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Flush lineage cache on task submission instead of execution #4942

Merged
merged 5 commits into from
Jun 7, 2019
Merged

Flush lineage cache on task submission instead of execution #4942

merged 5 commits into from
Jun 7, 2019

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stephanie-wang
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@stephanie-wang stephanie-wang commented Jun 6, 2019 •
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What do these changes do?

Tasks are now written to the GCS as soon as they are submitted to the local raylet, whereas before they were written after being assigned to a worker. This means that tasks are now evicted from the lineage cache as soon as possible, which should reduce the number of tasks that are in the lineage cache and therefore the size of the uncommitted lineage that gets piggy-backed on forwarded tasks. Previously, this was causing high task latency (#4359). Also, the sustainable throughput should now be about the same as if we were using the GCS alone.

This involves several changes:

  • Refactor the lineage cache so that each task can only be in either UNCOMMITTED or COMMITTING state. Later, I would like to add a COMMITTED state to indicate that a task has been acknowledged by the GCS but can't be evicted yet.
  • During task submission, forwarded tasks are added to the lineage cache as UNCOMMITTED and tasks submitted locally are committed immediately.
  • Actor execution dependencies are not updated based on execution order. This means that if an actor with multiple handles dies, we will not be able to exactly replay the tasks before the failure. In the future, we can re-enable this by writing tasks to the GCS a second time (upon execution).

Related issue number

First PR towards #4359. I will open another PR later that handles the scenario where the raylet where the task was originally submitted fails before it can complete the GCS write.

Linter

  • I've run scripts/format.sh to lint the changes in this PR.

@stephanie-wang
Refactor lineage cache:
7caadb2 
- only use states UNCOMMITTED AND COMMITTING
- flush tasks to the GCS as soon as they are submitted
@stephanie-wang
Update unit tests
c58642e
@stephanie-wang stephanie-wang requested review from robertnishihara, AmplabJenkins and pcmoritz and removed request for AmplabJenkins June 6, 2019 20:40
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pcmoritz commented Jun 6, 2019

Can you also briefly add to the description why these changes are needed? It's in preparation for getting rid of the task leases, right?

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Can you also briefly add to the description why these changes are needed? It's in preparation for getting rid of the task leases, right?

Ah no, it's actually just a separate robustness issue for maintaining consistent task latency (I updated the PR description).

robertnishihara
robertnishihara reviewed Jun 6, 2019
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This looks awesome!

src/ray/raylet/lineage_cache.cc
subscribe_tasks.insert(task_id);
// TODO(swang): Do not subscribe to tasks that are in the uncommitted
// lineage but were already COMMITTED at the sender.
RAY_CHECK(SubscribeTask(task_id));
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So the plan is for this to go away and to instead flush all lineage caches in the event of a failure, right?

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No, I think that would be in addition to this PR, not instead of...unless I'm misunderstanding what you mean by "this"?

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Oh, I was thinking the subscription would go away, but I agree now that it should stay.

src/ray/raylet/lineage_cache.cc Outdated Show resolved Hide resolved
src/ray/raylet/lineage_cache.h
UNCOMMITTED_READY,
/// The task has been written to the GCS and we are waiting for an
/// acknowledgement of the commit.
/// The task is uncommitted. Unless there is a failure, we will expect a
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It's probably worth documenting the fact that we're making inequality comparisons between these states. Someone inserting a state probably won't think about the ordering.

Or we can just not use the comparisons and be more explicit.

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Thanks, I'll update the documentation.

I actually like having the comparisons in this case because it forces us to think about what state transitions are okay.

src/ray/raylet/lineage_cache.h Outdated
COMMITTING,
// TODO(swang): Add a COMMITTED state for the following scenario: We
// received a commit acknowledgement for this task, but a task in its
// lineage has not yet been committed.
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Why don't we need this state in this PR?

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It's just a performance optimization to avoid subscribing to tasks that we already know have been committed. I'll update the TODO to reflect/clarify this.

src/ray/raylet/node_manager.cc Outdated Show resolved Hide resolved
stephanie-wang and others added 3 commits June 6, 2019 15:40
@stephanie-wang @robertnishihara
Update src/ray/raylet/lineage_cache.cc
e18d850 
Co-Authored-By: Robert Nishihara <robertnishihara@gmail.com>
@stephanie-wang @robertnishihara
Update src/ray/raylet/node_manager.cc
3a556e4 
Co-Authored-By: Robert Nishihara <robertnishihara@gmail.com>
@stephanie-wang
doc
311f568
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Test FAILed.
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@robertnishihara robertnishihara merged commit 873d45b into ray-project:master Jun 7, 2019
@robertnishihara robertnishihara deleted the flush-lineage-cache branch June 7, 2019 18:35
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@stephanie-wang stephanie-wang mentioned this pull request Jun 10, 2019
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stefanpantic added a commit to wingman-ai/ray that referenced this pull request Jun 21, 2019
@stefanpantic
Merge ray upstream into master (#35)
b850e14 
* [rllib] Remove dependency on TensorFlow (ray-project#4764)

* remove hard tf dep

* add test

* comment fix

* fix test

* Dynamic Custom Resources - create and delete resources (ray-project#3742)

* Update tutorial link in doc (ray-project#4777)

* [rllib] Implement learn_on_batch() in torch policy graph

* Fix `ray stop` by killing raylet before plasma (ray-project#4778)

* Fatal check if object store dies (ray-project#4763)

* [rllib] fix clip by value issue as TF upgraded (ray-project#4697)

*  fix clip_by_value issue

*  fix typo

* [autoscaler] Fix submit (ray-project#4782)

* Queue tasks in the raylet in between async callbacks (ray-project#4766)

* Add a SWAP TaskQueue so that we can keep track of tasks that are temporarily dequeued

* Fix bug where tasks that fail to be forwarded don't appear to be local by adding them to SWAP queue

* cleanups

* updates

* updates

* [Java][Bazel]  Refine auto-generated pom files (ray-project#4780)

* Bump version to 0.7.0 (ray-project#4791)

* [JAVA] setDefaultUncaughtExceptionHandler to log uncaught exception in user thread. (ray-project#4798)

* Add WorkerUncaughtExceptionHandler

* Fix

* revert bazel and pom

* [tune] Fix CLI test (ray-project#4801)

* Fix pom file generation (ray-project#4800)

* [rllib] Support continuous action distributions in IMPALA/APPO (ray-project#4771)

* [rllib] TensorFlow 2 compatibility (ray-project#4802)

* Change tagline in documentation and README. (ray-project#4807)

* Update README.rst, index.rst, tutorial.rst and  _config.yml

* [tune] Support non-arg submit (ray-project#4803)

* [autoscaler] rsync cluster (ray-project#4785)

* [tune] Remove extra parsing functionality (ray-project#4804)

* Fix Java worker log dir (ray-project#4781)

* [tune] Initial track integration (ray-project#4362)

Introduces a minimally invasive utility for logging experiment results. A broad requirement for this tool is that it should integrate seamlessly with Tune execution.

* [rllib] [RFC] Dynamic definition of loss functions and modularization support (ray-project#4795)

* dynamic graph

* wip

* clean up

* fix

* document trainer

* wip

* initialize the graph using a fake batch

* clean up dynamic init

* wip

* spelling

* use builder for ppo pol graph

* add ppo graph

* fix naming

* order

* docs

* set class name correctly

* add torch builder

* add custom model support in builder

* cleanup

* remove underscores

* fix py2 compat

* Update dynamic_tf_policy_graph.py

* Update tracking_dict.py

* wip

* rename

* debug level

* rename policy_graph -> policy in new classes

* fix test

* rename ppo tf policy

* port appo too

* forgot grads

* default policy optimizer

* make default config optional

* add config to optimizer

* use lr by default in optimizer

* update

* comments

* remove optimizer

* fix tuple actions support in dynamic tf graph

* [rllib] Rename PolicyGraph => Policy, move from evaluation/ to policy/ (ray-project#4819)

This implements some of the renames proposed in ray-project#4813
We leave behind backwards-compatibility aliases for *PolicyGraph and SampleBatch.

* [Java] Dynamic resource API in Java (ray-project#4824)

* Add default values for Wgym flags

* Fix import

* Fix issue when starting `raylet_monitor` (ray-project#4829)

* Refactor ID Serial 1: Separate ObjectID and TaskID from UniqueID (ray-project#4776)

* Enable BaseId.

* Change TaskID and make python test pass

* Remove unnecessary functions and fix test failure and change TaskID to
16 bytes.

* Java code change draft

* Refine

* Lint

* Update java/api/src/main/java/org/ray/api/id/TaskId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/api/src/main/java/org/ray/api/id/BaseId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/api/src/main/java/org/ray/api/id/BaseId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/api/src/main/java/org/ray/api/id/ObjectId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Address comment

* Lint

* Fix SINGLE_PROCESS

* Fix comments

* Refine code

* Refine test

* Resolve conflict

* Fix bug in which actor classes are not exported multiple times. (ray-project#4838)

* Bump Ray master version to 0.8.0.dev0 (ray-project#4845)

* Add section to bump version of master branch and cleanup release docs (ray-project#4846)

* Fix import

* Export remote functions when first used and also fix bug in which rem… (ray-project#4844)

* Export remote functions when first used and also fix bug in which remote functions and actor classes are not exported from workers during subsequent ray sessions.

* Documentation update

* Fix tests.

* Fix grammar

* Update wheel versions in documentation to 0.8.0.dev0 and 0.7.0. (ray-project#4847)

* [tune] Later expansion of local_dir (ray-project#4806)

* [rllib] [RFC] Deprecate Python 2 / RLlib (ray-project#4832)

* Fix a typo in kubernetes yaml (ray-project#4872)

* Move global state API out of global_state object. (ray-project#4857)

* Install bazel in autoscaler development configs. (ray-project#4874)

* [tune] Fix up Ax Search and Examples (ray-project#4851)

* update Ax for cleaner API

* docs update

* [rllib] Update concepts docs and add "Building Policies in Torch/TensorFlow" section (ray-project#4821)

* wip

* fix index

* fix bugs

* todo

* add imports

* note on get ph

* note on get ph

* rename to building custom algs

* add rnn state info

* [rllib] Fix error getting kl when simple_optimizer: True in multi-agent PPO

* Replace ReturnIds with NumReturns in TaskInfo to reduce the size (ray-project#4854)

* Refine TaskInfo

* Fix

* Add a test to print task info size

* Lint

* Refine

* Update deps commits of opencensus to support building with bzl 0.25.x (ray-project#4862)

* Update deps to support bzl 2.5.x

* Fix

* Upgrade arrow to latest master (ray-project#4858)

* [tune] Auto-init Ray + default SearchAlg (ray-project#4815)

* Bump version from 0.8.0.dev0 to 0.7.1. (ray-project#4890)

* [rllib] Allow access to batches prior to postprocessing (ray-project#4871)

* [rllib] Fix Multidiscrete support (ray-project#4869)

* Refactor redis callback handling (ray-project#4841)

* Add CallbackReply

* Fix

* fix linting by format.sh

* Fix linting

* Address comments.

* Fix

* Initial high-level code structure of CoreWorker. (ray-project#4875)

* Drop duplicated string format (ray-project#4897)

This string format is unnecessary. java_worker_options has been appended to the commandline later.

* Refactor ID Serial 2: change all ID functions to `CamelCase` (ray-project#4896)

* Hotfix for change of from_random to FromRandom (ray-project#4909)

* [rllib] Fix documentation on custom policies (ray-project#4910)

* wip

* add docs

* lint

* todo sections

* fix doc

* [rllib] Allow Torch policies access to full action input dict in extra_action_out_fn (ray-project#4894)

* fix torch extra out

* preserve setitem

* fix docs

* [tune] Pretty print params json in logger.py (ray-project#4903)

* [sgd] Distributed Training via PyTorch (ray-project#4797)

Implements distributed SGD using distributed PyTorch.

* [rllib] Rough port of DQN to build_tf_policy() pattern (ray-project#4823)

* fetching objects in parallel in _get_arguments_for_execution (ray-project#4775)

* [tune] Disallow setting resources_per_trial when it is already configured (ray-project#4880)

* disallow it

* import fix

* fix example

* fix test

* fix tests

* Update mock.py

* fix

* make less convoluted

* fix tests

* [rllib] Rename PolicyEvaluator => RolloutWorker (ray-project#4820)

* Fix local cluster yaml (ray-project#4918)

* [tune] Directional metrics for components (ray-project#4120) (ray-project#4915)

* [Core Worker] implement ObjectInterface and add test framework (ray-project#4899)

* [tune] Make PBT Quantile fraction configurable (ray-project#4912)

* Better organize ray_common module (ray-project#4898)

* Fix error

* [tune] Add requirements-dev.txt and update docs for contributing (ray-project#4925)

* Add requirements-dev.txt and update docs.

* Update doc/source/tune-contrib.rst

Co-Authored-By: Richard Liaw <rliaw@berkeley.edu>

* Unpin everything except for yapf.

* Fix compute actions return value

* Bump version from 0.7.1 to 0.8.0.dev1. (ray-project#4937)

* Update version number in documentation after release 0.7.0 -> 0.7.1 and 0.8.0.dev0 -> 0.8.0.dev1. (ray-project#4941)

* [doc] Update developer docs with bazel instructions (ray-project#4944)

* [C++] Add hash table to Redis-Module (ray-project#4911)

* Flush lineage cache on task submission instead of execution (ray-project#4942)

* [rllib] Add docs on how to use TF eager execution (ray-project#4927)

* [rllib] Port remainder of algorithms to build_trainer() pattern (ray-project#4920)

* Fix resource bookkeeping bug with acquiring unknown resource. (ray-project#4945)

* Update aws keys for uploading wheels to s3. (ray-project#4948)

* Upload wheels on Travis to branchname/commit_id. (ray-project#4949)

* [Java] Fix serializing issues of `RaySerializer` (ray-project#4887)

* Fix

* Address comment.

* fix (ray-project#4950)

* [Java] Add inner class `Builder` to build call options. (ray-project#4956)

* Add Builder class

* format

* Refactor by IDE

* Remove uncessary dependency

* Make release stress tests work and improve them. (ray-project#4955)

* Use proper session directory for debug_string.txt (ray-project#4960)

* [core] Use int64_t instead of int to keep track of fractional resources (ray-project#4959)

* [core worker] add task submission & execution interface (ray-project#4922)

* [sgd] Add non-distributed PyTorch runner (ray-project#4933)

* Add non-distributed PyTorch runner

* use dist.is_available() instead of checking OS

* Nicer exception

* Fix bug in choosing port

* Refactor some code

* Address comments

* Address comments

* Flush all tasks from local lineage cache after a node failure (ray-project#4964)

* Remove typing from setup.py install_requirements. (ray-project#4971)

* [Java] Fix bug of `BaseID` in multi-threading case. (ray-project#4974)

* [rllib] Fix DDPG example (ray-project#4973)

* Upgrade CI clang-format to 6.0 (ray-project#4976)

* [Core worker] add store & task provider (ray-project#4966)

* Fix bugs in the a3c code template. (ray-project#4984)

* Inherit Function Docstrings and other metedata (ray-project#4985)

* Fix a crash when unknown worker registering to raylet (ray-project#4992)

* [gRPC] Use gRPC for inter-node-manager communication (ray-project#4968)
@stephanie-wang stephanie-wang mentioned this pull request Jun 26, 2019
Merged
1 task
stefanpantic added a commit to wingman-ai/ray that referenced this pull request Jun 26, 2019
@stefanpantic
Merge with ray master (#36)
110aaab 
* [rllib] Remove dependency on TensorFlow (ray-project#4764)

* remove hard tf dep

* add test

* comment fix

* fix test

* Dynamic Custom Resources - create and delete resources (ray-project#3742)

* Update tutorial link in doc (ray-project#4777)

* [rllib] Implement learn_on_batch() in torch policy graph

* Fix `ray stop` by killing raylet before plasma (ray-project#4778)

* Fatal check if object store dies (ray-project#4763)

* [rllib] fix clip by value issue as TF upgraded (ray-project#4697)

*  fix clip_by_value issue

*  fix typo

* [autoscaler] Fix submit (ray-project#4782)

* Queue tasks in the raylet in between async callbacks (ray-project#4766)

* Add a SWAP TaskQueue so that we can keep track of tasks that are temporarily dequeued

* Fix bug where tasks that fail to be forwarded don't appear to be local by adding them to SWAP queue

* cleanups

* updates

* updates

* [Java][Bazel]  Refine auto-generated pom files (ray-project#4780)

* Bump version to 0.7.0 (ray-project#4791)

* [JAVA] setDefaultUncaughtExceptionHandler to log uncaught exception in user thread. (ray-project#4798)

* Add WorkerUncaughtExceptionHandler

* Fix

* revert bazel and pom

* [tune] Fix CLI test (ray-project#4801)

* Fix pom file generation (ray-project#4800)

* [rllib] Support continuous action distributions in IMPALA/APPO (ray-project#4771)

* [rllib] TensorFlow 2 compatibility (ray-project#4802)

* Change tagline in documentation and README. (ray-project#4807)

* Update README.rst, index.rst, tutorial.rst and  _config.yml

* [tune] Support non-arg submit (ray-project#4803)

* [autoscaler] rsync cluster (ray-project#4785)

* [tune] Remove extra parsing functionality (ray-project#4804)

* Fix Java worker log dir (ray-project#4781)

* [tune] Initial track integration (ray-project#4362)

Introduces a minimally invasive utility for logging experiment results. A broad requirement for this tool is that it should integrate seamlessly with Tune execution.

* [rllib] [RFC] Dynamic definition of loss functions and modularization support (ray-project#4795)

* dynamic graph

* wip

* clean up

* fix

* document trainer

* wip

* initialize the graph using a fake batch

* clean up dynamic init

* wip

* spelling

* use builder for ppo pol graph

* add ppo graph

* fix naming

* order

* docs

* set class name correctly

* add torch builder

* add custom model support in builder

* cleanup

* remove underscores

* fix py2 compat

* Update dynamic_tf_policy_graph.py

* Update tracking_dict.py

* wip

* rename

* debug level

* rename policy_graph -> policy in new classes

* fix test

* rename ppo tf policy

* port appo too

* forgot grads

* default policy optimizer

* make default config optional

* add config to optimizer

* use lr by default in optimizer

* update

* comments

* remove optimizer

* fix tuple actions support in dynamic tf graph

* [rllib] Rename PolicyGraph => Policy, move from evaluation/ to policy/ (ray-project#4819)

This implements some of the renames proposed in ray-project#4813
We leave behind backwards-compatibility aliases for *PolicyGraph and SampleBatch.

* [Java] Dynamic resource API in Java (ray-project#4824)

* Add default values for Wgym flags

* Fix import

* Fix issue when starting `raylet_monitor` (ray-project#4829)

* Refactor ID Serial 1: Separate ObjectID and TaskID from UniqueID (ray-project#4776)

* Enable BaseId.

* Change TaskID and make python test pass

* Remove unnecessary functions and fix test failure and change TaskID to
16 bytes.

* Java code change draft

* Refine

* Lint

* Update java/api/src/main/java/org/ray/api/id/TaskId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/api/src/main/java/org/ray/api/id/BaseId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/api/src/main/java/org/ray/api/id/BaseId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/api/src/main/java/org/ray/api/id/ObjectId.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Address comment

* Lint

* Fix SINGLE_PROCESS

* Fix comments

* Refine code

* Refine test

* Resolve conflict

* Fix bug in which actor classes are not exported multiple times. (ray-project#4838)

* Bump Ray master version to 0.8.0.dev0 (ray-project#4845)

* Add section to bump version of master branch and cleanup release docs (ray-project#4846)

* Fix import

* Export remote functions when first used and also fix bug in which rem… (ray-project#4844)

* Export remote functions when first used and also fix bug in which remote functions and actor classes are not exported from workers during subsequent ray sessions.

* Documentation update

* Fix tests.

* Fix grammar

* Update wheel versions in documentation to 0.8.0.dev0 and 0.7.0. (ray-project#4847)

* [tune] Later expansion of local_dir (ray-project#4806)

* [rllib] [RFC] Deprecate Python 2 / RLlib (ray-project#4832)

* Fix a typo in kubernetes yaml (ray-project#4872)

* Move global state API out of global_state object. (ray-project#4857)

* Install bazel in autoscaler development configs. (ray-project#4874)

* [tune] Fix up Ax Search and Examples (ray-project#4851)

* update Ax for cleaner API

* docs update

* [rllib] Update concepts docs and add "Building Policies in Torch/TensorFlow" section (ray-project#4821)

* wip

* fix index

* fix bugs

* todo

* add imports

* note on get ph

* note on get ph

* rename to building custom algs

* add rnn state info

* [rllib] Fix error getting kl when simple_optimizer: True in multi-agent PPO

* Replace ReturnIds with NumReturns in TaskInfo to reduce the size (ray-project#4854)

* Refine TaskInfo

* Fix

* Add a test to print task info size

* Lint

* Refine

* Update deps commits of opencensus to support building with bzl 0.25.x (ray-project#4862)

* Update deps to support bzl 2.5.x

* Fix

* Upgrade arrow to latest master (ray-project#4858)

* [tune] Auto-init Ray + default SearchAlg (ray-project#4815)

* Bump version from 0.8.0.dev0 to 0.7.1. (ray-project#4890)

* [rllib] Allow access to batches prior to postprocessing (ray-project#4871)

* [rllib] Fix Multidiscrete support (ray-project#4869)

* Refactor redis callback handling (ray-project#4841)

* Add CallbackReply

* Fix

* fix linting by format.sh

* Fix linting

* Address comments.

* Fix

* Initial high-level code structure of CoreWorker. (ray-project#4875)

* Drop duplicated string format (ray-project#4897)

This string format is unnecessary. java_worker_options has been appended to the commandline later.

* Refactor ID Serial 2: change all ID functions to `CamelCase` (ray-project#4896)

* Hotfix for change of from_random to FromRandom (ray-project#4909)

* [rllib] Fix documentation on custom policies (ray-project#4910)

* wip

* add docs

* lint

* todo sections

* fix doc

* [rllib] Allow Torch policies access to full action input dict in extra_action_out_fn (ray-project#4894)

* fix torch extra out

* preserve setitem

* fix docs

* [tune] Pretty print params json in logger.py (ray-project#4903)

* [sgd] Distributed Training via PyTorch (ray-project#4797)

Implements distributed SGD using distributed PyTorch.

* [rllib] Rough port of DQN to build_tf_policy() pattern (ray-project#4823)

* fetching objects in parallel in _get_arguments_for_execution (ray-project#4775)

* [tune] Disallow setting resources_per_trial when it is already configured (ray-project#4880)

* disallow it

* import fix

* fix example

* fix test

* fix tests

* Update mock.py

* fix

* make less convoluted

* fix tests

* [rllib] Rename PolicyEvaluator => RolloutWorker (ray-project#4820)

* Fix local cluster yaml (ray-project#4918)

* [tune] Directional metrics for components (ray-project#4120) (ray-project#4915)

* [Core Worker] implement ObjectInterface and add test framework (ray-project#4899)

* [tune] Make PBT Quantile fraction configurable (ray-project#4912)

* Better organize ray_common module (ray-project#4898)

* Fix error

* [tune] Add requirements-dev.txt and update docs for contributing (ray-project#4925)

* Add requirements-dev.txt and update docs.

* Update doc/source/tune-contrib.rst

Co-Authored-By: Richard Liaw <rliaw@berkeley.edu>

* Unpin everything except for yapf.

* Fix compute actions return value

* Bump version from 0.7.1 to 0.8.0.dev1. (ray-project#4937)

* Update version number in documentation after release 0.7.0 -> 0.7.1 and 0.8.0.dev0 -> 0.8.0.dev1. (ray-project#4941)

* [doc] Update developer docs with bazel instructions (ray-project#4944)

* [C++] Add hash table to Redis-Module (ray-project#4911)

* Flush lineage cache on task submission instead of execution (ray-project#4942)

* [rllib] Add docs on how to use TF eager execution (ray-project#4927)

* [rllib] Port remainder of algorithms to build_trainer() pattern (ray-project#4920)

* Fix resource bookkeeping bug with acquiring unknown resource. (ray-project#4945)

* Update aws keys for uploading wheels to s3. (ray-project#4948)

* Upload wheels on Travis to branchname/commit_id. (ray-project#4949)

* [Java] Fix serializing issues of `RaySerializer` (ray-project#4887)

* Fix

* Address comment.

* fix (ray-project#4950)

* [Java] Add inner class `Builder` to build call options. (ray-project#4956)

* Add Builder class

* format

* Refactor by IDE

* Remove uncessary dependency

* Make release stress tests work and improve them. (ray-project#4955)

* Use proper session directory for debug_string.txt (ray-project#4960)

* [core] Use int64_t instead of int to keep track of fractional resources (ray-project#4959)

* [core worker] add task submission & execution interface (ray-project#4922)

* [sgd] Add non-distributed PyTorch runner (ray-project#4933)

* Add non-distributed PyTorch runner

* use dist.is_available() instead of checking OS

* Nicer exception

* Fix bug in choosing port

* Refactor some code

* Address comments

* Address comments

* Flush all tasks from local lineage cache after a node failure (ray-project#4964)

* Remove typing from setup.py install_requirements. (ray-project#4971)

* [Java] Fix bug of `BaseID` in multi-threading case. (ray-project#4974)

* [rllib] Fix DDPG example (ray-project#4973)

* Upgrade CI clang-format to 6.0 (ray-project#4976)

* [Core worker] add store & task provider (ray-project#4966)

* Fix bugs in the a3c code template. (ray-project#4984)

* Inherit Function Docstrings and other metedata (ray-project#4985)

* Fix a crash when unknown worker registering to raylet (ray-project#4992)

* [gRPC] Use gRPC for inter-node-manager communication (ray-project#4968)

* Fix Java CI failure (ray-project#4995)

* fix handling of non-integral timeout values in signal.receive (ray-project#5002)

* temp fix for build (ray-project#5006)

* [tune] Tutorial UX Changes (ray-project#4990)

* add integration, iris, ASHA, recursive changes, set reuse_actors=True, and enable Analysis as a return object

* docstring

* fix up example

* fix

* cleanup tests

* experiment analysis

* Fix valgrind build by installing new version of valgrind (ray-project#5008)

* Fix no cpus test (ray-project#5009)

* Fix tensorflow-1.14 installation in jenkins (ray-project#5007)

* Add dynamic worker options for worker command. (ray-project#4970)

* Add fields for fbs

* WIP

* Fix complition errors

* Add java part

* FIx

* Fix

* Fix

* Fix lint

* Refine API

* address comments and add test

* Fix

* Address comment.

* Address comments.

* Fix linting

* Refine

* Fix lint

* WIP: address comment.

* Fix java

* Fix py

* Refin

* Fix

* Fix

* Fix linting

* Fix lint

* Address comments

* WIP

* Fix

* Fix

* minor refine

* Fix lint

* Fix raylet test.

* Fix lint

* Update src/ray/raylet/worker_pool.h

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Update java/runtime/src/main/java/org/ray/runtime/AbstractRayRuntime.java

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Address comments.

* Address comments.

* Fix test.

* Update src/ray/raylet/worker_pool.h

Co-Authored-By: Hao Chen <chenh1024@gmail.com>

* Address comments.

* Address comments.

* Fix

* Fix lint

* Fix lint

* Fix

* Address comments.

* Fix linting

* [docs] docs for running Tensorboard without sudo (ray-project#5015)

* Instructions for running Tensorboard without sudo

When we run Tensorboard to visualize the results of Ray outputs on multi-user clusters where we don't have sudo access, such as RISE clusters, a few commands need to first be run to make sure tensorboard can edit the tmp directory. This is a pretty common usecase so I figured we may as well put it in the documentation for Tune.

* Update tune-usage.rst

* [ci] Change Jenkins to py3 (ray-project#5022)

* conda3

* integration

* add nevergrad, remotedata

* pytest 0.3.1

* otherdockers

* setup

* tune

* [gRPC] Migrate gcs data structures to protobuf (ray-project#5024)

* [rllib] Add QMIX mixer parameters to optimizer param list (ray-project#5014)

* add mixer params

* Update qmix_policy.py

* [grpc] refactor rpc server to support multiple io services (ray-project#5023)

* [rllib] Give error if sample_async is used with pytorch for A3C (ray-project#5000)

* give error if sample_async is used with pytorch

* update

* Update a3c.py

* [tune] Update MNIST Example (ray-project#4991)

* Add entropy coeff schedule

* Revert "Merge with ray master"

This reverts commit 108bfa2, reversing
changes made to 2e0eec9.

* Revert "Revert "Merge with ray master""

This reverts commit 92c0f88.

* Remove entropy decay stuff
Edilmo added a commit to BonsaiAI/ray that referenced this pull request Feb 7, 2020
@Edilmo
Cherry picking changes from PR 4942
2d5e546 
This PR contains changes that help with memory issues
ray-project#4942
@Edilmo Edilmo mentioned this pull request Feb 7, 2020
Merged
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@pcmoritz pcmoritz pcmoritz approved these changes

@robertnishihara robertnishihara robertnishihara approved these changes

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@stephanie-wang @pcmoritz @AmplabJenkins @robertnishihara