Recently there have been a number of CI test failures due to direct or transitive dependency version upgrades. Printing out environment information for each test suite allows us to quickly check the diff between failed and successful runs.
**Notes:**
1. In this PR I just manually added `./ci/env/env_info.sh` to each test suite. We may want to generalize this in the future.
2. This is just for CI now, but is applicable to release tests as well.
Signed-off-by: Matthew Deng <matt@anyscale.com>
Removes all ML related code from `ray.util`
Removes:
- `ray.util.xgboost`
- `ray.util.lightgbm`
- `ray.util.horovod`
- `ray.util.ray_lightning`
Moves `ray.util.ml_utils` to other locations
Closes#23900
Signed-off-by: Amog Kamsetty <amogkamsetty@yahoo.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Co-authored-by: Kai Fricke <kai@anyscale.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 AIR CI build has been failing on master since #25022.
#25022 moved the tests that require credentials, but we left the bazel command in the build pipeline still. So even though all the tests are passing, the buildkite stage itself was failing since it tries run tests that require credentials, but these tests no longer exist in the directory. This is only a problem for master build since we don't run this command for PR builds.
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.
This PR
- adds an example on how to run Ray Train and log results to weights & biases
- adds functionality to the W&B plugin to store checkpoints
- fixes a bug introduced in #24017
- Adds a CI utility script to setup credentials
- Adds a CI utility script to remove test state from external services cc @simon-mo
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
Clean up the ci/ directory. This means getting rid of the travis/ path completely and moving the files into sensible subdirectories.
Details:
- Moves everything under ci/travis into subdirectories, e.g. ci/build, ci/lint, etc.
- Minor adjustments to some scripts (variable renames)
- Removes the outdated (unused) asan tests
Follow-up to #22748, enabling tests in CI.
Conditions: A new RAY_CI_ML_AFFECTED condition is added for this test suite. The package currently depends on Ray Data, and will be triggered accordingly.
Dependencies: Adding DATA_PROCESSING_TESTING dependencies (set for install-dependencies.sh) for now.
Adding a minimal test suite to catch any regressions from accidentally adding backend imports (e.g. `torch`, `tensorflow`, `horovod`) to the main import path.
**Example:** If I'm running Ray Train with `tensorflow`, I should not be required to have `torch` installed.
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.
Following #18987 this PR adds a docker-compose based local multi node cluster.
The fake multinode docker comprises two parts. The docker_monitor.py script is a watch script calling docker compose up whenever the docker-compose.yaml changes. The node provider creates and updates the docker compose according to the autoscaling requirements.
This mode fully supports autoscaling and comes with test utilities to start and connect to docker-compose autoscaling environments. There's also a sample test case showing how this can be used.
