Adds a Population-Based Training (as described in https://arxiv.org/abs/1711.09846) scheduler to Ray.tune. Currently mutates hyperparameters according to either a user-defined list of possible values to mutate to (necessary if hyperparameters can only be certain values ex. sgd_batch_size), or by a factor of 0.8 or 1.2.
* Bring cloudpickle version 0.5.2 inside the repo.
* Use internal copy of cloudpickle everywhere.
* Fix linting.
* Import ordering.
* Change __init__.py.
* Set pickler in serialization context.
* Don't check ray location.
Remove rllib dep: trainable is now a standalone abstract class that can be easily subclassed.
Clean up hyperband: fix debug string and add an example.
Remove YAML api / ScriptRunner: this was never really used.
Move ray.init() out of run_experiments(): This provides greater flexibility and should be less confusing since there isn't an implicit init() done there. Note that this is a breaking API change for tune.
* Add failing unit test for nondeterministic reconstruction
* Retry scheduling actor tasks if reassigned to local scheduler
* Update execution edges asynchronously upon dispatch for nondeterministic reconstruction
* Fix bug for updating checkpoint task execution dependencies
* Update comments for deterministic reconstruction
* cleanup
* Add (and skip) failing test case for nondeterministic reconstruction
* Suppress test output
* working multi action distribution and multiagent model
* currently working but the splits arent done in the right place
* added shared models
* added categorical support and mountain car example
* now compatible with generalized advantage estimation
* working multiagent code with discrete and continuous example
* moved reshaper to utils
* code review changes made, ppo action placeholder moved to model catalog, all multiagent code moved out of fcnet
* added examples in
* added PEP8 compliance
* examples are mostly pep8 compliant
* removed all flake errors
* added examples to jenkins tests
* fixed custom options bug
* added lines to let docker file find multiagent tests
* shortened example run length
* corrected nits
* fixed flake errors
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.
* 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
* 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.
* Give error if a worker has a version mismatch for Python Ray, or cloudpickle.
* Check version when attaching driver to cluster.
* Only do check if the version info is present.
* Bug fix.
* Fix typo.
* wip
* with test
* add timeout
* also add test for f
* remove on cleanup
* update
* wip
* fix tests
* mark actor removed in redis
* clang-format
* fix bug when no-inprogress tasks
* try to set task status done
* Add comment.
* Add basic functionality for Cython functions and actors
* Fix up per @pcmoritz comments
* Fixes per @richardliaw comments
* Fixes per @robertnishihara comments
* Forgot double quotes when updating masked_log
* Remove import typing for Python 2 compatibility
