So I have a AMD machine with many cores and 32GB of memory. When I do `pip install -e .`, my machine crashes since bazel tries to use all the cores, but quickly runs out of memory. It seems there is no native way to set environment variables to tell bazel to limit its resource consumption, but there is a `--local_cpu_resources` command-line option.
This PR exposes that to the `pip install` via an environment variable. I also went through the setup.py and documented all the environment variables I could find.
Datasets docs for last-mile preprocessing, particularly geared towards ML ingest. This gives groupby, aggregations, and random shuffling examples in the overview page (not present previously), adds some concreteness to our last-mile preprocessing positioning, and provides some preprocessing recipes for a few common transformations.
This PR adds support for publishing and subscribing to logs in Python via GCS pubsub. It also refactors the Python threaded subscriber to support subscribing and calling `close()` from multiple threads.
We can also move tests and logging support to another PR, but it will make the purpose of the refactoring seems less obvious.
block splitting and makes it off by default. This makes it easier to debug problems potentially related to this feature. Criteria for enabling by default:
- We're confident all nightly tests pass (currently, there may be an issue with large-scale groupby with block splitting).
- We're confident lineage-based reconstruction can work with block splitting.
This PR introduces a TrialCheckpoint class which is returned e.g. by ExperimentAnalysis.best_checkpoint. The class enables easy access to cloud storage locations (rather than just local directories before). It also comes with utilities to download, upload, and save trial checkpoints to local and cloud targets.
Address followup comments from https://github.com/ray-project/ray/pull/19863
- Add short "Concepts" section
- Add more section headings to break up the text
- Add "Workflow: Local Files" example
- Add "Workflow: Library development" example
This PR is the last PR that enables out of order execution. Previous PR: #20176
In this PR specifically, we added an execute_out_of_order option to .options call, which creates the actor with both out_of_order_submit_queue and out_of_order_scheduling queue.
this PR also added @simon-mo original case for testing.
The default block size of 500MiB seems too low for some common workloads, e.g. shuffling 500GB. This creates 1000 blocks which means 1 million intermediate shuffle objects until we implement #20500.
Why are these changes needed?
Add timeout(ms) param for Java ray.get. The API changes have been updated to doc ([Ray Core Walkthrough]->[Fetching Results]).
eg:
ObjectRef<Integer> objRef = Ray.put(1);
objRef.get(1000)
Ray.get(Ray.task(MyRayApp::slowFunction).remote(), 3000)
Related issue number
#20247
* Revert "Revert "[RLlib] POC: `PGTrainer` class that works by sub-classing, not `trainer_template.py`. (#20055)" (#20284)"
This reverts commit 246787cdd9.
Co-authored-by: sven1977 <svenmika1977@gmail.com>
This PR adds support for automatic block splitting on read and map transforms, to keep block size bounded to ~500MiB. This avoids potential OOM situations where a map task may consume too much intermediate Python heap memory, or too much object store shared memory for one block.
This PR removes global named actor and global PGs.
I believe these APIs are not used widely in OSS.
CPP part is not included in this PR.
@kfstorm @clay4444 @raulchen Please take a look if this change is reasonable.
IMPORTANT NOTE: This is a Java API change and will lead backward incompatibility in Java global named actor and global PG usage.
CPP part is not included in this PR.
INCLUDES:
Remove setGlobalName() and getGlobalActor() APIs.
Remove getGlobalPlacementGroup() and setGlobalPG
Add getActor(name, namespace) API
Add getPlacementGroup(name, namespace) API
Update doc pages.
