All workflow tasks are executed as remote functions that submitted from WorkflowManagmentActor. WorkflowManagmentActor is a detached long-running actor whose owner is the first driver in the cluster that runs the very first workflow execution. Therefore, for new drivers that run workflows, the loggings won't be properly published back to the driver because loggings are saved and published based on job_id and the job_id is always the first driver's job_id as the ownership goes like: first_driver -> WorkflowManagmentActor -> workflow executions using remote functions.
To solve this, during workflow execution, we pass the actual driver's job_id along with execution, and re-configure the logging files on each worker that runs the remote functions. Notice that we need to do this in multiple places as a workflow task is executed with more than one remote functions that are running in different workers.
This PR fixes a bug: when the task is pushed to a core worker but hasn't been scheduled to run cancel is not called which will lead to the get request hanging forever.
The fix is to call the `Cancel`.
The previous implementation of the reporting logic in HuggingFaceTrainer had a few edge cases that caused the training iterations and measured epochs to diverge. This new implementation should ensure that reporting is consistent.
On the ServeHead level, it is talking to serve api and controller to do deployment and clean up now. With this pr, it hides the deployment clean up logic into server.run() for code cleanness and easy to refactor in the future.
This PR modifies the KubeRay e2e autoscaling test so that one of its scaling commands is sent via the Ray Job Submission API.
This validates that the Ray Job Submission API works with KubeRay and, in particular, that the Ray Dashboard is correctly exposed.
Updating W&B Ray Tune Integration with new standards. Adding support to wandb service, the soon to be default way for multiprocessing + wandb run logging.
Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
This PR introduces a modification to the external markdown logic in doc build to restore the original file content after build is finished. This ensures that the files are not accidentally committed.
This PR allows for Ray to disable metrics collection. It was possible with RAY_enable_metrics_collection, but it didn't fully disable collection because there was a metrics collection happening from agent that wasn't properly disabled. This PR also adds tests.
The local environment setup of release tests (in client tests) can sometimes update dependencies of the `anyscale` package to an unsupported version. By re-installing the `anyscale` package after local env setup, we make sure that we can connect to the cluster. Note that this may lead to incompatibilities of the test script, however.
Failing pytest summaries for flaky tests that eventually succeed are not always cleaned up properly: https://buildkite.com/ray-project/ray-builders-branch/builds/7292#_
This PR ensures we only print summaries when we have at least one summary file (and not just the header file).
Users get error messages from client/server on actor failures, even if they already try-except'd the error. For example:
```
import ray
ray.init("ray://localhost:10001")
try:
ray.get_actor("doesnotexist")
except ValueError:
pass
```
Will still generate the log `Caught schedule exception` and `Exception from actor creation is ignored in destructor. To receive this exception in application code, call a method on the actor reference before its destructor is run.`. Reduce the level of these logs to debug by default.
Fixes checkpoints not being recorded in Tune's checkpoint manager if the first checkpoint has None value. This also deflakes `test_checkpoint_manager.py::CheckpointManagerTest`.
**TL;DR:** Don't clear for eager, clear all but non-lazy input blocks if lazy, clear everything if pipelining.
This PR provides more efficient and intuitive block clearing semantics for eager mode, lazy mode, and pipelining, while still supporting multiple operations applied to the same base dataset, i.e. fan-out. For example, two different map operations are applied to the same base `ds` in this example:
```python
ds = ray.data.range(10).map(lambda x: x+1)
ds1 = ds.map(lambda x: 2*x)
ds2 = ds.map(lambda x: 3*x)
```
If naively clear the blocks when executing the map to produce `ds1`, the map producing `ds2` will fail.
### Desired Semantics
- **Eager mode** - don’t clear input blocks, thereby supporting fan-out from cached data at any point in the stage chain without triggering unexpected recomputation.
- **Lazy mode** - if lazy datasource, clear the input blocks for every stage, relying on recomputing via stage lineage if fan-out occurs; if non-lazy datasource, do not clear source blocks for execution plan when executing first stage, but do clear input blocks for every subsequent stage.
- **Pipelines** - Same as lazy mode, although the only fan-out that can occur is from the pipeline source blocks when repeating a dataset/pipeline, so unintended intermediate recomputation will never happen.
#17581 introduced a warning about excess queuing for actors. Unfortunately since Ray 1.10.0, the metric used became wrong for async actors, resulting in bogus warnings when they are called more than 5000 times, even though there are not 5000 pending tasks.
The difference between 1.9.2 and 1.10.0 is that async actors tasks skip the queue in CoreWorkerClient::PushActorTask. However CoreWorkerClient::ClientProcessedUpToSeqno uses max_finished_seq_no_ which is never updated when the queue is skipped.
I think that a better metric for the amount of tasks that are pending submissions is the size of the internal queue CoreWorkerDirectActorTaskSubmitter::inflight_task_callbacks.
Currently nightly tests are unable to finish in a day because of concurrency group limit on `large` tests. This is an attempt to adjust the limits so buildkite can run / finish more tests. I will observe which tests fall into the `enormous` group and adjust the test resource / concurrency group limits again.
Fix CQL getting stuck when deprecated timesteps_per_iteration is used (use min_train_timesteps_per_reporting instead).
CQL does not perform sampling timesteps and the deprecated timesteps_per_iteration is automatically translated into the new min_sample_timesteps_per_reporting, but should be translated (only for CQL and other purely offline RL algos) into min_train_timesteps_per_reporting.
If timesteps_per_iteration, CQL lever leaves the first iteration as it thinks it's not done yet (sample timesteps always remain at 0).
For debugging client environments, it is helpful to print the installed pip packages.
Additionally, a fix for the environment of the ml_user_tune_rllib_connect_test is added. Additionally, anyscale import errors are reported verbosely to help debug missing packages.
The prefetch_blocks implementation doesn't work as expected. Due to ray.wait() doesn't given us fine grained control, today we block waiting any of the block returns. As I read the code, it may or may not actually fetching all the blocks.
A better way to ensure prefetching not blocking is to use ray remote function call, which is not blocking and ensures the blocks are fetched eventually.
Lint was still failing (but only caught with doctest):
```
File "../../python/ray/rllib/utils/numpy.py", line ?, in default
Failed example:
tree.traverse(make_action_immutable, d, top_down=False)
Exception raised:
Traceback (most recent call last):
File "/opt/miniconda/lib/python3.6/doctest.py", line 1330, in __run
compileflags, 1), test.globs)
File "<doctest default[4]>", line 1, in <module>
tree.traverse(make_action_immutable, d, top_down=False)
NameError: name 'make_action_immutable' is not defined
```
Adds a fast file metadata provider that trades comprehensive file metadata collection for speed of metadata collection, and which also disabled directory path expansion which can be very slow on some cloud storage service providers. This PR also refactors the Parquet datasource to be able to take advantage of both these changes and the content-type agnostic partitioning support from #23624.
This is the second PR of a series originally proposed in #23179.
