* Ray doc - changed python indentation to 4 spaces in documentation files actors.rst, api.rst, and example-*.rst
* Ray documentation - changed Python to 4 space indentation for files install-*.rst, installation-troubleshooting.rst, internals-overview.rst, serialization.rst, troubleshootin.rst, tutorial.rst, using-ray-*.rst
* Test example applications in Jenkins.
* Fix default upload_dir argument for Algorithm class.
* Fix evolution strategies.
* Comment out policy gradient example which doesn't seem to work.
* Set --env-name for evolution strategies.
* 4 space indentation for actor.py.
* 4 space indentation for worker.py.
* 4 space indentation for more files.
* 4 space indentation for some test files.
* Check indentation in Travis.
* 4 space indentation for some rl files.
* Fix failure test.
* Fix multi_node_test.
* 4 space indentation for more files.
* 4 space indentation for remaining files.
* Fixes.
* write config to s3
* add train file
* write performance to S3
* writing needs to be fixed, replacing result.json at the moment
* update
* add experiment_id
* more logging and example queries
* update
* add info
* fill in other algorithms
* fix linting
* convert readme to rst
* fixes
* simplejson -> json
* make files executable
* edit README.rst
* unify storing logs in S3 and on local filesystem
* use 'info' entry in TrainingResult for algorithm specific info
* don't install smart_open with ray
* fixes
* linting fixes
* Don't reconstruct all objects in every fetch request in local scheduler.
* Separate out fetch timer and reconstruction timer.
* Fix bug.
* Bug fix.
* Fix naming convention for global variables.
* Address comments.
* Make reconstruct_counter a static variable.
* Fix linting.
* Redo reconstruct handler using a set of objects to fetch.
* Fix linting.
* Replace set with vector.
* Updated task_profiles function to avoid future repetitive parsing.
* Fix indentation.
* Fixed according to comments.
* Included updated test for task_profiles function.
* Simplify test.
* Fix indentation.
* Fix.
* Dispatch regular and actor tasks when resources become available.
* Make actor methods do resource bookkeeping and add test.
* Remove unnecessary field.
* Fix linting.
* Fix actor test.
* Maintain set of actors with pending tasks to speed up task dispatch.
* Exit early from task dispatch if there are no resources available.
* Fix linting.
* Fix error.
* Fix bug related to iterator invalidation.
* When an actor is removed, remove it from the set of actors with pending tasks.
* Start up webui on ray.init
* Removed .ipynb checkpoint folders.
* Removed print statements in cleanup function.
* Fixed
* Removed extra file.
* Cleaned up ui.
* Don't start browser automatically in ray.init(), also copy the notebook every time so that changes don't persist.
* Update setup.py and installation instructions to install jupyter.
* Don't automatically install jupyter, don't start the UI if jupyter is not installed.
* Improve error message when failing to start UI.
* Replace a local scheduler ut_array with a std::vector.
* Replace vector of sizes in local scheduler with std::pair.
* Remove utarray include.
* Replace utarray with std::vector for reading local scheduler input messages.
* Remove more UT data structures.
* Remove UT includes.
* Fix linting.
* Include stdlib.h to find size_t.
* Remove includes of stdbool.h.
* Replace std::pair with TaskQueueEntry.
* Fix redis tests.
* Reinstate tests.
* configuration option for tensorflow debugger
* add model checkpointing
* fix linting
* make it possible to run without checkpointing
* fix
* loading from checkpoint and expose debugger through cli
* todo for filters
* Fix typo.
* Divide large get requests into smaller chunks.
* Divide fetches into smaller chunks.
* Wait longer in worker and manager before reissuing fetch requests if there are many outstanding fetch requests.
* Log warning if a handler in the local scheduler or plasma manager takes more than one second.
* added log_table function and a test
* fixed log_files and added task_profiles
* fixed formatting
* fixed linting errors
* fixes
* removed file
* more fixes
* hopefully fixed
* Small changes.
* Fix linting.
* Fix bug in log monitor.
* Small changes.
* Fix bug in travis.
* Including data_size and hash in the ResultTableReply.
* Included data_size and hash info in object_table.
* Fixed bugs in ray_redis_module.cc.
* Removing commented out code.
* Fixes
* Freed hash and data_size strings after using, and checked if they're null along with task_id and is_put.
* Changed it so that data_size is set correctly.
* Removed iostream import.
* Included a check to ensure that the Redis string to long long conversion was successful.
* Included separate data_size and hash null checks.
* Fixed bug.
* Made linting changes.
* Another linting error.
* Slight simplication.
* Log fatal error if plasma manager or local scheduler take too long to send heartbeat.
* Fix linting.
* Use int64_t for milliseconds since unix epoch.
* implement restarting workers after certain number of task executions
* Clean up python code.
* Don't start new worker when an actor disconnects.
* Move wait_for_pid_to_exit to test_utils.py.
* Add test.
* Fix linting errors.
* Fix linting.
* Fix typo.
* add tf metrics
* comments
* fix network scopes
* add doc
* initial work
* try with 3 virtual cpus
* clean up metrics
* use format string
* fix trace level
* back to pong
* always run summary on cpu
* plot intermediate and final sgd stats
* add back a global step
* update
* add timeline
* use staging area and reuse weights properly
* stage at cpu
* whoops, stage only the batch
* clean up a bit
* fix py flake
* wip
* create an optimizer graph per device
* print timeline on 5th batch instead
* print examples per second
* log placement for training ops
* force placement on cpu:0
* try separating weights onto different gpus
* try using nccl
* add cpu fallback
* remove space from date
* check has gpu device
* fix flag config
* checkpoint
* wip
* update
* add some timing
* trace loading
* try cpu
* revert that
* remove expensive test
* lint
* cleanups
* clean up timers
* clean it up a bit
* fix code for non-scalar action spaces
* address some nits
* fix quotes
* efficient shuffling between sgd epochs
* local scheduler
* redirect output files to be associated with workers rather than the local scheduler
* fixed formatting
* fixes
* Moved output redirection logic to worker.py.
* Changed write mode.
* Fixed formatting.
* Added comment.
* Reuse log file creation in services.py.
* Fix linting.
* Fix problem in which multiple processes attempt to create /tmp/raylogs at the same time.
* Added to troubleshooting documentation about whether redefining remote functions runs the new code version
* Minor correction to troubleshooting documentation
* Writing new documentation page for using Ray with GPUs
* Wrote new documentation page on using ray with gpus
* Add some more details.
* Added to troubleshooting documentation about whether redefining remote functions runs the new code version
* Minor correction to troubleshooting documentation
* Small rewordings.
