* wip
* Sat Dec 30 15:07:28 PST 2017
* log video
* video doesn't work well
* scenario integration
* Sat Dec 30 17:30:22 PST 2017
* Sat Dec 30 17:31:05 PST 2017
* Sat Dec 30 17:31:32 PST 2017
* Sat Dec 30 17:32:16 PST 2017
* Sat Dec 30 17:34:11 PST 2017
* Sat Dec 30 17:34:50 PST 2017
* Sat Dec 30 17:35:34 PST 2017
* Sat Dec 30 17:38:49 PST 2017
* Sat Dec 30 17:40:39 PST 2017
* Sat Dec 30 17:43:00 PST 2017
* Sat Dec 30 17:43:04 PST 2017
* Sat Dec 30 17:45:56 PST 2017
* Sat Dec 30 17:46:26 PST 2017
* Sat Dec 30 17:47:02 PST 2017
* Sat Dec 30 17:51:53 PST 2017
* Sat Dec 30 17:52:54 PST 2017
* Sat Dec 30 17:56:43 PST 2017
* Sat Dec 30 18:27:07 PST 2017
* Sat Dec 30 18:27:52 PST 2017
* fix train
* Sat Dec 30 18:41:51 PST 2017
* Sat Dec 30 18:54:11 PST 2017
* Sat Dec 30 18:56:22 PST 2017
* Sat Dec 30 19:05:04 PST 2017
* Sat Dec 30 19:05:23 PST 2017
* Sat Dec 30 19:11:53 PST 2017
* Sat Dec 30 19:14:31 PST 2017
* Sat Dec 30 19:16:20 PST 2017
* Sat Dec 30 19:18:05 PST 2017
* Sat Dec 30 19:18:45 PST 2017
* Sat Dec 30 19:22:44 PST 2017
* Sat Dec 30 19:24:41 PST 2017
* Sat Dec 30 19:26:57 PST 2017
* Sat Dec 30 19:40:37 PST 2017
* wip models
* reward bonus
* test prep
* Sun Dec 31 18:45:25 PST 2017
* Sun Dec 31 18:58:28 PST 2017
* Sun Dec 31 18:59:34 PST 2017
* Sun Dec 31 19:03:33 PST 2017
* Sun Dec 31 19:05:05 PST 2017
* Sun Dec 31 19:09:25 PST 2017
* fix train
* kill
* add tuple preprocessor
* Sun Dec 31 20:38:33 PST 2017
* Sun Dec 31 22:51:24 PST 2017
* Sun Dec 31 23:14:13 PST 2017
* Sun Dec 31 23:16:04 PST 2017
* Mon Jan 1 00:08:35 PST 2018
* Mon Jan 1 00:10:48 PST 2018
* Mon Jan 1 01:08:31 PST 2018
* Mon Jan 1 14:45:44 PST 2018
* Mon Jan 1 14:54:56 PST 2018
* Mon Jan 1 17:29:29 PST 2018
* switch to euclidean dists
* Mon Jan 1 17:39:27 PST 2018
* Mon Jan 1 17:41:47 PST 2018
* Mon Jan 1 17:44:18 PST 2018
* Mon Jan 1 17:47:09 PST 2018
* Mon Jan 1 20:31:02 PST 2018
* Mon Jan 1 20:39:33 PST 2018
* Mon Jan 1 20:40:55 PST 2018
* Mon Jan 1 20:55:06 PST 2018
* Mon Jan 1 21:05:52 PST 2018
* fix env path
* merge richards fix
* fix hash
* Mon Jan 1 22:04:00 PST 2018
* Mon Jan 1 22:25:29 PST 2018
* Mon Jan 1 22:30:42 PST 2018
* simplified reward function
* add framestack
* add env configs
* simplify speed reward
* Tue Jan 2 17:36:15 PST 2018
* Tue Jan 2 17:49:16 PST 2018
* Tue Jan 2 18:10:38 PST 2018
* add lane keeping simple mode
* Tue Jan 2 20:25:26 PST 2018
* Tue Jan 2 20:30:30 PST 2018
* Tue Jan 2 20:33:26 PST 2018
* Tue Jan 2 20:41:42 PST 2018
* ppo lane keep
* simplify discrete actions
* Tue Jan 2 21:41:05 PST 2018
* Tue Jan 2 21:49:03 PST 2018
* Tue Jan 2 22:12:23 PST 2018
* Tue Jan 2 22:14:42 PST 2018
* Tue Jan 2 22:20:59 PST 2018
* Tue Jan 2 22:23:43 PST 2018
* Tue Jan 2 22:26:27 PST 2018
* Tue Jan 2 22:27:20 PST 2018
* Tue Jan 2 22:44:00 PST 2018
* Tue Jan 2 22:57:58 PST 2018
* Tue Jan 2 23:08:51 PST 2018
* Tue Jan 2 23:11:32 PST 2018
* update dqn reward
* Thu Jan 4 12:29:40 PST 2018
* Thu Jan 4 12:30:26 PST 2018
* Update train_dqn.py
* fix
* docs
* Update README.rst
* Sat Dec 30 15:23:49 PST 2017
* comments
* Sun Dec 31 23:33:30 PST 2017
* Sun Dec 31 23:33:38 PST 2017
* Sun Dec 31 23:37:46 PST 2017
* Sun Dec 31 23:39:28 PST 2017
* Sun Dec 31 23:43:05 PST 2017
* Sun Dec 31 23:51:55 PST 2017
* Sun Dec 31 23:52:51 PST 2017
This adds (experimental) auto-scaling support for Ray clusters based on GCS load metrics. The auto-scaling algorithm is as follows:
Based on current (instantaneous) load information, we compute the approximate number of "used workers". This is based on the bottleneck resource, e.g. if 8/8 GPUs are used in a 8-node cluster but all the CPUs are idle, the number of used nodes is still counted as 8. This number can also be fractional.
We scale that number by 1 / target_utilization_fraction and round up to determine the target cluster size (subject to the max_workers constraint). The autoscaler control loop takes care of launching new nodes until the target cluster size is met.
When a node is idle for more than idle_timeout_minutes, we remove it from the cluster if that would not drop the cluster size below min_workers.
Note that we'll need to update the wheel in the example yaml file after this PR is merged.
* revamp saving
* smaller jpgs
* hide verbose
* Tue Dec 19 22:25:01 PST 2017
* make sure temp dirs sort lexiographically
* save total reward too
* zero pad i
* 160x160 dqn
* ever higher res dqn
* Adding dataframe object and minor APIs
* Adding reduce functionality
* Adding some print and making reduce work on current Ray
* Cleanup
* Added new functionality and docs.
* Adding more functionality.
* New functionality with older cleanup
* Complying with flake8 formatting
* Added tests and addressed reviewer comments
* Complying with flake8.
* Adding pandas to travis and requirements doc
* Fixing flake8 failures
* Fixing flake8 errors from imports
* Fixing import error
* Fixing import errors
* Addressing reviewer comments
* Addressing lint error
* Define execution dependencies flatbuffer and add to Redis commands
* Convert TaskSpec to TaskExecutionSpec
* Add execution dependencies to Python bindings
* Submitting actor tasks uses execution dependency API instead of dummy argument
* Fix dependency getters and some cleanup for fetching missing dependencies
* C++ convention
* Make TaskExecutionSpec a C++ class
* Convert local scheduler to use TaskExecutionSpec class
* Convert some pointers to references
* Finish conversion to TaskExecutionSpec class
* fix
* Fix
* Fix memory errors?
* Cast flatbuffers GetSize to size_t
* Fixes
* add more retries in global scheduler unit test
* fix linting and cast fbb.GetSize to size_t
* Style and doc
* Fix linting and simplify from_flatbuf.
* Raise exception if pyarrow is imported before ray.
* Pip install pyarrow when building doc so we don't have to mock it.
* Raise ImportError instead of Exception.
* trying to fix jenkins tests
* comment out more tests
* remove pytorch stuff
* use non-monotonic clock (monotonic not supported on python 2.7)
* whitespace
This introduces rllib.Evaluator and rllib.Optimizer classes. Optimizers encapsulate a particular distributed optimization strategy for RL. Evaluators encapsulate the model graph, and once implemented, any Optimizer may be "plugged in" to any algorithm that implements the Evaluator interface.
* Enable scheduling with custom resource labels.
* Fix.
* Minor fixes and ref counting fix.
* Linting
* Use .data() instead of .c_str().
* Fix linting.
* Fix ResourcesTest.testGPUIDs test by waiting for workers to start up.
* Sleep in test so that all tasks are submitted before any completes.
