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.
As the integration logging callbacks are commonly used with AIR Trainers, they should be moved from the tune package to the air package. The old imports will still work, but raise a deprecation warning.
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.
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 is a notebook showing how to tune an xgboost model and analyze the results.
Also adds a `get_dataframe()` method to `ResultsGrid` to fetch the trial results.
Depends on #24483 for toctree.
