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robertnishihara
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This already happens in the cluster case, so it only affects behavior in the local case.

I can arguments for not redirecting the worker output.

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retest this please

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https://travis-ci.org/ray-project/ray/jobs/355733563 -- no module names signals, I wonder what changed here.

@pcmoritz pcmoritz merged commit 4bccabd into ray-project:master Mar 21, 2018
@pcmoritz pcmoritz deleted the redirectoutput branch March 21, 2018 01:14
royf added a commit to royf/ray that referenced this pull request Apr 22, 2018
* commit 'f69cbd35d4e86f2a3c2ace875aaf8166edb69f5d': (64 commits)
  Bump version to 0.4.0. (ray-project#1745)
  Fix monitor.py bottleneck by removing excess Redis queries. (ray-project#1786)
  Convert the ObjectTable implementation to a Log (ray-project#1779)
  Acquire worker lock when importing actor. (ray-project#1783)
  Introduce a log interface for the new GCS (ray-project#1771)
  [tune] Fix linting error (ray-project#1777)
  [tune] Added pbt with keras on cifar10 dataset example (ray-project#1729)
  Add a GCS table for the xray task flatbuffer (ray-project#1775)
  [tune] Change tune resource request syntax to be less confusing (ray-project#1764)
  Remove from X import Y convention in RLlib ES. (ray-project#1774)
  Check if the provider is external before getting the config. (ray-project#1743)
  Request and cancel notifications in the new GCS API (ray-project#1758)
  Fix resource bookkeeping for blocked actor methods. (ray-project#1766)
  Fix bug when connecting another driver in local case. (ray-project#1760)
  Define string prefixes for all tables in the new GCS API (ray-project#1755)
  [rllib] Update RLlib to work with new actor scheduling behavior (ray-project#1754)
  Redirect output of all processes by default. (ray-project#1752)
  Add API for getting total cluster resources. (ray-project#1736)
  Always send actor creation tasks to the global scheduler. (ray-project#1757)
  Print error when actor takes too long to start, and refactor error me… (ray-project#1747)
  ...

# Conflicts:
#	python/ray/rllib/__init__.py
#	python/ray/rllib/dqn/dqn.py
#	python/ray/rllib/dqn/dqn_evaluator.py
#	python/ray/rllib/dqn/dqn_replay_evaluator.py
#	python/ray/rllib/optimizers/__init__.py
#	python/ray/rllib/tuned_examples/pong-dqn.yaml
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3 participants