hvzukgpJ6FAVEpB6stH_CiNMjY/edit#
This PR adds a team name to each test suite.
If the name is not specified, it will be reported as unspecified.
If you are running a local test, and if the new test suite doesn't have a team name specified, it will raise an exception (in this way, we can avoid missing team names in the future).
Note that we will aggregate all of test config into a single file, nightly_test.yaml.
* use nightly
* switch ml cpu to ray cpu
* fix
* add pytest
* add more pytest
* add constraint
* add tensorflow
* fix merge conflict
* add tblib
* fix
* add back uninstall
* Create a core set of algorithms tests to run nightly.
* Run release tests under tf, tf2, and torch frameworks.
* Fix
* Add eager_tracing option for tf2 framework.
* make sure core tests can run in parallel.
* cql
* Report progress while running nightly/weekly tests.
* Innclude SAC in nightly lineup.
* Revert changes to learning_tests
* rebrand to performance test.
* update build_pipeline.py with new performance_tests name.
* Record stats.
* bug fix, need to populate experiments dict.
* Alphabetize yaml files.
* Allow specifying frameworks. And do not run tf2 by default.
* remove some debugging code.
* fix
* Undo testing changes.
* Do not run CQL regression for now.
* LINT.
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* Add an RLlib Tune experiment to UserTest suite.
* Add ray.init()
* Move example script to example/tune/, so it can be imported as module.
* add __init__.py so our new module will get included in python wheel.
* Add block device to RLlib test instances.
* Reduce disk size a little bit.
* Add metrics reporting
* Allow max of 5 workers to accomodate all the worker tasks.
* revert disk size change.
* Minor updates
* Trigger build
* set max num workers
* Add a compute cfg for autoscaled cpu and gpu nodes.
* use 1gpu instance.
* install tblib for debugging worker crashes.
* Manually upgrade to pytorch 1.9.0
* -y
* torch=1.9.0
* install torch on driver
* Add an RLlib Tune experiment to UserTest suite.
* Add ray.init()
* Move example script to example/tune/, so it can be imported as module.
* add __init__.py so our new module will get included in python wheel.
* Add block device to RLlib test instances.
* Reduce disk size a little bit.
* Add metrics reporting
* Allow max of 5 workers to accomodate all the worker tasks.
* revert disk size change.
* Minor updates
* Trigger build
* set max num workers
* Add a compute cfg for autoscaled cpu and gpu nodes.
* use 1gpu instance.
* install tblib for debugging worker crashes.
* Manually upgrade to pytorch 1.9.0
* -y
* torch=1.9.0
* install torch on driver
* bump timeout
* Write a more informational result dict.
* Revert changes to compute config files that are not used.
* add smoke test
* update
* reduce timeout
* Reduce the # of env per worker to 1.
* Small fix for getting trial_states
* Trigger build
* simply result dict
* lint
* more lint
* fix smoke test
Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
