Updates TensorflowPredictor to use the new _predict_pandas API.
Also as agreed upon offline, removes the extra configurations from TensorflowPredictor (column selection, concatenation) in favor of having this be done via a Preprocessor.
Add external hook to /api/component_activities endpoint in dashboard snapshot router
Change is_active field of RayActivityResponse to take an enum RayActivityStatus instead of bool. This is a backward incompatible change, but should be ok because [dashboard] Add component_activities API #25996 wasn't included in any branch cuts. RayActivityResponse now supports informing when there was an error getting the activity observation and the reason.
We currently use our own serialization to ship checkpoints as objects. Instead we should use the Checkpoint class. This PR also adds support to create results from checkpoints pointing to object references.
Depends on #26351
Signed-off-by: Kai Fricke <kai@anyscale.com>
#25655 refactored syncing but it introduced a regression - previously, dirs of any size could have been synced, but now only dirs below the default limit of 1 GB can be. This PR fixes this regression allowing dirs of any size to be synced.
With this PR, files put into directory checkpoints that were dict checkpoints will be serialized and retained when a subsequent to_dict() is called. This is to enable storing additional files, as e.g. needed by Ray Tune.
Signed-off-by: Kai Fricke <kai@anyscale.com>
We added drop_columns() API to datasets in #26200, so updating documentation here to use the new API - doc/source/data/examples/nyc_taxi_basic_processing.ipynb. In addition, fixing some minor typos after proofreading the datasets documentation.
Uses the new AIR Train API for examples and tests.
The `Result` object gets a new attribute - `log_dir`, pointing to the Trial's `logdir` allowing users to access tensorboard logs and artifacts of other loggers.
This PR only deals with "low hanging fruit" - tests that need substantial rewriting or Train user guide are not touched. Those will be updated in followup PRs.
Tests and examples that concern deprecated features or which are duplicated in AIR have been removed or disabled.
Requires https://github.com/ray-project/ray/pull/25943 to be merged in first
Alternative to #26356 - here we just pin raydp-nightly and resolve the dependency issues in follow-up PRs.
This is to quickly unblock CI.
Signed-off-by: Kai Fricke <kai@anyscale.com>
This PR unified the semantics of some workflow APIs.
Those workflow APIs acts on workflow tasks so they could be blocked for a long time. So we have both the blocking and non-blocking versions for them: xxx for blocking and xxx_async for non-blocking APIs.
This is a simple example that shows how to do OCR with Ray Datasets. It includes:
- How to upload and download the dataset to and from S3
- How to run OCR on the dataset with tesseract
- How to use actors to keep around and re-use a spaCy context for doing NLP on the data
Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com>
The existing docs didn't work for me and these updates did. 🤷♀️ I selectively pulled this stuff out of the CI (which ideally would just be runnable locally).
In Ray 2.0, we want to achieve api server HA.
Originally serve endpoints are in head node.
This pr moves serve endpoints to dashboard agents, so they will be HA due to multiple replica of dashboard agent.
When detecting resource capacities to advertise to Ray, the Ray operator takes into account requests. This doesn't make sense -- taking a min of resources and limits definitely doesn't make sense. Only limits should be considered.
Revert back to using nightly base images instead of pinning to 1.12.1. Pinning the docker image had led to uncaught errors in the past. Instead, we should be using nightly to make sure release tests will work on the most up to date versions of docker/cluster envs. If there are any test failures, the underlying issues should be fixed rather than pinning the docker image.
Co-authored-by: Kai Fricke <kai@anyscale.com>
* Avoid depending on `CoreWorkerProcess::GetCoreWorker()` in local mode.
* Fix bug in `LocalModeObjectStore::PutRaw`.
* Remove unused `TaskExecutor::Execute` method.
* Use `Process::Wait` instead of sleep when invoking `ray start` and `ray stop`.
