this is a temp fix of #25556. When the dtype from the pandas dataframe gives object, we set the dtype to be None and make use of the auto-inferring of the type in the conversion.
Why are these changes needed?
Wasn't able to build Ray following these instructions : https://docs.ray.io/en/latest/ray-contribute/development.html#building-ray
It fails to run
pip install -e . --verbose # Add --user if you see a permission denied error.
I have a local installation of protobuf via Homebrew and bazel is using its headers against the protobuf that it is pulling into its sandbox. It is a known issue with bazel and one of the workarounds is to block the local dir so it doesn't accidentally pick up the header if someone happens to have it installed locally.
Manually tested by running
bazel build --verbose_failures --sandbox_debug -- //:ray_pkg
Without the fix I would get an error similar to https://gist.github.com/clarng/ff7b7bf7672802d1e3e07b4b509e4fc8
With the fix it builds
This PR includes / depends on #25709
The two concepts of Syncer and SyncClient are confusing, as is the current API for passing custom sync functions.
This PR refactors Tune's syncing behavior. The Sync client concept is hard deprecated. Instead, we offer a well defined Syncer API that can be extended to provide own syncing functionality. However, the default will be to use Ray AIRs file transfer utilities.
New API:
- Users can pass `syncer=CustomSyncer` which implements the `Syncer` API
- Otherwise our off-the-shelf syncing is used
- As before, syncing to cloud disables syncing to driver
Changes:
- Sync client is removed
- Syncer interface introduced
- _DefaultSyncer is a wrapper around the URI upload/download API from Ray AIR
- SyncerCallback only uses remote tasks to synchronize data
- Rsync syncing is fully depracated and removed
- Docker and kubernetes-specific syncing is fully deprecated and removed
- Testing is improved to use `file://` URIs instead of mock sync clients
## Why are these changes needed?
This is to refactor the interaction of state cli to API server from a hard-coded request workflow to `SubmissionClient` based.
See #24956 for more details.
## Summary
<!-- Please give a short summary of the change and the problem this solves. -->
- Created a `StateApiClient` that inherits from the `SubmissionClient` and refactor various listing commands into class methods.
## Related issue number
Closes#24956Closes#25578
Adds notes explaining that Ray's support on Azure, Aliyun, and SLURM is community-maintained.
Rephrases the mention of K8s support in the intro.
This PR replaces https://github.com/ray-project/ray/pull/25504.
## Why are these changes needed?
When schedule actors on pg, instead of iterating all nodes in the cluster resource, This optimize will directly queries corresponding nodes by looking at pg location index.
This optimization can reduce the complexity of the algorithm from O (N) to o (1)，and N is the number of nodes. In particular, the more nodes in large-scale clusters, the better the optimization effect.
**This PR only optimize schedule by gcs, I will submit a PR for raylet scheduling later.**
In ant group, Now we have achieved the optimization in the GCS scheduling mode and obtained the following performance test results.
1、The average time of selecting nodes is reduced from 330us to 30us, and the performance is improved by about 11 times.
2、The total time of creating & executing 12,000 actors ranges from 271 (s) - > 225 (s) on average. Reduce time consumption by 17%.
More detailed solution information is in the issue.
## Related issue number
[Core/PG/Schedule]Optimize the scheduling performance of actors/tasks with PG specified #23881
This is carved out from https://github.com/ray-project/ray/pull/25558.
tlrd: checkpoint.py current doesn't support the following
```
a. from fs to dict checkpoint;
b. drop some marker to dict checkpoint;
c. convert back to fs checkpoint;
d. convert back to dict checkpoint.
Assert that the marker should still be there
```
It will be easier to develop if we could use a tool to organize / sort imports and not have to move them around by hand.
This PR shows how we could do this with isort (black doesn't quite do this per https://github.com/psf/black/issues/333)
After this PR lands everyone will need to update their formatter to include isort if they don't have it already, i.e.
pip install -r ./python/requirements_linters.txt
All future file changes will go through isort and may introduce a slightly larger PR the first time as it will clean up the imports.
The plan is to land this PR and also clean up the rest of the code in parallel by using this PR to format the codebase (so people won't get surprised by the formatter if the file hasn't been touched yet)
Co-authored-by: Clarence Ng <clarence@anyscale.com>
Follow another approach mentioned in #25350.
The scaling config is now converted to the dataclass letting us use a single function for validation of both user supplied dicts and dataclasses. This PR also fixes the fact the scaling config wasn't validated in the GBDT Trainer and validates that allowed keys set in Trainers are present in the dataclass.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
The multi node testing utility currently does not support controlling cluster state from within Ray tasks or actors., but it currently requires Ray client. This makes it impossible to properly test e.g. fault tolerance, as the driver has to be executed on the client machine in order to control cluster state. However, this client machine is not part of the Ray cluster and can't schedule tasks on the local node - which is required by some utilities, e.g. checkpoint to driver syncing.
This PR introduces a remote control API for the multi node cluster utility that utilizes a Ray queue to communicate with an execution thread. That way we can instruct cluster commands from within the Ray cluster.
Closes#25588
NVIDIA recently pushed updates to the CUDA image removing support for end of life drivers. Therefore, the default AMIs that we previously had for OSS cluster launcher are not able to run the Ray GPU Docker images.
This PR updates the default AMIs to the latest Deep Learning versions. In general, we should periodically update these AMIs, especially when we add support for new CUDA versions.
I manually confirmed that the nightly Ray docker images work with the new AMI in us-west-2.
This PR implements the basic log APIs. For the better APIs (like higher level APIs like ray logs actors), it will be implemented after the internal API review is done.
# If there's only 1 match, print a file content. Otherwise, print all files that match glob.
ray logs [glob_filter] --node-id=[head node by default]
Args:
--tail: Tail the last X lines
--follow: Follow the new logs
--actor-id: The actor id
--pid --node-ip: For worker logs
--node-id: The node id of the log
--interval: When --follow is specified, logs are printed with this interval. (should we remove it?)
Including the Bazel build files in the wheel leads to problems if the Ray wheels are brought in as a dependency from another bazel workspace, since that workspace will not recurse into the directories of the wheel that contain BUILD files -- this can lead to dropped files.
This only happens for macOS wheels, on linux wheels the BUILD files were already excluded.
Timeout is only introduced in GcsClient due to the reason that ray client is not defining the timeout well for their API and it's a lot of effort to make it work e2e. For built-in component, we should use GcsClient directly.
This PR use GcsClient to replace the old one to integrate GCS HA with Ray Serve.
This PR fixes two issues with the __array__ protocol on the tensor extension:
1. The __array__ protocol on TensorArrayElement was missing the dtype parameter, causing np.asarray(tae, dtype=some_dtype) calls to fail. This PR adds support for the dtype argument.
2. TensorArray and TensorArrayElement didn't support NumPy's scalar casting semantics for single-element tensors. This PR adds support for these scalar casting semantics.
Ray's gRPC server wrapper configures a max active call setting for each handler. When the max active call is -1, the handler is supposed to allow handling unlimited number of requests concurrently. However in practice it is often observed that handlers configured with unlimited active calls are still handling at most 100 requests concurrently.
This is a result of the existing logic:
At a high level, each gRPC method is associated with a number of ServerCall objects (acting as "tags") in the gRPC completion queue. When there is no tag for a method, gRPC server thread will not be able to poll requests from the method call from the completion queue. After a request is polled from the completion queue, it is processed by the polling gRPC server thread, then queued to an eventloop.
When a handler is in the "unlimited" mode, it creates when a new ServerCall object (tag) before actual processing. The problem is that new ServerCalls are created on the eventloop instead of the gRPC server thread. When the event loop runs a callback from the gRPC server, the callback creates a new ServerCall object, and can run the gRPC handler to completion if the handler does not have any async step. So overall, the event loop will not run more callbacks than the initial number of ServerCalls, which is 100 in the "unlimited" mode.
The solution is to create a new ServerCall in the gRPC server thread, before sending the ServerCall to the eventloop.
Running some night tests to verify the fix does not introduce instabilities: https://buildkite.com/ray-project/release-tests-branch/builds/652
Also, looking into adding gRPC server / client stress tests with large number of concurrent requests.
