Per the [Ray docs contributing guide](https://docs.ray.io/en/master/ray-contribute/docs.html), code chunks should be in `.py` files and pulled in via `literalinclude` rather than placed directly in `.rst` files. This PR takes a small step in doing this for the RLlib docs, specifically for the training API doc page.
Note that I had to make some changes to the code itself so that it would run, namely adding missing numpy imports and changing `model.from_batch(...)` to `model(...)` in a couple places.
Co-authored-by: Max Pumperla <max.pumperla@googlemail.com>
This PR makes several improvements to the Datasets' tensor story. See the issues for each item for more details.
- Automatically infer tensor blocks (single-column tables representing a single tensor) when returning NumPy ndarrays from map_batches(), map(), and flat_map().
- Automatically infer tensor columns when building tabular blocks in general.
- Fixes shuffling and sorting for tensor columns
This should improve the UX/efficiency of the following:
- Working with pure-tensor datasets in general.
- Mapping tensor UDFs over pure-tensor, a better foundation for tensor-native preprocessing for end-users and AIR.
* [runtime env] runtime env inheritance refactor (#22244)
Runtime Environments is already GA in Ray 1.6.0. The latest doc is [here](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments). And now, we already supported a [inheritance](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance) behavior as follows (copied from the doc):
- The runtime_env["env_vars"] field will be merged with the runtime_env["env_vars"] field of the parent. This allows for environment variables set in the parent’s runtime environment to be automatically propagated to the child, even if new environment variables are set in the child’s runtime environment.
- Every other field in the runtime_env will be overridden by the child, not merged. For example, if runtime_env["py_modules"] is specified, it will replace the runtime_env["py_modules"] field of the parent.
We think this runtime env merging logic is so complex and confusing to users because users can't know the final runtime env before the jobs are run.
Current PR tries to do a refactor and change the behavior of Runtime Environments inheritance. Here is the new behavior:
- **If there is no runtime env option when we create actor, inherit the parent runtime env.**
- **Otherwise, use the optional runtime env directly and don't do the merging.**
Add a new API named `ray.runtime_env.get_current_runtime_env()` to get the parent runtime env and modify this dict by yourself. Like:
```Actor.options(runtime_env=ray.runtime_env.get_current_runtime_env().update({"X": "Y"}))```
This new API also can be used in ray client.
This PR adds a FAQ to Datasets docs.
Docs preview: https://ray--24932.org.readthedocs.build/en/24932/
## Checks
- [x] I've run `scripts/format.sh` to lint the changes in this PR.
- [x] I've included any doc changes needed for https://docs.ray.io/en/master/.
- [x] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
- [x] Unit tests
- [ ] Release tests
- [ ] This PR is not tested :(
Co-authored-by: Eric Liang <ekhliang@gmail.com>
This PR adds a dedicated docs page for examples, and adds a basic e2e tabular data processing example on the NYC taxi dataset.
The goal of this example is to demonstrate basic data reading, inspection, transformations, and shuffling, along with ingestion into dummy model trainers and doing dummy batch inference, for tabular (Parquet) data.
This PR overhauls the "Accessing Datasets", adding proper coverage of each data consuming methods, including the ML framework exchange APIs (to_torch() and to_tf()).
This PR moves all exception classes from runtime module to api module. It's aiming to eliminate the confusion about ray exceptions. It means that Ray users don't need to touch runtime module when API programming after this PR.
Note that this should be merged onto 2.0.
This PR knocks off a few miscellaneous GA docs P0s given in our docs tracker. Namely:
- Documents Datasets resource allocation model.
- De-emphasizes global/windowed shuffling.
- Documents lazy execution mode, and expands our execution model docs in general.
This is part of the Dataset GA doc fix effort to update/improve the documentation.
This PR revamps the Getting Started page.
What are the changes:
- Focus on basic/core features that are bread-and-butter for users, leave the advanced features out
- Focus on high level introduction, leave the detailed spec out (e.g. what are possible batch_types for map_batches() API)
- Use more realistic (yet still simple) data example that's familiar to people (IRIS dataset in this case)
- Use the same data example throughout to make it context-switch free
- Use runnable code rather than faked
- Reference to the code from doc, instead of inlining them in the doc
Co-authored-by: Ubuntu <ubuntu@ip-172-31-32-136.us-west-2.compute.internal>
Co-authored-by: Eric Liang <ekhliang@gmail.com>
This example simply doesn't run as is. We can bring it back up again later, if it makes sense. But it's not clear what the variables used there, like actor are. Fixes#21328
Signed-off-by: Max Pumperla <max.pumperla@googlemail.com>
as per discussion on slack, this should avoid having old versions of the docs indexed by search engines, see readthedocs/readthedocs.org#2430 for reference.
Signed-off-by: Max Pumperla <max.pumperla@googlemail.com>
This PR is a general overhaul of the "Creating Datasets" feature guide, providing complete coverage of all (public) dataset creation APIs and highlighting features and quirks of the individual APIs, data modalities, storage backends, etc. In order to keep the page from getting too long and keeping it easy to navigate, tabbed views are used heavily.
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 support for tensor columns in the to_tf() and to_torch() APIs.
For Torch, this involves an explicit extension array check and (zero-copy) conversion of the tensor column to a NumPy array before converting the column to a Torch tensor.
For TensorFlow, this involves bypassing df.values when converting tensor feature columns to NumPy arrays, instead manually creating a single NumPy array from the column Series.
In both cases, I think that the UX around heterogeneous feature columns and squeezing the column dimension could be improved, but I'm saving that for a future PR.
Adds a Dataset.split_proportionately method that allows the user to split a dataset using proportions. This is a very common use-case for eg. train-test splitting. The implementation is a thin wrapper over Dataset.split_at_indices.
Additionally, this PR adds a ray.ml.train_test_split function intended to provide a familiar API to ML practitioners.
Updates the landing page to match the format and content of Tune's. Added some shorter quickstarts and sharpened up the messaging in our "Why choose Serve?" section, those are the main content changes.
I also moved all of the `doc_code` into one directory and added a bazel target that should run all of the examples added there. Split into a separate PR: https://github.com/ray-project/ray/pull/24736.
Why are these changes needed?
Current documentation code in Message passing using Ray Queue can be enhanced, for better demonstration of the message queue.
It creates 10 tasks but only 2 consumers, and each consumer consumes one task then exit. Therefore, the output is a bit vague:
(consumer pid=1022727) got work 0
(consumer pid=1022595) got work 1
So I make consumer working until the queue is empty. The output shows consumer 1 and 2 working in parallel:
(consumer pid=1030876) consumer 0 got work 0
(consumer pid=1030876) consumer 0 got work 1
(consumer pid=1030876) consumer 0 got work 3
(consumer pid=1030876) consumer 0 got work 5
(consumer pid=1030876) consumer 0 got work 7
(consumer pid=1030876) consumer 0 got work 9
(consumer pid=1030949) consumer 1 got work 2
(consumer pid=1030949) consumer 1 got work 4
(consumer pid=1030949) consumer 1 got work 6
(consumer pid=1030949) consumer 1 got work 8
P.S. Also fix a typo in doc.
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.
