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.
Running ray status with the changes from #20359
while running an autoscaler older than those changes
results in an error on input "head_ip" to LoadMetricsSummary.
See #20359 (comment)
This PR fixes the bug by restoring head_ip as an optional parameter of LoadMetricsSummary.
non_terminated_nodes calls are expensive for some node provider implementations.
This PR refactors autoscaler._update() such that it results in at most one non_terminated_nodes call.
Conceptually, the change is that the autoscaler only needs a consistent view of the world once per update interval.
The structure of an autoscaler update is now
call non_terminated_nodes to update internal state
update autoscaler status strings
terminate nodes we don't need, removing them from internal state as we go
run node updaters if needed
get nodes to launch based on internal state
There's a small operational difference introduced:
Previously -- After a node is created, its NodeUpdater thread is initiated immediately.
Now -- After a node is created, its NodeUpdater thread is initiated in the next autoscaler update.
This typically will not introduce latency, since the time to get SSH access (a few minutes) is much longer than the autoscaler update interval (5 seconds by default).
Along the way, I've removed the local_ip initialization parameter for LoadMetrics because it was confusing and not useful (and caused some tests to fail)
## Why are these changes needed?
In python, redis rpush is used to broadcast and store the keys. In this PR, we use gcs kv to store the keys. pubsub is still using redis which need to be remove later.
The protocol before this PR:
- worker subscribe to redis key spaces
- worker write the key of function/actor to (export:sqn, key)
- so the other worker will be notified and start to load the data by checking export:sqn
This depends on redis for both kv and pubsub, and this PR fix the kv part.
After this PR:
- worker subscribe to redis key space
- For exporting:
- worker will find the first key not being hold. This is guaranteed by internal kv which right now is a single thread, atomic db. The worker will just check until it find one key not existing and write it (this is single operation). One optimization right now is to use the import counter as the start offset since this counter means all keys before the counter has already been used.
- worker will then write a dummy key to redis key space for broadcasting
- For importer
- It's working as before, but instead of reading from redis, it will read from gcs kv.
This is part in redis removal project.
## Related issue number
https://github.com/ray-project/ray/issues/19443
Remerging #19789 with some fixes for Dask-on-Ray 1TB sort:
- Fixes a bug where the timer was not getting reset correctly
- Increased timeout to 10min just to be safe
- Changed the error to a unique exception ObjectFetchTimedOutError to improve debugging.
This exception should usually indicate a system-level bug.
Propagates environment variables to BackendExecutor actor using runtime envs.
Also actually run test_callbacks in CI.
Note that there is an issue with runtime envs: #20587. But this only happens if you shutdown Ray and start a new session again.
This PR reverts the previous revert with the following minor changes.
Worker capping is off by default.
The cap feature flag is on the for the tests that explicitely require it.
Adds a RAY_DATA_DISABLE_PROGRESS_BARS env var to control the default progress bar behavior. The default value is "0". Setting it to "1" disables progress bars, unless they are reenabled again by the set_progress_bars method.
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
## Why are these changes needed?
Publisher and subscriber for logs, in driver, dashboard and tests are refactored to make it easier to support using Ray pubsub for logs. Actual support of Ray pubsub for logs will be added later in #20492.
This PR does not intend to introduce any behavior change.
## Related issue number
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.
Why are these changes needed?
This is a serial of PRs to make CoreWorkerProcess thread-safe and CoreWorker Code easy to read. [#19675#19677#19678#19679]
Move CoreWorkerOptions out of core_worker.h; makes the code easier to read.
Next PR: #19677
The ray-ml image depends on numpy ~=1.19.2 via the tensorflow==2.6 requirement. Unfortunately that's incompatible with Dataset (see here #20258 (comment)).
This PR upgrades the numpy dependency only for the nightly test.
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.
Before this PR, `ds.iter_batches()` would yield no batches if `prefetch_blocks > ds.num_blocks()` was given, since the sliding window semantics were to return no windows if `window_size > len(iterable)`. This PR tweaks the sliding window implementation to always return at least one window, even if the one window is smaller than the given window size.
This should fix the long running release tests that are failing to build their app configs.
It seems like pip install ray[all] now downgrades the ray version. It's unclear why, but most likely, a dependency has pinned the ray version now. This PR explicitely install the version of Ray that we want after the pip install ray[all] to fix the problem.
Xgboosts train_small timed out because of a CPU borrowing feature related to placement groups. The root bug will be fixed in the coming weeks, but this PR makes the release test consistently pass by requesting 0 CPUs for the remote wrapper script.
