m5.16xlarge instances have 64 CPU and 256GB memory, which are overkill for scheduling tests that do not have a lot of computations. Use smaller instance m5.4xlarge to save cost and make allocating instances easier.
Currently when Raylets die, it is hard to figure out:
if a Raylet died at all in a cluster. Usually we have to check on nodes where a number of workers died and see if the Raylet has died as well.
reason of Raylet's death.
With this PR, if a Raylet dies from a reason other than SIGTERM, the dashboard agent will report the failure along with last 20 lines of the Raylet log.
This exposes a low-cost way to perform a pseudo global shuffle.
For extremely large datasets that span multiple nodes, contiguous blocks will often be colocated on the same node. This leads to hot spots during iteration of the dataset in which single nodes (1) must send a lot of data over the network, and (2) perform lots of disk reads if the dataset is spilled to disk.
This allows the workload to be spread across the nodes on which the dataset blocks are on.
Stats construction on the from_arrow and from_numpy (and from_pandas with Pandas block support disabled) is currently broken since we weren't resolving the block metadata before passing it to the stats, causing future ds.stats() calls to fail. This PR fixes this and adds some test coverage.
Drivebys:
- Adds stats for from_pandas() zero-copy path (metadata fetch only).
- Changes "from_numpy" stats stage name to "from_numpy_refs", to be consistent with stats for other from_*() APIs.
Currently, team:ml spans all ML (Tune, Train, AIR) tests and rllib tests. rllib tests are much more flaky and it would be good to split them up in the flaky test tracker. This PR changes Rllib-tests from team:ml to team:rllib to enable this separation.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
The current inheritance behavior for runtime_envs enables the following workflow for Jobs: A working_dir can be set in the Jobs API, and then inside the driver script, if a new per-task runtime_env is defined, it will automatically inherit the driver's working_dir.
There is an ongoing discussion about the best approach for runtime_env inheritance going forward: https://github.com/ray-project/ray/issues/25484, in which we noted that there were no tests covering this behavior.
This PR adds integration tests for the above behavior. If we ultimately decide to abandon the current inheritance behavior and instead have child runtime envs completely overwrite the parent runtime env, this test will fail, reminding us to do the following:
- Update the internal runtime_env usage in Ray Tune to use the `ray.get_runtime_context().runtime_env.update` API
- Update the documentation for Ray Jobs telling users to use `ray.get_runtime_context().runtime_env.update` and update this test
Breaks the hard dependency on Preprocessor imports for type hints in AIR. Preparation for move of Preprocessors to `ray.data`.
Trainer still has a hard dependency due to an `isinstance` check.
This test was flaky because actor tasks can fail if submitted when the actor process is failed or restarting. This PR changes the test to be more stressful so that the error is easier to reproduce and changes the max_retries parameter to -1 so that the actor task will succeed.
Related issue number
Closes#24942.
Unreverts #24812, skipping the memory releasing tests that are already flaky. We have a separate issue tracking the unskipping of these memory releasing tests, once we find a more reliable way to test them.
* Revert "Revert "Revert "Revert "[Datasets] [Tensor Story - 1/2] Automatically provide tensor views to UDFs and infer tensor blocks for pure-tensor datasets."" (#25031)" (#25057)"
This reverts commit fb2933a78f.
* Skip shuffle memory release test.
**Update**: This PR is now part 3 of a three PR group to consolidate the checkpoints.
1. Part 1 adds the common checkpoint management class #24771
2. Part 2 adds the integration for Ray Train #24772
3. This PR builds on #24772 and includes all changes. It moves the Ray Tune integration to use the new common checkpoint manager class.
Old PR description:
This PR consolidates the Ray Train and Tune checkpoint managers. These concepts previously did something very similar but in different modules. To simplify maintenance in the future, we've consolidated the common core.
- This PR keeps full compatibility with the previous interfaces and implementations. This means that for now, Train and Tune will have separate CheckpointManagers that both extend the common core
- This PR prepares Tune to move to a CheckpointStrategy object
- In follow-up PRs, we can further unify interfacing with the common core, possibly removing any train- or tune-specific adjustments (e.g. moving to setup on init rather on runtime for Ray Train)
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
