## What do these changes do?
* Improved --no-cuda handling
* Removed deprecated Variable usage
## Related issue number
Fixes#3873
<!-- Are there any issues opened that will be resolved by merging this change? -->
Breaks the node provider node getter into cached and non-cached versions.
Fixes#3930 by updating the node label finger print before updating labels.
Fixes#3935 by refreshing node cache if node ip is not found.
- NodeUpdater gets its' IP in parallel now (no longer in __init__)
- We use persistent connections in SSH (temp folder created only for ray; ControlMaster)
- hash_runtime_conf was performing a pointless hexlify step, wasting time on large files
- We use NodeUpdaterThreads and share the NodeProvider; NodeUpdaterProcess is removed
- AWSNodeProvider caches nodes more aggressively
- NodeProvider now has a shim batch terminate_nodes() call; AWSNodeProvider parallelises it; the autoscaler uses it
- AWSNodeProvider batches EC2 update_tags calls
- Logging changes throughout to provide standardised timing information for profiling
- Pulled out a few unnecessary is_running calls (NodeUpdater will loop waiting for SSH anyway)
## Related issue number
Issue #3599
In earlier PRs, PR#3585 and PR#3637, export_policy_model and export_policy_checkpoint were introduced for users to export TensorFlow model and checkpoint.
For Ray Tune users, these APIs are not accessible through YAML configurations.
In this pull request, export_formats option is provided to enable users to choose the desired export format.
* Factor out starting Ray processes.
* Detect flags through environment variables.
* Return ProcessInfo from start_ray_process.
* Print valgrind errors at exit.
* Test valgrind in travis.
* Some valgrind fixes.
* Undo raylet monitor change.
* Only test plasma store in valgrind.
* add marvil policy graph
* fix typo
* add offline optimizer and enable running marwil
* fix loss function
* add maintaining the moving average of advantage norm
* use sync replay optimizer for unifying
* remove offline optimizer and use sync replay optimizer
* format by yapf
* add imitation learning objective
* fix according to eric's review
* format by yapf
* revise
* add test data
* marwil
* Refactor code about ray.ObjectID.
* remove from_random and use nil_id instead of constructor
* remove id() in hash
* Lint and fix
* Change driver id to ObjectID
* Replace binary_to_hex(ObjectID.id()) to ObjectID.hex()
