add docs for gitbook (#1919)

docs-gb/PULL_REQUEST_TEMPLATE/pull_request_template.md

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+# Pull Request
+
+## Description
+<!-- Provide a brief description of the purpose of this pull request -->
+
+## Changes Made
+<!-- Describe the changes introduced by this pull request -->
+
+## Related Issues
+<!-- Mention any related issues or tickets that are being addressed by this pull request -->
+
+## Screenshots (if applicable)
+<!-- Add screenshots or images to visually represent the changes, if applicable -->
+
+## Checklist
+<!-- Make sure to check the items below before submitting your pull request -->
+
+- [ ] Code follows the project's style guidelines
+- [ ] All tests related to the changes pass successfully
+- [ ] Documentation is updated (if necessary)
+- [ ] Code is reviewed by at least one other team member
+- [ ] Any breaking changes are communicated and documented
+
+## Additional Notes
+<!-- Add any additional notes or context that may be helpful for reviewers -->
+

docs-gb/README.md

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+# MLServer
+
+An open source inference server for your machine learning models.
+
+[![video_play_icon](https://user-images.githubusercontent.com/10466106/151803854-75d17c32-541c-4eee-b589-d45b07ea486d.png)](https://www.youtube.com/watch?v=aZHe3z-8C_w)
+
+## Overview
+
+MLServer aims to provide an easy way to start serving your machine learning
+models through a REST and gRPC interface, fully compliant with [KFServing's V2
+Dataplane](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html)
+spec. Watch a quick video introducing the project [here](https://www.youtube.com/watch?v=aZHe3z-8C_w).
+
+- Multi-model serving, letting users run multiple models within the same
+  process.
+- Ability to run [inference in parallel for vertical
+  scaling](https://mlserver.readthedocs.io/en/latest/user-guide/parallel-inference.html)
+  across multiple models through a pool of inference workers.
+- Support for [adaptive
+  batching](https://mlserver.readthedocs.io/en/latest/user-guide/adaptive-batching.html),
+  to group inference requests together on the fly.
+- Scalability with deployment in Kubernetes native frameworks, including
+  [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/graph/protocols.html#v2-kfserving-protocol) and
+  [KServe (formerly known as KFServing)](https://kserve.github.io/website/modelserving/v1beta1/sklearn/v2/), where
+  MLServer is the core Python inference server used to serve machine learning
+  models.
+- Support for the standard [V2 Inference Protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html) on
+  both the gRPC and REST flavours, which has been standardised and adopted by
+  various model serving frameworks.
+
+You can read more about the goals of this project on the [initial design
+document](https://docs.google.com/document/d/1C2uf4SaAtwLTlBCciOhvdiKQ2Eay4U72VxAD4bXe7iU/edit?usp=sharing).
+
+## Usage
+
+You can install the `mlserver` package running:
+
+```bash
+pip install mlserver
+```
+
+Note that to use any of the optional [inference runtimes](#inference-runtimes),
+you'll need to install the relevant package.
+For example, to serve a `scikit-learn` model, you would need to install the
+`mlserver-sklearn` package:
+
+```bash
+pip install mlserver-sklearn
+```
+
+For further information on how to use MLServer, you can check any of the
+[available examples](#examples).
+
+## Inference Runtimes
+
+Inference runtimes allow you to define how your model should be used within
+MLServer.
+You can think of them as the **backend glue** between MLServer and your machine
+learning framework of choice.
+You can read more about [inference runtimes in their documentation
+page](./docs/runtimes/index.md).
+
+Out of the box, MLServer comes with a set of pre-packaged runtimes which let
+you interact with a subset of common frameworks.
+This allows you to start serving models saved in these frameworks straight
+away.
+However, it's also possible to **[write custom
+runtimes](./docs/runtimes/custom.md)**.
+
+Out of the box, MLServer provides support for:
+
+| Framework     | Supported | Documentation                                                    |
+| ------------- | --------- | ---------------------------------------------------------------- |
+| Scikit-Learn  | ✅        | [MLServer SKLearn](./runtimes/sklearn)                           |
+| XGBoost       | ✅        | [MLServer XGBoost](./runtimes/xgboost)                           |
+| Spark MLlib   | ✅        | [MLServer MLlib](./runtimes/mllib)                               |
+| LightGBM      | ✅        | [MLServer LightGBM](./runtimes/lightgbm)                         |
+| CatBoost      | ✅        | [MLServer CatBoost](./runtimes/catboost)                         |
+| Tempo         | ✅        | [`github.com/SeldonIO/tempo`](https://github.com/SeldonIO/tempo) |
+| MLflow        | ✅        | [MLServer MLflow](./runtimes/mlflow)                             |
+| Alibi-Detect  | ✅        | [MLServer Alibi Detect](./runtimes/alibi-detect)                 |
+| Alibi-Explain | ✅        | [MLServer Alibi Explain](./runtimes/alibi-explain)               |
+| HuggingFace   | ✅        | [MLServer HuggingFace](./runtimes/huggingface)                   |
+
+## Supported Python Versions
+
+🔴 Unsupported
+
+🟠 Deprecated: To be removed in a future version
+
+🟢 Supported
+
+🔵 Untested
+
+| Python Version | Status |
+| -------------- | ------ |
+| 3.7            | 🔴     |
+| 3.8            | 🔴     |
+| 3.9            | 🟢     |
+| 3.10           | 🟢     |
+| 3.11           | 🔵     |
+| 3.12           | 🔵     |
+
+## Examples
+
+To see MLServer in action, check out [our full list of
+examples](./docs/examples/index.md).
+You can find below a few selected examples showcasing how you can leverage
+MLServer to start serving your machine learning models.
+
+- [Serving a `scikit-learn` model](./docs/examples/sklearn/README.md)
+- [Serving a `xgboost` model](./docs/examples/xgboost/README.md)
+- [Serving a `lightgbm` model](./docs/examples/lightgbm/README.md)
+- [Serving a `catboost` model](./docs/examples/catboost/README.md)
+- [Serving a `tempo` pipeline](./docs/examples/tempo/README.md)
+- [Serving a custom model](./docs/examples/custom/README.md)
+- [Serving an `alibi-detect` model](./docs/examples/alibi-detect/README.md)
+- [Serving a `HuggingFace` model](./docs/examples/huggingface/README.md)
+- [Multi-Model Serving with multiple frameworks](./docs/examples/mms/README.md)
+- [Loading / unloading models from a model repository](./docs/examples/model-repository/README.md)
+
+## Developer Guide
+
+### Versioning
+
+Both the main `mlserver` package and the [inference runtimes
+packages](./docs/runtimes/index.md) try to follow the same versioning schema.
+To bump the version across all of them, you can use the
+[`./hack/update-version.sh`](./hack/update-version.sh) script.
+
+We generally keep the version as a placeholder for an upcoming version.
+
+For example:
+
+```bash
+./hack/update-version.sh 0.2.0.dev1
+```
+
+### Testing
+
+To run all of the tests for MLServer and the runtimes, use:
+
+```bash
+make test
+```
+
+To run run tests for a single file, use something like:
+
+```bash
+tox -e py3 -- tests/batch_processing/test_rest.py
+```

docs-gb/SUMMARY.md

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+* [MLServer](README.md)
+* [Getting Started](getting-started.md)
+* [User Guide](user-guide/index.md)
+  * [Content Types (and Codecs)](user-guide/content-type.md)
+  * [OpenAPI Support](user-guide/openapi.md)
+  * [Parallel Inference](user-guide/parallel-inference.md)
+  * [Adaptive Batching](user-guide/adaptive-batching.md)
+  * [Custom Inference Runtimes](user-guide/custom.md)
+  * [Metrics](user-guide/metrics.md)
+  * [Deployment](user-guide/deployment/README.md)
+    * [Seldon Core](user-guide/deployment/seldon-core.md)
+    * [KServe](user-guide/deployment/kserve.md)
+  * [Streaming](user-guide/streaming.md)
+* [Inference Runtimes](runtimes/README.md)
+  * [SKLearn](runtimes/sklearn.md)
+  * [XGBoost](runtimes/xgboost.md)
+  * [MLFlow](runtimes/mlflow.md)
+  * [Spark MlLib](runtimes/mllib.md)
+  * [LightGBM](runtimes/lightgbm.md)
+  * [Catboost](runtimes/catboost.md)
+  * [Alibi-Detect](runtimes/alibi-detect.md)
+  * [Alibi-Explain](runtimes/alibi-explain.md)
+  * [HuggingFace](runtimes/huggingface.md)
+  * [Custom](runtimes/custom.md)
+* [Reference](reference/README.md)
+  * [MLServer Settings](reference/settings.md)
+  * [Model Settings](reference/model-settings.md)
+  * [MLServer CLI](reference/cli.md)
+  * [Python API](reference/python-api/README.md)
+    * [MLModel](reference/api/model.md)
+    * [Types](reference/api/types.md)
+    * [Codecs](reference/api/codecs.md)
+    * [Metrics](reference/api/metrics.md)
+* [Examples](examples/README.md)
+  * [Serving Scikit-Learn models](examples/sklearn/README.md)
+  * [Serving XGBoost models](examples/xgboost/README.md)
+  * [Serving LightGBM models](examples/lightgbm/README.md)
+  * [Serving MLflow models](examples/mlflow/README.md)
+  * [Serving a custom model](examples/custom/README.md)
+  * [Serving Alibi-Detect models](examples/alibi-detect/README.md)
+  * [Serving HuggingFace Transformer Models](examples/huggingface/README.md)
+  * [Multi-Model Serving](examples/mms/README.md)
+  * [Model Repository API](examples/model-repository/README.md)
+  * [Content Type Decoding](examples/content-type/README.md)
+  * [Custom Conda environments in MLServer](examples/conda/README.md)
+  * [Serving a custom model with JSON serialization](examples/custom-json/README.md)
+  * [Serving models through Kafka](examples/kafka/README.md)
+  * [Streaming](examples/streaming/README.md)
+  * [Deploying a Custom Tensorflow Model with MLServer and Seldon Core](examples/cassava/README.md)
+* [Changelog](changelog.md)