`test_traceback.py` was taking ~55s to finish recently, and since today it starts to time out at 60s more frequently. All test cases do succeed so increase its test time out for now. We will look into if there is any performance regression separately.
This is the second last PR to improve `ActorDiedError` exception.
This propagates Actor death cause metadata to the ray error object. In this way, we can raise a better actor died error exception.
After this PR is merged, I will add more metadata to each error message and write a documentation that explains when each error happens.
TODO
- [x] Fix test failures
- [x] Add unit tests
- [x] Fix Java/cpp cases
Follow up PRs
- Not allowing nullptr for RayErrorInfo input.
GCS pubsub uses long polling, so the subscriber waits instead of returning None from polling when there is no buffered log. It needs a different heuristic to decide if the driver is not keeping up with logs from the worker.
Making some minor fixes.
1. Update input `batch_size` to be global batch size. Introduce `worker_batch_size` so each iteration trains same global batch size.
2. Update dataset `size` calculation to only refer to the fraction of the data that is trained on each worker. This allows calculations (e.g. training progress, accuracy) to be correct.
3. Add `model.train()` for generality.
4. Remove `smoke-test` flag since it's not really being used.
- Tolerate GRPC deadline exceeded and transient failures in Python GCS AIO subscribers, which becomes consistent with Python GCS synchronous subscribers.
- Tolerate any exception in dashboard for subscribing to logs and error info, which becomes consistent with how dashboard handles GRPC errors for obtaining node stats.
When a node is dead, reference table should remove locations for those objects on the node. Otherwise locality-aware scheduling will schedule tasks to the dead node.
This is a minimum viable product for Ray Autoscaler integration with Kuberay. It is not ready for prime time/general use, but should be enough for interested parties to get started (see the documentation in kuberay.md).
GCS, when running as an individual component, can cause other components to fail in case of crashes.
Here are two main cases covered in this patch:
1. monitor.py will raise an exception when disconnected from GCS.
2. When GCS becomes available later than other components, the missing KV of GCS address can cause other components to fail to start.
In our patch, we fixed these two issues as well as increased the timeout for redis connection which was too small.
Co-authored-by: Mingwei Tian <mwtian@anyscale.com>
Prior to this PR, sort, groupby, and aggregate defined separate types for extracting values from Dataset records. This was confusing since the user had to understand the differences between the different key types (which were basically exactly the same).
This PR defines a common key type: KeyFn, which is simply Union[None, str, Callable[[T], Any]]. This is used as sort(KeyFn...), aggregate(Agg(KeyFn)...), groupby(KeyFn).agg(Agg(KeyFn), ...).
It also unifies the error generation paths to a common _validate_key_fn utility. This also improves the errors generated when passing explicit AggregateFn classes, which previously failed in the workers if invalid.
See #21458. Currently, Tune keeps its own list of alive node IPs, but this information is only updated every 10 seconds and is usually stale when a new node is added. Because of this, the first trial scheduled on this node is usually marked as failed. This PR adds a test confirming this behavior and gets rid of the unneeded code path.
Co-authored-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Co-authored-by: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com>
In test_client_reconnect.py, each test case starts a Ray cluster via client server's default_connect_handler(). The Ray cluster shuts down implicitly when the start_middleman_server() ended and Python GC'es the client server. After turning on GCS pubsub, the time when client server is GC'ed changes. Sometimes the Ray cluster from a previous test cases stays alive after the next test case starts and shuts down later, leading to test failures due to lost data or crashes (race during worker shutdown, will be investigated separately).
This PR makes sure each test case shuts down its Ray cluster.
This PR fixed and reenabled tests in HA mode
- //python/ray/tests:test_healthcheck
- //python/ray/tests:test_autoscaler_drain_node_api
- //python/ray/tests:test_ray_debugger
When cleanup the function table, we use the prefix to delete the data. But right now prefix contains binary data and it won't work well with redis keys/scan which use `*` in the pattern.
For example, when job id increases to 41, it'll delete the keys for job 1 which leads to the new worker failing to import the function.
This PR uses hex of job id to avoid this.
This PR adds pandas block format support by implementing `PandasRow`, `PandasBlockBuilder`, `PandasBlockAccessor`.
Note that `sort_and_partition`, `combine`, `merge_sorted_blocks`, `aggregate_combined_blocks` in `PandasBlockAccessor` redirects to arrow block format implementation for now. They'll be implemented in a later PR.
Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com>
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Why are these changes needed?
fix dlmalloc allocate bug, details in here #21310
* fix dlmalloc bug
* make lint happy
* make lint happy
* fix by comment
* use _check_spilled_mb
* add cpp UT
If a task is re-executed on failure, it will deterministically generate the same IDs for any ray.put or .remote task calls because it uses its own task ID as a seed. This can cause problems if those objects conflict with previous versions that still exist in the cluster.
This PR adds the execution attempt number to the current task ID seed. This avoids collisions with any ObjectIDs generated by the previous execution attempt of the task.
Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com>
In Ray, functions are exported to the function table during runtime. But it's not cleaned up after use. This PR garbage collects the resource when there is no job/detached actor referencing the resource.
Ideally, we should move the function table imports/exports feature to core, so gcs function manager is introduced, and currently, it's for reference counting only.
External Redis should still be supported with GCS bootstrapping, to avoid breaking users.
In GCS mode, some logic are removed for external Redis:
- Printing external Redis addresses to terminal: hard to implement across `ray start`, `ray.init()` and Ray cluster util.
- Starting local Redis if external Redis is unavailable: failing loudly here seems more appropriate.
Also, re-enable a few tests which restarts GCS in GCS bootstrapping mode, by using external Redis for KV storage.
Currently we install OpenSSH on the fly in fake multinode docker testing. Instead we can speed testing up a fair bit by building a Docker image which includes OpenSSH first and then run tests with this image.
This PR adds a new method to the Searcher class, add_evaluated_trials. This method wraps around add_evaluated_point and allows the user to pass a Trial, list of Trials or ExperimentAnalysis to load into the searcher. Furthermore, this PR updates the HEBO version to the latest and removes outdated documentation, and adds add_evaluated_point methods to Dragonfly and SkOpt searchers.
tune does not run smoothly on Windows. This cleans up some blockers:
- use cross-platform shutils.get_terminal_size instead of Popen(stty)
- somehow Trainer.workers is None at the end of test_commands.py, so the cleanup command was erroring. The error was not fatal, but was printing in the logs.
- if run locally, the log files are all written to the same location, so the rync-based syncing solution is not needed. This is the real fix for issue #20747
