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
**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>
The package "ml" should be renamed to "air".
Main question: Keep a `ml.py` with `from ray.air import *` for some level of backwards compatibility?
I'd go for no to force people to use the new structure.
The TrialExecutor base class was a stub and has been deprecated long ago; direct inheritance was disabled. This PR removes the base class and moves the remaining functionality into the RayTrialExecutor.
Currently, we are not running doc notebooks in CI due to a bazel misconfiguration - we are using `glob` in a top level package in order to get the paths for the notebooks, but those are contained inside subpackages, which glob purposefully ignores. Therefore, the lists of notebooks to run are empty. This PR fixes that by:
* Running the `py_test_run_all_notebooks` macro inside the relevant subpackages
* Editing the `test_myst_doc.py` script to allow for recursive search for the target file, allowing to deal with mismatches between `name` and `data` arguments in `py_test_run_all_notebooks`
* Setting the `allow_empty=False` flag inside `glob` calls in our macros to ensure that this oversight is caught early
* Enabling detection of changes in doc folder for `*.ipynb` and `BUILD` files
This PR also adds a GPU runner for doc tests, allowing one of our examples to pass - and setting the infra for more to come. Finally, a misconfigured path for one set of doc tests is also fixed.
Rolling out next deprecation cycle:
- DeprecationWarnings that were `warnings.warn` or `logger.warn` before are now raised errors
- Raised Deprecation warnings are now removed
- Notably, this involves deprecating the TrialCheckpoint functionality and associated cloud tests
- Added annotations to deprecation warning for when to fully remove
Ray SGD v1 has been denoted as a deprecated API for a while. This PR fully deprecates Ray SGD v1. An error will be raised if ray.util.sgd package is attempted to be imported.
Closes#16435
* Revert "Revert "[tune] Also interrupt training when SIGUSR1 received" (#24085)"
This reverts commit 00595653ed.
Failure in windows has been addressed by conditionally registering the signal handler if available.
Ray Tune currently gracefully stops training on SIGINT. However, the Ray core worker prevents SIGINT (and SIGTERM) to be processed by child tasks, which means that Ray Tune runs that are started in remote tasks (e.g. via Ray client) cannot be gracefully interrupted.
In k8s-based cloud tests that used the Ray client to kick off a Ray Tune run, this lead to test flakiness, as final experiment state could not be gracefully persisted to cloud storage.
This PR adds support for SIGUSR1 in addition to SIGINT to interrupt training gracefully.
- Adds links to Job Submission from existing library tutorials where `ray submit` is used. When Jobs becomes GA, we should fully replace the uses of `ray submit` with Ray job submission and ensure this is tested.
- Adds docstrings for the Jobs SDK, which automatically show up in the API reference
- Improve the Job Submission main page
- Add a "Deployment Guide" landing page explaining when to use Ray Client vs Ray Jobs
Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
This PR makes a number of major overhauls to the Ray core docs:
Add a key-concepts section for {Tasks, Actors, Objects, Placement Groups, Env Deps}.
Re-org the user guide to align with key concepts.
Rewrite the walkthrough to link to mini-walkthroughs in the key concept sections.
Minor tweaks and additional transition material.
Example for running notebooks on our docs directly in the browser by connecting to a binder instance launched on demand.
If this seems useful we can extend this to other examples gradually.
Signed-off-by: Max Pumperla <max.pumperla@googlemail.com>
This is a down scoped change. For the full overview picture of Tune control loop, see [`Tune control loop refactoring`](https://docs.google.com/document/d/1RDsW7SVzwMPZfA0WLOPA4YTqbRyXIHGYmBenJk33HaE/edit#heading=h.2za3bbxbs5gn)
1. Previously there are separate waits on pg ready and other events. As a result, there are quite a few timing tweaks that are inefficient, hard to understand and unit test. This PR consolidates into a single wait that is handled by TrialRunner in each step.
- A few event types are introduced, and their mapping into scenarios
* PG_READY --> Should place a trial onto it. If somehow there is no trial to be placed there, the pg will be put in _ready momentarily. This is due to historically resources is conceptualized as a pull based model.
* NO_RUNNING_TRIALS_TIME_OUT --> possibly not sufficient resources case
* TRAINING_RESULT
* SAVING_RESULT
* RESTORING_RESULT
* YIELD --> This just means that simply taking very long to train. We need to punt back to the main loop to print out status info etc.
2. Previously TrialCleanup is not very efficient and can be racing between Trainable.stop() and `return_placement_group`. This PR streamlines the Trial cleanup process by explicitly let Trainable.stop() to finish followed by `return_placement_group(pg)`. Note, graceful shutdown is needed in cases like `pause_trial` where checkpointing to memory needs to be given the time to happen before the actor is gone.
3. There are quite some env variables removed (timing tweaks), that I consider OK to proceed without deprecation cycle.
Continuing docs overhaul, tune now has:
- [x] better landing page
- [x] a getting started guide
- [x] user guide was cut down, partially merged with FAQ, and partially integrated with tutorials
- [x] the new user guide contains guides to tune features and practical integrations
- [x] we rewrote some of the feature guides for clarity
- [x] we got rid of sphinx-gallery for this sub-project (only data and core left), as it looks bad and is unnecessarily complicated anyway (plus, makes the build slower)
- [x] sphinx-gallery examples are now moved to markdown notebook, as started in #22030.
- [x] Examples are tested in the new framework, of course.
There's still a lot one can do, but this is already getting too large. Will follow up with more fine-tuning next week.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
This PR adds a `CometLoggerCallback` to the Tune Integrations, allowing users to log runs from Ray to [Comet](https://www.comet.ml/site/).
Co-authored-by: Michael Cullan <mjcullan@gmail.com>
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
This PR consolidates both #21667 and #21759 (look there for features), but improves on them in the following way:
- [x] we reverted renaming of existing projects `tune`, `rllib`, `train`, `cluster`, `serve`, `raysgd` and `data` so that links won't break. I think my consolidation efforts with the `ray-` prefix were a little overeager in that regard. It's better like this. Only the creation of `ray-core` was a necessity, and some files moved into the `rllib` folder, so that should be relatively benign.
- [x] Additionally, we added Algolia `docsearch`, screenshot below. This is _much_ better than our current search. Caveat: there's a sphinx dependency that needs to be replaced (`sphinx-tabs`) by another, newer one (`sphinx-panels`), as the former prevents loading of the `algolia.js` library. Will follow-up in the next PR (hoping this one doesn't get re-re-re-re-reverted).
Added hyperameters to the concetp section since it's important to explain what they are and added diagrams help readeer visualize the difference between model and hyperparameters
Signed-off-by: Jules S.Damji <jules@anyscale.com>
Co-authored-by: Jules S.Damji <jules@anyscale.com>
