Remove more ray docs

website/docs/usage/index.md

@@ -75,7 +75,6 @@ spaCy's [`setup.cfg`](%%GITHUB_SPACY/setup.cfg) for details on what's included.
 | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `lookups`        | Install [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) for data tables for lemmatization and lexeme normalization. The data is serialized with trained pipelines, so you only need this package if you want to train your own models. |
 | `transformers`   | Install [`spacy-transformers`](https://github.com/explosion/spacy-transformers). The package will be installed automatically when you install a transformer-based pipeline.                                                                                    |
-| `ray`            | Install [`spacy-ray`](https://github.com/explosion/spacy-ray) to add CLI commands for [parallel training](/usage/training#parallel-training).                                                                                                                  |
 | `cuda`, ...      | Install spaCy with GPU support provided by [CuPy](https://cupy.chainer.org) for your given CUDA version. See the GPU [installation instructions](#gpu) for details and options.                                                                                |
 | `apple`          | Install [`thinc-apple-ops`](https://github.com/explosion/thinc-apple-ops) to improve performance on an Apple M1.                                                                                                                                               |
 | `ja`, `ko`, `th` | Install additional dependencies required for tokenization for the [languages](/usage/models#languages).                                                                                                                                                        |

website/docs/usage/projects.md

@@ -1014,54 +1014,6 @@ https://github.com/explosion/projects/blob/v3/integrations/fastapi/scripts/main.
 
 ---
 
-### Ray {#ray} <IntegrationLogo name="ray" width={100} height="auto" align="right" />
-
-> #### Installation
->
-> ```cli
-> $ pip install -U %%SPACY_PKG_NAME[ray]%%SPACY_PKG_FLAGS
-> # Check that the CLI is registered
-> $ python -m spacy ray --help
-> ```
-
-[Ray](https://ray.io/) is a fast and simple framework for building and running
-**distributed applications**. You can use Ray for parallel and distributed
-training with spaCy via our lightweight
-[`spacy-ray`](https://github.com/explosion/spacy-ray) extension package. If the
-package is installed in the same environment as spaCy, it will automatically add
-[`spacy ray`](/api/cli#ray) commands to your spaCy CLI. See the usage guide on
-[parallel training](/usage/training#parallel-training) for more details on how
-it works under the hood.
-
-<Project id="integrations/ray">
-
-Get started with parallel training using our project template. It trains a
-simple model on a Universal Dependencies Treebank and lets you parallelize the
-training with Ray.
-
-</Project>
-
-You can integrate [`spacy ray train`](/api/cli#ray-train) into your
-`project.yml` just like the regular training command and pass it the config, and
-optional output directory or remote storage URL and config overrides if needed.
-
-<!-- prettier-ignore -->
-```yaml
-### project.yml
-commands:
-  - name: "ray"
-    help: "Train a model via parallel training with Ray"
-    script:
-      - "python -m spacy ray train configs/config.cfg -o training/ --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy"
-    deps:
-      - "corpus/train.spacy"
-      - "corpus/dev.spacy"
-    outputs:
-      - "training/model-best"
-```
-
----
-
 ### Weights & Biases {#wandb} <IntegrationLogo name="wandb" width={175} height="auto" align="right" />
 
 [Weights & Biases](https://www.wandb.com/) is a popular platform for experiment

website/docs/usage/training.md

@@ -1572,77 +1572,6 @@ token-based annotations like the dependency parse or entity labels, you'll need
 to take care to adjust the `Example` object so its annotations match and remain
 valid.
 
-## Parallel & distributed training with Ray {#parallel-training}
-
-> #### Installation
->
-> ```cli
-> $ pip install -U %%SPACY_PKG_NAME[ray]%%SPACY_PKG_FLAGS
-> # Check that the CLI is registered
-> $ python -m spacy ray --help
-> ```
-
-[Ray](https://ray.io/) is a fast and simple framework for building and running
-**distributed applications**. You can use Ray to train spaCy on one or more
-remote machines, potentially speeding up your training process. Parallel
-training won't always be faster though – it depends on your batch size, models,
-and hardware.
-
-<Infobox variant="warning">
-
-To use Ray with spaCy, you need the
-[`spacy-ray`](https://github.com/explosion/spacy-ray) package installed.
-Installing the package will automatically add the `ray` command to the spaCy
-CLI.
-
-</Infobox>
-
-The [`spacy ray train`](/api/cli#ray-train) command follows the same API as
-[`spacy train`](/api/cli#train), with a few extra options to configure the Ray
-setup. You can optionally set the `--address` option to point to your Ray
-cluster. If it's not set, Ray will run locally.
-
-```cli
-python -m spacy ray train config.cfg --n-workers 2
-```
-
-<Project id="integrations/ray">
-
-Get started with parallel training using our project template. It trains a
-simple model on a Universal Dependencies Treebank and lets you parallelize the
-training with Ray.
-
-</Project>
-
-### How parallel training works {#parallel-training-details}
-
-Each worker receives a shard of the **data** and builds a copy of the **model
-and optimizer** from the [`config.cfg`](#config). It also has a communication
-channel to **pass gradients and parameters** to the other workers. Additionally,
-each worker is given ownership of a subset of the parameter arrays. Every
-parameter array is owned by exactly one worker, and the workers are given a
-mapping so they know which worker owns which parameter.
-
-![Illustration of setup](../images/spacy-ray.svg)
-
-As training proceeds, every worker will be computing gradients for **all** of
-the model parameters. When they compute gradients for parameters they don't own,
-they'll **send them to the worker** that does own that parameter, along with a
-version identifier so that the owner can decide whether to discard the gradient.
-Workers use the gradients they receive and the ones they compute locally to
-update the parameters they own, and then broadcast the updated array and a new
-version ID to the other workers.
-
-This training procedure is **asynchronous** and **non-blocking**. Workers always
-push their gradient increments and parameter updates, they do not have to pull
-them and block on the result, so the transfers can happen in the background,
-overlapped with the actual training work. The workers also do not have to stop
-and wait for each other ("synchronize") at the start of each batch. This is very
-useful for spaCy, because spaCy is often trained on long documents, which means
-**batches can vary in size** significantly. Uneven workloads make synchronous
-gradient descent inefficient, because if one batch is slow, all of the other
-workers are stuck waiting for it to complete before they can continue.
-
 ## Internal training API {#api}
 
 <Infobox variant="danger">