* 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.
* 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
* add tf metrics
* comments
* fix network scopes
* add doc
* use format string
* fix trace level
* plot intermediate and final sgd stats
* add back a global step
* Perform ray.register_class under the hood.
* Fix bug.
* Release worker lock when waiting for imports to arrive in get.
* Remove calls to register_class from examples and tests.
* Clear serialization state between tests.
* Fix bug and add test for multiple custom classes with same name.
* Fix failure test.
* Fix linting and cleanups to python code.
* Fixes to documentation.
* Implement recursion depth for recursively registering classes.
* Fix linting.
* Push warning to user if waiting for class for too long.
* Fix typos.
* Don't export FunctionToRun if pickling the function fails.
* Don't broadcast class definition when pickling class.
* Test examples for pep8 compliance.
* Make rl_pong example pep8 compliant.
* Make policy gradient example pep8 compliant.
* Make lbfgs example pep8 compliant.
* Make hyperopt example pep8 compliant.
* Make a3c example pep8 compliant.
* Make evolution strategies example pep8 compliant.
* Make resnet example pep8 compliant.
* Fix.
* Direction substitution of @ray.remote -> @ray.task.
* Changes to make '@ray.task' work.
* Instantiate actors with Class.remote() instead of Class().
* Convert actor instantiation in tests and examples from Class() to Class.remote().
* Change actor method invocation from object.method() to object.method.remote().
* Update tests and examples to invoke actor methods with .remote().
* Fix bugs in jenkins tests.
* Fix example applications.
* Change @ray.task back to @ray.remote.
* Changes to make @ray.actor -> @ray.remote work.
* Direct substitution of @ray.actor -> @ray.remote.
* Fixes.
* Raise exception if @ray.actor decorator is used.
* Simplify ActorMethod class.
* pong on pixels working (not cleaned up)
* make training compatible with all atari games
* cartpole runs
* Update documentation and usage for policy gradients.
Philipp Moritz