diff --git a/docs-gb/.gitbook/assets/kserve-logo.png b/docs-gb/.gitbook/assets/kserve-logo.png new file mode 100644 index 000000000..11675d395 Binary files /dev/null and b/docs-gb/.gitbook/assets/kserve-logo.png differ diff --git a/docs-gb/.gitbook/assets/seldon-core-logo.png b/docs-gb/.gitbook/assets/seldon-core-logo.png new file mode 100644 index 000000000..1aa6dbe72 Binary files /dev/null and b/docs-gb/.gitbook/assets/seldon-core-logo.png differ diff --git a/docs-gb/PULL_REQUEST_TEMPLATE/pull_request_template.md b/docs-gb/PULL_REQUEST_TEMPLATE/pull_request_template.md new file mode 100644 index 000000000..d33cfade8 --- /dev/null +++ b/docs-gb/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -0,0 +1,26 @@ +# Pull Request + +## Description + + +## Changes Made + + +## Related Issues + + +## Screenshots (if applicable) + + +## Checklist + + +- [ ] 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 + + diff --git a/docs-gb/README.md b/docs-gb/README.md new file mode 100644 index 000000000..d37232864 --- /dev/null +++ b/docs-gb/README.md @@ -0,0 +1,151 @@ +# 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 +``` diff --git a/docs-gb/SUMMARY.md b/docs-gb/SUMMARY.md new file mode 100644 index 000000000..a269677d9 --- /dev/null +++ b/docs-gb/SUMMARY.md @@ -0,0 +1,50 @@ +* [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) \ No newline at end of file diff --git a/docs-gb/changelog.md b/docs-gb/changelog.md new file mode 100644 index 000000000..c8c18719e --- /dev/null +++ b/docs-gb/changelog.md @@ -0,0 +1,529 @@ +# Changelog + + + +## [1.6.0](https://github.com/SeldonIO/MLServer/releases/tag/1.6.0) - 26 Jun 2024 + + ## Overview + + +### Upgrades + MLServer supports Pydantic V2. + +### Features + MLServer supports streaming data to and from your models. + + Streaming support is available for both the REST and gRPC servers: + * for the REST server is limited only to server streaming. This means that the client sends a single request to the server, and the server responds with a stream of data. + * for the gRPC server is available for both client and server streaming. This means that the client sends a stream of data to the server, and the server responds with a stream of data. + + See our [docs](https://mlserver.readthedocs.io/en/1.6.0/user-guide/streaming.html) and [example](https://mlserver.readthedocs.io/en/1.6.0/examples/streaming/README.html) for more details. + +## What's Changed +* fix(ci): fix typo in CI name by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1623 +* Update CHANGELOG by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1624 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1634 +* Fix mlserver_huggingface settings device type by [@geodavic](https://github.com/geodavic) in https://github.com/SeldonIO/MLServer/pull/1486 +* fix: Adjust HF tests post-merge of PR [#1486](https://github.com/SeldonIO/MLServer/issues/1486) by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1635 +* Update README.md w licensing clarification by [@paulb-seldon](https://github.com/paulb-seldon) in https://github.com/SeldonIO/MLServer/pull/1636 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1642 +* fix(ci): optimise disk space for GH workers by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1644 +* build: Update maintainers by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1659 +* fix: Missing f-string directives by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1677 +* build: Add Catboost runtime to Dependabot by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1689 +* Fix JSON input shapes by [@ReveStobinson](https://github.com/ReveStobinson) in https://github.com/SeldonIO/MLServer/pull/1679 +* build(deps): bump alibi-detect from 0.11.5 to 0.12.0 by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1702 +* build(deps): bump alibi from 0.9.5 to 0.9.6 by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1704 +* Docs correction - Updated README.md in mlflow to match column names order by [@vivekk0903](https://github.com/vivekk0903) in https://github.com/SeldonIO/MLServer/pull/1703 +* fix(runtimes): Remove unused Pydantic dependencies by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1725 +* test: Detect generate failures by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1729 +* build: Add granularity in types generation by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1749 +* Migrate to Pydantic v2 by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1748 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1753 +* Revert "build(deps): bump uvicorn from 0.28.0 to 0.29.0" by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1758 +* refactor(pydantic): Remaining migrations for deprecated functions by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1757 +* Fixed openapi dataplane.yaml by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1752 +* fix(pandas): Use Pydantic v2 compatible type by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1760 +* Fix Pandas codec decoding from numpy arrays by [@lhnwrk](https://github.com/lhnwrk) in https://github.com/SeldonIO/MLServer/pull/1751 +* build: Bump versions for Read the Docs by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1761 +* docs: Remove quotes around local TOC by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1764 +* Spawn worker in custom environment by [@lhnwrk](https://github.com/lhnwrk) in https://github.com/SeldonIO/MLServer/pull/1739 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1767 +* basic contributing guide on contributing and opening a PR by [@bohemia420](https://github.com/bohemia420) in https://github.com/SeldonIO/MLServer/pull/1773 +* Inference streaming support by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1750 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1779 +* build: Lock GitHub runners' OS by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1765 +* Removed text-model form benchmarking by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1790 +* Bumped mlflow to 2.13.1 and gunicorn to 22.0.0 by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1791 +* Build(deps): Update to poetry version 1.8.3 in docker build by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1792 +* Bumped werkzeug to 3.0.3 by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1793 +* Docs streaming by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1789 +* Bump uvicorn 0.30.1 by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1795 +* Fixes for all-runtimes by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1794 +* Fix BaseSettings import for pydantic v2 by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1798 +* Bumped preflight version to 1.9.7 by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1797 +* build: Install dependencies only in Tox environments by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1785 +* Bumped to 1.6.0.dev2 by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1803 +* Fix CI/CD macos-huggingface by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1805 +* Fixed macos kafka CI by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1807 +* Update poetry lock by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1808 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1813 +* Fix/macos all runtimes by [@RobertSamoilescu](https://github.com/RobertSamoilescu) in https://github.com/SeldonIO/MLServer/pull/1823 +* fix: Update stale reviewer in licenses.yml workflow by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1824 +* ci: Merge changes from master to release branch by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1825 + +## New Contributors +* [@paulb-seldon](https://github.com/paulb-seldon) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1636 +* [@ReveStobinson](https://github.com/ReveStobinson) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1679 +* [@vivekk0903](https://github.com/vivekk0903) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1703 +* [@RobertSamoilescu](https://github.com/RobertSamoilescu) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1752 +* [@lhnwrk](https://github.com/lhnwrk) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1751 +* [@bohemia420](https://github.com/bohemia420) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1773 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.5.0...1.6.0 + +[Changes][1.6.0] + + + +## [1.5.0](https://github.com/SeldonIO/MLServer/releases/tag/1.5.0) - 05 Mar 2024 + +## What's Changed + +* Update CHANGELOG by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1592 +* build: Migrate away from Node v16 actions by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1596 +* build: Bump version and improve release doc by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1602 +* build: Upgrade stale packages (fastapi, starlette, tensorflow, torch) by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1603 +* fix(ci): tests and security workflow fixes by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1608 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1612 +* fix(ci): Missing quote in CI test for all_runtimes by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1617 +* build(docker): Bump dependencies by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1618 +* docs: List supported Python versions by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1591 +* fix(ci): Have separate smaller tasks for release by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1619 + + +## Notes +* We remove support for python 3.8, check https://github.com/SeldonIO/MLServer/pull/1603 for more info. Docker images for mlserver are already using python 3.10. + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.4.0...1.5.0 + +[Changes][1.5.0] + + + +## [1.4.0](https://github.com/SeldonIO/MLServer/releases/tag/1.4.0) - 28 Feb 2024 + + + +## What's Changed +* Free up some space for GH actions by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1282 +* Introduce tracing with OpenTelemetry by [@vtaskow](https://github.com/vtaskow) in https://github.com/SeldonIO/MLServer/pull/1281 +* Update release CI to use Poetry by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1283 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1284 +* Add support for white-box explainers to alibi-explain runtime by [@ascillitoe](https://github.com/ascillitoe) in https://github.com/SeldonIO/MLServer/pull/1279 +* Update CHANGELOG by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1294 +* Fix build-wheels.sh error when copying to output path by [@lc525](https://github.com/lc525) in https://github.com/SeldonIO/MLServer/pull/1286 +* Fix typo by [@strickvl](https://github.com/strickvl) in https://github.com/SeldonIO/MLServer/pull/1289 +* feat(logging): Distinguish logs from different models by [@vtaskow](https://github.com/vtaskow) in https://github.com/SeldonIO/MLServer/pull/1302 +* Make sure we use our Response class by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1314 +* Adding Quick-Start Guide to docs by [@ramonpzg](https://github.com/ramonpzg) in https://github.com/SeldonIO/MLServer/pull/1315 +* feat(logging): Provide JSON-formatted structured logging as option by [@vtaskow](https://github.com/vtaskow) in https://github.com/SeldonIO/MLServer/pull/1308 +* Bump in conda version and mamba solver by [@dtpryce](https://github.com/dtpryce) in https://github.com/SeldonIO/MLServer/pull/1298 +* feat(huggingface): Merge model settings by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1337 +* feat(huggingface): Load local artefacts in HuggingFace runtime by [@vtaskow](https://github.com/vtaskow) in https://github.com/SeldonIO/MLServer/pull/1319 +* Document and test behaviour around NaN by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1346 +* Address flakiness on 'mlserver build' tests by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1363 +* Bump Poetry and lockfiles by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1369 +* Bump Miniforge3 to 23.3.1 by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1372 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1373 +* Improved huggingface batch logic by [@ajsalow](https://github.com/ajsalow) in https://github.com/SeldonIO/MLServer/pull/1336 +* Add inference params support to MLFlow's custom invocation endpoint (… by [@M4nouel](https://github.com/M4nouel) in https://github.com/SeldonIO/MLServer/pull/1375 +* Increase build space for runtime builds by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1385 +* Fix minor typo in `sklearn` README by [@krishanbhasin-gc](https://github.com/krishanbhasin-gc) in https://github.com/SeldonIO/MLServer/pull/1402 +* Add catboost classifier support by [@krishanbhasin-gc](https://github.com/krishanbhasin-gc) in https://github.com/SeldonIO/MLServer/pull/1403 +* added model_kwargs to huggingface model by [@nanbo-liu](https://github.com/nanbo-liu) in https://github.com/SeldonIO/MLServer/pull/1417 +* Re-generate License Info by [@github-actions](https://github.com/github-actions) in https://github.com/SeldonIO/MLServer/pull/1456 +* Local response cache implementation by [@SachinVarghese](https://github.com/SachinVarghese) in https://github.com/SeldonIO/MLServer/pull/1440 +* fix link to custom runtimes by [@kretes](https://github.com/kretes) in https://github.com/SeldonIO/MLServer/pull/1467 +* Improve typing on `Environment` class by [@krishanbhasin-gc](https://github.com/krishanbhasin-gc) in https://github.com/SeldonIO/MLServer/pull/1469 +* build(dependabot): Change reviewers by [@jesse-c](https://github.com/jesse-c) in https://github.com/SeldonIO/MLServer/pull/1548 +* MLServer changes from internal fork - deps and CI updates by [@sakoush](https://github.com/sakoush) in https://github.com/SeldonIO/MLServer/pull/1588 + +## New Contributors +* [@vtaskow](https://github.com/vtaskow) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1281 +* [@lc525](https://github.com/lc525) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1286 +* [@strickvl](https://github.com/strickvl) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1289 +* [@ramonpzg](https://github.com/ramonpzg) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1315 +* [@jesse-c](https://github.com/jesse-c) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1337 +* [@ajsalow](https://github.com/ajsalow) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1336 +* [@M4nouel](https://github.com/M4nouel) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1375 +* [@nanbo-liu](https://github.com/nanbo-liu) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1417 +* [@kretes](https://github.com/kretes) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1467 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.3.5...1.4.0 + +[Changes][1.4.0] + + + +## [1.3.5](https://github.com/SeldonIO/MLServer/releases/tag/1.3.5) - 10 Jul 2023 + + + +### What's Changed + +* Rename HF codec to `hf` by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1268 +* Publish is_drift metric to Prom by [@joshsgoldstein](https://github.com/joshsgoldstein) in https://github.com/SeldonIO/MLServer/pull/1263 + +### New Contributors +* [@joshsgoldstein](https://github.com/joshsgoldstein) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1263 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.3.4...1.3.5 + +[Changes][1.3.5] + + + +## [1.3.4](https://github.com/SeldonIO/MLServer/releases/tag/1.3.4) - 21 Jun 2023 + + + +### What's Changed + +* Silent logging by [@dtpryce](https://github.com/dtpryce) in https://github.com/SeldonIO/MLServer/pull/1230 +* Fix `mlserver infer` with `BYTES` by [@RafalSkolasinski](https://github.com/RafalSkolasinski) in https://github.com/SeldonIO/MLServer/pull/1213 + +### New Contributors +* [@dtpryce](https://github.com/dtpryce) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1230 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.3.3...1.3.4 + +[Changes][1.3.4] + + + +## [1.3.3](https://github.com/SeldonIO/MLServer/releases/tag/1.3.3) - 05 Jun 2023 + + + +### What's Changed + +* Add default LD_LIBRARY_PATH env var by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1120 +* Adding cassava tutorial (mlserver + seldon core) by [@edshee](https://github.com/edshee) in https://github.com/SeldonIO/MLServer/pull/1156 +* Add docs around converting to / from JSON by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1165 +* Document SKLearn available outputs by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1167 +* Fix minor typo in `alibi-explain` tests by [@ascillitoe](https://github.com/ascillitoe) in https://github.com/SeldonIO/MLServer/pull/1170 +* Add support for `.ubj` models and improve XGBoost docs by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1168 +* Fix content type annotations for pandas codecs by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1162 +* Added option to configure the grpc histogram by [@cristiancl25](https://github.com/cristiancl25) in https://github.com/SeldonIO/MLServer/pull/1143 +* Add OS classifiers to project's metadata by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1171 +* Don't use `qsize` for parallel worker queue by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1169 +* Fix small typo in Python API docs by [@krishanbhasin-gc](https://github.com/krishanbhasin-gc) in https://github.com/SeldonIO/MLServer/pull/1174 +* Fix star import in `mlserver.codecs.*` by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1172 + +### New Contributors +* [@cristiancl25](https://github.com/cristiancl25) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1143 +* [@krishanbhasin-gc](https://github.com/krishanbhasin-gc) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1174 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.3.2...1.3.3 + +[Changes][1.3.3] + + + +## [1.3.2](https://github.com/SeldonIO/MLServer/releases/tag/1.3.2) - 10 May 2023 + + + +### What's Changed +* Use default initialiser if not using a custom env by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1104 +* Add support for online drift detectors by [@ascillitoe](https://github.com/ascillitoe) in https://github.com/SeldonIO/MLServer/pull/1108 +* added intera and inter op parallelism parameters to the hugggingface … by [@saeid93](https://github.com/saeid93) in https://github.com/SeldonIO/MLServer/pull/1081 +* Fix settings reference in runtime docs by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1109 +* Bump Alibi libs requirements by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1121 +* Add default LD_LIBRARY_PATH env var by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1120 +* Ignore both .metrics and .envs folders by [@adriangonz](https://github.com/adriangonz) in https://github.com/SeldonIO/MLServer/pull/1132 + +### New Contributors +* [@ascillitoe](https://github.com/ascillitoe) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1108 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.3.1...1.3.2 + +[Changes][1.3.2] + + + +## [1.3.1](https://github.com/SeldonIO/MLServer/releases/tag/1.3.1) - 27 Apr 2023 + +### What's Changed + +- Move OpenAPI schemas into Python package ([#1095](https://github.com/SeldonIO/MLServer/issues/1095)) + +[Changes][1.3.1] + + + +## [1.3.0](https://github.com/SeldonIO/MLServer/releases/tag/1.3.0) - 27 Apr 2023 + +> WARNING :warning: : The `1.3.0` has been yanked from PyPi due to a packaging issue. This should have been now resolved in `>= 1.3.1`. + +### What's Changed + +#### Custom Model Environments + +More often that not, your custom runtimes will depend on external 3rd party dependencies which are not included within the main MLServer package - or different versions of the same package (e.g. `scikit-learn==1.1.0` vs `scikit-learn==1.2.0`). In these cases, to load your custom runtime, MLServer will need access to these dependencies. + +In MLServer `1.3.0`, it is now [possible to load this custom set of dependencies by providing them](https://mlserver.readthedocs.io/en/latest/user-guide/custom.html#loading-a-custom-python-environment), through an [environment tarball](https://mlserver.readthedocs.io/en/latest/examples/conda/README.html), whose path can be specified within your `model-settings.json` file. This custom environment will get provisioned on the fly after loading a model - alongside the default environment and any other custom environments. + +Under the hood, each of these environments will run their own separate pool of workers. + +![image](https://user-images.githubusercontent.com/1577620/234797983-aa52c353-2d2f-4261-a078-06bfe62cae87.png) + +#### Custom Metrics + +The MLServer framework now includes a simple interface that allows you to register and keep track of any [custom metrics](https://mlserver.readthedocs.io/en/latest/user-guide/metrics.html#custom-metrics): + +- `[mlserver.register()](https://mlserver.readthedocs.io/en/latest/reference/api/metrics.html#mlserver.register)`: Register a new metric. +- `[mlserver.log()](https://mlserver.readthedocs.io/en/latest/reference/api/metrics.html#mlserver.log)`: Log a new set of metric / value pairs. + +Custom metrics will generally be registered in the `[load()](https://mlserver.readthedocs.io/en/latest/reference/api/model.html#mlserver.MLModel.load)` method and then used in the `[predict()](https://mlserver.readthedocs.io/en/latest/reference/api/model.html#mlserver.MLModel.predict)` method of your [custom runtime](https://mlserver.readthedocs.io/en/latest/user-guide/custom.html). These metrics can then be polled and queried via [Prometheus](https://mlserver.readthedocs.io/en/latest/user-guide/metrics.html#settings). + +![image](https://user-images.githubusercontent.com/1577620/234798211-9e538439-4914-4aa6-9c3f-539a66e3ce54.png) + +#### OpenAPI + +MLServer `1.3.0` now includes an autogenerated Swagger UI which can be used to interact dynamically with the Open Inference Protocol. + +The autogenerated Swagger UI can be accessed under the `/v2/docs` endpoint. + +![https://mlserver.readthedocs.io/en/latest/_images/swagger-ui.png](https://mlserver.readthedocs.io/en/latest/_images/swagger-ui.png) + +Alongside the [general API documentation](https://mlserver.readthedocs.io/en/latest/user-guide/openapi.html#Swagger-UI), MLServer also exposes now a set of API docs tailored to individual models, showing the specific endpoints available for each one. + +The model-specific autogenerated Swagger UI can be accessed under the following endpoints: + +- `/v2/models/{model_name}/docs` +- `/v2/models/{model_name}/versions/{model_version}/docs` + +#### HuggingFace Improvements + +MLServer now includes improved Codec support for all the main different types that can be returned by HugginFace models - ensuring that the values returned via the Open Inference Protocol are more semantic and meaningful. + +Massive thanks to [@pepesi](https://github.com/pepesi) for taking the lead on improving the HuggingFace runtime! + +#### Support for Custom Model Repositories + +Internally, MLServer leverages a Model Repository implementation which is used to discover and find different models (and their versions) available to load. The latest version of MLServer will now allow you to swap this for your own model repository implementation - letting you integrate against your own model repository workflows. + +This is exposed via the [model_repository_implementation](https://mlserver.readthedocs.io/en/latest/reference/settings.html#mlserver.settings.Settings.model_repository_implementation) flag of your `settings.json` configuration file. + +Thanks to [@jgallardorama](https://github.com/jgallardorama) (aka [@jgallardorama-itx](https://github.com/jgallardorama-itx) ) for his effort contributing this feature! + +#### Batch and Worker Queue Metrics + +MLServer `1.3.0` introduces a [new set of metrics](https://mlserver.readthedocs.io/en/latest/user-guide/metrics.html#default-metrics) to increase visibility around two of its internal queues: + +- [Adaptive batching](https://mlserver.readthedocs.io/en/latest/user-guide/adaptive-batching.html) queue: used to accumulate request batches on the fly. +- [Parallel inference](https://mlserver.readthedocs.io/en/latest/user-guide/parallel-inference.html) queue: used to send over requests to the inference worker pool. + +Many thanks to [@alvarorsant](https://github.com/alvarorsant) for taking the time to implement this highly requested feature! + +#### Image Size Optimisations + +The latest version of MLServer includes a few optimisations around image size, which help reduce the size of the official set of images by more than ~60% - making them more convenient to use and integrate within your workloads. In the case of the full `seldonio/mlserver:1.3.0` image (including all runtimes and dependencies), this means going from 10GB down to ~3GB. + +#### Python API Documentation + +Alongside its built-in inference runtimes, MLServer also exposes a Python framework that you can use to extend MLServer and write your own codecs and inference runtimes. The MLServer official docs now include a [reference page](https://mlserver.readthedocs.io/en/latest/reference/api/index.html) documenting the main components of this framework in more detail. + +### New Contributors +* [@rio](https://github.com/rio) made their first contribution in https://github.com/SeldonIO/MLServer/pull/864 +* [@pepesi](https://github.com/pepesi) made their first contribution in https://github.com/SeldonIO/MLServer/pull/692 +* [@jgallardorama](https://github.com/jgallardorama) made their first contribution in https://github.com/SeldonIO/MLServer/pull/849 +* [@alvarorsant](https://github.com/alvarorsant) made their first contribution in https://github.com/SeldonIO/MLServer/pull/860 +* [@gawsoftpl](https://github.com/gawsoftpl) made their first contribution in https://github.com/SeldonIO/MLServer/pull/950 +* [@stephen37](https://github.com/stephen37) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1033 +* [@sauerburger](https://github.com/sauerburger) made their first contribution in https://github.com/SeldonIO/MLServer/pull/1064 + +[Changes][1.3.0] + + + +## [1.2.4](https://github.com/SeldonIO/MLServer/releases/tag/1.2.4) - 10 Mar 2023 + + + + + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.2.3...1.2.4 + +[Changes][1.2.4] + + + +## [1.2.3](https://github.com/SeldonIO/MLServer/releases/tag/1.2.3) - 16 Jan 2023 + + + + + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.2.2...1.2.3 + +[Changes][1.2.3] + + + +## [1.2.2](https://github.com/SeldonIO/MLServer/releases/tag/1.2.2) - 16 Jan 2023 + + + + + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.2.1...1.2.2 + +[Changes][1.2.2] + + + +## [1.2.1](https://github.com/SeldonIO/MLServer/releases/tag/1.2.1) - 19 Dec 2022 + + + + + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.2.0...1.2.1 + +[Changes][1.2.1] + + + +## [1.2.0](https://github.com/SeldonIO/MLServer/releases/tag/1.2.0) - 25 Nov 2022 + + + +### What's Changed + +#### Simplified Interface for Custom Runtimes + +MLServer now exposes an alternative [_“simplified”_ interface](https://mlserver.readthedocs.io/en/latest/user-guide/custom.html#simplified-interface) which can be used to write custom runtimes. This interface can be enabled by decorating your predict() method with the `mlserver.codecs.decode_args` decorator, and it lets you specify in the method signature both how you want your request payload to be decoded and how to encode the response back. + +Based on the information provided in the method signature, MLServer will automatically decode the request payload into the different inputs specified as keyword arguments. Under the hood, this is implemented through [MLServer’s codecs and content types system](https://mlserver.readthedocs.io/en/latest/user-guide/content-type.html). + +```python +from mlserver import MLModel +from mlserver.codecs import decode_args + +class MyCustomRuntime(MLModel): + + async def load(self) -> bool: + # TODO: Replace for custom logic to load a model artifact + self._model = load_my_custom_model() + self.ready = True + return self.ready + + @decode_args + async def predict(self, questions: List[str], context: List[str]) -> np.ndarray: + # TODO: Replace for custom logic to run inference + return self._model.predict(questions, context) +``` + +#### Built-in Templates for Custom Runtimes + +To make it easier to write your own custom runtimes, MLServer now ships with a `mlserver init` command that will generate a templated project. This project will include a skeleton with folders, unit tests, Dockerfiles, etc. for you to fill. + +![image1](https://user-images.githubusercontent.com/1577620/203810614-f4daa32e-8b1d-4bea-9b02-959b1d054596.gif) + +#### Dynamic Loading of Custom Runtimes + +MLServer now lets you [load custom runtimes dynamically](https://mlserver.readthedocs.io/en/latest/user-guide/custom.html#loading-a-custom-mlserver-runtime) into a running instance of MLServer. Once you have your custom runtime ready, all you need to do is to move it to your model folder, next to your `model-settings.json` configuration file. + +For example, if we assume a flat model repository where each folder represents a model, you would end up with a folder structure like the one below: + +``` +. +├── models +│   └── sum-model +│   ├── model-settings.json +│   ├── models.py +``` + +#### Batch Inference Client + +This release of MLServer introduces a new [`mlserver infer`](https://mlserver.readthedocs.io/en/latest/reference/cli.html#mlserver-infer) command, which will let you run inference over a large batch of input data on the client side. Under the hood, this command will stream a large set of inference requests from specified input file, arrange them in microbatches, orchestrate the request / response lifecycle, and will finally write back the obtained responses into output file. + +#### Parallel Inference Improvements + +The `1.2.0` release of MLServer, includes a number of fixes around the parallel inference pool focused on improving the architecture to optimise memory usage and reduce latency. These changes include (but are not limited to): + +- The main MLServer process won’t load an extra replica of the model anymore. Instead, all computing will occur on the parallel inference pool. +- The worker pool will now ensure that all requests are executed on each worker’s AsyncIO loop, thus optimising compute time vs IO time. +- Several improvements around logging from the inference workers. + +#### Dropped support for Python 3.7 + +MLServer has now dropped support for Python `3.7`. Going forward, only `3.8`, `3.9` and `3.10` will be supported (with `3.8` being used in our official set of images). + +#### Move to UBI Base Images + +The official set of MLServer images has now moved to use [UBI 9](https://www.redhat.com/en/blog/introducing-red-hat-universal-base-image) as a base image. This ensures support to run MLServer in OpenShift clusters, as well as a well-maintained baseline for our images. + +#### Support for MLflow 2.0 + +In line with MLServer’s close relationship with the MLflow team, this release of MLServer introduces support for the recently released MLflow 2.0. This introduces changes to the drop-in MLflow “scoring protocol” support, in the MLflow runtime for MLServer, to ensure it’s aligned with MLflow 2.0. + +MLServer is also shipped as a dependency of MLflow, therefore you can try it out today by installing MLflow as: + +```bash +$ pip install mlflow[extras] +``` + +To learn more about how to use MLServer directly from the MLflow CLI, check out the [MLflow docs](https://www.mlflow.org/docs/latest/models.html#serving-with-mlserver). + + +### New Contributors +* [@johnpaulett](https://github.com/johnpaulett) made their first contribution in https://github.com/SeldonIO/MLServer/pull/633 +* [@saeid93](https://github.com/saeid93) made their first contribution in https://github.com/SeldonIO/MLServer/pull/711 +* [@RafalSkolasinski](https://github.com/RafalSkolasinski) made their first contribution in https://github.com/SeldonIO/MLServer/pull/720 +* [@dumaas](https://github.com/dumaas) made their first contribution in https://github.com/SeldonIO/MLServer/pull/742 +* [@Salehbigdeli](https://github.com/Salehbigdeli) made their first contribution in https://github.com/SeldonIO/MLServer/pull/776 +* [@regen100](https://github.com/regen100) made their first contribution in https://github.com/SeldonIO/MLServer/pull/839 + +**Full Changelog**: https://github.com/SeldonIO/MLServer/compare/1.1.0...1.2.0 + +[Changes][1.2.0] + + + +## [v1.2.0.dev1](https://github.com/SeldonIO/MLServer/releases/tag/1.2.0.dev1) - 01 Aug 2022 + + + +[Changes][1.2.0.dev1] + + + +## [v1.1.0](https://github.com/SeldonIO/MLServer/releases/tag/1.1.0) - 01 Aug 2022 + + + +[Changes][1.1.0] + + +[1.6.0]: https://github.com/SeldonIO/MLServer/compare/1.5.0...1.6.0 +[1.5.0]: https://github.com/SeldonIO/MLServer/compare/1.4.0...1.5.0 +[1.4.0]: https://github.com/SeldonIO/MLServer/compare/1.3.5...1.4.0 +[1.3.5]: https://github.com/SeldonIO/MLServer/compare/1.3.4...1.3.5 +[1.3.4]: https://github.com/SeldonIO/MLServer/compare/1.3.3...1.3.4 +[1.3.3]: https://github.com/SeldonIO/MLServer/compare/1.3.2...1.3.3 +[1.3.2]: https://github.com/SeldonIO/MLServer/compare/1.3.1...1.3.2 +[1.3.1]: https://github.com/SeldonIO/MLServer/compare/1.3.0...1.3.1 +[1.3.0]: https://github.com/SeldonIO/MLServer/compare/1.2.4...1.3.0 +[1.2.4]: https://github.com/SeldonIO/MLServer/compare/1.2.3...1.2.4 +[1.2.3]: https://github.com/SeldonIO/MLServer/compare/1.2.2...1.2.3 +[1.2.2]: https://github.com/SeldonIO/MLServer/compare/1.2.1...1.2.2 +[1.2.1]: https://github.com/SeldonIO/MLServer/compare/1.2.0...1.2.1 +[1.2.0]: https://github.com/SeldonIO/MLServer/compare/1.2.0.dev1...1.2.0 +[1.2.0.dev1]: https://github.com/SeldonIO/MLServer/compare/1.1.0...1.2.0.dev1 +[1.1.0]: https://github.com/SeldonIO/MLServer/tree/1.1.0 + + diff --git a/docs-gb/examples/Makefile b/docs-gb/examples/Makefile new file mode 100644 index 000000000..0dafe3833 --- /dev/null +++ b/docs-gb/examples/Makefile @@ -0,0 +1,16 @@ +.SUFFIXES: .ipynb .md + +EXAMPLES := $(wildcard */*.ipynb) +EXAMPLES_MD=$(patsubst %.ipynb, %.md, $(EXAMPLES)) + +all: $(EXAMPLES_MD) + +.ipynb.md: + jupyter nbconvert \ + $< \ + --ClearOutputPreprocessor.enabled=True \ + --to markdown \ + --output $(notdir $@) + + +.PHONY: all diff --git a/docs-gb/examples/README.md b/docs-gb/examples/README.md new file mode 100644 index 000000000..30766c5e6 --- /dev/null +++ b/docs-gb/examples/README.md @@ -0,0 +1,40 @@ +# Examples + +To see MLServer in action you can check out the examples below. +These are end-to-end notebooks, showing how to serve models with MLServer. + +## Inference Runtimes + +If you are interested in how MLServer interacts with particular model +frameworks, you can check the following examples. These focus on showcasing the different +[inference runtimes](../runtimes/index.md) that ship with MLServer out of the box. +Note that, for **advanced use cases**, you can also write your own custom +inference runtime (see the [example below on custom models](./custom/README.md)). + +- [Serving Scikit-Learn models](./sklearn/README.md) +- [Serving XGBoost models](./xgboost/README.md) +- [Serving LightGBM models](./lightgbm/README.md) +- [Serving CatBoost models](./catboost/README.md) +- [Serving MLflow models](./mlflow/README.md) +- [Serving custom models](./custom/README.md) +- [Serving Alibi Detect models](./alibi-detect/README.md) +- [Serving HuggingFace models](./huggingface/README.md) + +## MLServer Features + +To see some of the advanced features included in MLServer (e.g. multi-model +serving), check out the examples below. + +- [Multi-Model Serving with multiple frameworks](./mms/README.md) +- [Loading / unloading models from a model repository](./model-repository/README.md) +- [Content-Type Decoding](./content-type/README.md) +- [Custom Conda environment](./conda/README.md) +- [Serving custom models requiring JSON inputs or outputs](./custom-json/README.md) +- [Serving models through Kafka](./kafka/README.md) +- [Streaming inference](./streaming/README.md) + +## Tutorials + +Tutorials are designed to be *beginner-friendly* and walk through accomplishing a series of tasks using MLServer (and other tools). + +- [Deploying a Custom Tensorflow Model with MLServer and Seldon Core](./cassava/README.md) diff --git a/docs-gb/examples/alibi-detect/.gitignore b/docs-gb/examples/alibi-detect/.gitignore new file mode 100644 index 000000000..0e5de0ae9 --- /dev/null +++ b/docs-gb/examples/alibi-detect/.gitignore @@ -0,0 +1,2 @@ +alibi-detector-artifacts/* +!alibi-detector-artifacts/.gitkeep diff --git a/docs-gb/examples/alibi-detect/README.ipynb b/docs-gb/examples/alibi-detect/README.ipynb new file mode 100644 index 000000000..d8d0fc136 --- /dev/null +++ b/docs-gb/examples/alibi-detect/README.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving Alibi-Detect models\n", + "\n", + "Out of the box, `mlserver` supports the deployment and serving of [alibi-detect](https://docs.seldon.io/projects/alibi-detect/en/latest/index.html) models. Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. In this example, we will cover how we can create a detector configuration to then serve it using `mlserver`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetch reference data\n", + "\n", + "The first step will be to fetch a reference data and other relevant metadata for an `alibi-detect` model. \n", + "\n", + "For that, we will use the [alibi](https://github.com/SeldonIO/alibi) library to get the adult dataset with [demographic features from a 1996 US census](https://archive.ics.uci.edu/ml/datasets/census+income).\n", + "\n", + "**Note**: Install `alibi` dataset dependencies and `alibi-detect` for detector configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install alibi alibi_detect" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import alibi\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adult = alibi.datasets.fetch_adult()\n", + "X, y = adult.data, adult.target\n", + "feature_names = adult.feature_names\n", + "category_map = adult.category_map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_ref = 10000\n", + "n_test = 10000\n", + "\n", + "X_ref, X_t0, X_t1 = X[:n_ref], X[n_ref:n_ref + n_test], X[n_ref + n_test:n_ref + 2 * n_test]\n", + "categories_per_feature = {f: None for f in list(category_map.keys())}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drift Detector Configuration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example is based on the Categorical and mixed type data drift detection on income prediction tabular data from the [alibi-detect](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/cd_chi2ks_adult.html) documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating detector and saving configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from alibi_detect.cd import TabularDrift\n", + "cd_tabular = TabularDrift(X_ref, p_val=.05, categories_per_feature=categories_per_feature)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from alibi_detect.utils.saving import save_detector\n", + "filepath = \"alibi-detector-artifacts\"\n", + "save_detector(cd_tabular, filepath)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Detecting data drift directly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "preds = cd_tabular.predict(X_t0,drift_type=\"feature\")\n", + "\n", + "labels = ['No!', 'Yes!']\n", + "print(f\"Threshold {preds['data']['threshold']}\")\n", + "for f in range(cd_tabular.n_features):\n", + " fname = feature_names[f]\n", + " is_drift = (preds['data']['p_val'][f] < preds['data']['threshold']).astype(int)\n", + " stat_val, p_val = preds['data']['distance'][f], preds['data']['p_val'][f]\n", + " print(f'{fname} -- Drift? {labels[is_drift]} -- Chi2 {stat_val:.3f} -- p-value {p_val:.3f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have the reference data and other configuration parameters, the next step will be to serve it using `mlserver`. \n", + "For that, we will need to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"income-tabular-drift\",\n", + " \"implementation\": \"mlserver_alibi_detect.AlibiDetectRuntime\",\n", + " \"parameters\": {\n", + " \"uri\": \"./alibi-detector-artifacts\",\n", + " \"version\": \"v0.1.0\",\n", + " \"extra\": {\n", + " \"predict_parameters\":{\n", + " \"drift_type\": \"feature\"\n", + " }\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running mlserver start command. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our alibi-detect model being served by `mlserver`. To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": X_t0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": X_t0.tolist(),\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/income-tabular-drift/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View model response" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "response_dict = json.loads(response.text)\n", + "\n", + "labels = ['No!', 'Yes!']\n", + "for f in range(cd_tabular.n_features):\n", + " stat = 'Chi2' if f in list(categories_per_feature.keys()) else 'K-S'\n", + " fname = feature_names[f]\n", + " is_drift = response_dict['outputs'][0]['data'][f]\n", + " stat_val, p_val = response_dict['outputs'][1]['data'][f], response_dict['outputs'][2]['data'][f]\n", + " print(f'{fname} -- Drift? {labels[is_drift]} -- Chi2 {stat_val:.3f} -- p-value {p_val:.3f}')" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "5377649fd9904e56b1a72eb79a8e3137babc0e642f8d1adb411c1391b7f9d00d" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/alibi-detect/README.md b/docs-gb/examples/alibi-detect/README.md new file mode 100644 index 000000000..332f13db6 --- /dev/null +++ b/docs-gb/examples/alibi-detect/README.md @@ -0,0 +1,184 @@ +# Serving Alibi-Detect models + +Out of the box, `mlserver` supports the deployment and serving of [alibi_detect](https://docs.seldon.io/projects/alibi-detect/en/latest/index.html) models. Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. In this example, we will cover how we can create a detector configuration to then serve it using `mlserver`. + +## Fetch reference data + +The first step will be to fetch a reference data and other relevant metadata for an `alibi-detect` model. + +For that, we will use the [alibi](https://github.com/SeldonIO/alibi) library to get the adult dataset with [demographic features from a 1996 US census](https://archive.ics.uci.edu/ml/datasets/census+income). + +````{note} +Install `alibi` library for dataset dependencies and `alibi_detect` library for detector configuration from Pypi +```python +!pip install alibi alibi_detect +``` +```` + +```python +import alibi +import matplotlib.pyplot as plt +import numpy as np +``` + +```python +adult = alibi.datasets.fetch_adult() +X, y = adult.data, adult.target +feature_names = adult.feature_names +category_map = adult.category_map +``` + +```python +n_ref = 10000 +n_test = 10000 + +X_ref, X_t0, X_t1 = X[:n_ref], X[n_ref:n_ref + n_test], X[n_ref + n_test:n_ref + 2 * n_test] +categories_per_feature = {f: None for f in list(category_map.keys())} +``` + +## Drift Detector Configuration + +This example is based on the Categorical and mixed type data drift detection on income prediction tabular data from the [alibi-detect](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/cd_chi2ks_adult.html) documentation. + +### Creating detector and saving configuration + +```python +from alibi_detect.cd import TabularDrift +cd_tabular = TabularDrift(X_ref, p_val=.05, categories_per_feature=categories_per_feature) +``` + +```python +from alibi_detect.utils.saving import save_detector +filepath = "alibi-detector-artifacts" +save_detector(cd_tabular, filepath) +``` + +### Detecting data drift directly + +```python +preds = cd_tabular.predict(X_t0,drift_type="feature") + +labels = ['No!', 'Yes!'] +print(f"Threshold {preds['data']['threshold']}") +for f in range(cd_tabular.n_features): + fname = feature_names[f] + is_drift = (preds['data']['p_val'][f] < preds['data']['threshold']).astype(int) + stat_val, p_val = preds['data']['distance'][f], preds['data']['p_val'][f] + print(f'{fname} -- Drift? {labels[is_drift]} -- Chi2 {stat_val:.3f} -- p-value {p_val:.3f}') +``` + + Threshold 0.05 + Age -- Drift? No! -- Chi2 0.012 -- p-value 0.508 + Workclass -- Drift? No! -- Chi2 8.487 -- p-value 0.387 + Education -- Drift? No! -- Chi2 4.753 -- p-value 0.576 + Marital Status -- Drift? No! -- Chi2 3.160 -- p-value 0.368 + Occupation -- Drift? No! -- Chi2 8.194 -- p-value 0.415 + Relationship -- Drift? No! -- Chi2 0.485 -- p-value 0.993 + Race -- Drift? No! -- Chi2 0.587 -- p-value 0.965 + Sex -- Drift? No! -- Chi2 0.217 -- p-value 0.641 + Capital Gain -- Drift? No! -- Chi2 0.002 -- p-value 1.000 + Capital Loss -- Drift? No! -- Chi2 0.002 -- p-value 1.000 + Hours per week -- Drift? No! -- Chi2 0.012 -- p-value 0.508 + Country -- Drift? No! -- Chi2 9.991 -- p-value 0.441 + +## Serving + +Now that we have the reference data and other configuration parameters, the next step will be to serve it using `mlserver`. +For that, we will need to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +### `settings.json` + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + + Overwriting settings.json + +### `model-settings.json` + +```python +%%writefile model-settings.json +{ + "name": "income-tabular-drift", + "implementation": "mlserver_alibi_detect.AlibiDetectRuntime", + "parameters": { + "uri": "./alibi-detector-artifacts", + "version": "v0.1.0", + "extra": { + "predict_parameters":{ + "drift_type": "feature" + } + } + } +} +``` + + Overwriting model-settings.json + +### Start serving our model + +Now that we have our config in-place, we can start the server by running mlserver start command. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our alibi-detect model being served by `mlserver`. To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + +```python +import requests + +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": X_t0.shape, + "datatype": "FP32", + "data": X_t0.tolist(), + } + ] +} + +endpoint = "http://localhost:8080/v2/models/income-tabular-drift/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) +``` + +### View model response + +```python +import json +response_dict = json.loads(response.text) + +labels = ['No!', 'Yes!'] +for f in range(cd_tabular.n_features): + stat = 'Chi2' if f in list(categories_per_feature.keys()) else 'K-S' + fname = feature_names[f] + is_drift = response_dict['outputs'][0]['data'][f] + stat_val, p_val = response_dict['outputs'][1]['data'][f], response_dict['outputs'][2]['data'][f] + print(f'{fname} -- Drift? {labels[is_drift]} -- Chi2 {stat_val:.3f} -- p-value {p_val:.3f}') +``` + + Age -- Drift? No! -- Chi2 0.012 -- p-value 0.508 + Workclass -- Drift? No! -- Chi2 8.487 -- p-value 0.387 + Education -- Drift? No! -- Chi2 4.753 -- p-value 0.576 + Marital Status -- Drift? No! -- Chi2 3.160 -- p-value 0.368 + Occupation -- Drift? No! -- Chi2 8.194 -- p-value 0.415 + Relationship -- Drift? No! -- Chi2 0.485 -- p-value 0.993 + Race -- Drift? No! -- Chi2 0.587 -- p-value 0.965 + Sex -- Drift? No! -- Chi2 0.217 -- p-value 0.641 + Capital Gain -- Drift? No! -- Chi2 0.002 -- p-value 1.000 + Capital Loss -- Drift? No! -- Chi2 0.002 -- p-value 1.000 + Hours per week -- Drift? No! -- Chi2 0.012 -- p-value 0.508 + Country -- Drift? No! -- Chi2 9.991 -- p-value 0.441 diff --git a/docs-gb/examples/alibi-detect/alibi-detector-artifacts/.gitkeep b/docs-gb/examples/alibi-detect/alibi-detector-artifacts/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/docs-gb/examples/alibi-detect/model-settings.json b/docs-gb/examples/alibi-detect/model-settings.json new file mode 100644 index 000000000..5b68cbb6a --- /dev/null +++ b/docs-gb/examples/alibi-detect/model-settings.json @@ -0,0 +1,13 @@ +{ + "name": "income-tabular-drift", + "implementation": "mlserver_alibi_detect.AlibiDetectRuntime", + "parameters": { + "uri": "./alibi-detector-artifacts", + "version": "v0.1.0", + "extra": { + "predict_parameters":{ + "drift_type": "feature" + } + } + } +} diff --git a/docs-gb/examples/alibi-detect/settings.json b/docs-gb/examples/alibi-detect/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/alibi-detect/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/alibi-explain/data/mnist_anchor_image/explainer.dill b/docs-gb/examples/alibi-explain/data/mnist_anchor_image/explainer.dill new file mode 100644 index 000000000..a511a6546 Binary files /dev/null and b/docs-gb/examples/alibi-explain/data/mnist_anchor_image/explainer.dill differ diff --git a/docs-gb/examples/alibi-explain/data/mnist_anchor_image/meta.dill b/docs-gb/examples/alibi-explain/data/mnist_anchor_image/meta.dill new file mode 100644 index 000000000..b6e386601 Binary files /dev/null and b/docs-gb/examples/alibi-explain/data/mnist_anchor_image/meta.dill differ diff --git a/docs-gb/examples/alibi-explain/data/mnist_anchor_image/segmentation_fn.dill b/docs-gb/examples/alibi-explain/data/mnist_anchor_image/segmentation_fn.dill new file mode 100644 index 000000000..108113260 Binary files /dev/null and b/docs-gb/examples/alibi-explain/data/mnist_anchor_image/segmentation_fn.dill differ diff --git a/docs-gb/examples/alibi-explain/model-settings.json b/docs-gb/examples/alibi-explain/model-settings.json new file mode 100644 index 000000000..0fa42390d --- /dev/null +++ b/docs-gb/examples/alibi-explain/model-settings.json @@ -0,0 +1,14 @@ +{ + "name": "anchor-image-explain-model", + "implementation": "mlserver_alibi_explain.AlibiExplainRuntime", + "parallel_workers": 0, + "parameters": { + "uri": "./data/mnist_anchor_image", + "version": "v0.1.0", + "extra": { + "explainer_type": "anchor_image", + "infer_uri": "http://localhost:42315/v2/models/test-pytorch-mnist/infer" + } + } +} + diff --git a/docs-gb/examples/alibi-explain/settings.json b/docs-gb/examples/alibi-explain/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/alibi-explain/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/cassava/README.md b/docs-gb/examples/cassava/README.md new file mode 100644 index 000000000..8a573f802 --- /dev/null +++ b/docs-gb/examples/cassava/README.md @@ -0,0 +1,314 @@ +# Deploying a Custom Tensorflow Model with MLServer and Seldon Core + +## Background + +### Intro + +This tutorial walks through the steps required to take a python ML model from your machine to a production deployment on Kubernetes. More specifically we'll cover: +- Running the model locally +- Turning the ML model into an API +- Containerizing the model +- Storing the container in a registry +- Deploying the model to Kubernetes (with Seldon Core) +- Scaling the model + +The tutorial comes with an accompanying video which you might find useful as you work through the steps: +[![video_play_icon](img/video_play.png)](https://youtu.be/3bR25_qpokM) + +The slides used in the video can be found [here](img/slides.pdf). + +### The Use Case + +For this tutorial, we're going to use the [Cassava dataset](https://www.tensorflow.org/datasets/catalog/cassava) available from the Tensorflow Catalog. This dataset includes leaf images from the cassava plant. Each plant can be classified as either "healthly" or as having one of four diseases (Mosaic Disease, Bacterial Blight, Green Mite, Brown Streak Disease). + +![cassava_examples](img/cassava_examples.png) + +We won't go through the steps of training the classifier. Instead, we'll be using a pre-trained one available on TensorFlow Hub. You can find the [model details here](https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2). + +## Getting Set Up + +The easiest way to run this example is to clone the repository located [here](https://github.com/SeldonIO/cassava-example): + +```bash +git clone https://github.com/SeldonIO/cassava-example.git +``` + +If you've already cloned the MLServer repository, you can also find it in `docs/examples/cassava`. + +Once you've done that, you can just run: + +```bash +cd cassava-example/ +``` + +```Python +pip install -r requirements.txt +``` + +And it'll set you up with all the libraries required to run the code. + +## Running The Python App + +The starting point for this tutorial is python script `app.py`. This is typical of the kind of python code we'd run standalone or in a jupyter notebook. Let's familiarise ourself with the code: + +```Python +from helpers import plot, preprocess +import tensorflow as tf +import tensorflow_datasets as tfds +import tensorflow_hub as hub + +# Fixes an issue with Jax and TF competing for GPU +tf.config.experimental.set_visible_devices([], 'GPU') + +# Load the model +model_path = './model' +classifier = hub.KerasLayer(model_path) + +# Load the dataset and store the class names +dataset, info = tfds.load('cassava', with_info=True) +class_names = info.features['label'].names + ['unknown'] + +# Select a batch of examples and plot them +batch_size = 9 +batch = dataset['validation'].map(preprocess).batch(batch_size).as_numpy_iterator() +examples = next(batch) +plot(examples, class_names) + +# Generate predictions for the batch and plot them against their labels +predictions = classifier(examples['image']) +predictions_max = tf.argmax(predictions, axis=-1) +print(predictions_max) +plot(examples, class_names, predictions_max) +``` + +First up, we're importing a couple of functions from our `helpers.py` file: +- `plot` provides the visualisation of the samples, labels and predictions. +- `preprocess` is used to resize images to 224x224 pixels and normalize the RGB values. + +The rest of the code is fairly self-explanatory from the comments. We load the model and dataset, select some examples, make predictions and then plot the results. + +Try it yourself by running: + +```Bash +python app.py +``` + +Here's what our setup currently looks like: +![step_1](img/step_1.png) + +## Creating an API for The Model + +The problem with running our code like we did earlier is that it's not accessible to anyone who doesn't have the python script (and all of it's dependencies). A good way to solve this is to turn our model into an API. + +Typically people turn to popular python web servers like [Flask](https://github.com/pallets/flask) or [FastAPI](https://github.com/tiangolo/fastapi). This is a good approach and gives us lots of flexibility but it also requires us to do a lot of the work ourselves. We need to impelement routes, set up logging, capture metrics and define an API schema among other things. A simpler way to tackle this problem is to use an inference server. For this tutorial we're going to use the open source [MLServer](https://github.com/SeldonIO/MLServer) framework. + +MLServer supports a bunch of [inference runtimes](https://mlserver.readthedocs.io/en/stable/runtimes/index.html) out of the box, but it also supports [custom python code](https://mlserver.readthedocs.io/en/stable/user-guide/custom.html) which is what we'll use for our Tensorflow model. + +### Setting Things Up + +In order to get our model ready to run on MLServer we need to wrap it in a single python class with two methods, `load()` and `predict()`. Let's take a look at the code (found in `model/serve-model.py`): + +```Python +from mlserver import MLModel +from mlserver.codecs import decode_args +import numpy as np +import tensorflow as tf +import tensorflow_hub as hub + +# Define a class for our Model, inheriting the MLModel class from MLServer +class CassavaModel(MLModel): + + # Load the model into memory + async def load(self) -> bool: + tf.config.experimental.set_visible_devices([], 'GPU') + model_path = '.' + self._model = hub.KerasLayer(model_path) + self.ready = True + return self.ready + + # Logic for making predictions against our model + @decode_args + async def predict(self, payload: np.ndarray) -> np.ndarray: + # convert payload to tf.tensor + payload_tensor = tf.constant(payload) + + # Make predictions + predictions = self._model(payload_tensor) + predictions_max = tf.argmax(predictions, axis=-1) + + # convert predictions to np.ndarray + response_data = np.array(predictions_max) + + return response_data +``` + +The `load()` method is used to define any logic required to set up our model for inference. In our case, we're loading the model weights into `self._model`. The `predict()` method is where we include all of our prediction logic. + +You may notice that we've slightly modified our code from earlier (in `app.py`). The biggest change is that it is now wrapped in a single class `CassavaModel`. + +The only other task we need to do to run our model on MLServer is to specify a `model-settings.json` file: + +```Json +{ + "name": "cassava", + "implementation": "serve-model.CassavaModel" +} +``` + +This is a simple configuration file that tells MLServer how to handle our model. In our case, we've provided a name for our model and told MLServer where to look for our model class (`serve-model.CassavaModel`). + +### Serving The Model + +We're now ready to serve our model with MLServer. To do that we can simply run: + +```bash +mlserver start model/ +``` + +MLServer will now start up, load our cassava model and provide access through both a REST and gRPC API. + +### Making Predictions Using The API + +Now that our API is up and running. Open a new terminal window and navigate back to the root of this repository. We can then send predictions to our api using the `test.py` file by running: + +```bash +python test.py --local +``` + +Our setup has now evloved and looks like this: +![step_2](img/step_2.png) + +## Containerizing The Model + +[Containers](https://en.wikipedia.org/wiki/Containerization_(computing)) are an easy way to package our application together with it's runtime and dependencies. More importantly, containerizing our model allows it to run in a variety of different environments. + +> **Note:** you will need [Docker](https://www.docker.com/) installed to run this section of the tutorial. You'll also need a [docker hub](https://hub.docker.com/) account or another container registry. + +Taking our model and packaging it into a container manually can be a pretty tricky process and requires knowledge of writing Dockerfiles. Thankfully MLServer removes this complexity and provides us with a simple `build` command. + +Before we run this command, we need to provide our dependencies in either a `requirements.txt` or a `conda.env` file. The requirements file we'll use for this example is stored in `model/requirements.txt`: + +``` +tensorflow==2.12.0 +tensorflow-hub==0.13.0 +``` + +> Notice that we didn't need to include `mlserver` in our requirements? That's because the builder image has mlserver included already. + +We're now ready to build our container image using: + +```bash +mlserver build model/ -t [YOUR_CONTAINER_REGISTRY]/[IMAGE_NAME] +``` + +Make sure you replace `YOUR_CONTAINER_REGISTRY` and `IMAGE_NAME` with your dockerhub username and a suitable name e.g. "bobsmith/cassava". + +MLServer will now build the model into a container image for us. We can check the output of this by running: + +```bash +docker images +``` + +Finally, we want to send this container image to be stored in our container registry. We can do this by running: + +```bash +docker push [YOUR_CONTAINER_REGISTRY]/[IMAGE_NAME] +``` + +Our setup now looks like this. Where our model has been packaged and sent to a container registry: +![step_3](img/step_3.png) + +## Deploying to Kubernetes + +Now that we've turned our model into a production-ready API, containerized it and pushed it to a registry, it's time to deploy our model. + +We're going to use a popular open source framework called [Seldon Core](https://github.com/seldonio/seldon-core) to deploy our model. Seldon Core is great because it combines all of the awesome cloud-native features we get from [Kubernetes](https://kubernetes.io/) but it also adds machine-learning specific features. + +*This tutorial assumes you already have a Seldon Core cluster up and running. If that's not the case, head over the [installation instructions](https://docs.seldon.io/projects/seldon-core/en/latest/nav/installation.html) and get set up first. You'll also need to install the `kubectl` command line interface.* + +### Creating the Deployment + +To create our deployment with Seldon Core we need to create a small configuration file that looks like this: + +*You can find this file named `deployment.yaml` in the base folder of this tutorial's repository.* + +```yaml +apiVersion: machinelearning.seldon.io/v1 +kind: SeldonDeployment +metadata: + name: cassava +spec: + protocol: v2 + predictors: + - componentSpecs: + - spec: + containers: + - image: YOUR_CONTAINER_REGISTRY/IMAGE_NAME + name: cassava + imagePullPolicy: Always + graph: + name: cassava + type: MODEL + name: cassava +``` + +Make sure you replace `YOUR_CONTAINER_REGISTRY` and `IMAGE_NAME` with your dockerhub username and a suitable name e.g. "bobsmith/cassava". + +We can apply this configuration file to our Kubernetes cluster just like we would for any other Kubernetes object using: + +```bash +kubectl create -f deployment.yaml +``` + +To check our deployment is up and running we can run: + +```bash +kubectl get pods +``` + +We should see `STATUS = Running` once our deployment has finalized. + +### Testing the Deployment + +Now that our model is up and running on a Kubernetes cluster (via Seldon Core), we can send some test inference requests to make sure it's working. + +To do this, we simply run the `test.py` file in the following way: + +```bash +python test.py --remote +``` + +This script will randomly select some test samples, send them to the cluster, gather the predictions and then plot them for us. + +**A note on running this yourself:** +*This example is set up to connect to a kubernetes cluster running locally on your machine. If yours is local too, you'll need to make sure you [port forward](https://docs.seldon.io/projects/seldon-core/en/latest/install/kind.html#local-port-forwarding) before sending requests. If your cluster is remote, you'll need to change the `inference_url` variable on line 21 of `test.py`.* + +Having deployed our model to kubernetes and tested it, our setup now looks like this: +![step_4](img/step_4.png) + +## Scaling the Model + +Our model is now running in a production environment and able to handle requests from external sources. This is awesome but what happens as the number of requests being sent to our model starts to increase? Eventually, we'll reach the limit of what a single server can handle. Thankfully, we can get around this problem by scaling our model [horizontally](https://en.wikipedia.org/wiki/Scalability#Horizontal_or_scale_out). + +Kubernetes and Seldon Core make this really easy to do by simply running: + +```bash +kubectl scale sdep cassava --replicas=3 +``` + +We can replace the `--replicas=3` with any number we want to scale to. + +To watch the servers scaling out we can run: + +```bash +kubectl get pods --watch +``` + +Once the new replicas have finished rolling out, our setup now looks like this: +![step_5](img/step_5.png) + + +In this tutorial we've scaled the model out manually to show how it works. In a real environment we'd want to set up [auto-scaling](https://docs.seldon.io/projects/seldon-core/en/latest/graph/scaling.html#autoscaling-seldon-deployments) to make sure our prediction API is always online and performing as expected. + + + diff --git a/docs-gb/examples/cassava/app.py b/docs-gb/examples/cassava/app.py new file mode 100644 index 000000000..1f941ccb4 --- /dev/null +++ b/docs-gb/examples/cassava/app.py @@ -0,0 +1,27 @@ +from helpers import plot, preprocess +import tensorflow as tf +import tensorflow_datasets as tfds +import tensorflow_hub as hub + +# Fixes an issue with Jax and TF competing for GPU +tf.config.experimental.set_visible_devices([], "GPU") + +# Load the model +model_path = "./model" +classifier = hub.KerasLayer(model_path) + +# Load the dataset and store the class names +dataset, info = tfds.load("cassava", with_info=True) +class_names = info.features["label"].names + ["unknown"] + +# Select a batch of examples and plot them +batch_size = 9 +batch = dataset["validation"].map(preprocess).batch(batch_size).as_numpy_iterator() +examples = next(batch) +plot(examples, class_names) + +# Generate predictions for the batch and plot them against their labels +predictions = classifier(examples["image"]) +predictions_max = tf.argmax(predictions, axis=-1) +print(predictions_max) +plot(examples, class_names, predictions_max) diff --git a/docs-gb/examples/cassava/deployment.yaml b/docs-gb/examples/cassava/deployment.yaml new file mode 100644 index 000000000..41ee0b237 --- /dev/null +++ b/docs-gb/examples/cassava/deployment.yaml @@ -0,0 +1,17 @@ +apiVersion: machinelearning.seldon.io/v1 +kind: SeldonDeployment +metadata: + name: cassava +spec: + protocol: v2 + predictors: + - componentSpecs: + - spec: + containers: + - image: edshee/cassava + name: cassava + imagePullPolicy: Always + graph: + name: cassava + type: MODEL + name: cassava diff --git a/docs-gb/examples/cassava/helpers.py b/docs-gb/examples/cassava/helpers.py new file mode 100644 index 000000000..047be5b0f --- /dev/null +++ b/docs-gb/examples/cassava/helpers.py @@ -0,0 +1,63 @@ +import numpy as np +import matplotlib.pyplot as plt +import tensorflow as tf + + +# Plotting function, shows the datapoints as a grid of images +# (with labels and predictions) +def plot(examples, class_names, predictions=None): + # Label mapping + name_map = dict( + cmd="Mosaic Disease", + cbb="Bacterial Blight", + cgm="Green Mite", + cbsd="Brown Streak Disease", + healthy="Healthy", + unknown="Unknown", + ) + + # Get the images, labels, and optionally predictions + images = examples["image"] + labels = examples["label"] + batch_size = len(images) + if predictions is None: + predictions = batch_size * [None] + + # Configure the layout of the grid + x = np.ceil(np.sqrt(batch_size)) + y = np.ceil(batch_size / x) + fig = plt.figure(figsize=(x * 6, y * 7)) + + for i, (image, label, prediction) in enumerate(zip(images, labels, predictions)): + # Render the image + ax = fig.add_subplot(x, y, i + 1) + ax.imshow(image, aspect="auto") + ax.grid(False) + ax.set_xticks([]) + ax.set_yticks([]) + + # Display the label and optionally prediction + x_label = "Label: " + name_map[class_names[label]] + if prediction is not None: + x_label = ( + "Prediction: " + name_map[class_names[prediction]] + "\n" + x_label + ) + ax.xaxis.label.set_color("green" if label == prediction else "red") + ax.set_xlabel(x_label) + + plt.show() + + +# Preprocess images to be the right format for the model +def preprocess(data): + image = data["image"] + + # Normalize [0, 255] to [0, 1] + image = tf.cast(image, tf.float32) + image = image / 255.0 + + # Resize the images to 224 x 224 + image = tf.image.resize(image, (224, 224)) + + data["image"] = image + return data diff --git a/docs-gb/examples/cassava/img/cassava_examples.png b/docs-gb/examples/cassava/img/cassava_examples.png new file mode 100644 index 000000000..9eaac7617 Binary files /dev/null and b/docs-gb/examples/cassava/img/cassava_examples.png 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b/docs-gb/examples/cassava/model/requirements.txt new file mode 100644 index 000000000..c7015344e --- /dev/null +++ b/docs-gb/examples/cassava/model/requirements.txt @@ -0,0 +1,2 @@ +tensorflow==2.12.1 +tensorflow-hub==0.13.0 diff --git a/docs-gb/examples/cassava/model/saved_model.pb b/docs-gb/examples/cassava/model/saved_model.pb new file mode 100755 index 000000000..9801b7705 Binary files /dev/null and b/docs-gb/examples/cassava/model/saved_model.pb differ diff --git a/docs-gb/examples/cassava/model/serve-model.py b/docs-gb/examples/cassava/model/serve-model.py new file mode 100644 index 000000000..43acabb65 --- /dev/null +++ b/docs-gb/examples/cassava/model/serve-model.py @@ -0,0 +1,31 @@ +from mlserver import MLModel +from mlserver.codecs import decode_args +import numpy as np +import tensorflow as tf +import tensorflow_hub as hub + + +# Define a class for our Model, inheriting the MLModel class from MLServer +class CassavaModel(MLModel): + # Load the model into memory + async def load(self) -> bool: + tf.config.experimental.set_visible_devices([], "GPU") + model_path = "." + self._model = hub.KerasLayer(model_path) + self.ready = True + return self.ready + + # Logic for making predictions against our model + @decode_args + async def predict(self, payload: np.ndarray) -> np.ndarray: + # convert payload to tf.tensor + payload_tensor = tf.constant(payload) + + # Make predictions + predictions = self._model(payload_tensor) + predictions_max = tf.argmax(predictions, axis=-1) + + # convert predictions to np.ndarray + response_data = np.array(predictions_max) + + return response_data diff --git a/docs-gb/examples/cassava/model/settings.json b/docs-gb/examples/cassava/model/settings.json new file mode 100644 index 000000000..07c1d90da --- /dev/null +++ b/docs-gb/examples/cassava/model/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} \ No newline at end of file diff --git a/docs-gb/examples/cassava/model/variables/variables.data-00000-of-00001 b/docs-gb/examples/cassava/model/variables/variables.data-00000-of-00001 new file mode 100755 index 000000000..c715b88a5 Binary files /dev/null and b/docs-gb/examples/cassava/model/variables/variables.data-00000-of-00001 differ diff --git a/docs-gb/examples/cassava/model/variables/variables.index b/docs-gb/examples/cassava/model/variables/variables.index new file mode 100755 index 000000000..4bc4ffad8 Binary files /dev/null and b/docs-gb/examples/cassava/model/variables/variables.index differ diff --git a/docs-gb/examples/cassava/requirements.txt b/docs-gb/examples/cassava/requirements.txt new file mode 100644 index 000000000..a27eb2ede --- /dev/null +++ b/docs-gb/examples/cassava/requirements.txt @@ -0,0 +1,3 @@ +mlserver==1.3.2 +tensorflow==2.12.1 +tensorflow-hub==0.13.0 diff --git a/docs-gb/examples/cassava/test.py b/docs-gb/examples/cassava/test.py new file mode 100644 index 000000000..0a6eeb6ca --- /dev/null +++ b/docs-gb/examples/cassava/test.py @@ -0,0 +1,72 @@ +import sys +from helpers import preprocess, plot +import numpy as np +import requests +from mlserver.types import InferenceRequest +from mlserver.codecs import NumpyCodec +import tensorflow_datasets as tfds +import tensorflow as tf + +# Disable tensorflow debugging output +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +# Inference variables +if len(sys.argv) < 2: + sys.exit("Please provide inference mode (--local or --remote)") +if sys.argv[1] == "--local": + inference_url = "http://localhost:8080/v2/models/cassava/infer" +elif sys.argv[1] == "--remote": + inference_url = "http://localhost:8080/seldon/default/cassava/v2/models/infer" +else: + sys.exit("Please provide inference mode (--local or --remote)") +batch_size = 16 + +# Load the dataset and class names +print("Lodaing dataset...") +dataset, info = tfds.load("cassava", with_info=True) +class_names = info.features["label"].names + ["unknown"] + +# Shuffle the dataset with a buffer size equal to the number +# of examples in the 'validation' split +validation_dataset = dataset["validation"] +buffer_size = info.splits["validation"].num_examples +shuffled_validation_dataset = validation_dataset.shuffle(buffer_size) + +# Select a batch of examples from the validation dataset +batch = ( + shuffled_validation_dataset.map(preprocess).batch(batch_size).as_numpy_iterator() +) +examples = next(batch) + +# Convert the TensorFlow tensor to a numpy array +input_data = np.array(examples["image"]) + +# Build the inference request +inference_request = InferenceRequest( + inputs=[NumpyCodec.encode_input(name="payload", payload=input_data)] +) + +# Send the inference request and capture response +print("Sending Inference Request...") +res = requests.post(inference_url, json=inference_request.model_dump()) +print("Got Response...") + +# Parse the JSON string into a Python dictionary +response_dict = res.json() + +# Extract the data array and shape from the output, assuming only +# one output or the target output is at index 0 +data_list = response_dict["outputs"][0]["data"] +data_shape = response_dict["outputs"][0]["shape"] + +# Convert the data list to a numpy array and reshape it +data_array = np.array(data_list).reshape(data_shape) +print("Predictions:", data_array) + +# Convert the numpy array to tf tensor +data_tensor = tf.convert_to_tensor(np.squeeze(data_array), dtype=tf.int64) + +# Plot the examples with their predictions +plot(examples, class_names, data_tensor) diff --git a/docs-gb/examples/catboost/README.ipynb b/docs-gb/examples/catboost/README.ipynb new file mode 100644 index 000000000..cd731f67f --- /dev/null +++ b/docs-gb/examples/catboost/README.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving CatBoost models\n", + "\n", + "Out of the box, `mlserver` supports the deployment and serving of `catboost` models.\n", + "By default, it will assume that these models have been [serialised using the `save_model()` method](https://catboost.ai/en/docs/concepts/python-reference_catboost_save_model).\n", + "\n", + "In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "To test the CatBoost Server, first we need to generate a simple CatBoost model using Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from catboost import CatBoostClassifier\n", + "\n", + "train_data = np.random.randint(0, 100, size=(100, 10))\n", + "train_labels = np.random.randint(0, 2, size=(100))\n", + "\n", + "model = CatBoostClassifier(iterations=2,\n", + " depth=2,\n", + " learning_rate=1,\n", + " loss_function='Logloss',\n", + " verbose=True)\n", + "model.fit(train_data, train_labels)\n", + "model.save_model('model.cbm')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our model will be persisted as a file named `model.cbm`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. \n", + "For that, we will need to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"catboost\",\n", + " \"implementation\": \"mlserver_catboost.CatboostModel\",\n", + " \"parameters\": {\n", + " \"uri\": \"./model.cbm\",\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import requests\n", + "import numpy as np\n", + "\n", + "test_data = np.random.randint(0, 100, size=(1, 10))\n", + "\n", + "x_0 = test_data[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict-prob\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/catboost/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "print(response.json())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs-gb/examples/catboost/README.md b/docs-gb/examples/catboost/README.md new file mode 100644 index 000000000..c4a6c0768 --- /dev/null +++ b/docs-gb/examples/catboost/README.md @@ -0,0 +1,104 @@ +# Serving CatBoost models + +Out of the box, `mlserver` supports the deployment and serving of `catboost` models. +By default, it will assume that these models have been [serialised using the `save_model()` method](https://catboost.ai/en/docs/concepts/python-reference_catboost_save_model). + +In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`. + +## Training + +To test the CatBoost Server, first we need to generate a simple CatBoost model using Python. + + +```python +import numpy as np +from catboost import CatBoostClassifier + +train_data = np.random.randint(0, 100, size=(100, 10)) +train_labels = np.random.randint(0, 2, size=(100)) + +model = CatBoostClassifier(iterations=2, + depth=2, + learning_rate=1, + loss_function='Logloss', + verbose=True) +model.fit(train_data, train_labels) +model.save_model('model.cbm') +``` + +Our model will be persisted as a file named `model.cbm`. + +## Serving + +Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. +For that, we will need to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + +### `model-settings.json` + + +```python +%%writefile model-settings.json +{ + "name": "catboost", + "implementation": "mlserver_catboost.CatboostModel", + "parameters": { + "uri": "./model.cbm", + "version": "v0.1.0" + } +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests +import numpy as np + +test_data = np.random.randint(0, 100, size=(1, 10)) + +x_0 = test_data[0:1] +inference_request = { + "inputs": [ + { + "name": "predict-prob", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/catboost/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +print(response.json()) +``` diff --git a/docs-gb/examples/catboost/model-settings.json b/docs-gb/examples/catboost/model-settings.json new file mode 100644 index 000000000..7967b0c63 --- /dev/null +++ b/docs-gb/examples/catboost/model-settings.json @@ -0,0 +1,8 @@ +{ + "name": "catboost", + "implementation": "mlserver_catboost.CatboostModel", + "parameters": { + "uri": "./model.cbm", + "version": "v0.1.0" + } +} \ No newline at end of file diff --git a/docs-gb/examples/catboost/model.cbm b/docs-gb/examples/catboost/model.cbm new file mode 100644 index 000000000..874dac119 Binary files /dev/null and b/docs-gb/examples/catboost/model.cbm differ diff --git a/docs-gb/examples/catboost/settings.json b/docs-gb/examples/catboost/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/catboost/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/conda/Makefile b/docs-gb/examples/conda/Makefile new file mode 100644 index 000000000..bca77ba83 --- /dev/null +++ b/docs-gb/examples/conda/Makefile @@ -0,0 +1,5 @@ +old-sklearn.tar.gz: environment.yml + conda env create --force -f environment.yml + conda pack --force -n old-sklearn -o old-sklearn.tar.gz + conda env remove -n old-sklearn + diff --git a/docs-gb/examples/conda/README.ipynb b/docs-gb/examples/conda/README.ipynb new file mode 100644 index 000000000..b9d6e20aa --- /dev/null +++ b/docs-gb/examples/conda/README.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Custom Conda environments in MLServer\n", + "\n", + "It's not unusual that model runtimes require extra dependencies that are not direct dependencies of MLServer.\n", + "This is the case when we want to use [custom runtimes](../custom/README), but also when our model artifacts are the output of older versions of a toolkit (e.g. models trained with an older version of SKLearn).\n", + "\n", + "In these cases, since these dependencies (or dependency versions) are not known in advance by MLServer, they **won't be included in the default `seldonio/mlserver` Docker image**.\n", + "To cover these cases, the **`seldonio/mlserver` Docker image allows you to load custom environments** before starting the server itself.\n", + "\n", + "This example will walk you through how to create and save an custom environment, so that it can be loaded in MLServer without any extra change to the `seldonio/mlserver` Docker image. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define our environment\n", + "\n", + "For this example, we will create a custom environment to serve a model trained with an older version of Scikit-Learn. \n", + "The first step will be define this environment, using a [`environment.yml`](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually). \n", + "\n", + "Note that these environments can also be created on the fly as we go, and then serialised later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile environment.yml\n", + "\n", + "name: old-sklearn\n", + "channels:\n", + " - conda-forge\n", + "dependencies:\n", + " - python == 3.8\n", + " - scikit-learn == 0.24.2\n", + " - joblib == 0.17.0\n", + " - requests\n", + " - pip\n", + " - pip:\n", + " - mlserver == 1.1.0\n", + " - mlserver-sklearn == 1.1.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train model in our custom environment\n", + "\n", + "To illustrate the point, we will train a Scikit-Learn model using our older environment.\n", + "\n", + "The first step will be to create and activate an environment which reflects what's outlined in our `environment.yml` file.\n", + "\n", + "> **NOTE:** If you are running this from a Jupyter Notebook, you will need to restart your Jupyter instance so that it runs from this environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!conda env create --force -f environment.yml\n", + "!conda activate old-sklearn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now train and save a Scikit-Learn model using the older version of our environment.\n", + "This model will be serialised as `model.joblib`.\n", + "\n", + "You can find more details of this process in the [Scikit-Learn example](../sklearn/README)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original source code and more details can be found in:\n", + "# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html\n", + "\n", + "# Import datasets, classifiers and performance metrics\n", + "from sklearn import datasets, svm, metrics\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# The digits dataset\n", + "digits = datasets.load_digits()\n", + "\n", + "# To apply a classifier on this data, we need to flatten the image, to\n", + "# turn the data in a (samples, feature) matrix:\n", + "n_samples = len(digits.images)\n", + "data = digits.images.reshape((n_samples, -1))\n", + "\n", + "# Create a classifier: a support vector classifier\n", + "classifier = svm.SVC(gamma=0.001)\n", + "\n", + "# Split data into train and test subsets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data, digits.target, test_size=0.5, shuffle=False)\n", + "\n", + "# We learn the digits on the first half of the digits\n", + "classifier.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "model_file_name = \"model.joblib\"\n", + "joblib.dump(classifier, model_file_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Serialise our custom environment\n", + "\n", + "Lastly, we will need to serialise our environment in the format expected by MLServer.\n", + "To do that, we will use a tool called [`conda-pack`](https://conda.github.io/conda-pack/).\n", + "\n", + "This tool, will save a portable version of our environment as a `.tar.gz` file, also known as _tarball_." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!conda pack --force -n old-sklearn -o old-sklearn.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving \n", + "\n", + "Now that we have defined our environment (and we've got a sample artifact trained in that environment), we can move to serving our model.\n", + "\n", + "To do that, we will first need to select the right runtime through a `model-settings.json` config file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"mnist-svm\",\n", + " \"implementation\": \"mlserver_sklearn.SKLearnModel\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then spin up our model, using our custom environment, leveraging MLServer's Docker image.\n", + "Keep in mind that **you will need Docker installed in your machine to run this example**.\n", + "\n", + "Our Docker command will need to take into account the following points:\n", + "\n", + "- Mount the example's folder as a volume so that it can be accessed from within the container.\n", + "- Let MLServer know that our custom environment's tarball can be found as `old-sklearn.tar.gz`.\n", + "- Expose port `8080` so that we can send requests from the outside. \n", + "\n", + "From the command line, this can be done using Docker's CLI as:\n", + "\n", + "```bash\n", + "docker run -it --rm \\\n", + " -v \"$PWD\":/mnt/models \\\n", + " -e \"MLSERVER_ENV_TARBALL=/mnt/models/old-sklearn.tar.gz\" \\\n", + " -p 8080:8080 \\\n", + " seldonio/mlserver:1.1.0-slim\n", + "```\n", + "\n", + "Note that we need to keep the server running in the background while we send requests.\n", + "Therefore, it's best to run this command on a separate terminal session." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/mnist-svm/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/conda/README.md b/docs-gb/examples/conda/README.md new file mode 100644 index 000000000..cd2514398 --- /dev/null +++ b/docs-gb/examples/conda/README.md @@ -0,0 +1,172 @@ +# Custom Conda environments in MLServer + +It's not unusual that model runtimes require extra dependencies that are not direct dependencies of MLServer. +This is the case when we want to use [custom runtimes](../custom/README), but also when our model artifacts are the output of older versions of a toolkit (e.g. models trained with an older version of SKLearn). + +In these cases, since these dependencies (or dependency versions) are not known in advance by MLServer, they **won't be included in the default `seldonio/mlserver` Docker image**. +To cover these cases, the **`seldonio/mlserver` Docker image allows you to load custom environments** before starting the server itself. + +This example will walk you through how to create and save an custom environment, so that it can be loaded in MLServer without any extra change to the `seldonio/mlserver` Docker image. + +## Define our environment + +For this example, we will create a custom environment to serve a model trained with an older version of Scikit-Learn. +The first step will be define this environment, using a [`environment.yml`](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually). + +Note that these environments can also be created on the fly as we go, and then serialised later. + + +```python +%%writefile environment.yml + +name: old-sklearn +channels: + - conda-forge +dependencies: + - python == 3.8 + - scikit-learn == 0.24.2 + - joblib == 0.17.0 + - requests + - pip + - pip: + - mlserver == 1.1.0 + - mlserver-sklearn == 1.1.0 +``` + +### Train model in our custom environment + +To illustrate the point, we will train a Scikit-Learn model using our older environment. + +The first step will be to create and activate an environment which reflects what's outlined in our `environment.yml` file. + +> **NOTE:** If you are running this from a Jupyter Notebook, you will need to restart your Jupyter instance so that it runs from this environment. + + +```python +!conda env create --force -f environment.yml +!conda activate old-sklearn +``` + +We can now train and save a Scikit-Learn model using the older version of our environment. +This model will be serialised as `model.joblib`. + +You can find more details of this process in the [Scikit-Learn example](../sklearn/README). + + +```python +# Original source code and more details can be found in: +# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html + +# Import datasets, classifiers and performance metrics +from sklearn import datasets, svm, metrics +from sklearn.model_selection import train_test_split + +# The digits dataset +digits = datasets.load_digits() + +# To apply a classifier on this data, we need to flatten the image, to +# turn the data in a (samples, feature) matrix: +n_samples = len(digits.images) +data = digits.images.reshape((n_samples, -1)) + +# Create a classifier: a support vector classifier +classifier = svm.SVC(gamma=0.001) + +# Split data into train and test subsets +X_train, X_test, y_train, y_test = train_test_split( + data, digits.target, test_size=0.5, shuffle=False) + +# We learn the digits on the first half of the digits +classifier.fit(X_train, y_train) +``` + + +```python +import joblib + +model_file_name = "model.joblib" +joblib.dump(classifier, model_file_name) +``` + +### Serialise our custom environment + +Lastly, we will need to serialise our environment in the format expected by MLServer. +To do that, we will use a tool called [`conda-pack`](https://conda.github.io/conda-pack/). + +This tool, will save a portable version of our environment as a `.tar.gz` file, also known as _tarball_. + + +```python +!conda pack --force -n old-sklearn -o old-sklearn.tar.gz +``` + +## Serving + +Now that we have defined our environment (and we've got a sample artifact trained in that environment), we can move to serving our model. + +To do that, we will first need to select the right runtime through a `model-settings.json` config file. + + +```python +%%writefile model-settings.json +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel" +} +``` + +We can then spin up our model, using our custom environment, leveraging MLServer's Docker image. +Keep in mind that **you will need Docker installed in your machine to run this example**. + +Our Docker command will need to take into account the following points: + +- Mount the example's folder as a volume so that it can be accessed from within the container. +- Let MLServer know that our custom environment's tarball can be found as `old-sklearn.tar.gz`. +- Expose port `8080` so that we can send requests from the outside. + +From the command line, this can be done using Docker's CLI as: + +```bash +docker run -it --rm \ + -v "$PWD":/mnt/models \ + -e "MLSERVER_ENV_TARBALL=/mnt/models/old-sklearn.tar.gz" \ + -p 8080:8080 \ + seldonio/mlserver:1.1.0-slim +``` + +Note that we need to keep the server running in the background while we send requests. +Therefore, it's best to run this command on a separate terminal session. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests + +x_0 = X_test[0:1] +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/mnist-svm/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + + +```python + +``` diff --git a/docs-gb/examples/conda/environment.yml b/docs-gb/examples/conda/environment.yml new file mode 100644 index 000000000..90b9c4ea3 --- /dev/null +++ b/docs-gb/examples/conda/environment.yml @@ -0,0 +1,13 @@ + +name: old-sklearn +channels: + - conda-forge +dependencies: + - python == 3.8 + - scikit-learn == 0.24.2 + - joblib == 0.17.0 + - requests + - pip + - pip: + - mlserver == 1.1.0 + - mlserver-sklearn == 1.1.0 diff --git a/docs-gb/examples/conda/model-settings.json b/docs-gb/examples/conda/model-settings.json new file mode 100644 index 000000000..26b6c1e16 --- /dev/null +++ b/docs-gb/examples/conda/model-settings.json @@ -0,0 +1,4 @@ +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel" +} diff --git a/docs-gb/examples/conda/model.joblib b/docs-gb/examples/conda/model.joblib new file mode 100644 index 000000000..508296340 Binary files /dev/null and b/docs-gb/examples/conda/model.joblib differ diff --git a/docs-gb/examples/content-type/README.ipynb b/docs-gb/examples/content-type/README.ipynb new file mode 100644 index 000000000..ff6209f14 --- /dev/null +++ b/docs-gb/examples/content-type/README.ipynb @@ -0,0 +1,646 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "regional-affair", + "metadata": {}, + "source": [ + "# Content Type Decoding\n", + "\n", + "MLServer extends the V2 inference protocol by adding support for a `content_type` annotation.\n", + "This annotation can be provided either through the model metadata `parameters`, or through the input `parameters`.\n", + "By leveraging the `content_type` annotation, we can provide the necessary information to MLServer so that it can _decode_ the input payload from the \"wire\" V2 protocol to something meaningful to the model / user (e.g. a NumPy array).\n", + "\n", + "This example will walk you through some examples which illustrate how this works, and how it can be extended." + ] + }, + { + "cell_type": "markdown", + "id": "suburban-damage", + "metadata": {}, + "source": [ + "## Echo Inference Runtime\n", + "\n", + "To start with, we will write a _dummy_ runtime which just prints the input, the _decoded_ input and returns it.\n", + "This will serve as a testbed to showcase how the `content_type` support works.\n", + "\n", + "Later on, we will extend this runtime by adding custom _codecs_ that will decode our V2 payload to custom types." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "alleged-tunnel", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile runtime.py\n", + "import json\n", + "\n", + "from mlserver import MLModel\n", + "from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput\n", + "from mlserver.codecs import DecodedParameterName\n", + "\n", + "_to_exclude = {\n", + " \"parameters\": {DecodedParameterName, \"headers\"},\n", + " 'inputs': {\"__all__\": {\"parameters\": {DecodedParameterName, \"headers\"}}}\n", + "}\n", + "\n", + "class EchoRuntime(MLModel):\n", + " async def predict(self, payload: InferenceRequest) -> InferenceResponse:\n", + " outputs = []\n", + " for request_input in payload.inputs:\n", + " decoded_input = self.decode(request_input)\n", + " print(f\"------ Encoded Input ({request_input.name}) ------\")\n", + " as_dict = request_input.dict(exclude=_to_exclude) # type: ignore\n", + " print(json.dumps(as_dict, indent=2))\n", + " print(f\"------ Decoded input ({request_input.name}) ------\")\n", + " print(decoded_input)\n", + " \n", + " outputs.append(\n", + " ResponseOutput(\n", + " name=request_input.name,\n", + " datatype=request_input.datatype,\n", + " shape=request_input.shape,\n", + " data=request_input.data\n", + " )\n", + " )\n", + " \n", + " return InferenceResponse(model_name=self.name, outputs=outputs)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "impaired-principle", + "metadata": {}, + "source": [ + "As you can see above, this runtime will decode the incoming payloads by calling the `self.decode()` helper method.\n", + "This method will check what's the right content type for each input in the following order:\n", + "\n", + "1. Is there any content type defined in the `inputs[].parameters.content_type` field within the **request payload**?\n", + "2. Is there any content type defined in the `inputs[].parameters.content_type` field within the **model metadata**?\n", + "3. Is there any default content type that should be assumed?\n" + ] + }, + { + "cell_type": "markdown", + "id": "chronic-halloween", + "metadata": {}, + "source": [ + "### Model Settings\n", + "\n", + "In order to enable this runtime, we will also create a `model-settings.json` file.\n", + "This file should be present (or accessible from) in the folder where we run `mlserver start .`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "accessory-jerusalem", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "\n", + "{\n", + " \"name\": \"content-type-example\",\n", + " \"implementation\": \"runtime.EchoRuntime\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "canadian-somalia", + "metadata": {}, + "source": [ + "## Request Inputs\n", + "\n", + "Our initial step will be to decide the content type based on the incoming `inputs[].parameters` field.\n", + "For this, we will start our MLServer in the background (e.g. running `mlserver start .`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "criminal-personality", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "payload = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"parameters-np\",\n", + " \"datatype\": \"INT32\",\n", + " \"shape\": [2, 2],\n", + " \"data\": [1, 2, 3, 4],\n", + " \"parameters\": {\n", + " \"content_type\": \"np\"\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"parameters-str\",\n", + " \"datatype\": \"BYTES\",\n", + " \"shape\": [1],\n", + " \"data\": \"hello world 😁\",\n", + " \"parameters\": {\n", + " \"content_type\": \"str\"\n", + " }\n", + " }\n", + " ]\n", + "}\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:8080/v2/models/content-type-example/infer\",\n", + " json=payload\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c092ef24-4055-4391-ab2e-b44aa36f135a", + "metadata": {}, + "source": [ + "### Codecs\n", + "\n", + "As you've probably already noticed, writing request payloads compliant with both the V2 Inference Protocol requires a certain knowledge about both the V2 spec and the structure expected by each content type.\n", + "To account for this and simplify usage, the MLServer package exposes a set of utilities which will help you interact with your models via the V2 protocol.\n", + "\n", + "These helpers are mainly shaped as _\"codecs\"_.\n", + "That is, abstractions which know how to _\"encode\"_ and _\"decode\"_ arbitrary Python datatypes to and from the V2 Inference Protocol.\n", + "\n", + "Generally, we recommend using the existing set of codecs to generate your V2 payloads.\n", + "This will ensure that requests and responses follow the right structure, and should provide a more seamless experience.\n", + "\n", + "Following with our previous example, the same code could be rewritten using codecs as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4c20511-2af8-4a12-968b-0eb43e92ef04", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import numpy as np\n", + "\n", + "from mlserver.types import InferenceRequest, InferenceResponse\n", + "from mlserver.codecs import NumpyCodec, StringCodec\n", + "\n", + "parameters_np = np.array([[1, 2], [3, 4]])\n", + "parameters_str = [\"hello world 😁\"]\n", + "\n", + "payload = InferenceRequest(\n", + " inputs=[\n", + " NumpyCodec.encode_input(\"parameters-np\", parameters_np),\n", + " # The `use_bytes=False` flag will ensure that the encoded payload is JSON-compatible\n", + " StringCodec.encode_input(\"parameters-str\", parameters_str, use_bytes=False),\n", + " ]\n", + ")\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:8080/v2/models/content-type-example/infer\",\n", + " json=payload.model_dump()\n", + ")\n", + "\n", + "response_payload = InferenceResponse.parse_raw(response.text)\n", + "print(NumpyCodec.decode_output(response_payload.outputs[0]))\n", + "print(StringCodec.decode_output(response_payload.outputs[1]))" + ] + }, + { + "cell_type": "markdown", + "id": "d143ef5b-6f8f-41e2-9de5-91806899d94b", + "metadata": {}, + "source": [ + "Note that the rewritten snippet now makes use of the built-in `InferenceRequest` class, which represents a V2 inference request.\n", + "On top of that, it also uses the `NumpyCodec` and `StringCodec` implementations, which know how to encode a Numpy array and a list of strings into V2-compatible request inputs." + ] + }, + { + "cell_type": "markdown", + "id": "false-blind", + "metadata": {}, + "source": [ + "### Model Metadata\n", + "\n", + "Our next step will be to define the expected content type through the model metadata.\n", + "This can be done by extending the `model-settings.json` file, and adding a section on inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "enclosed-russia", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "\n", + "{\n", + " \"name\": \"content-type-example\",\n", + " \"implementation\": \"runtime.EchoRuntime\",\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"metadata-np\",\n", + " \"datatype\": \"INT32\",\n", + " \"shape\": [2, 2],\n", + " \"parameters\": {\n", + " \"content_type\": \"np\"\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"metadata-str\",\n", + " \"datatype\": \"BYTES\",\n", + " \"shape\": [11],\n", + " \"parameters\": {\n", + " \"content_type\": \"str\"\n", + " }\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "linear-logic", + "metadata": {}, + "source": [ + "After adding this metadata, we will re-start MLServer (e.g. `mlserver start .`) and we will send a new request without any explicit `parameters`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "independent-yacht", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "payload = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"metadata-np\",\n", + " \"datatype\": \"INT32\",\n", + " \"shape\": [2, 2],\n", + " \"data\": [1, 2, 3, 4],\n", + " },\n", + " {\n", + " \"name\": \"metadata-str\",\n", + " \"datatype\": \"BYTES\",\n", + " \"shape\": [11],\n", + " \"data\": \"hello world 😁\",\n", + " }\n", + " ]\n", + "}\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:8080/v2/models/content-type-example/infer\",\n", + " json=payload\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "waiting-samuel", + "metadata": {}, + "source": [ + "As you should be able to see in the server logs, MLServer will cross-reference the input names against the model metadata to find the right content type." + ] + }, + { + "cell_type": "markdown", + "id": "documentary-butterfly", + "metadata": {}, + "source": [ + "### Custom Codecs\n", + "\n", + "There may be cases where a custom inference runtime may need to encode / decode to custom datatypes.\n", + "As an example, we can think of computer vision models which may only operate with `pillow` image objects.\n", + "\n", + "In these scenarios, it's possible to extend the `Codec` interface to write our custom encoding logic.\n", + "A `Codec`, is simply an object which defines a `decode()` and `encode()` methods.\n", + "To illustrate how this would work, we will extend our custom runtime to add a custom `PillowCodec`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "simple-reducing", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile runtime.py\n", + "import io\n", + "import json\n", + "\n", + "from PIL import Image\n", + "\n", + "from mlserver import MLModel\n", + "from mlserver.types import (\n", + " InferenceRequest,\n", + " InferenceResponse,\n", + " RequestInput,\n", + " ResponseOutput,\n", + ")\n", + "from mlserver.codecs import NumpyCodec, register_input_codec, DecodedParameterName\n", + "from mlserver.codecs.utils import InputOrOutput\n", + "\n", + "\n", + "_to_exclude = {\n", + " \"parameters\": {DecodedParameterName},\n", + " \"inputs\": {\"__all__\": {\"parameters\": {DecodedParameterName}}},\n", + "}\n", + "\n", + "\n", + "@register_input_codec\n", + "class PillowCodec(NumpyCodec):\n", + " ContentType = \"img\"\n", + " DefaultMode = \"L\"\n", + "\n", + " @classmethod\n", + " def can_encode(cls, payload: Image) -> bool:\n", + " return isinstance(payload, Image)\n", + "\n", + " @classmethod\n", + " def _decode(cls, input_or_output: InputOrOutput) -> Image:\n", + " if input_or_output.datatype != \"BYTES\":\n", + " # If not bytes, assume it's an array\n", + " image_array = super().decode_input(input_or_output) # type: ignore\n", + " return Image.fromarray(image_array, mode=cls.DefaultMode)\n", + "\n", + " encoded = input_or_output.data\n", + " if isinstance(encoded, str):\n", + " encoded = encoded.encode()\n", + "\n", + " return Image.frombytes(\n", + " mode=cls.DefaultMode, size=input_or_output.shape, data=encoded\n", + " )\n", + "\n", + " @classmethod\n", + " def encode_output(cls, name: str, payload: Image) -> ResponseOutput: # type: ignore\n", + " byte_array = io.BytesIO()\n", + " payload.save(byte_array, mode=cls.DefaultMode)\n", + "\n", + " return ResponseOutput(\n", + " name=name, shape=payload.size, datatype=\"BYTES\", data=byte_array.getvalue()\n", + " )\n", + "\n", + " @classmethod\n", + " def decode_output(cls, response_output: ResponseOutput) -> Image:\n", + " return cls._decode(response_output)\n", + "\n", + " @classmethod\n", + " def encode_input(cls, name: str, payload: Image) -> RequestInput: # type: ignore\n", + " output = cls.encode_output(name, payload)\n", + " return RequestInput(\n", + " name=output.name,\n", + " shape=output.shape,\n", + " datatype=output.datatype,\n", + " data=output.data,\n", + " )\n", + "\n", + " @classmethod\n", + " def decode_input(cls, request_input: RequestInput) -> Image:\n", + " return cls._decode(request_input)\n", + "\n", + "\n", + "class EchoRuntime(MLModel):\n", + " async def predict(self, payload: InferenceRequest) -> InferenceResponse:\n", + " outputs = []\n", + " for request_input in payload.inputs:\n", + " decoded_input = self.decode(request_input)\n", + " print(f\"------ Encoded Input ({request_input.name}) ------\")\n", + " as_dict = request_input.dict(exclude=_to_exclude) # type: ignore\n", + " print(json.dumps(as_dict, indent=2))\n", + " print(f\"------ Decoded input ({request_input.name}) ------\")\n", + " print(decoded_input)\n", + "\n", + " outputs.append(\n", + " ResponseOutput(\n", + " name=request_input.name,\n", + " datatype=request_input.datatype,\n", + " shape=request_input.shape,\n", + " data=request_input.data,\n", + " )\n", + " )\n", + "\n", + " return InferenceResponse(model_name=self.name, outputs=outputs)" + ] + }, + { + "cell_type": "markdown", + "id": "communist-childhood", + "metadata": {}, + "source": [ + "We should now be able to restart our instance of MLServer (i.e. with the `mlserver start .` command), to send a few test requests." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "underlying-judgment", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "payload = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"image-int32\",\n", + " \"datatype\": \"INT32\",\n", + " \"shape\": [8, 8],\n", + " \"data\": [\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 1, 0\n", + " ],\n", + " \"parameters\": {\n", + " \"content_type\": \"img\"\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"image-bytes\",\n", + " \"datatype\": \"BYTES\",\n", + " \"shape\": [8, 8],\n", + " \"data\": (\n", + " \"10101010\"\n", + " \"10101010\"\n", + " \"10101010\"\n", + " \"10101010\"\n", + " \"10101010\"\n", + " \"10101010\"\n", + " \"10101010\"\n", + " \"10101010\"\n", + " ),\n", + " \"parameters\": {\n", + " \"content_type\": \"img\"\n", + " }\n", + " }\n", + " ]\n", + "}\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:8080/v2/models/content-type-example/infer\",\n", + " json=payload\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "creative-roads", + "metadata": {}, + "source": [ + "As you should be able to see in the MLServer logs, the server is now able to decode the payload into a Pillow image.\n", + "This example also illustrates how `Codec` objects can be compatible with multiple `datatype` values (e.g. tensor and `BYTES` in this case)." + ] + }, + { + "cell_type": "markdown", + "id": "flexible-series", + "metadata": {}, + "source": [ + "## Request Codecs\n", + "\n", + "So far, we've seen how you can specify codecs so that they get applied at the input level.\n", + "However, it is also possible to use request-wide codecs that aggregate multiple inputs to decode the payload.\n", + "This is usually relevant for cases where the models expect a multi-column input type, like a Pandas DataFrame.\n", + "\n", + "To illustrate this, we will first tweak our `EchoRuntime` so that it prints the decoded contents at the request level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "identical-somerset", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile runtime.py\n", + "import json\n", + "\n", + "from mlserver import MLModel\n", + "from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput\n", + "from mlserver.codecs import DecodedParameterName\n", + "\n", + "_to_exclude = {\n", + " \"parameters\": {DecodedParameterName},\n", + " 'inputs': {\"__all__\": {\"parameters\": {DecodedParameterName}}}\n", + "}\n", + "\n", + "class EchoRuntime(MLModel):\n", + " async def predict(self, payload: InferenceRequest) -> InferenceResponse:\n", + " print(\"------ Encoded Input (request) ------\")\n", + " as_dict = payload.dict(exclude=_to_exclude) # type: ignore\n", + " print(json.dumps(as_dict, indent=2))\n", + " print(\"------ Decoded input (request) ------\")\n", + " decoded_request = None\n", + " if payload.parameters:\n", + " decoded_request = getattr(payload.parameters, DecodedParameterName)\n", + " print(decoded_request)\n", + " \n", + " outputs = []\n", + " for request_input in payload.inputs:\n", + " outputs.append(\n", + " ResponseOutput(\n", + " name=request_input.name,\n", + " datatype=request_input.datatype,\n", + " shape=request_input.shape,\n", + " data=request_input.data\n", + " )\n", + " )\n", + " \n", + " return InferenceResponse(model_name=self.name, outputs=outputs)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "labeled-onion", + "metadata": {}, + "source": [ + "We should now be able to restart our instance of MLServer (i.e. with the `mlserver start .` command), to send a few test requests." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "satellite-texas", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "payload = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"parameters-np\",\n", + " \"datatype\": \"INT32\",\n", + " \"shape\": [2, 2],\n", + " \"data\": [1, 2, 3, 4],\n", + " \"parameters\": {\n", + " \"content_type\": \"np\"\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"parameters-str\",\n", + " \"datatype\": \"BYTES\",\n", + " \"shape\": [2, 11],\n", + " \"data\": [\"hello world 😁\", \"bye bye 😁\"],\n", + " \"parameters\": {\n", + " \"content_type\": \"str\"\n", + " }\n", + " }\n", + " ],\n", + " \"parameters\": {\n", + " \"content_type\": \"pd\"\n", + " }\n", + "}\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:8080/v2/models/content-type-example/infer\",\n", + " json=payload\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "figured-member", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs-gb/examples/content-type/README.md b/docs-gb/examples/content-type/README.md new file mode 100644 index 000000000..cd905cf3c --- /dev/null +++ b/docs-gb/examples/content-type/README.md @@ -0,0 +1,478 @@ +# Content Type Decoding + +MLServer extends the V2 inference protocol by adding support for a `content_type` annotation. +This annotation can be provided either through the model metadata `parameters`, or through the input `parameters`. +By leveraging the `content_type` annotation, we can provide the necessary information to MLServer so that it can _decode_ the input payload from the "wire" V2 protocol to something meaningful to the model / user (e.g. a NumPy array). + +This example will walk you through some examples which illustrate how this works, and how it can be extended. + +## Echo Inference Runtime + +To start with, we will write a _dummy_ runtime which just prints the input, the _decoded_ input and returns it. +This will serve as a testbed to showcase how the `content_type` support works. + +Later on, we will extend this runtime by adding custom _codecs_ that will decode our V2 payload to custom types. + + +```python +%%writefile runtime.py +import json + +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput +from mlserver.codecs import DecodedParameterName + +_to_exclude = { + "parameters": {DecodedParameterName, "headers"}, + 'inputs': {"__all__": {"parameters": {DecodedParameterName, "headers"}}} +} + +class EchoRuntime(MLModel): + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + outputs = [] + for request_input in payload.inputs: + decoded_input = self.decode(request_input) + print(f"------ Encoded Input ({request_input.name}) ------") + as_dict = request_input.dict(exclude=_to_exclude) # type: ignore + print(json.dumps(as_dict, indent=2)) + print(f"------ Decoded input ({request_input.name}) ------") + print(decoded_input) + + outputs.append( + ResponseOutput( + name=request_input.name, + datatype=request_input.datatype, + shape=request_input.shape, + data=request_input.data + ) + ) + + return InferenceResponse(model_name=self.name, outputs=outputs) + +``` + +As you can see above, this runtime will decode the incoming payloads by calling the `self.decode()` helper method. +This method will check what's the right content type for each input in the following order: + +1. Is there any content type defined in the `inputs[].parameters.content_type` field within the **request payload**? +2. Is there any content type defined in the `inputs[].parameters.content_type` field within the **model metadata**? +3. Is there any default content type that should be assumed? + + +### Model Settings + +In order to enable this runtime, we will also create a `model-settings.json` file. +This file should be present (or accessible from) in the folder where we run `mlserver start .`. + + +```python +%%writefile model-settings.json + +{ + "name": "content-type-example", + "implementation": "runtime.EchoRuntime" +} +``` + +## Request Inputs + +Our initial step will be to decide the content type based on the incoming `inputs[].parameters` field. +For this, we will start our MLServer in the background (e.g. running `mlserver start .`) + + +```python +import requests + +payload = { + "inputs": [ + { + "name": "parameters-np", + "datatype": "INT32", + "shape": [2, 2], + "data": [1, 2, 3, 4], + "parameters": { + "content_type": "np" + } + }, + { + "name": "parameters-str", + "datatype": "BYTES", + "shape": [1], + "data": "hello world 😁", + "parameters": { + "content_type": "str" + } + } + ] +} + +response = requests.post( + "http://localhost:8080/v2/models/content-type-example/infer", + json=payload +) +``` + +### Codecs + +As you've probably already noticed, writing request payloads compliant with both the V2 Inference Protocol requires a certain knowledge about both the V2 spec and the structure expected by each content type. +To account for this and simplify usage, the MLServer package exposes a set of utilities which will help you interact with your models via the V2 protocol. + +These helpers are mainly shaped as _"codecs"_. +That is, abstractions which know how to _"encode"_ and _"decode"_ arbitrary Python datatypes to and from the V2 Inference Protocol. + +Generally, we recommend using the existing set of codecs to generate your V2 payloads. +This will ensure that requests and responses follow the right structure, and should provide a more seamless experience. + +Following with our previous example, the same code could be rewritten using codecs as: + + +```python +import requests +import numpy as np + +from mlserver.types import InferenceRequest, InferenceResponse +from mlserver.codecs import NumpyCodec, StringCodec + +parameters_np = np.array([[1, 2], [3, 4]]) +parameters_str = ["hello world 😁"] + +payload = InferenceRequest( + inputs=[ + NumpyCodec.encode_input("parameters-np", parameters_np), + # The `use_bytes=False` flag will ensure that the encoded payload is JSON-compatible + StringCodec.encode_input("parameters-str", parameters_str, use_bytes=False), + ] +) + +response = requests.post( + "http://localhost:8080/v2/models/content-type-example/infer", + json=payload.model_dump() +) + +response_payload = InferenceResponse.parse_raw(response.text) +print(NumpyCodec.decode_output(response_payload.outputs[0])) +print(StringCodec.decode_output(response_payload.outputs[1])) +``` + +Note that the rewritten snippet now makes use of the built-in `InferenceRequest` class, which represents a V2 inference request. +On top of that, it also uses the `NumpyCodec` and `StringCodec` implementations, which know how to encode a Numpy array and a list of strings into V2-compatible request inputs. + +### Model Metadata + +Our next step will be to define the expected content type through the model metadata. +This can be done by extending the `model-settings.json` file, and adding a section on inputs. + + +```python +%%writefile model-settings.json + +{ + "name": "content-type-example", + "implementation": "runtime.EchoRuntime", + "inputs": [ + { + "name": "metadata-np", + "datatype": "INT32", + "shape": [2, 2], + "parameters": { + "content_type": "np" + } + }, + { + "name": "metadata-str", + "datatype": "BYTES", + "shape": [11], + "parameters": { + "content_type": "str" + } + } + ] +} +``` + +After adding this metadata, we will re-start MLServer (e.g. `mlserver start .`) and we will send a new request without any explicit `parameters`. + + +```python +import requests + +payload = { + "inputs": [ + { + "name": "metadata-np", + "datatype": "INT32", + "shape": [2, 2], + "data": [1, 2, 3, 4], + }, + { + "name": "metadata-str", + "datatype": "BYTES", + "shape": [11], + "data": "hello world 😁", + } + ] +} + +response = requests.post( + "http://localhost:8080/v2/models/content-type-example/infer", + json=payload +) +``` + +As you should be able to see in the server logs, MLServer will cross-reference the input names against the model metadata to find the right content type. + +### Custom Codecs + +There may be cases where a custom inference runtime may need to encode / decode to custom datatypes. +As an example, we can think of computer vision models which may only operate with `pillow` image objects. + +In these scenarios, it's possible to extend the `Codec` interface to write our custom encoding logic. +A `Codec`, is simply an object which defines a `decode()` and `encode()` methods. +To illustrate how this would work, we will extend our custom runtime to add a custom `PillowCodec`. + + +```python +%%writefile runtime.py +import io +import json + +from PIL import Image + +from mlserver import MLModel +from mlserver.types import ( + InferenceRequest, + InferenceResponse, + RequestInput, + ResponseOutput, +) +from mlserver.codecs import NumpyCodec, register_input_codec, DecodedParameterName +from mlserver.codecs.utils import InputOrOutput + + +_to_exclude = { + "parameters": {DecodedParameterName}, + "inputs": {"__all__": {"parameters": {DecodedParameterName}}}, +} + + +@register_input_codec +class PillowCodec(NumpyCodec): + ContentType = "img" + DefaultMode = "L" + + @classmethod + def can_encode(cls, payload: Image) -> bool: + return isinstance(payload, Image) + + @classmethod + def _decode(cls, input_or_output: InputOrOutput) -> Image: + if input_or_output.datatype != "BYTES": + # If not bytes, assume it's an array + image_array = super().decode_input(input_or_output) # type: ignore + return Image.fromarray(image_array, mode=cls.DefaultMode) + + encoded = input_or_output.data + if isinstance(encoded, str): + encoded = encoded.encode() + + return Image.frombytes( + mode=cls.DefaultMode, size=input_or_output.shape, data=encoded + ) + + @classmethod + def encode_output(cls, name: str, payload: Image) -> ResponseOutput: # type: ignore + byte_array = io.BytesIO() + payload.save(byte_array, mode=cls.DefaultMode) + + return ResponseOutput( + name=name, shape=payload.size, datatype="BYTES", data=byte_array.getvalue() + ) + + @classmethod + def decode_output(cls, response_output: ResponseOutput) -> Image: + return cls._decode(response_output) + + @classmethod + def encode_input(cls, name: str, payload: Image) -> RequestInput: # type: ignore + output = cls.encode_output(name, payload) + return RequestInput( + name=output.name, + shape=output.shape, + datatype=output.datatype, + data=output.data, + ) + + @classmethod + def decode_input(cls, request_input: RequestInput) -> Image: + return cls._decode(request_input) + + +class EchoRuntime(MLModel): + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + outputs = [] + for request_input in payload.inputs: + decoded_input = self.decode(request_input) + print(f"------ Encoded Input ({request_input.name}) ------") + as_dict = request_input.dict(exclude=_to_exclude) # type: ignore + print(json.dumps(as_dict, indent=2)) + print(f"------ Decoded input ({request_input.name}) ------") + print(decoded_input) + + outputs.append( + ResponseOutput( + name=request_input.name, + datatype=request_input.datatype, + shape=request_input.shape, + data=request_input.data, + ) + ) + + return InferenceResponse(model_name=self.name, outputs=outputs) +``` + +We should now be able to restart our instance of MLServer (i.e. with the `mlserver start .` command), to send a few test requests. + + +```python +import requests + +payload = { + "inputs": [ + { + "name": "image-int32", + "datatype": "INT32", + "shape": [8, 8], + "data": [ + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0, + 1, 0, 1, 0, 1, 0, 1, 0 + ], + "parameters": { + "content_type": "img" + } + }, + { + "name": "image-bytes", + "datatype": "BYTES", + "shape": [8, 8], + "data": ( + "10101010" + "10101010" + "10101010" + "10101010" + "10101010" + "10101010" + "10101010" + "10101010" + ), + "parameters": { + "content_type": "img" + } + } + ] +} + +response = requests.post( + "http://localhost:8080/v2/models/content-type-example/infer", + json=payload +) +``` + +As you should be able to see in the MLServer logs, the server is now able to decode the payload into a Pillow image. +This example also illustrates how `Codec` objects can be compatible with multiple `datatype` values (e.g. tensor and `BYTES` in this case). + +## Request Codecs + +So far, we've seen how you can specify codecs so that they get applied at the input level. +However, it is also possible to use request-wide codecs that aggregate multiple inputs to decode the payload. +This is usually relevant for cases where the models expect a multi-column input type, like a Pandas DataFrame. + +To illustrate this, we will first tweak our `EchoRuntime` so that it prints the decoded contents at the request level. + + +```python +%%writefile runtime.py +import json + +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput +from mlserver.codecs import DecodedParameterName + +_to_exclude = { + "parameters": {DecodedParameterName}, + 'inputs': {"__all__": {"parameters": {DecodedParameterName}}} +} + +class EchoRuntime(MLModel): + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + print("------ Encoded Input (request) ------") + as_dict = payload.dict(exclude=_to_exclude) # type: ignore + print(json.dumps(as_dict, indent=2)) + print("------ Decoded input (request) ------") + decoded_request = None + if payload.parameters: + decoded_request = getattr(payload.parameters, DecodedParameterName) + print(decoded_request) + + outputs = [] + for request_input in payload.inputs: + outputs.append( + ResponseOutput( + name=request_input.name, + datatype=request_input.datatype, + shape=request_input.shape, + data=request_input.data + ) + ) + + return InferenceResponse(model_name=self.name, outputs=outputs) + +``` + +We should now be able to restart our instance of MLServer (i.e. with the `mlserver start .` command), to send a few test requests. + + +```python +import requests + +payload = { + "inputs": [ + { + "name": "parameters-np", + "datatype": "INT32", + "shape": [2, 2], + "data": [1, 2, 3, 4], + "parameters": { + "content_type": "np" + } + }, + { + "name": "parameters-str", + "datatype": "BYTES", + "shape": [2, 11], + "data": ["hello world 😁", "bye bye 😁"], + "parameters": { + "content_type": "str" + } + } + ], + "parameters": { + "content_type": "pd" + } +} + +response = requests.post( + "http://localhost:8080/v2/models/content-type-example/infer", + json=payload +) +``` + + +```python + +``` diff --git a/docs-gb/examples/content-type/model-settings.json b/docs-gb/examples/content-type/model-settings.json new file mode 100644 index 000000000..0565febb2 --- /dev/null +++ b/docs-gb/examples/content-type/model-settings.json @@ -0,0 +1,23 @@ + +{ + "name": "content-type-example", + "implementation": "runtime.EchoRuntime", + "inputs": [ + { + "name": "metadata-np", + "datatype": "INT32", + "shape": [2, 2], + "parameters": { + "content_type": "np" + } + }, + { + "name": "metadata-str", + "datatype": "BYTES", + "shape": [11], + "parameters": { + "content_type": "str" + } + } + ] +} diff --git a/docs-gb/examples/content-type/runtime.py b/docs-gb/examples/content-type/runtime.py new file mode 100644 index 000000000..5e3d98985 --- /dev/null +++ b/docs-gb/examples/content-type/runtime.py @@ -0,0 +1,103 @@ +import io +import json + +from PIL import Image + +from mlserver import MLModel +from mlserver.types import ( + InferenceRequest, + InferenceResponse, + RequestInput, + ResponseOutput, +) +from mlserver.codecs import InputCodec, register_input_codec, DecodedParameterName +from mlserver.codecs.utils import InputOrOutput + + +_to_exclude = { + "parameters": {DecodedParameterName}, + "inputs": {"__all__": {"parameters": {DecodedParameterName}}}, +} + + +@register_input_codec +class PillowCodec(InputCodec): + ContentType = "img" + DefaultMode = "L" + + @classmethod + def can_encode(cls, payload: Image.Image) -> bool: + return isinstance(payload, Image.Image) + + @classmethod + def _decode(cls, input_or_output: InputOrOutput) -> Image.Image: + if input_or_output.datatype != "BYTES": + # If not bytes, assume it's an array + image_array = super().decode_input(input_or_output) # type: ignore + return Image.fromarray(image_array, mode=cls.DefaultMode) + + encoded = input_or_output.data + if isinstance(encoded, str): + encoded = encoded.encode() + + return Image.frombytes( + mode=cls.DefaultMode, size=input_or_output.shape, data=encoded + ) + + @classmethod + def encode_output( # type: ignore + cls, + name: str, + payload: Image.Image, + ) -> ResponseOutput: # type: ignore + byte_array = io.BytesIO() + payload.save(byte_array, mode=cls.DefaultMode) + + return ResponseOutput( + name=name, shape=payload.size, datatype="BYTES", data=byte_array.getvalue() + ) + + @classmethod + def decode_output(cls, response_output: ResponseOutput) -> Image.Image: + return cls._decode(response_output) + + @classmethod + def encode_input( # type: ignore + cls, + name: str, + payload: Image.Image, + ) -> RequestInput: # type: ignore + output = cls.encode_output(name, payload) + return RequestInput( + name=output.name, + shape=output.shape, + datatype=output.datatype, + data=output.data, + ) + + @classmethod + def decode_input(cls, request_input: RequestInput) -> Image.Image: + return cls._decode(request_input) + + +class EchoRuntime(MLModel): + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + outputs = [] + for request_input in payload.inputs: + decoded_input = self.decode(request_input) + print(f"------ Encoded Input ({request_input.name}) ------") + as_dict = request_input.dict(exclude=_to_exclude) # type: ignore + print(json.dumps(as_dict, indent=2)) + print(f"------ Decoded input ({request_input.name}) ------") + print(decoded_input) + + outputs.append( + ResponseOutput( + name=request_input.name, + datatype=request_input.datatype, + shape=request_input.shape, + data=request_input.data, + ) + ) + + return InferenceResponse(model_name=self.name, outputs=outputs) diff --git a/docs-gb/examples/custom-json/README.ipynb b/docs-gb/examples/custom-json/README.ipynb new file mode 100644 index 000000000..475bbb575 --- /dev/null +++ b/docs-gb/examples/custom-json/README.ipynb @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving a custom model with JSON serialization\n", + "\n", + "The `mlserver` package comes with inference runtime implementations for `scikit-learn` and `xgboost` models.\n", + "However, some times we may also need to roll out our own inference server, with custom logic to perform inference.\n", + "To support this scenario, MLServer makes it really easy to create your own extensions, which can then be containerised and deployed in a production environment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this example, we create a simple `Hello World JSON` model that parses and modifies a JSON data chunk. This is often useful as a means to\n", + "quickly bootstrap existing models that utilize JSON based model inputs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "The next step will be to serve our model using `mlserver`. \n", + "For that, we will first implement an extension which serve as the _runtime_ to perform inference using our custom `Hello World JSON` model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Custom inference runtime\n", + "\n", + "This is a trivial model to demonstrate how to conceptually work with JSON inputs / outputs. In this example:\n", + "\n", + "- Parse the JSON input from the client\n", + "- Create a JSON response echoing back the client request as well as a server generated message\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile jsonmodels.py\n", + "import json\n", + "\n", + "from typing import Dict, Any\n", + "from mlserver import MLModel, types\n", + "from mlserver.codecs import StringCodec\n", + "\n", + "\n", + "class JsonHelloWorldModel(MLModel):\n", + " async def load(self) -> bool:\n", + " # Perform additional custom initialization here.\n", + " print(\"Initialize model\")\n", + "\n", + " # Set readiness flag for model\n", + " return await super().load()\n", + "\n", + " async def predict(self, payload: types.InferenceRequest) -> types.InferenceResponse:\n", + " request = self._extract_json(payload)\n", + " response = {\n", + " \"request\": request,\n", + " \"server_response\": \"Got your request. Hello from the server.\",\n", + " }\n", + " response_bytes = json.dumps(response).encode(\"UTF-8\")\n", + "\n", + " return types.InferenceResponse(\n", + " id=payload.id,\n", + " model_name=self.name,\n", + " model_version=self.version,\n", + " outputs=[\n", + " types.ResponseOutput(\n", + " name=\"echo_response\",\n", + " shape=[len(response_bytes)],\n", + " datatype=\"BYTES\",\n", + " data=[response_bytes],\n", + " parameters=types.Parameters(content_type=\"str\"),\n", + " )\n", + " ],\n", + " )\n", + "\n", + " def _extract_json(self, payload: types.InferenceRequest) -> Dict[str, Any]:\n", + " inputs = {}\n", + " for inp in payload.inputs:\n", + " inputs[inp.name] = json.loads(\n", + " \"\".join(self.decode(inp, default_codec=StringCodec))\n", + " )\n", + "\n", + " return inputs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Settings files\n", + "\n", + "The next step will be to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"json-hello-world\",\n", + " \"implementation\": \"jsonmodels.JsonHelloWorldModel\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request (REST)\n", + "\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import json\n", + "from mlserver.types import InferenceResponse\n", + "from mlserver.codecs.string import StringRequestCodec\n", + "from pprint import PrettyPrinter\n", + "\n", + "pp = PrettyPrinter(indent=1)\n", + "\n", + "inputs = {\"name\": \"Foo Bar\", \"message\": \"Hello from Client (REST)!\"}\n", + "\n", + "# NOTE: this uses characters rather than encoded bytes. It is recommended that you use the `mlserver` types to assist in the correct encoding.\n", + "inputs_string = json.dumps(inputs)\n", + "\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"echo_request\",\n", + " \"shape\": [len(inputs_string)],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [inputs_string],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/json-hello-world/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "print(f\"full response:\\n\")\n", + "print(response)\n", + "# retrive text output as dictionary\n", + "inference_response = InferenceResponse.parse_raw(response.text)\n", + "raw_json = StringRequestCodec.decode_response(inference_response)\n", + "output = json.loads(raw_json[0])\n", + "print(f\"\\ndata part:\\n\")\n", + "pp.pprint(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request (gRPC)\n", + "\n", + "\n", + "Utilizing string data with the gRPC interface can be a bit tricky. To ensure we are correctly handling inputs and\n", + "outputs we will be handled correctly.\n", + "\n", + "For simplicity in this case, we leverage the Python types that `mlserver` provides out of the box. Alternatively,\n", + "the gRPC stubs can be generated regenerated from the V2 specification directly for use by non-Python as well as \n", + "Python clients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import json\n", + "import grpc\n", + "from mlserver.codecs.string import StringRequestCodec\n", + "import mlserver.grpc.converters as converters\n", + "import mlserver.grpc.dataplane_pb2_grpc as dataplane\n", + "import mlserver.types as types\n", + "from pprint import PrettyPrinter\n", + "\n", + "pp = PrettyPrinter(indent=1)\n", + "\n", + "model_name = \"json-hello-world\"\n", + "inputs = {\"name\": \"Foo Bar\", \"message\": \"Hello from Client (gRPC)!\"}\n", + "inputs_bytes = json.dumps(inputs).encode(\"UTF-8\")\n", + "\n", + "inference_request = types.InferenceRequest(\n", + " inputs=[\n", + " types.RequestInput(\n", + " name=\"echo_request\",\n", + " shape=[len(inputs_bytes)],\n", + " datatype=\"BYTES\",\n", + " data=[inputs_bytes],\n", + " parameters=types.Parameters(content_type=\"str\"),\n", + " )\n", + " ]\n", + ")\n", + "\n", + "inference_request_g = converters.ModelInferRequestConverter.from_types(\n", + " inference_request, model_name=model_name, model_version=None\n", + ")\n", + "\n", + "grpc_channel = grpc.insecure_channel(\"localhost:8081\")\n", + "grpc_stub = dataplane.GRPCInferenceServiceStub(grpc_channel)\n", + "\n", + "response = grpc_stub.ModelInfer(inference_request_g)\n", + "\n", + "print(f\"full response:\\n\")\n", + "print(response)\n", + "# retrive text output as dictionary\n", + "inference_response = converters.ModelInferResponseConverter.to_types(response)\n", + "raw_json = StringRequestCodec.decode_response(inference_response)\n", + "output = json.loads(raw_json[0])\n", + "print(f\"\\ndata part:\\n\")\n", + "pp.pprint(output)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('central')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.15" + }, + "vscode": { + "interpreter": { + "hash": "2465c4f56298bc06dbdad3e7519856d346ec0e9edf6ba2c905f0af711583810e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/custom-json/README.md b/docs-gb/examples/custom-json/README.md new file mode 100644 index 000000000..de67aa24f --- /dev/null +++ b/docs-gb/examples/custom-json/README.md @@ -0,0 +1,218 @@ +# Serving a custom model with JSON serialization + +The `mlserver` package comes with inference runtime implementations for `scikit-learn` and `xgboost` models. +However, some times we may also need to roll out our own inference server, with custom logic to perform inference. +To support this scenario, MLServer makes it really easy to create your own extensions, which can then be containerised and deployed in a production environment. + +## Overview + +In this example, we create a simple `Hello World JSON` model that parses and modifies a JSON data chunk. This is often useful as a means to +quickly bootstrap existing models that utilize JSON based model inputs. + +## Serving + +The next step will be to serve our model using `mlserver`. +For that, we will first implement an extension which serve as the _runtime_ to perform inference using our custom `Hello World JSON` model. + +### Custom inference runtime + +This is a trivial model to demonstrate how to conceptually work with JSON inputs / outputs. In this example: + +- Parse the JSON input from the client +- Create a JSON response echoing back the client request as well as a server generated message + + + +```python +%%writefile jsonmodels.py +import json + +from typing import Dict, Any +from mlserver import MLModel, types +from mlserver.codecs import StringCodec + + +class JsonHelloWorldModel(MLModel): + async def load(self) -> bool: + # Perform additional custom initialization here. + print("Initialize model") + + # Set readiness flag for model + return await super().load() + + async def predict(self, payload: types.InferenceRequest) -> types.InferenceResponse: + request = self._extract_json(payload) + response = { + "request": request, + "server_response": "Got your request. Hello from the server.", + } + response_bytes = json.dumps(response).encode("UTF-8") + + return types.InferenceResponse( + id=payload.id, + model_name=self.name, + model_version=self.version, + outputs=[ + types.ResponseOutput( + name="echo_response", + shape=[len(response_bytes)], + datatype="BYTES", + data=[response_bytes], + parameters=types.Parameters(content_type="str"), + ) + ], + ) + + def _extract_json(self, payload: types.InferenceRequest) -> Dict[str, Any]: + inputs = {} + for inp in payload.inputs: + inputs[inp.name] = json.loads( + "".join(self.decode(inp, default_codec=StringCodec)) + ) + + return inputs + +``` + +### Settings files + +The next step will be to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +#### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + +#### `model-settings.json` + + +```python +%%writefile model-settings.json +{ + "name": "json-hello-world", + "implementation": "jsonmodels.JsonHelloWorldModel" +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request (REST) + + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests +import json +from mlserver.types import InferenceResponse +from mlserver.codecs.string import StringRequestCodec +from pprint import PrettyPrinter + +pp = PrettyPrinter(indent=1) + +inputs = {"name": "Foo Bar", "message": "Hello from Client (REST)!"} + +# NOTE: this uses characters rather than encoded bytes. It is recommended that you use the `mlserver` types to assist in the correct encoding. +inputs_string = json.dumps(inputs) + +inference_request = { + "inputs": [ + { + "name": "echo_request", + "shape": [len(inputs_string)], + "datatype": "BYTES", + "data": [inputs_string], + } + ] +} + +endpoint = "http://localhost:8080/v2/models/json-hello-world/infer" +response = requests.post(endpoint, json=inference_request) + +print(f"full response:\n") +print(response) +# retrive text output as dictionary +inference_response = InferenceResponse.parse_raw(response.text) +raw_json = StringRequestCodec.decode_response(inference_response) +output = json.loads(raw_json[0]) +print(f"\ndata part:\n") +pp.pprint(output) +``` + +### Send test inference request (gRPC) + + +Utilizing string data with the gRPC interface can be a bit tricky. To ensure we are correctly handling inputs and +outputs we will be handled correctly. + +For simplicity in this case, we leverage the Python types that `mlserver` provides out of the box. Alternatively, +the gRPC stubs can be generated regenerated from the V2 specification directly for use by non-Python as well as +Python clients. + + +```python +import requests +import json +import grpc +from mlserver.codecs.string import StringRequestCodec +import mlserver.grpc.converters as converters +import mlserver.grpc.dataplane_pb2_grpc as dataplane +import mlserver.types as types +from pprint import PrettyPrinter + +pp = PrettyPrinter(indent=1) + +model_name = "json-hello-world" +inputs = {"name": "Foo Bar", "message": "Hello from Client (gRPC)!"} +inputs_bytes = json.dumps(inputs).encode("UTF-8") + +inference_request = types.InferenceRequest( + inputs=[ + types.RequestInput( + name="echo_request", + shape=[len(inputs_bytes)], + datatype="BYTES", + data=[inputs_bytes], + parameters=types.Parameters(content_type="str"), + ) + ] +) + +inference_request_g = converters.ModelInferRequestConverter.from_types( + inference_request, model_name=model_name, model_version=None +) + +grpc_channel = grpc.insecure_channel("localhost:8081") +grpc_stub = dataplane.GRPCInferenceServiceStub(grpc_channel) + +response = grpc_stub.ModelInfer(inference_request_g) + +print(f"full response:\n") +print(response) +# retrive text output as dictionary +inference_response = converters.ModelInferResponseConverter.to_types(response) +raw_json = StringRequestCodec.decode_response(inference_response) +output = json.loads(raw_json[0]) +print(f"\ndata part:\n") +pp.pprint(output) +``` diff --git a/docs-gb/examples/custom-json/jsonmodels.py b/docs-gb/examples/custom-json/jsonmodels.py new file mode 100644 index 000000000..72c8399de --- /dev/null +++ b/docs-gb/examples/custom-json/jsonmodels.py @@ -0,0 +1,46 @@ +import json + +from typing import Dict, Any +from mlserver import MLModel, types +from mlserver.codecs import StringCodec + + +class JsonHelloWorldModel(MLModel): + async def load(self) -> bool: + # Perform additional custom initialization here. + print("Initialize model") + + # Set readiness flag for model + return await super().load() + + async def predict(self, payload: types.InferenceRequest) -> types.InferenceResponse: + request = self._extract_json(payload) + response = { + "request": request, + "server_response": "Got your request. Hello from the server.", + } + response_bytes = json.dumps(response).encode("UTF-8") + + return types.InferenceResponse( + id=payload.id, + model_name=self.name, + model_version=self.version, + outputs=[ + types.ResponseOutput( + name="echo_response", + shape=[len(response_bytes)], + datatype="BYTES", + data=[response_bytes], + parameters=types.Parameters(content_type="str"), + ) + ], + ) + + def _extract_json(self, payload: types.InferenceRequest) -> Dict[str, Any]: + inputs = {} + for inp in payload.inputs: + inputs[inp.name] = json.loads( + "".join(self.decode(inp, default_codec=StringCodec)) + ) + + return inputs diff --git a/docs-gb/examples/custom-json/model-settings.json b/docs-gb/examples/custom-json/model-settings.json new file mode 100644 index 000000000..c02bde27e --- /dev/null +++ b/docs-gb/examples/custom-json/model-settings.json @@ -0,0 +1,4 @@ +{ + "name": "json-hello-world", + "implementation": "jsonmodels.JsonHelloWorldModel" +} diff --git a/docs-gb/examples/custom-json/settings.json b/docs-gb/examples/custom-json/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/custom-json/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/custom/README.ipynb b/docs-gb/examples/custom/README.ipynb new file mode 100644 index 000000000..ed47008f9 --- /dev/null +++ b/docs-gb/examples/custom/README.ipynb @@ -0,0 +1,507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving a custom model\n", + "\n", + "The `mlserver` package comes with inference runtime implementations for `scikit-learn` and `xgboost` models.\n", + "However, some times we may also need to roll out our own inference server, with custom logic to perform inference.\n", + "To support this scenario, MLServer makes it really easy to create your own extensions, which can then be containerised and deployed in a production environment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this example, we will train a [`numpyro` model](http://num.pyro.ai/en/stable/). \n", + "The `numpyro` library streamlines the implementation of probabilistic models, abstracting away advanced inference and training algorithms.\n", + "\n", + "Out of the box, `mlserver` doesn't provide an inference runtime for `numpyro`.\n", + "However, through this example we will see how easy is to develop our own." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "The first step will be to train our model.\n", + "This will be a very simple bayesian regression model, based on an example provided in the [`numpyro` docs](https://nbviewer.jupyter.org/github/pyro-ppl/numpyro/blob/master/notebooks/source/bayesian_regression.ipynb).\n", + "\n", + "Since this is a probabilistic model, during training we will compute an approximation to the posterior distribution of our model using MCMC." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original source code and more details can be found in:\n", + "# https://nbviewer.jupyter.org/github/pyro-ppl/numpyro/blob/master/notebooks/source/bayesian_regression.ipynb\n", + "\n", + "\n", + "import numpyro\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from numpyro import distributions as dist\n", + "from jax import random\n", + "from numpyro.infer import MCMC, NUTS\n", + "\n", + "DATASET_URL = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv\"\n", + "dset = pd.read_csv(DATASET_URL, sep=\";\")\n", + "\n", + "standardize = lambda x: (x - x.mean()) / x.std()\n", + "\n", + "dset[\"AgeScaled\"] = dset.MedianAgeMarriage.pipe(standardize)\n", + "dset[\"MarriageScaled\"] = dset.Marriage.pipe(standardize)\n", + "dset[\"DivorceScaled\"] = dset.Divorce.pipe(standardize)\n", + "\n", + "\n", + "def model(marriage=None, age=None, divorce=None):\n", + " a = numpyro.sample(\"a\", dist.Normal(0.0, 0.2))\n", + " M, A = 0.0, 0.0\n", + " if marriage is not None:\n", + " bM = numpyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n", + " M = bM * marriage\n", + " if age is not None:\n", + " bA = numpyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n", + " A = bA * age\n", + " sigma = numpyro.sample(\"sigma\", dist.Exponential(1.0))\n", + " mu = a + M + A\n", + " numpyro.sample(\"obs\", dist.Normal(mu, sigma), obs=divorce)\n", + "\n", + "\n", + "# Start from this source of randomness. We will split keys for subsequent operations.\n", + "rng_key = random.PRNGKey(0)\n", + "rng_key, rng_key_ = random.split(rng_key)\n", + "\n", + "num_warmup, num_samples = 1000, 2000\n", + "\n", + "# Run NUTS.\n", + "kernel = NUTS(model)\n", + "mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples)\n", + "mcmc.run(\n", + " rng_key_, marriage=dset.MarriageScaled.values, divorce=dset.DivorceScaled.values\n", + ")\n", + "mcmc.print_summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving our trained model\n", + "\n", + "Now that we have _trained_ our model, the next step will be to save it so that it can be loaded afterwards at serving-time.\n", + "Note that, since this is a probabilistic model, we will only need to save the traces that approximate the posterior distribution over latent parameters.\n", + "\n", + "This will get saved in a `numpyro-divorce.json` file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "samples = mcmc.get_samples()\n", + "serialisable = {}\n", + "for k, v in samples.items():\n", + " serialisable[k] = np.asarray(v).tolist()\n", + "\n", + "model_file_name = \"numpyro-divorce.json\"\n", + "with open(model_file_name, \"w\") as model_file:\n", + " json.dump(serialisable, model_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "The next step will be to serve our model using `mlserver`. \n", + "For that, we will first implement an extension which serve as the _runtime_ to perform inference using our custom `numpyro` model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Custom inference runtime\n", + "\n", + "Our custom inference wrapper should be responsible of:\n", + "\n", + "- Loading the model from the set samples we saved previously.\n", + "- Running inference using our model structure, and the posterior approximated from the samples.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load models.py\n", + "import json\n", + "import numpyro\n", + "import numpy as np\n", + "\n", + "from jax import random\n", + "from mlserver import MLModel\n", + "from mlserver.codecs import decode_args\n", + "from mlserver.utils import get_model_uri\n", + "from numpyro.infer import Predictive\n", + "from numpyro import distributions as dist\n", + "from typing import Optional\n", + "\n", + "\n", + "class NumpyroModel(MLModel):\n", + " async def load(self) -> bool:\n", + " model_uri = await get_model_uri(self._settings)\n", + " with open(model_uri) as model_file:\n", + " raw_samples = json.load(model_file)\n", + "\n", + " self._samples = {}\n", + " for k, v in raw_samples.items():\n", + " self._samples[k] = np.array(v)\n", + "\n", + " self._predictive = Predictive(self._model, self._samples)\n", + "\n", + " return True\n", + "\n", + " @decode_args\n", + " async def predict(\n", + " self,\n", + " marriage: Optional[np.ndarray] = None,\n", + " age: Optional[np.ndarray] = None,\n", + " divorce: Optional[np.ndarray] = None,\n", + " ) -> np.ndarray:\n", + " predictions = self._predictive(\n", + " rng_key=random.PRNGKey(0), marriage=marriage, age=age, divorce=divorce\n", + " )\n", + "\n", + " obs = predictions[\"obs\"]\n", + " obs_mean = obs.mean()\n", + "\n", + " return np.asarray(obs_mean)\n", + "\n", + " def _model(self, marriage=None, age=None, divorce=None):\n", + " a = numpyro.sample(\"a\", dist.Normal(0.0, 0.2))\n", + " M, A = 0.0, 0.0\n", + " if marriage is not None:\n", + " bM = numpyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n", + " M = bM * marriage\n", + " if age is not None:\n", + " bA = numpyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n", + " A = bA * age\n", + " sigma = numpyro.sample(\"sigma\", dist.Exponential(1.0))\n", + " mu = a + M + A\n", + " numpyro.sample(\"obs\", dist.Normal(mu, sigma), obs=divorce)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Settings files\n", + "\n", + "The next step will be to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load model-settings.json\n", + "{\n", + " \"name\": \"numpyro-divorce\",\n", + " \"implementation\": \"models.NumpyroModel\",\n", + " \"parameters\": {\n", + " \"uri\": \"./numpyro-divorce.json\"\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import numpy as np\n", + "\n", + "from mlserver.types import InferenceRequest\n", + "from mlserver.codecs import NumpyCodec\n", + "\n", + "x_0 = np.array([28.0])\n", + "inference_request = InferenceRequest(\n", + " inputs=[\n", + " NumpyCodec.encode_input(name=\"marriage\", payload=x_0)\n", + " ]\n", + ")\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/numpyro-divorce/infer\"\n", + "response = requests.post(endpoint, json=inference_request.model_dump())\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deployment\n", + "\n", + "Now that we have written and tested our custom model, the next step is to deploy it.\n", + "With that goal in mind, the rough outline of steps will be to first build a custom image containing our code, and then deploy it.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specifying requirements\n", + "MLServer will automatically find your requirements.txt file and install necessary python packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load requirements.txt\n", + "numpy==1.22.4\n", + "numpyro==0.8.0\n", + "jax==0.2.24\n", + "jaxlib==0.3.7\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building a custom image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{note}\n", + "This section expects that Docker is available and running in the background. \n", + "```\n", + "\n", + "MLServer offers helpers to build a custom Docker image containing your code.\n", + "In this example, we will use the `mlserver build` subcommand to create an image, which we'll be able to deploy later.\n", + "\n", + "\n", + "Note that this section expects that Docker is available and running in the background, as well as a functional cluster with Seldon Core installed and some familiarity with `kubectl`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%%bash\n", + "mlserver build . -t 'my-custom-numpyro-server:0.1.0'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure that the image is fully functional, we can spin up a container and then send a test request. To start the container, you can run something along the following lines in a separate terminal:\n", + "\n", + "```bash\n", + "docker run -it --rm -p 8080:8080 my-custom-numpyro-server:0.1.0\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from mlserver.types import InferenceRequest\n", + "from mlserver.codecs import NumpyCodec\n", + "\n", + "x_0 = np.array([28.0])\n", + "inference_request = InferenceRequest(\n", + " inputs=[\n", + " NumpyCodec.encode_input(name=\"marriage\", payload=x_0)\n", + " ]\n", + ")\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/numpyro-divorce/infer\"\n", + "response = requests.post(endpoint, json=inference_request.model_dump())\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we should be able to see, the server running within our Docker image responds as expected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploying our custom image\n", + "\n", + "```{note}\n", + "This section expects access to a functional Kubernetes cluster with Seldon Core installed and some familiarity with `kubectl`. \n", + "```\n", + "\n", + "Now that we've built a custom image and verified that it works as expected, we can move to the next step and deploy it.\n", + "There is a large number of tools out there to deploy images.\n", + "However, for our example, we will focus on deploying it to a cluster running [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/).\n", + "\n", + "```{note}\n", + "Also consider that depending on your Kubernetes installation Seldon Core might expect to get the container image from a public container registry like [Docker hub](https://hub.docker.com/) or [Google Container Registry](https://cloud.google.com/container-registry). For that you need to do an extra step of pushing the container to the registry using `docker tag /` and `docker push /` and also updating the `image` section of the yaml file to `/`. \n", + "```\n", + "\n", + "For that, we will need to create a `SeldonDeployment` resource which instructs Seldon Core to deploy a model embedded within our custom image and compliant with the [V2 Inference Protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2).\n", + "This can be achieved by _applying_ (i.e. `kubectl apply`) a `SeldonDeployment` manifest to the cluster, similar to the one below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile seldondeployment.yaml\n", + "apiVersion: machinelearning.seldon.io/v1\n", + "kind: SeldonDeployment\n", + "metadata:\n", + " name: numpyro-model\n", + "spec:\n", + " protocol: v2\n", + " predictors:\n", + " - name: default\n", + " graph:\n", + " name: numpyro-divorce\n", + " type: MODEL\n", + " componentSpecs:\n", + " - spec:\n", + " containers:\n", + " - name: numpyro-divorce\n", + " image: my-custom-numpyro-server:0.1.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + }, + "vscode": { + "interpreter": { + "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/custom/README.md b/docs-gb/examples/custom/README.md new file mode 100644 index 000000000..4bf01139b --- /dev/null +++ b/docs-gb/examples/custom/README.md @@ -0,0 +1,334 @@ +# Serving a custom model + +The `mlserver` package comes with inference runtime implementations for `scikit-learn` and `xgboost` models. +However, some times we may also need to roll out our own inference server, with custom logic to perform inference. +To support this scenario, MLServer makes it really easy to create your own extensions, which can then be containerised and deployed in a production environment. + +## Overview + +In this example, we will train a [`numpyro` model](http://num.pyro.ai/en/stable/). +The `numpyro` library streamlines the implementation of probabilistic models, abstracting away advanced inference and training algorithms. + +Out of the box, `mlserver` doesn't provide an inference runtime for `numpyro`. +However, through this example we will see how easy is to develop our own. + +## Training + +The first step will be to train our model. +This will be a very simple bayesian regression model, based on an example provided in the [`numpyro` docs](https://nbviewer.jupyter.org/github/pyro-ppl/numpyro/blob/master/notebooks/source/bayesian_regression.ipynb). + +Since this is a probabilistic model, during training we will compute an approximation to the posterior distribution of our model using MCMC. + +```python +# Original source code and more details can be found in: +# https://nbviewer.jupyter.org/github/pyro-ppl/numpyro/blob/master/notebooks/source/bayesian_regression.ipynb + + +import numpyro +import numpy as np +import pandas as pd + +from numpyro import distributions as dist +from jax import random +from numpyro.infer import MCMC, NUTS + +DATASET_URL = "https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv" +dset = pd.read_csv(DATASET_URL, sep=";") + +standardize = lambda x: (x - x.mean()) / x.std() + +dset["AgeScaled"] = dset.MedianAgeMarriage.pipe(standardize) +dset["MarriageScaled"] = dset.Marriage.pipe(standardize) +dset["DivorceScaled"] = dset.Divorce.pipe(standardize) + + +def model(marriage=None, age=None, divorce=None): + a = numpyro.sample("a", dist.Normal(0.0, 0.2)) + M, A = 0.0, 0.0 + if marriage is not None: + bM = numpyro.sample("bM", dist.Normal(0.0, 0.5)) + M = bM * marriage + if age is not None: + bA = numpyro.sample("bA", dist.Normal(0.0, 0.5)) + A = bA * age + sigma = numpyro.sample("sigma", dist.Exponential(1.0)) + mu = a + M + A + numpyro.sample("obs", dist.Normal(mu, sigma), obs=divorce) + + +# Start from this source of randomness. We will split keys for subsequent operations. +rng_key = random.PRNGKey(0) +rng_key, rng_key_ = random.split(rng_key) + +num_warmup, num_samples = 1000, 2000 + +# Run NUTS. +kernel = NUTS(model) +mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples) +mcmc.run( + rng_key_, marriage=dset.MarriageScaled.values, divorce=dset.DivorceScaled.values +) +mcmc.print_summary() +``` + +### Saving our trained model + +Now that we have _trained_ our model, the next step will be to save it so that it can be loaded afterwards at serving-time. +Note that, since this is a probabilistic model, we will only need to save the traces that approximate the posterior distribution over latent parameters. + +This will get saved in a `numpyro-divorce.json` file. + +```python +import json + +samples = mcmc.get_samples() +serialisable = {} +for k, v in samples.items(): + serialisable[k] = np.asarray(v).tolist() + +model_file_name = "numpyro-divorce.json" +with open(model_file_name, "w") as model_file: + json.dump(serialisable, model_file) +``` + +## Serving + +The next step will be to serve our model using `mlserver`. +For that, we will first implement an extension which serve as the _runtime_ to perform inference using our custom `numpyro` model. + +### Custom inference runtime + +Our custom inference wrapper should be responsible of: + +- Loading the model from the set samples we saved previously. +- Running inference using our model structure, and the posterior approximated from the samples. + +```python +# %load models.py +import json +import numpyro +import numpy as np + +from jax import random +from mlserver import MLModel +from mlserver.codecs import decode_args +from mlserver.utils import get_model_uri +from numpyro.infer import Predictive +from numpyro import distributions as dist +from typing import Optional + + +class NumpyroModel(MLModel): + async def load(self) -> bool: + model_uri = await get_model_uri(self._settings) + with open(model_uri) as model_file: + raw_samples = json.load(model_file) + + self._samples = {} + for k, v in raw_samples.items(): + self._samples[k] = np.array(v) + + self._predictive = Predictive(self._model, self._samples) + + return True + + @decode_args + async def predict( + self, + marriage: Optional[np.ndarray] = None, + age: Optional[np.ndarray] = None, + divorce: Optional[np.ndarray] = None, + ) -> np.ndarray: + predictions = self._predictive( + rng_key=random.PRNGKey(0), marriage=marriage, age=age, divorce=divorce + ) + + obs = predictions["obs"] + obs_mean = obs.mean() + + return np.asarray(obs_mean) + + def _model(self, marriage=None, age=None, divorce=None): + a = numpyro.sample("a", dist.Normal(0.0, 0.2)) + M, A = 0.0, 0.0 + if marriage is not None: + bM = numpyro.sample("bM", dist.Normal(0.0, 0.5)) + M = bM * marriage + if age is not None: + bA = numpyro.sample("bA", dist.Normal(0.0, 0.5)) + A = bA * age + sigma = numpyro.sample("sigma", dist.Exponential(1.0)) + mu = a + M + A + numpyro.sample("obs", dist.Normal(mu, sigma), obs=divorce) + +``` + +### Settings files + +The next step will be to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +#### `settings.json` + +```python +# %load settings.json +{ + "debug": "true" +} + +``` + +#### `model-settings.json` + +```python +# %load model-settings.json +{ + "name": "numpyro-divorce", + "implementation": "models.NumpyroModel", + "parameters": { + "uri": "./numpyro-divorce.json" + } +} + +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + +```python +import requests +import numpy as np + +from mlserver.types import InferenceRequest +from mlserver.codecs import NumpyCodec + +x_0 = np.array([28.0]) +inference_request = InferenceRequest( + inputs=[ + NumpyCodec.encode_input(name="marriage", payload=x_0) + ] +) + +endpoint = "http://localhost:8080/v2/models/numpyro-divorce/infer" +response = requests.post(endpoint, json=inference_request.model_dump()) + +response.json() +``` + +## Deployment + +Now that we have written and tested our custom model, the next step is to deploy it. +With that goal in mind, the rough outline of steps will be to first build a custom image containing our code, and then deploy it. + +### Specifying requirements + +MLServer will automatically find your requirements.txt file and install necessary python packages + +```python +# %load requirements.txt +numpy==1.22.4 +numpyro==0.8.0 +jax==0.2.24 +jaxlib==0.3.7 + +``` + +### Building a custom image + +```{note} +This section expects that Docker is available and running in the background. +``` + +MLServer offers helpers to build a custom Docker image containing your code. +In this example, we will use the `mlserver build` subcommand to create an image, which we'll be able to deploy later. + +Note that this section expects that Docker is available and running in the background, as well as a functional cluster with Seldon Core installed and some familiarity with `kubectl`. + +```bash +%%bash +mlserver build . -t 'my-custom-numpyro-server:0.1.0' +``` + +To ensure that the image is fully functional, we can spin up a container and then send a test request. To start the container, you can run something along the following lines in a separate terminal: + +```bash +docker run -it --rm -p 8080:8080 my-custom-numpyro-server:0.1.0 +``` + +```python +import numpy as np + +from mlserver.types import InferenceRequest +from mlserver.codecs import NumpyCodec + +x_0 = np.array([28.0]) +inference_request = InferenceRequest( + inputs=[ + NumpyCodec.encode_input(name="marriage", payload=x_0) + ] +) + +endpoint = "http://localhost:8080/v2/models/numpyro-divorce/infer" +response = requests.post(endpoint, json=inference_request.model_dump()) + +response.json() +``` + +As we should be able to see, the server running within our Docker image responds as expected. + +### Deploying our custom image + +```{note} +This section expects access to a functional Kubernetes cluster with Seldon Core installed and some familiarity with `kubectl`. +``` + +Now that we've built a custom image and verified that it works as expected, we can move to the next step and deploy it. +There is a large number of tools out there to deploy images. +However, for our example, we will focus on deploying it to a cluster running [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/). + +```{note} +Also consider that depending on your Kubernetes installation Seldon Core might expect to get the container image from a public container registry like [Docker hub](https://hub.docker.com/) or [Google Container Registry](https://cloud.google.com/container-registry). For that you need to do an extra step of pushing the container to the registry using `docker tag /` and `docker push /` and also updating the `image` section of the yaml file to `/`. +``` + +For that, we will need to create a `SeldonDeployment` resource which instructs Seldon Core to deploy a model embedded within our custom image and compliant with the [V2 Inference Protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2). +This can be achieved by _applying_ (i.e. `kubectl apply`) a `SeldonDeployment` manifest to the cluster, similar to the one below: + +```python +%%writefile seldondeployment.yaml +apiVersion: machinelearning.seldon.io/v1 +kind: SeldonDeployment +metadata: + name: numpyro-model +spec: + protocol: v2 + predictors: + - name: default + graph: + name: numpyro-divorce + type: MODEL + componentSpecs: + - spec: + containers: + - name: numpyro-divorce + image: my-custom-numpyro-server:0.1.0 +``` + +```python + +``` diff --git a/docs-gb/examples/custom/model-settings.json b/docs-gb/examples/custom/model-settings.json new file mode 100644 index 000000000..a1183fed4 --- /dev/null +++ b/docs-gb/examples/custom/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "numpyro-divorce", + "implementation": "models.NumpyroModel", + "parameters": { + "uri": "./numpyro-divorce.json" + } +} diff --git a/docs-gb/examples/custom/models.py b/docs-gb/examples/custom/models.py new file mode 100644 index 000000000..9d19914e4 --- /dev/null +++ b/docs-gb/examples/custom/models.py @@ -0,0 +1,55 @@ +import json +import numpyro +import numpy as np + +from jax import random +from mlserver import MLModel +from mlserver.codecs import decode_args +from mlserver.utils import get_model_uri +from numpyro.infer import Predictive +from numpyro import distributions as dist +from typing import Optional + + +class NumpyroModel(MLModel): + async def load(self) -> bool: + model_uri = await get_model_uri(self._settings) + with open(model_uri) as model_file: + raw_samples = json.load(model_file) + + self._samples = {} + for k, v in raw_samples.items(): + self._samples[k] = np.array(v) + + self._predictive = Predictive(self._model, self._samples) + + return True + + @decode_args + async def predict( + self, + marriage: Optional[np.ndarray] = None, + age: Optional[np.ndarray] = None, + divorce: Optional[np.ndarray] = None, + ) -> np.ndarray: + predictions = self._predictive( + rng_key=random.PRNGKey(0), marriage=marriage, age=age, divorce=divorce + ) + + obs = predictions["obs"] + obs_mean = obs.mean() + + return np.asarray(obs_mean) + + def _model(self, marriage=None, age=None, divorce=None): + a = numpyro.sample("a", dist.Normal(0.0, 0.2)) + M, A = 0.0, 0.0 + if marriage is not None: + bM = numpyro.sample("bM", dist.Normal(0.0, 0.5)) + M = bM * marriage + if age is not None: + bA = numpyro.sample("bA", dist.Normal(0.0, 0.5)) + A = bA * age + sigma = numpyro.sample("sigma", dist.Exponential(1.0)) + mu = a + M + A + numpyro.sample("obs", dist.Normal(mu, sigma), obs=divorce) diff --git a/docs-gb/examples/custom/numpyro-divorce.json b/docs-gb/examples/custom/numpyro-divorce.json new file mode 100644 index 000000000..7ac262eef --- /dev/null +++ b/docs-gb/examples/custom/numpyro-divorce.json @@ -0,0 +1 @@ +{"a": [0.16836269199848175, 0.1115364134311676, -0.07910443842411041, -0.11816214770078659, 0.08452288806438446, -0.030391020700335503, 0.026638995856046677, 0.10505851358175278, 0.061130501329898834, -0.1337549239397049, 0.05167083814740181, 0.16501200199127197, 0.14894653856754303, 0.01701565459370613, 0.07465790957212448, 0.04534807428717613, -0.04508528858423233, -0.13797052204608917, -0.2078494280576706, -0.059156011790037155, -0.0020909535232931376, 0.04330433905124664, -0.08640143275260925, 0.07770322263240814, -0.12219710648059845, 0.11799821257591248, 0.05668610706925392, -0.08289697021245956, -0.1818048059940338, 0.0777725875377655, -0.12004752457141876, 0.1720149964094162, 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at end of file diff --git a/docs-gb/examples/custom/requirements.txt b/docs-gb/examples/custom/requirements.txt new file mode 100644 index 000000000..fe4a9486f --- /dev/null +++ b/docs-gb/examples/custom/requirements.txt @@ -0,0 +1,4 @@ +numpy==1.22.4 +numpyro==0.8.0 +jax==0.2.24 +jaxlib==0.3.7 diff --git a/docs-gb/examples/custom/seldondeployment.yaml b/docs-gb/examples/custom/seldondeployment.yaml new file mode 100644 index 000000000..02a3bd9dc --- /dev/null +++ b/docs-gb/examples/custom/seldondeployment.yaml @@ -0,0 +1,16 @@ +apiVersion: machinelearning.seldon.io/v1 +kind: SeldonDeployment +metadata: + name: numpyro-model +spec: + protocol: v2 + predictors: + - name: default + graph: + name: numpyro-divorce + type: MODEL + componentSpecs: + - spec: + containers: + - name: numpyro-divorce + image: my-custom-numpyro-server:0.1.0 diff --git a/docs-gb/examples/custom/settings.json b/docs-gb/examples/custom/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/custom/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/huggingface/.gitignore b/docs-gb/examples/huggingface/.gitignore new file mode 100644 index 000000000..e6ee07331 --- /dev/null +++ b/docs-gb/examples/huggingface/.gitignore @@ -0,0 +1 @@ +mlruns diff --git a/docs-gb/examples/huggingface/README.ipynb b/docs-gb/examples/huggingface/README.ipynb new file mode 100644 index 000000000..aee20b946 --- /dev/null +++ b/docs-gb/examples/huggingface/README.ipynb @@ -0,0 +1,748 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c519626", + "metadata": {}, + "source": [ + "# Serving HuggingFace Transformer Models\n", + "\n", + "Out of the box, MLServer supports the deployment and serving of HuggingFace Transformer models with the following features:\n", + "\n", + "- Loading of Transformer Model artifacts from the Hugging Face Hub.\n", + "- Model quantization & optimization using the Hugging Face Optimum library\n", + "- Request batching for GPU optimization (via adaptive batching and request batching)\n", + "\n", + "In this example, we will showcase some of this features using an example model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b5b2588c", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required dependencies\n", + "import requests" + ] + }, + { + "cell_type": "markdown", + "id": "f1fed35e", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Since we're using a pretrained model, we can skip straight to serving." + ] + }, + { + "cell_type": "markdown", + "id": "a205410c", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6df62443", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c6a6e8b2", + "metadata": {}, + "source": [ + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "id": "b664c591", + "metadata": {}, + "source": [ + "### Send test inference request\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "759ad7df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"this is a test\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "0edbcf72", + "metadata": {}, + "source": [ + "### Using Optimum Optimized Models\n", + "\n", + "We can also leverage the Optimum library that allows us to access quantized and optimized models. \n", + "\n", + "We can download pretrained optimized models from the hub if available by enabling the `optimum_model` flag:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6d185281", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\",\n", + " \"optimum_model\": true\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "90925ef0", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI. As before this needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6945db96", + "metadata": {}, + "source": [ + "### Send Test Request to Optimum Optimized Model\n", + "\n", + "The request can now be sent using the same request structure but using optimized models for better performance." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "39d8b438", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"this is a test\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "ad88a78f", + "metadata": {}, + "source": [ + "## Testing Supported Tasks\n", + "\n", + "We can support multiple other transformers other than just text generation, below includes examples for a few other tasks supported.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e9ee06c8", + "metadata": {}, + "source": [ + "### Question Answering" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4492dc01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"question-answering\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9abf39e0", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d7aaf365", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"question\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"what is your name?\"],\n", + " },\n", + " {\n", + " \"name\": \"context\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"Hello, I am Seldon, how is it going\"],\n", + " },\n", + " ]\n", + "}\n", + "\n", + "requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "5aa51356", + "metadata": {}, + "source": [ + "### Sentiment Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8e70c7d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-classification\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "ed336525", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2f704413", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is terrible!\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "fe6655d9", + "metadata": {}, + "source": [ + "## GPU Acceleration\n", + "\n", + "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", + "\n", + "### Testing with CPU\n", + "\n", + "We first test the time taken with the device=-1 which configures CPU by default" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "827472eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"max_batch_size\": 128,\n", + " \"max_batch_time\": 1,\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"device\": -1\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "eceb4d12", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "888501c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 66.42268538899953\n" + ] + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"text_inputs\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "# Benchmark time\n", + "import time\n", + "\n", + "start_time = time.monotonic()\n", + "\n", + "requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ")\n", + "\n", + "print(f\"Elapsed time: {time.monotonic() - start_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "92756662", + "metadata": {}, + "source": [ + "We can see that it takes 81 seconds which is 8 times longer than the gpu example below." + ] + }, + { + "cell_type": "markdown", + "id": "cd2e208c", + "metadata": {}, + "source": [ + "### Testing with GPU\n", + "\n", + "IMPORTANT: Running the code below requries having a machine with GPU configured correctly to work for Tensorflow/Pytorch.\n", + " \n", + "Now we'll run the benchmark with GPU configured, which we can do by setting `device=0`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "032b8f4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"device\": 0\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1b6bd339", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 11.27933280000434\n" + ] + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"text_inputs\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "# Benchmark time\n", + "import time\n", + "\n", + "start_time = time.monotonic()\n", + "\n", + "requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ")\n", + "\n", + "print(f\"Elapsed time: {time.monotonic() - start_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c929108c", + "metadata": {}, + "source": [ + "We can see that the elapsed time is 8 times less than the CPU version!" + ] + }, + { + "cell_type": "markdown", + "id": "5d790b37", + "metadata": {}, + "source": [ + "### Adaptive Batching with GPU\n", + "\n", + "We can also see how the adaptive batching capabilities can allow for GPU acceleration by grouping multiple incoming requests so they get processed in GPU batch.\n", + "\n", + "In our case we can enable adaptive batching with the `max_batch_size` which in our case we will set it ot 128.\n", + "\n", + "We will also configure `max_batch_time` which specifies` the maximum amount of time the MLServer orchestrator will wait before sending for inference." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "810a4abe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"max_batch_size\": 128,\n", + " \"max_batch_time\": 1,\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\",\n", + " \"device\": 0\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "e063ccb6", + "metadata": {}, + "source": [ + "In order to achieve the throughput required of 50 requests per second, we will use the tool `vegeta` which performs load testing.\n", + "\n", + "We can now see that we are able to see that the requests are batched and we receive 100% success eventhough the requests are sent one-by-one." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a787909", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requests [total, rate, throughput] 150, 50.34, 22.28\n", + "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", + "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", + "Bytes In [total, mean] 60978, 406.52\n", + "Bytes Out [total, mean] 12300, 82.00\n", + "Success [ratio] 100.00%\n", + "Status Codes [code:count] 200:150 \n", + "Error Set:\n" + ] + } + ], + "source": [ + "%%bash\n", + "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", + " | vegeta \\\n", + " -cpus=\"2\" \\\n", + " attack \\\n", + " -duration=\"3s\" \\\n", + " -rate=\"50\" \\\n", + " -format=json \\\n", + " | vegeta \\\n", + " report \\\n", + " -type=text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ddcb458", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs-gb/examples/huggingface/README.md b/docs-gb/examples/huggingface/README.md new file mode 100644 index 000000000..b4c3cd1c9 --- /dev/null +++ b/docs-gb/examples/huggingface/README.md @@ -0,0 +1,456 @@ +# Serving HuggingFace Transformer Models + +Out of the box, MLServer supports the deployment and serving of HuggingFace Transformer models with the following features: + +- Loading of Transformer Model artifacts from the Hugging Face Hub. +- Model quantization & optimization using the Hugging Face Optimum library +- Request batching for GPU optimization (via adaptive batching and request batching) + +In this example, we will showcase some of this features using an example model. + + +```python +# Import required dependencies +import requests +``` + +## Serving + +Since we're using a pretrained model, we can skip straight to serving. + +### `model-settings.json` + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "text-generation", + "pretrained_model": "distilgpt2" + } + } +} +``` + + Overwriting ./model-settings.json + + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + + + +```python +inference_request = { + "inputs": [ + { + "name": "args", + "shape": [1], + "datatype": "BYTES", + "data": ["this is a test"], + } + ] +} + +requests.post( + "http://localhost:8080/v2/models/transformer/infer", json=inference_request +).json() +``` + + + + + {'model_name': 'transformer', + 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d', + 'parameters': {}, + 'outputs': [{'name': 'output', + 'shape': [1, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'hg_jsonlist'}, + 'data': ['{"generated_text": "this is a testnet with 1-3,000-bit nodes as nodes."}']}]} + + + +### Using Optimum Optimized Models + +We can also leverage the Optimum library that allows us to access quantized and optimized models. + +We can download pretrained optimized models from the hub if available by enabling the `optimum_model` flag: + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "text-generation", + "pretrained_model": "distilgpt2", + "optimum_model": true + } + } +} +``` + + Overwriting ./model-settings.json + + +Once again, you are able to run the model using the MLServer CLI. As before this needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +### Send Test Request to Optimum Optimized Model + +The request can now be sent using the same request structure but using optimized models for better performance. + + +```python +inference_request = { + "inputs": [ + { + "name": "args", + "shape": [1], + "datatype": "BYTES", + "data": ["this is a test"], + } + ] +} + +requests.post( + "http://localhost:8080/v2/models/transformer/infer", json=inference_request +).json() +``` + + + + + {'model_name': 'transformer', + 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef', + 'parameters': {}, + 'outputs': [{'name': 'output', + 'shape': [1, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'hg_jsonlist'}, + 'data': ['{"generated_text": "this is a test of the \\"safe-code-safe-code-safe-code\\" approach. The method only accepts two parameters as parameters: the code. The parameter \'unsafe-code-safe-code-safe-code\' should"}']}]} + + + +## Testing Supported Tasks + +We can support multiple other transformers other than just text generation, below includes examples for a few other tasks supported. + + + +### Question Answering + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "question-answering" + } + } +} +``` + + Overwriting ./model-settings.json + + +Once again, you are able to run the model using the MLServer CLI. + +```shell +mlserver start . +``` + + +```python +inference_request = { + "inputs": [ + { + "name": "question", + "shape": [1], + "datatype": "BYTES", + "data": ["what is your name?"], + }, + { + "name": "context", + "shape": [1], + "datatype": "BYTES", + "data": ["Hello, I am Seldon, how is it going"], + }, + ] +} + +requests.post( + "http://localhost:8080/v2/models/transformer/infer", json=inference_request +).json() +``` + + + + + {'model_name': 'transformer', + 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197', + 'parameters': {}, + 'outputs': [{'name': 'output', + 'shape': [1, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'hg_jsonlist'}, + 'data': ['{"score": 0.9869915843009949, "start": 12, "end": 18, "answer": "Seldon"}']}]} + + + +### Sentiment Analysis + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "text-classification" + } + } +} +``` + + Overwriting ./model-settings.json + + +Once again, you are able to run the model using the MLServer CLI. + +```shell +mlserver start . +``` + + +```python +inference_request = { + "inputs": [ + { + "name": "args", + "shape": [1], + "datatype": "BYTES", + "data": ["This is terrible!"], + } + ] +} + +requests.post( + "http://localhost:8080/v2/models/transformer/infer", json=inference_request +).json() +``` + + + + + {'model_name': 'transformer', + 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b', + 'parameters': {}, + 'outputs': [{'name': 'output', + 'shape': [1, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'hg_jsonlist'}, + 'data': ['{"label": "NEGATIVE", "score": 0.9996137022972107}']}]} + + + +## GPU Acceleration + +We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters + +### Testing with CPU + +We first test the time taken with the device=-1 which configures CPU by default + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "max_batch_size": 128, + "max_batch_time": 1, + "parameters": { + "extra": { + "task": "text-generation", + "device": -1 + } + } +} +``` + + Overwriting ./model-settings.json + + +Once again, you are able to run the model using the MLServer CLI. + +```shell +mlserver start . +``` + + +```python +inference_request = { + "inputs": [ + { + "name": "text_inputs", + "shape": [1], + "datatype": "BYTES", + "data": ["This is a generation for the work" for i in range(512)], + } + ] +} + +# Benchmark time +import time + +start_time = time.monotonic() + +requests.post( + "http://localhost:8080/v2/models/transformer/infer", json=inference_request +) + +print(f"Elapsed time: {time.monotonic() - start_time}") +``` + + Elapsed time: 66.42268538899953 + + +We can see that it takes 81 seconds which is 8 times longer than the gpu example below. + +### Testing with GPU + +IMPORTANT: Running the code below requries having a machine with GPU configured correctly to work for Tensorflow/Pytorch. + +Now we'll run the benchmark with GPU configured, which we can do by setting `device=0` + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "text-generation", + "device": 0 + } + } +} +``` + + Overwriting ./model-settings.json + + + +```python +inference_request = { + "inputs": [ + { + "name": "text_inputs", + "shape": [1], + "datatype": "BYTES", + "data": ["This is a generation for the work" for i in range(512)], + } + ] +} + +# Benchmark time +import time + +start_time = time.monotonic() + +requests.post( + "http://localhost:8080/v2/models/transformer/infer", json=inference_request +) + +print(f"Elapsed time: {time.monotonic() - start_time}") +``` + + Elapsed time: 11.27933280000434 + + +We can see that the elapsed time is 8 times less than the CPU version! + +### Adaptive Batching with GPU + +We can also see how the adaptive batching capabilities can allow for GPU acceleration by grouping multiple incoming requests so they get processed in GPU batch. + +In our case we can enable adaptive batching with the `max_batch_size` which in our case we will set it ot 128. + +We will also configure `max_batch_time` which specifies` the maximum amount of time the MLServer orchestrator will wait before sending for inference. + + +```python +%%writefile ./model-settings.json +{ + "name": "transformer", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "max_batch_size": 128, + "max_batch_time": 1, + "parameters": { + "extra": { + "task": "text-generation", + "pretrained_model": "distilgpt2", + "device": 0 + } + } +} +``` + + Overwriting ./model-settings.json + + +In order to achieve the throughput required of 50 requests per second, we will use the tool `vegeta` which performs load testing. + +We can now see that we are able to see that the requests are batched and we receive 100% success eventhough the requests are sent one-by-one. + + +```bash +%%bash +jq -ncM '{"method": "POST", "header": {"Content-Type": ["application/json"] }, "url": "http://localhost:8080/v2/models/transformer/infer", "body": "{\"inputs\":[{\"name\":\"text_inputs\",\"shape\":[1],\"datatype\":\"BYTES\",\"data\":[\"test\"]}]}" | @base64 }' \ + | vegeta \ + -cpus="2" \ + attack \ + -duration="3s" \ + -rate="50" \ + -format=json \ + | vegeta \ + report \ + -type=text +``` + + Requests [total, rate, throughput] 150, 50.34, 22.28 + Duration [total, attack, wait] 6.732s, 2.98s, 3.753s + Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s + Bytes In [total, mean] 60978, 406.52 + Bytes Out [total, mean] 12300, 82.00 + Success [ratio] 100.00% + Status Codes [code:count] 200:150 + Error Set: + + + +```python + +``` diff --git a/docs-gb/examples/kafka/.gitignore b/docs-gb/examples/kafka/.gitignore new file mode 100644 index 000000000..2a003536c --- /dev/null +++ b/docs-gb/examples/kafka/.gitignore @@ -0,0 +1 @@ +kafka_* diff --git a/docs-gb/examples/kafka/README.ipynb b/docs-gb/examples/kafka/README.ipynb new file mode 100644 index 000000000..343544977 --- /dev/null +++ b/docs-gb/examples/kafka/README.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving models through Kafka\n", + "\n", + "Out of the box, MLServer provides support to receive inference requests from Kafka.\n", + "The Kafka server can run side-by-side with the REST and gRPC ones, and adds a new interface to interact with your model.\n", + "The inference responses coming back from your model, will also get written back to their own output topic.\n", + "\n", + "In this example, we will showcase the integration with Kafka by serving a [Scikit-Learn](../sklearn/README) model thorugh Kafka." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Kafka\n", + "\n", + "We are going to start by running a simple local docker deployment of kafka that we can test against. This will be a minimal cluster that will consist of a single zookeeper node and a single broker.\n", + "\n", + "You need to have Java installed in order for it to work correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget https://apache.mirrors.nublue.co.uk/kafka/2.8.0/kafka_2.12-2.8.0.tgz\n", + "!tar -zxvf kafka_2.12-2.8.0.tgz\n", + "!./kafka_2.12-2.8.0/bin/kafka-storage.sh format -t OXn8RTSlQdmxwjhKnSB_6A -c ./kafka_2.12-2.8.0/config/kraft/server.properties" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the no-zookeeper kafka broker\n", + "\n", + "Now you can just run it with the following command outside the terminal:\n", + "```\n", + "!./kafka_2.12-2.8.0/bin/kafka-server-start.sh ./kafka_2.12-2.8.0/config/kraft/server.properties\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Topics\n", + "\n", + "Now we can create the input and output topics required" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!./kafka_2.12-2.8.0/bin/kafka-topics.sh --create --topic mlserver-input --partitions 1 --replication-factor 1 --bootstrap-server localhost:9092\n", + "!./kafka_2.12-2.8.0/bin/kafka-topics.sh --create --topic mlserver-output --partitions 1 --replication-factor 1 --bootstrap-server localhost:9092" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "The first step will be to train a simple `scikit-learn` model.\n", + "For that, we will use the [MNIST example from the `scikit-learn` documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) which trains an SVM model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original source code and more details can be found in:\n", + "# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html\n", + "\n", + "# Import datasets, classifiers and performance metrics\n", + "from sklearn import datasets, svm, metrics\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# The digits dataset\n", + "digits = datasets.load_digits()\n", + "\n", + "# To apply a classifier on this data, we need to flatten the image, to\n", + "# turn the data in a (samples, feature) matrix:\n", + "n_samples = len(digits.images)\n", + "data = digits.images.reshape((n_samples, -1))\n", + "\n", + "# Create a classifier: a support vector classifier\n", + "classifier = svm.SVC(gamma=0.001)\n", + "\n", + "# Split data into train and test subsets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data, digits.target, test_size=0.5, shuffle=False)\n", + "\n", + "# We learn the digits on the first half of the digits\n", + "classifier.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving our trained model\n", + "\n", + "To save our trained model, we will serialise it using `joblib`.\n", + "While this is not a perfect approach, it's currently the recommended method to persist models to disk in the [`scikit-learn` documentation](https://scikit-learn.org/stable/modules/model_persistence.html).\n", + "\n", + "Our model will be persisted as a file named `mnist-svm.joblib`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "model_file_name = \"mnist-svm.joblib\"\n", + "joblib.dump(classifier, model_file_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. \n", + "For that, we will need to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.).\n", + "\n", + "Note that, the `settings.json` file will contain our Kafka configuration, including the address of the Kafka broker and the input / output topics that will be used for inference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\",\n", + " \"kafka_enabled\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"mnist-svm\",\n", + " \"implementation\": \"mlserver_sklearn.SKLearnModel\",\n", + " \"parameters\": {\n", + " \"uri\": \"./mnist-svm.joblib\",\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/mnist-svm/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send inference request through Kafka\n", + "\n", + "Now that we have verified that our server is accepting REST requests, we will try to send a new inference request through Kafka.\n", + "For this, we just need to send a request to the `mlserver-input` topic (which is the default input topic):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from kafka import KafkaProducer\n", + "\n", + "producer = KafkaProducer(bootstrap_servers=\"localhost:9092\")\n", + "\n", + "headers = {\n", + " \"mlserver-model\": b\"mnist-svm\",\n", + " \"mlserver-version\": b\"v0.1.0\",\n", + "}\n", + "\n", + "producer.send(\n", + " \"mlserver-input\",\n", + " json.dumps(inference_request).encode(\"utf-8\"),\n", + " headers=list(headers.items()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the message has gone into the queue, the Kafka server running within MLServer should receive this message and run inference.\n", + "The prediction output should then get posted into an output queue, which will be named `mlserver-output` by default." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from kafka import KafkaConsumer\n", + "\n", + "consumer = KafkaConsumer(\n", + " \"mlserver-output\",\n", + " bootstrap_servers=\"localhost:9092\",\n", + " auto_offset_reset=\"earliest\")\n", + "\n", + "for msg in consumer:\n", + " print(f\"key: {msg.key}\")\n", + " print(f\"value: {msg.value}\\n\")\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we should now be able to see above, the results of our inference request should now be visible in the output Kafka queue." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/kafka/README.md b/docs-gb/examples/kafka/README.md new file mode 100644 index 000000000..b547d7b21 --- /dev/null +++ b/docs-gb/examples/kafka/README.md @@ -0,0 +1,208 @@ +# Serving models through Kafka + +Out of the box, MLServer provides support to receive inference requests from Kafka. +The Kafka server can run side-by-side with the REST and gRPC ones, and adds a new interface to interact with your model. +The inference responses coming back from your model, will also get written back to their own output topic. + +In this example, we will showcase the integration with Kafka by serving a [Scikit-Learn](../sklearn/README) model thorugh Kafka. + +## Run Kafka + +We are going to start by running a simple local docker deployment of kafka that we can test against. This will be a minimal cluster that will consist of a single zookeeper node and a single broker. + +You need to have Java installed in order for it to work correctly. + + +```python +!wget https://apache.mirrors.nublue.co.uk/kafka/2.8.0/kafka_2.12-2.8.0.tgz +!tar -zxvf kafka_2.12-2.8.0.tgz +!./kafka_2.12-2.8.0/bin/kafka-storage.sh format -t OXn8RTSlQdmxwjhKnSB_6A -c ./kafka_2.12-2.8.0/config/kraft/server.properties +``` + +### Run the no-zookeeper kafka broker + +Now you can just run it with the following command outside the terminal: +``` +!./kafka_2.12-2.8.0/bin/kafka-server-start.sh ./kafka_2.12-2.8.0/config/kraft/server.properties +``` + +### Create Topics + +Now we can create the input and output topics required + + +```python +!./kafka_2.12-2.8.0/bin/kafka-topics.sh --create --topic mlserver-input --partitions 1 --replication-factor 1 --bootstrap-server localhost:9092 +!./kafka_2.12-2.8.0/bin/kafka-topics.sh --create --topic mlserver-output --partitions 1 --replication-factor 1 --bootstrap-server localhost:9092 +``` + +## Training + +The first step will be to train a simple `scikit-learn` model. +For that, we will use the [MNIST example from the `scikit-learn` documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) which trains an SVM model. + + +```python +# Original source code and more details can be found in: +# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html + +# Import datasets, classifiers and performance metrics +from sklearn import datasets, svm, metrics +from sklearn.model_selection import train_test_split + +# The digits dataset +digits = datasets.load_digits() + +# To apply a classifier on this data, we need to flatten the image, to +# turn the data in a (samples, feature) matrix: +n_samples = len(digits.images) +data = digits.images.reshape((n_samples, -1)) + +# Create a classifier: a support vector classifier +classifier = svm.SVC(gamma=0.001) + +# Split data into train and test subsets +X_train, X_test, y_train, y_test = train_test_split( + data, digits.target, test_size=0.5, shuffle=False) + +# We learn the digits on the first half of the digits +classifier.fit(X_train, y_train) +``` + +### Saving our trained model + +To save our trained model, we will serialise it using `joblib`. +While this is not a perfect approach, it's currently the recommended method to persist models to disk in the [`scikit-learn` documentation](https://scikit-learn.org/stable/modules/model_persistence.html). + +Our model will be persisted as a file named `mnist-svm.joblib` + + +```python +import joblib + +model_file_name = "mnist-svm.joblib" +joblib.dump(classifier, model_file_name) +``` + +## Serving + +Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. +For that, we will need to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +Note that, the `settings.json` file will contain our Kafka configuration, including the address of the Kafka broker and the input / output topics that will be used for inference. + +### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true", + "kafka_enabled": "true" +} +``` + +### `model-settings.json` + + +```python +%%writefile model-settings.json +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "uri": "./mnist-svm.joblib", + "version": "v0.1.0" + } +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests + +x_0 = X_test[0:1] +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/mnist-svm/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +### Send inference request through Kafka + +Now that we have verified that our server is accepting REST requests, we will try to send a new inference request through Kafka. +For this, we just need to send a request to the `mlserver-input` topic (which is the default input topic): + + +```python +import json +from kafka import KafkaProducer + +producer = KafkaProducer(bootstrap_servers="localhost:9092") + +headers = { + "mlserver-model": b"mnist-svm", + "mlserver-version": b"v0.1.0", +} + +producer.send( + "mlserver-input", + json.dumps(inference_request).encode("utf-8"), + headers=list(headers.items())) +``` + +Once the message has gone into the queue, the Kafka server running within MLServer should receive this message and run inference. +The prediction output should then get posted into an output queue, which will be named `mlserver-output` by default. + + +```python +from kafka import KafkaConsumer + +consumer = KafkaConsumer( + "mlserver-output", + bootstrap_servers="localhost:9092", + auto_offset_reset="earliest") + +for msg in consumer: + print(f"key: {msg.key}") + print(f"value: {msg.value}\n") + break +``` + +As we should now be able to see above, the results of our inference request should now be visible in the output Kafka queue. + + +```python + +``` diff --git a/docs-gb/examples/kafka/inference-request.json b/docs-gb/examples/kafka/inference-request.json new file mode 100644 index 000000000..9e1a9fdd1 --- /dev/null +++ b/docs-gb/examples/kafka/inference-request.json @@ -0,0 +1,15 @@ +{ + "id": "request-0", + "inputs": [ + { + "name": "predict", + "shape": [3], + "datatype": "INT32", + "data": [ 0., 0., 1., 11., 14., 15., 3., 0., 0., 1., 13., 16., 12., + 16., 8., 0., 0., 8., 16., 4., 6., 16., 5., 0., 0., 5., + 15., 11., 13., 14., 0., 0., 0., 0., 2., 12., 16., 13., 0., + 0., 0., 0., 0., 13., 16., 16., 6., 0., 0., 0., 0., 16., + 16., 16., 7., 0., 0., 0., 0., 11., 13., 12., 1., 0.] + } + ] +} diff --git a/docs-gb/examples/kafka/mnist-svm.joblib b/docs-gb/examples/kafka/mnist-svm.joblib new file mode 100644 index 000000000..783b1f158 Binary files /dev/null and b/docs-gb/examples/kafka/mnist-svm.joblib differ diff --git a/docs-gb/examples/kafka/model-settings.json b/docs-gb/examples/kafka/model-settings.json new file mode 100644 index 000000000..6dab01679 --- /dev/null +++ b/docs-gb/examples/kafka/model-settings.json @@ -0,0 +1,8 @@ +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "uri": "./mnist-svm.joblib", + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/kafka/settings.json b/docs-gb/examples/kafka/settings.json new file mode 100644 index 000000000..635566736 --- /dev/null +++ b/docs-gb/examples/kafka/settings.json @@ -0,0 +1,4 @@ +{ + "debug": "true", + "kafka_enabled": "true" +} diff --git a/docs-gb/examples/lightgbm/README.ipynb b/docs-gb/examples/lightgbm/README.ipynb new file mode 100644 index 000000000..2f803d522 --- /dev/null +++ b/docs-gb/examples/lightgbm/README.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving LightGBM models\n", + "\n", + "Out of the box, `mlserver` supports the deployment and serving of `lightgbm` models.\n", + "By default, it will assume that these models have been [serialised using the `bst.save_model()` method](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html).\n", + "\n", + "In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "To test the LightGBM Server, first we need to generate a simple LightGBM model using Python. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import lightgbm as lgb\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "import os\n", + "\n", + "model_dir = \".\"\n", + "BST_FILE = \"iris-lightgbm.bst\"\n", + "\n", + "iris = load_iris()\n", + "y = iris['target']\n", + "X = iris['data']\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n", + "dtrain = lgb.Dataset(X_train, label=y_train)\n", + "\n", + "params = {\n", + " 'objective':'multiclass', \n", + " 'metric':'softmax',\n", + " 'num_class': 3\n", + "}\n", + "lgb_model = lgb.train(params=params, train_set=dtrain)\n", + "model_file = os.path.join(model_dir, BST_FILE)\n", + "lgb_model.save_model(model_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our model will be persisted as a file named `iris-lightgbm.bst`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. \n", + "For that, we will need to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"iris-lgb\",\n", + " \"implementation\": \"mlserver_lightgbm.LightGBMModel\",\n", + " \"parameters\": {\n", + " \"uri\": \"./iris-lightgbm.bst\",\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict-prob\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8788/v2/models/iris-lgb/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see above, the model predicted the probability for each class, and the probability of class `1` is the biggest, close to `0.99`, which matches what's on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_test[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/lightgbm/README.md b/docs-gb/examples/lightgbm/README.md new file mode 100644 index 000000000..4e79fa0ce --- /dev/null +++ b/docs-gb/examples/lightgbm/README.md @@ -0,0 +1,117 @@ +# Serving LightGBM models + +Out of the box, `mlserver` supports the deployment and serving of `lightgbm` models. +By default, it will assume that these models have been [serialised using the `bst.save_model()` method](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html). + +In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`. + +## Training + +To test the LightGBM Server, first we need to generate a simple LightGBM model using Python. + + +```python +import lightgbm as lgb +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +import os + +model_dir = "." +BST_FILE = "iris-lightgbm.bst" + +iris = load_iris() +y = iris['target'] +X = iris['data'] +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) +dtrain = lgb.Dataset(X_train, label=y_train) + +params = { + 'objective':'multiclass', + 'metric':'softmax', + 'num_class': 3 +} +lgb_model = lgb.train(params=params, train_set=dtrain) +model_file = os.path.join(model_dir, BST_FILE) +lgb_model.save_model(model_file) +``` + +Our model will be persisted as a file named `iris-lightgbm.bst`. + +## Serving + +Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. +For that, we will need to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + +### `model-settings.json` + + +```python +%%writefile model-settings.json +{ + "name": "iris-lgb", + "implementation": "mlserver_lightgbm.LightGBMModel", + "parameters": { + "uri": "./iris-lightgbm.bst", + "version": "v0.1.0" + } +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests + +x_0 = X_test[0:1] +inference_request = { + "inputs": [ + { + "name": "predict-prob", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/iris-lgb/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +As we can see above, the model predicted the probability for each class, and the probability of class `1` is the biggest, close to `0.99`, which matches what's on the test set. + + +```python +y_test[0] +``` diff --git a/docs-gb/examples/lightgbm/iris-lightgbm.bst b/docs-gb/examples/lightgbm/iris-lightgbm.bst new file mode 100644 index 000000000..454353fa0 --- /dev/null +++ b/docs-gb/examples/lightgbm/iris-lightgbm.bst @@ -0,0 +1,5537 @@ +tree +version=v3 +num_class=3 +num_tree_per_iteration=3 +label_index=0 +max_feature_idx=3 +objective=multiclass num_class:3 +feature_names=Column_0 Column_1 Column_2 Column_3 +feature_infos=[4.2999999999999998:7.9000000000000004] [2:4.4000000000000004] [1:6.9000000000000004] [0.10000000000000001:2.5] +tree_sizes=524 708 524 636 736 632 638 734 634 638 721 636 640 723 638 746 738 637 742 721 742 743 636 741 744 738 741 744 620 739 744 720 741 743 634 740 745 619 743 742 633 740 747 721 741 745 636 739 636 735 741 636 619 738 640 723 741 637 638 742 638 619 739 745 637 741 637 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1] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 2] +[early_stopping_round: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[min_gain_to_split: 0] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 4] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[feature_contri: ] +[forcedsplits_filename: ] +[forcedbins_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[verbosity: 1] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[histogram_pool_size: -1] +[data_random_seed: 1] +[output_model: LightGBM_model.txt] +[snapshot_freq: -1] +[input_model: ] +[output_result: LightGBM_predict_result.txt] +[initscore_filename: ] +[valid_data_initscores: ] +[pre_partition: 0] +[enable_bundle: 1] +[max_conflict_rate: 0] +[is_enable_sparse: 1] +[sparse_threshold: 0.8] +[use_missing: 1] +[zero_as_missing: 0] +[two_round: 0] +[save_binary: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[predict_raw_score: 0] +[predict_leaf_index: 0] +[predict_contrib: 0] +[num_iteration_predict: -1] +[pred_early_stop: 0] +[pred_early_stop_freq: 10] +[pred_early_stop_margin: 10] +[convert_model_language: ] +[convert_model: gbdt_prediction.cpp] +[num_class: 3] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[max_position: 20] +[lambdamart_norm: 1] +[label_gain: ] +[metric_freq: 1] +[is_provide_training_metric: 0] +[eval_at: ] +[multi_error_top_k: 1] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] + +end of parameters + +pandas_categorical:null diff --git a/docs-gb/examples/lightgbm/model-settings.json b/docs-gb/examples/lightgbm/model-settings.json new file mode 100644 index 000000000..a9f1264a5 --- /dev/null +++ b/docs-gb/examples/lightgbm/model-settings.json @@ -0,0 +1,8 @@ +{ + "name": "iris-lgb", + "implementation": "mlserver_lightgbm.LightGBMModel", + "parameters": { + "uri": "./iris-lightgbm.bst", + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/lightgbm/settings.json b/docs-gb/examples/lightgbm/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/lightgbm/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/mlflow/.gitignore b/docs-gb/examples/mlflow/.gitignore new file mode 100644 index 000000000..e6ee07331 --- /dev/null +++ b/docs-gb/examples/mlflow/.gitignore @@ -0,0 +1 @@ +mlruns diff --git a/docs-gb/examples/mlflow/README.ipynb b/docs-gb/examples/mlflow/README.ipynb new file mode 100644 index 000000000..3ca8b5968 --- /dev/null +++ b/docs-gb/examples/mlflow/README.ipynb @@ -0,0 +1,507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c519626", + "metadata": {}, + "source": [ + "# Serving MLflow models\n", + "\n", + "Out of the box, MLServer supports the deployment and serving of MLflow models with the following features:\n", + "\n", + "- Loading of MLflow Model artifacts.\n", + "- Support of dataframes, dict-of-tensors and tensor inputs.\n", + "\n", + "In this example, we will showcase some of this features using an example model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "655ea442", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.core.magic import register_line_cell_magic\n", + "\n", + "@register_line_cell_magic\n", + "def writetemplate(line, cell):\n", + " with open(line, 'w') as f:\n", + " f.write(cell.format(**globals()))" + ] + }, + { + "cell_type": "markdown", + "id": "6b029264", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "The first step will be to train and serialise a MLflow model.\n", + "For that, we will use the [linear regression examle from the MLflow docs](https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6c4dd0c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# %load src/train.py\n", + "# Original source code and more details can be found in:\n", + "# https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html\n", + "\n", + "# The data set used in this example is from\n", + "# http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n", + "# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.\n", + "# Modeling wine preferences by data mining from physicochemical properties.\n", + "# In Decision Support Systems, Elsevier, 47(4):547-553, 2009.\n", + "\n", + "import warnings\n", + "import sys\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import ElasticNet\n", + "from urllib.parse import urlparse\n", + "import mlflow\n", + "import mlflow.sklearn\n", + "from mlflow.models.signature import infer_signature\n", + "\n", + "import logging\n", + "\n", + "logging.basicConfig(level=logging.WARN)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "\n", + "def eval_metrics(actual, pred):\n", + " rmse = np.sqrt(mean_squared_error(actual, pred))\n", + " mae = mean_absolute_error(actual, pred)\n", + " r2 = r2_score(actual, pred)\n", + " return rmse, mae, r2\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " warnings.filterwarnings(\"ignore\")\n", + " np.random.seed(40)\n", + "\n", + " # Read the wine-quality csv file from the URL\n", + " csv_url = (\n", + " \"http://archive.ics.uci.edu/ml\"\n", + " \"/machine-learning-databases/wine-quality/winequality-red.csv\"\n", + " )\n", + " try:\n", + " data = pd.read_csv(csv_url, sep=\";\")\n", + " except Exception as e:\n", + " logger.exception(\n", + " \"Unable to download training & test CSV, \"\n", + " \"check your internet connection. Error: %s\",\n", + " e,\n", + " )\n", + "\n", + " # Split the data into training and test sets. (0.75, 0.25) split.\n", + " train, test = train_test_split(data)\n", + "\n", + " # The predicted column is \"quality\" which is a scalar from [3, 9]\n", + " train_x = train.drop([\"quality\"], axis=1)\n", + " test_x = test.drop([\"quality\"], axis=1)\n", + " train_y = train[[\"quality\"]]\n", + " test_y = test[[\"quality\"]]\n", + "\n", + " alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5\n", + " l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5\n", + "\n", + " with mlflow.start_run():\n", + " lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n", + " lr.fit(train_x, train_y)\n", + "\n", + " predicted_qualities = lr.predict(test_x)\n", + "\n", + " (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)\n", + "\n", + " print(\"Elasticnet model (alpha=%f, l1_ratio=%f):\" % (alpha, l1_ratio))\n", + " print(\" RMSE: %s\" % rmse)\n", + " print(\" MAE: %s\" % mae)\n", + " print(\" R2: %s\" % r2)\n", + "\n", + " mlflow.log_param(\"alpha\", alpha)\n", + " mlflow.log_param(\"l1_ratio\", l1_ratio)\n", + " mlflow.log_metric(\"rmse\", rmse)\n", + " mlflow.log_metric(\"r2\", r2)\n", + " mlflow.log_metric(\"mae\", mae)\n", + "\n", + " tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n", + " model_signature = infer_signature(train_x, train_y)\n", + "\n", + " # Model registry does not work with file store\n", + " if tracking_url_type_store != \"file\":\n", + "\n", + " # Register the model\n", + " # There are other ways to use the Model Registry,\n", + " # which depends on the use case,\n", + " # please refer to the doc for more information:\n", + " # https://mlflow.org/docs/latest/model-registry.html#api-workflow\n", + " mlflow.sklearn.log_model(\n", + " lr,\n", + " \"model\",\n", + " registered_model_name=\"ElasticnetWineModel\",\n", + " signature=model_signature,\n", + " )\n", + " else:\n", + " mlflow.sklearn.log_model(lr, \"model\", signature=model_signature)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7d20bab", + "metadata": {}, + "outputs": [], + "source": [ + "!python src/train.py" + ] + }, + { + "cell_type": "markdown", + "id": "8ca5814a", + "metadata": {}, + "source": [ + "The training script will also serialise our trained model, leveraging the [MLflow Model format](https://www.mlflow.org/docs/latest/models.html).\n", + "By default, we should be able to find the saved artifact under the `mlruns` folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fa51242", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "[experiment_file_path] = !ls -td ./mlruns/0/* | head -1\n", + "model_path = os.path.join(experiment_file_path, \"artifacts\", \"model\")\n", + "print(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e20a516b", + "metadata": {}, + "outputs": [], + "source": [ + "!ls {model_path} " + ] + }, + { + "cell_type": "markdown", + "id": "f1fed35e", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and serialised our model, we are ready to start serving it.\n", + "For that, the initial step will be to set up a `model-settings.json` that instructs MLServer to load our artifact using the MLflow Inference Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6df62443", + "metadata": {}, + "outputs": [], + "source": [ + "%%writetemplate ./model-settings.json\n", + "{{\n", + " \"name\": \"wine-classifier\",\n", + " \"implementation\": \"mlserver_mlflow.MLflowRuntime\",\n", + " \"parameters\": {{\n", + " \"uri\": \"{model_path}\"\n", + " }}\n", + "}}" + ] + }, + { + "cell_type": "markdown", + "id": "c6a6e8b2", + "metadata": {}, + "source": [ + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "id": "b664c591", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually.\n", + "\n", + "Note that, the request specifies the value `pd` as its *content type*, whereas every input specifies the *content type* `np`.\n", + "These parameters will instruct MLServer to:\n", + "\n", + "- Convert every input value to a NumPy array, using the data type and shape information provided.\n", + "- Group all the different inputs into a Pandas DataFrame, using their names as the column names.\n", + "\n", + "To learn more about how MLServer uses content type parameters, you can check this [worked out example](../content-type/README.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "759ad7df", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"fixed acidity\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [7.4],\n", + " },\n", + " {\n", + " \"name\": \"volatile acidity\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [0.7000],\n", + " },\n", + " {\n", + " \"name\": \"citric acid\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [0],\n", + " },\n", + " {\n", + " \"name\": \"residual sugar\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [1.9],\n", + " },\n", + " {\n", + " \"name\": \"chlorides\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [0.076],\n", + " },\n", + " {\n", + " \"name\": \"free sulfur dioxide\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [11],\n", + " },\n", + " {\n", + " \"name\": \"total sulfur dioxide\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [34],\n", + " },\n", + " {\n", + " \"name\": \"density\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [0.9978],\n", + " },\n", + " {\n", + " \"name\": \"pH\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [3.51],\n", + " },\n", + " {\n", + " \"name\": \"sulphates\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [0.56],\n", + " },\n", + " {\n", + " \"name\": \"alcohol\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"FP32\",\n", + " \"data\": [9.4],\n", + " },\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/wine-classifier/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "id": "0bfc1203", + "metadata": {}, + "source": [ + "As we can see in the output above, the predicted quality score for our input wine was `5.57`." + ] + }, + { + "cell_type": "markdown", + "id": "b2fcd821", + "metadata": {}, + "source": [ + "### MLflow Scoring Protocol\n", + "\n", + "MLflow currently ships with an [scoring server with its own protocol](https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models).\n", + "In order to provide a drop-in replacement, the MLflow runtime in MLServer also exposes a custom endpoint which matches the signature of the MLflow's `/invocations` endpoint.\n", + "\n", + "As an example, we can try to send the same request that sent previously, but using MLflow's protocol.\n", + "Note that, in both cases, the request will be handled by the same MLServer instance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84361b87", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "inference_request = {\n", + " \"dataframe_split\": {\n", + " \"columns\": [\n", + " \"alcohol\",\n", + " \"chlorides\",\n", + " \"citric acid\",\n", + " \"density\",\n", + " \"fixed acidity\",\n", + " \"free sulfur dioxide\",\n", + " \"pH\",\n", + " \"residual sugar\",\n", + " \"sulphates\",\n", + " \"total sulfur dioxide\",\n", + " \"volatile acidity\",\n", + " ],\n", + " \"data\": [[7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4]]\n", + " }\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/invocations\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "id": "8be5c51c", + "metadata": {}, + "source": [ + "As we can see above, the predicted quality for our input is `5.57`, matching the prediction we obtained above." + ] + }, + { + "cell_type": "markdown", + "id": "dc9c0c19", + "metadata": {}, + "source": [ + "### MLflow Model Signature\n", + "\n", + "MLflow lets users define a [_model signature_](https://www.mlflow.org/docs/latest/models.html#model-signature-and-input-example), where they can specify what types of inputs does the model accept, and what types of outputs it returns. \n", + "Similarly, the [V2 inference protocol](https://github.com/kubeflow/kfserving/tree/master/docs/predict-api/v2) employed by MLServer defines a [_metadata endpoint_](https://github.com/kubeflow/kfserving/blob/master/docs/predict-api/v2/required_api.md#model-metadata) which can be used to query what inputs and outputs does the model accept.\n", + "However, even though they serve similar functions, the data schemas used by each one of them are not compatible between them.\n", + "\n", + "To solve this, if your model defines a MLflow model signature, MLServer will convert _on-the-fly_ this signature to a metadata schema compatible with the V2 Inference Protocol.\n", + "This will also include specifying any extra [content type](../content-type/README.md) that is required to correctly decode / encode your data.\n", + "\n", + "As an example, we can first have a look at the model signature saved for our MLflow model.\n", + "This can be seen directly on the `MLModel` file saved by our model.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0a0400a", + "metadata": {}, + "outputs": [], + "source": [ + "!cat {model_path}/MLmodel" + ] + }, + { + "cell_type": "markdown", + "id": "cde189a4", + "metadata": {}, + "source": [ + "We can then query the metadata endpoint, to see the model metadata inferred by MLServer from our test model's signature.\n", + "For this, we will use the `/v2/models/wine-classifier/` endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a5d0080", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/wine-classifier\"\n", + "response = requests.get(endpoint)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "id": "eeb11edd", + "metadata": {}, + "source": [ + "As we should be able to see, the model metadata now matches the information contained in our model signature, including any extra content types necessary to decode our data correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d185281", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs-gb/examples/mlflow/README.md b/docs-gb/examples/mlflow/README.md new file mode 100644 index 000000000..06cb6724a --- /dev/null +++ b/docs-gb/examples/mlflow/README.md @@ -0,0 +1,356 @@ +# Serving MLflow models + +Out of the box, MLServer supports the deployment and serving of MLflow models with the following features: + +- Loading of MLflow Model artifacts. +- Support of dataframes, dict-of-tensors and tensor inputs. + +In this example, we will showcase some of this features using an example model. + + +```python +from IPython.core.magic import register_line_cell_magic + +@register_line_cell_magic +def writetemplate(line, cell): + with open(line, 'w') as f: + f.write(cell.format(**globals())) +``` + +## Training + +The first step will be to train and serialise a MLflow model. +For that, we will use the [linear regression examle from the MLflow docs](https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html). + + +```python +# %load src/train.py +# Original source code and more details can be found in: +# https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html + +# The data set used in this example is from +# http://archive.ics.uci.edu/ml/datasets/Wine+Quality +# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. +# Modeling wine preferences by data mining from physicochemical properties. +# In Decision Support Systems, Elsevier, 47(4):547-553, 2009. + +import warnings +import sys + +import pandas as pd +import numpy as np +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +from sklearn.model_selection import train_test_split +from sklearn.linear_model import ElasticNet +from urllib.parse import urlparse +import mlflow +import mlflow.sklearn +from mlflow.models.signature import infer_signature + +import logging + +logging.basicConfig(level=logging.WARN) +logger = logging.getLogger(__name__) + + +def eval_metrics(actual, pred): + rmse = np.sqrt(mean_squared_error(actual, pred)) + mae = mean_absolute_error(actual, pred) + r2 = r2_score(actual, pred) + return rmse, mae, r2 + + +if __name__ == "__main__": + warnings.filterwarnings("ignore") + np.random.seed(40) + + # Read the wine-quality csv file from the URL + csv_url = ( + "http://archive.ics.uci.edu/ml" + "/machine-learning-databases/wine-quality/winequality-red.csv" + ) + try: + data = pd.read_csv(csv_url, sep=";") + except Exception as e: + logger.exception( + "Unable to download training & test CSV, " + "check your internet connection. Error: %s", + e, + ) + + # Split the data into training and test sets. (0.75, 0.25) split. + train, test = train_test_split(data) + + # The predicted column is "quality" which is a scalar from [3, 9] + train_x = train.drop(["quality"], axis=1) + test_x = test.drop(["quality"], axis=1) + train_y = train[["quality"]] + test_y = test[["quality"]] + + alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 + l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 + + with mlflow.start_run(): + lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) + lr.fit(train_x, train_y) + + predicted_qualities = lr.predict(test_x) + + (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) + + print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio)) + print(" RMSE: %s" % rmse) + print(" MAE: %s" % mae) + print(" R2: %s" % r2) + + mlflow.log_param("alpha", alpha) + mlflow.log_param("l1_ratio", l1_ratio) + mlflow.log_metric("rmse", rmse) + mlflow.log_metric("r2", r2) + mlflow.log_metric("mae", mae) + + tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme + model_signature = infer_signature(train_x, train_y) + + # Model registry does not work with file store + if tracking_url_type_store != "file": + + # Register the model + # There are other ways to use the Model Registry, + # which depends on the use case, + # please refer to the doc for more information: + # https://mlflow.org/docs/latest/model-registry.html#api-workflow + mlflow.sklearn.log_model( + lr, + "model", + registered_model_name="ElasticnetWineModel", + signature=model_signature, + ) + else: + mlflow.sklearn.log_model(lr, "model", signature=model_signature) + +``` + + +```python +!python src/train.py +``` + +The training script will also serialise our trained model, leveraging the [MLflow Model format](https://www.mlflow.org/docs/latest/models.html). +By default, we should be able to find the saved artifact under the `mlruns` folder. + + +```python +import os + +[experiment_file_path] = !ls -td ./mlruns/0/* | head -1 +model_path = os.path.join(experiment_file_path, "artifacts", "model") +print(model_path) +``` + + +```python +!ls {model_path} +``` + +## Serving + +Now that we have trained and serialised our model, we are ready to start serving it. +For that, the initial step will be to set up a `model-settings.json` that instructs MLServer to load our artifact using the MLflow Inference Runtime. + + +```python +%%writetemplate ./model-settings.json +{{ + "name": "wine-classifier", + "implementation": "mlserver_mlflow.MLflowRuntime", + "parameters": {{ + "uri": "{model_path}" + }} +}} +``` + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + +Note that, the request specifies the value `pd` as its *content type*, whereas every input specifies the *content type* `np`. +These parameters will instruct MLServer to: + +- Convert every input value to a NumPy array, using the data type and shape information provided. +- Group all the different inputs into a Pandas DataFrame, using their names as the column names. + +To learn more about how MLServer uses content type parameters, you can check this [worked out example](../content-type/README.md). + + +```python +import requests + +inference_request = { + "inputs": [ + { + "name": "fixed acidity", + "shape": [1], + "datatype": "FP32", + "data": [7.4], + }, + { + "name": "volatile acidity", + "shape": [1], + "datatype": "FP32", + "data": [0.7000], + }, + { + "name": "citric acid", + "shape": [1], + "datatype": "FP32", + "data": [0], + }, + { + "name": "residual sugar", + "shape": [1], + "datatype": "FP32", + "data": [1.9], + }, + { + "name": "chlorides", + "shape": [1], + "datatype": "FP32", + "data": [0.076], + }, + { + "name": "free sulfur dioxide", + "shape": [1], + "datatype": "FP32", + "data": [11], + }, + { + "name": "total sulfur dioxide", + "shape": [1], + "datatype": "FP32", + "data": [34], + }, + { + "name": "density", + "shape": [1], + "datatype": "FP32", + "data": [0.9978], + }, + { + "name": "pH", + "shape": [1], + "datatype": "FP32", + "data": [3.51], + }, + { + "name": "sulphates", + "shape": [1], + "datatype": "FP32", + "data": [0.56], + }, + { + "name": "alcohol", + "shape": [1], + "datatype": "FP32", + "data": [9.4], + }, + ] +} + +endpoint = "http://localhost:8080/v2/models/wine-classifier/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +As we can see in the output above, the predicted quality score for our input wine was `5.57`. + +### MLflow Scoring Protocol + +MLflow currently ships with an [scoring server with its own protocol](https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models). +In order to provide a drop-in replacement, the MLflow runtime in MLServer also exposes a custom endpoint which matches the signature of the MLflow's `/invocations` endpoint. + +As an example, we can try to send the same request that sent previously, but using MLflow's protocol. +Note that, in both cases, the request will be handled by the same MLServer instance. + + +```python +import requests + +inference_request = { + "dataframe_split": { + "columns": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + ], + "data": [[7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4]] + } +} + +endpoint = "http://localhost:8080/invocations" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +As we can see above, the predicted quality for our input is `5.57`, matching the prediction we obtained above. + +### MLflow Model Signature + +MLflow lets users define a [_model signature_](https://www.mlflow.org/docs/latest/models.html#model-signature-and-input-example), where they can specify what types of inputs does the model accept, and what types of outputs it returns. +Similarly, the [V2 inference protocol](https://github.com/kubeflow/kfserving/tree/master/docs/predict-api/v2) employed by MLServer defines a [_metadata endpoint_](https://github.com/kubeflow/kfserving/blob/master/docs/predict-api/v2/required_api.md#model-metadata) which can be used to query what inputs and outputs does the model accept. +However, even though they serve similar functions, the data schemas used by each one of them are not compatible between them. + +To solve this, if your model defines a MLflow model signature, MLServer will convert _on-the-fly_ this signature to a metadata schema compatible with the V2 Inference Protocol. +This will also include specifying any extra [content type](../content-type/README.md) that is required to correctly decode / encode your data. + +As an example, we can first have a look at the model signature saved for our MLflow model. +This can be seen directly on the `MLModel` file saved by our model. + + + + + +```python +!cat {model_path}/MLmodel +``` + +We can then query the metadata endpoint, to see the model metadata inferred by MLServer from our test model's signature. +For this, we will use the `/v2/models/wine-classifier/` endpoint. + + +```python +import requests + + +endpoint = "http://localhost:8080/v2/models/wine-classifier" +response = requests.get(endpoint) + +response.json() +``` + +As we should be able to see, the model metadata now matches the information contained in our model signature, including any extra content types necessary to decode our data correctly. + + +```python + +``` diff --git a/docs-gb/examples/mlflow/model-settings.json b/docs-gb/examples/mlflow/model-settings.json new file mode 100644 index 000000000..39c98e953 --- /dev/null +++ b/docs-gb/examples/mlflow/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "wine-classifier", + "implementation": "mlserver_mlflow.MLflowRuntime", + "parameters": { + "uri": "./mlruns/0/6cd26d62883641188b4f7616725ed838/artifacts/model" + } +} diff --git a/docs-gb/examples/mlflow/src/train.py b/docs-gb/examples/mlflow/src/train.py new file mode 100644 index 000000000..1278b1dc9 --- /dev/null +++ b/docs-gb/examples/mlflow/src/train.py @@ -0,0 +1,102 @@ +# Original source code and more details can be found in: +# https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html + +# The data set used in this example is from +# http://archive.ics.uci.edu/ml/datasets/Wine+Quality +# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. +# Modeling wine preferences by data mining from physicochemical properties. +# In Decision Support Systems, Elsevier, 47(4):547-553, 2009. + +import warnings +import sys + +import pandas as pd +import numpy as np +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +from sklearn.model_selection import train_test_split +from sklearn.linear_model import ElasticNet +from urllib.parse import urlparse +import mlflow +import mlflow.sklearn +from mlflow.models.signature import infer_signature + +import logging + +logging.basicConfig(level=logging.WARN) +logger = logging.getLogger(__name__) + + +def eval_metrics(actual, pred): + rmse = np.sqrt(mean_squared_error(actual, pred)) + mae = mean_absolute_error(actual, pred) + r2 = r2_score(actual, pred) + return rmse, mae, r2 + + +if __name__ == "__main__": + warnings.filterwarnings("ignore") + np.random.seed(40) + + # Read the wine-quality csv file from the URL + csv_url = ( + "http://archive.ics.uci.edu/ml" + "/machine-learning-databases/wine-quality/winequality-red.csv" + ) + try: + data = pd.read_csv(csv_url, sep=";") + except Exception as e: + logger.exception( + "Unable to download training & test CSV, " + "check your internet connection. Error: %s", + e, + ) + + # Split the data into training and test sets. (0.75, 0.25) split. + train, test = train_test_split(data) + + # The predicted column is "quality" which is a scalar from [3, 9] + train_x = train.drop(["quality"], axis=1) + test_x = test.drop(["quality"], axis=1) + train_y = train[["quality"]] + test_y = test[["quality"]] + + alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 + l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 + + with mlflow.start_run(): + lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) + lr.fit(train_x, train_y) + + predicted_qualities = lr.predict(test_x) + + (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) + + print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio)) + print(" RMSE: %s" % rmse) + print(" MAE: %s" % mae) + print(" R2: %s" % r2) + + mlflow.log_param("alpha", alpha) + mlflow.log_param("l1_ratio", l1_ratio) + mlflow.log_metric("rmse", rmse) + mlflow.log_metric("r2", r2) + mlflow.log_metric("mae", mae) + + tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme + model_signature = infer_signature(train_x, train_y) + + # Model registry does not work with file store + if tracking_url_type_store != "file": + # Register the model + # There are other ways to use the Model Registry, + # which depends on the use case, + # please refer to the doc for more information: + # https://mlflow.org/docs/latest/model-registry.html#api-workflow + mlflow.sklearn.log_model( + lr, + "model", + registered_model_name="ElasticnetWineModel", + signature=model_signature, + ) + else: + mlflow.sklearn.log_model(lr, "model", signature=model_signature) diff --git a/docs-gb/examples/mllib/data/.part-00000-3b569ac9-e283-4220-ba6c-31088ed23cb8-c000.snappy.parquet.crc b/docs-gb/examples/mllib/data/.part-00000-3b569ac9-e283-4220-ba6c-31088ed23cb8-c000.snappy.parquet.crc new file mode 100644 index 000000000..e62ae8b4d Binary files /dev/null and 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000000000..e69de29bb diff --git a/docs-gb/examples/mllib/metadata/part-00000 b/docs-gb/examples/mllib/metadata/part-00000 new file mode 100644 index 000000000..333195048 --- /dev/null +++ b/docs-gb/examples/mllib/metadata/part-00000 @@ -0,0 +1 @@ +{"class":"org.apache.spark.mllib.classification.LogisticRegressionModel","version":"1.0","numFeatures":692,"numClasses":2} diff --git a/docs-gb/examples/mllib/model-settings.json b/docs-gb/examples/mllib/model-settings.json new file mode 100644 index 000000000..56eff7193 --- /dev/null +++ b/docs-gb/examples/mllib/model-settings.json @@ -0,0 +1,9 @@ +{ + "name": "spark-mllib", + "implementation": "mlserver-mllib.MLlibModel", + "parameters": { + "uri": "./", + "format": "LogisticRegression", + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/mms/Makefile b/docs-gb/examples/mms/Makefile new file mode 100644 index 000000000..db6e4ee5b --- /dev/null +++ b/docs-gb/examples/mms/Makefile @@ -0,0 +1,5 @@ +upload: + gsutil cp -r models/* gs://seldon-models/mlserver/mms + gsutil cp -r models/mnist-svm gs://seldon-models/mlserver/mms-sklearn + gsutil cp -r models/mushroom-xgboost gs://seldon-models/mlserver/mms-xgboost + diff --git a/docs-gb/examples/mms/README.ipynb b/docs-gb/examples/mms/README.ipynb new file mode 100644 index 000000000..a565461da --- /dev/null +++ b/docs-gb/examples/mms/README.ipynb @@ -0,0 +1,363 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Model Serving\n", + "\n", + "MLServer has been built with Multi-Model Serving (MMS) in mind.\n", + "This means that, within a single instance of MLServer, you can serve multiple models under different paths.\n", + "This also includes multiple versions of the same model.\n", + "\n", + "This notebook shows an example of how you can leverage MMS with MLServer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "We will first start by training 2 different models:\n", + "\n", + "| Name | Framework | Source | Trained Model Path |\n", + "| ------------------ | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------- |\n", + "| `mnist-svm` | `scikit-learn` | [MNIST example from the `scikit-learn` documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) | `./models/mnist-svm/model.joblib` |\n", + "| `mushroom-xgboost` | `xgboost` | [Mushrooms example from the `xgboost` Getting Started guide](https://xgboost.readthedocs.io/en/latest/get_started.html#python) | `./models/mushroom-xgboost/model.json` |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training our `mnist-svm` model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original source code and more details can be found in:\n", + "# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html\n", + "\n", + "# Import datasets, classifiers and performance metrics\n", + "from sklearn import datasets, svm, metrics\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# The digits dataset\n", + "digits = datasets.load_digits()\n", + "\n", + "# To apply a classifier on this data, we need to flatten the image, to\n", + "# turn the data in a (samples, feature) matrix:\n", + "n_samples = len(digits.images)\n", + "data = digits.images.reshape((n_samples, -1))\n", + "\n", + "# Create a classifier: a support vector classifier\n", + "classifier = svm.SVC(gamma=0.001)\n", + "\n", + "# Split data into train and test subsets\n", + "X_train, X_test_digits, y_train, y_test_digits = train_test_split(\n", + " data, digits.target, test_size=0.5, shuffle=False)\n", + "\n", + "# We learn the digits on the first half of the digits\n", + "classifier.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "import os\n", + "\n", + "mnist_svm_path = os.path.join(\"models\", \"mnist-svm\")\n", + "os.makedirs(mnist_svm_path, exist_ok=True)\n", + "\n", + "mnist_svm_model_path = os.path.join(mnist_svm_path, \"model.joblib\")\n", + "joblib.dump(classifier, mnist_svm_model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training our `mushroom-xgboost` model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original code and extra details can be found in:\n", + "# https://xgboost.readthedocs.io/en/latest/get_started.html#python\n", + "\n", + "import os\n", + "import xgboost as xgb\n", + "import requests\n", + "\n", + "from urllib.parse import urlparse\n", + "from sklearn.datasets import load_svmlight_file\n", + "\n", + "\n", + "TRAIN_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.train'\n", + "TEST_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.test'\n", + "\n", + "\n", + "def _download_file(url: str) -> str:\n", + " parsed = urlparse(url)\n", + " file_name = os.path.basename(parsed.path)\n", + " file_path = os.path.join(os.getcwd(), file_name)\n", + " \n", + " res = requests.get(url)\n", + " \n", + " with open(file_path, 'wb') as file:\n", + " file.write(res.content)\n", + " \n", + " return file_path\n", + "\n", + "train_dataset_path = _download_file(TRAIN_DATASET_URL)\n", + "test_dataset_path = _download_file(TEST_DATASET_URL)\n", + "\n", + "# NOTE: Workaround to load SVMLight files from the XGBoost example\n", + "X_train, y_train = load_svmlight_file(train_dataset_path)\n", + "X_test_agar, y_test_agar = load_svmlight_file(test_dataset_path)\n", + "X_train = X_train.toarray()\n", + "X_test_agar = X_test_agar.toarray()\n", + "\n", + "# read in data\n", + "dtrain = xgb.DMatrix(data=X_train, label=y_train)\n", + "\n", + "# specify parameters via map\n", + "param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic' }\n", + "num_round = 2\n", + "bst = xgb.train(param, dtrain, num_round)\n", + "\n", + "bst" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "mushroom_xgboost_path = os.path.join(\"models\", \"mushroom-xgboost\")\n", + "os.makedirs(mushroom_xgboost_path, exist_ok=True)\n", + "\n", + "mushroom_xgboost_model_path = os.path.join(mushroom_xgboost_path, \"model.json\")\n", + "bst.save_model(mushroom_xgboost_model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "The next step will be serving both our models within the same MLServer instance.\n", + "For that, we will just need to create a `model-settings.json` file local to each of our models and a server-wide `settings.json`.\n", + "That is,\n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `models/mnist-svm/model-settings.json`: holds the configuration specific to our `mnist-svm` model (e.g. input type, runtime to use, etc.).\n", + "- `models/mushroom-xgboost/model-settings.json`: holds the configuration specific to our `mushroom-xgboost` model (e.g. input type, runtime to use, etc.).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `models/mnist-svm/model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile models/mnist-svm/model-settings.json\n", + "{\n", + " \"name\": \"mnist-svm\",\n", + " \"implementation\": \"mlserver_sklearn.SKLearnModel\",\n", + " \"parameters\": {\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `models/mushroom-xgboost/model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile models/mushroom-xgboost/model-settings.json\n", + "{\n", + " \"name\": \"mushroom-xgboost\",\n", + " \"implementation\": \"mlserver_xgboost.XGBoostModel\",\n", + " \"parameters\": {\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "By this point, we should have both our models getting served by MLServer.\n", + "To make sure that everything is working as expected, let's send a request from each test set.\n", + "\n", + "For that, we can use the Python types that the `mlserver` package provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing our `mnist-svm` model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test_digits[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/mnist-svm/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing our `mushroom-xgboost` model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test_agar[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/mushroom-xgboost/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/mms/README.md b/docs-gb/examples/mms/README.md new file mode 100644 index 000000000..6a477cf46 --- /dev/null +++ b/docs-gb/examples/mms/README.md @@ -0,0 +1,242 @@ +# Multi-Model Serving + +MLServer has been built with [Multi-Model Serving (MMS)](https://www.seldon.io/what-is-multi-model-serving-and-how-does-it-transform-your-ml-infrastructure) in mind. +This means that, within a single instance of MLServer, you can serve multiple models under different paths. +This also includes multiple versions of the same model. + +This notebook shows an example of how you can leverage MMS with MLServer. + +## Training + +We will first start by training 2 different models: + +| Name | Framework | Source | Trained Model Path | +| ------------------ | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------- | +| `mnist-svm` | `scikit-learn` | [MNIST example from the `scikit-learn` documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) | `./models/mnist-svm/model.joblib` | +| `mushroom-xgboost` | `xgboost` | [Mushrooms example from the `xgboost` Getting Started guide](https://xgboost.readthedocs.io/en/latest/get_started.html#python) | `./models/mushroom-xgboost/model.json` | + + +### Training our `mnist-svm` model + + +```python +# Original source code and more details can be found in: +# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html + +# Import datasets, classifiers and performance metrics +from sklearn import datasets, svm, metrics +from sklearn.model_selection import train_test_split + +# The digits dataset +digits = datasets.load_digits() + +# To apply a classifier on this data, we need to flatten the image, to +# turn the data in a (samples, feature) matrix: +n_samples = len(digits.images) +data = digits.images.reshape((n_samples, -1)) + +# Create a classifier: a support vector classifier +classifier = svm.SVC(gamma=0.001) + +# Split data into train and test subsets +X_train, X_test_digits, y_train, y_test_digits = train_test_split( + data, digits.target, test_size=0.5, shuffle=False) + +# We learn the digits on the first half of the digits +classifier.fit(X_train, y_train) +``` + + +```python +import joblib +import os + +mnist_svm_path = os.path.join("models", "mnist-svm") +os.makedirs(mnist_svm_path, exist_ok=True) + +mnist_svm_model_path = os.path.join(mnist_svm_path, "model.joblib") +joblib.dump(classifier, mnist_svm_model_path) +``` + +### Training our `mushroom-xgboost` model + + +```python +# Original code and extra details can be found in: +# https://xgboost.readthedocs.io/en/latest/get_started.html#python + +import os +import xgboost as xgb +import requests + +from urllib.parse import urlparse +from sklearn.datasets import load_svmlight_file + + +TRAIN_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.train' +TEST_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.test' + + +def _download_file(url: str) -> str: + parsed = urlparse(url) + file_name = os.path.basename(parsed.path) + file_path = os.path.join(os.getcwd(), file_name) + + res = requests.get(url) + + with open(file_path, 'wb') as file: + file.write(res.content) + + return file_path + +train_dataset_path = _download_file(TRAIN_DATASET_URL) +test_dataset_path = _download_file(TEST_DATASET_URL) + +# NOTE: Workaround to load SVMLight files from the XGBoost example +X_train, y_train = load_svmlight_file(train_dataset_path) +X_test_agar, y_test_agar = load_svmlight_file(test_dataset_path) +X_train = X_train.toarray() +X_test_agar = X_test_agar.toarray() + +# read in data +dtrain = xgb.DMatrix(data=X_train, label=y_train) + +# specify parameters via map +param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic' } +num_round = 2 +bst = xgb.train(param, dtrain, num_round) + +bst +``` + + +```python +import os + +mushroom_xgboost_path = os.path.join("models", "mushroom-xgboost") +os.makedirs(mushroom_xgboost_path, exist_ok=True) + +mushroom_xgboost_model_path = os.path.join(mushroom_xgboost_path, "model.json") +bst.save_model(mushroom_xgboost_model_path) +``` + +## Serving + +The next step will be serving both our models within the same MLServer instance. +For that, we will just need to create a `model-settings.json` file local to each of our models and a server-wide `settings.json`. +That is, + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `models/mnist-svm/model-settings.json`: holds the configuration specific to our `mnist-svm` model (e.g. input type, runtime to use, etc.). +- `models/mushroom-xgboost/model-settings.json`: holds the configuration specific to our `mushroom-xgboost` model (e.g. input type, runtime to use, etc.). + + + +### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + +### `models/mnist-svm/model-settings.json` + + +```python +%%writefile models/mnist-svm/model-settings.json +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "version": "v0.1.0" + } +} +``` + +### `models/mushroom-xgboost/model-settings.json` + + +```python +%%writefile models/mushroom-xgboost/model-settings.json +{ + "name": "mushroom-xgboost", + "implementation": "mlserver_xgboost.XGBoostModel", + "parameters": { + "version": "v0.1.0" + } +} + +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +## Testing + +By this point, we should have both our models getting served by MLServer. +To make sure that everything is working as expected, let's send a request from each test set. + +For that, we can use the Python types that the `mlserver` package provides out of box, or we can build our request manually. + +### Testing our `mnist-svm` model + + +```python +import requests + +x_0 = X_test_digits[0:1] +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/mnist-svm/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +### Testing our `mushroom-xgboost` model + + +```python +import requests + +x_0 = X_test_agar[0:1] +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/mushroom-xgboost/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + + +```python + +``` diff --git a/docs-gb/examples/mms/agaricus.txt.test b/docs-gb/examples/mms/agaricus.txt.test new file mode 100644 index 000000000..83bdd2666 --- /dev/null +++ b/docs-gb/examples/mms/agaricus.txt.test @@ -0,0 +1,1611 @@ +0 1:1 9:1 19:1 21:1 24:1 34:1 36:1 39:1 42:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 117:1 122:1 +1 3:1 9:1 19:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 118:1 124:1 +0 1:1 9:1 20:1 21:1 24:1 34:1 36:1 39:1 41:1 53:1 56:1 65:1 69:1 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100644 index 000000000..5554718b9 --- /dev/null +++ b/docs-gb/examples/mms/models/mnist-svm/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/mms/models/mnist-svm/model.joblib b/docs-gb/examples/mms/models/mnist-svm/model.joblib new file mode 100644 index 000000000..8fdf30764 Binary files /dev/null and b/docs-gb/examples/mms/models/mnist-svm/model.joblib differ diff --git a/docs-gb/examples/mms/models/mushroom-xgboost/model-settings.json b/docs-gb/examples/mms/models/mushroom-xgboost/model-settings.json new file mode 100644 index 000000000..6c31bacc8 --- /dev/null +++ b/docs-gb/examples/mms/models/mushroom-xgboost/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "mushroom-xgboost", + "implementation": "mlserver_xgboost.XGBoostModel", + "parameters": { + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/mms/models/mushroom-xgboost/model.json b/docs-gb/examples/mms/models/mushroom-xgboost/model.json new file mode 100644 index 000000000..05990f0d2 --- /dev/null +++ b/docs-gb/examples/mms/models/mushroom-xgboost/model.json @@ -0,0 +1 @@ +{"learner":{"attributes":{"best_iteration":"1","best_ntree_limit":"2"},"feature_names":[],"feature_types":[],"gradient_booster":{"model":{"gbtree_model_param":{"num_trees":"2","size_leaf_vector":"0"},"tree_info":[0,0],"trees":[{"base_weights":[-7.150529E-2,1.2955159E0,-1.8666193E0,1.7121772E0,-1.7004405E0,-1.9407086E0,1.8596492E0],"categories":[],"categories_nodes":[],"categories_segments":[],"categories_sizes":[],"default_left":[true,true,true,false,false,false,false],"id":0,"left_children":[1,3,5,-1,-1,-1,-1],"loss_changes":[4.000531E3,1.158212E3,1.9817383E2,0E0,0E0,0E0,0E0],"parents":[2147483647,0,0,1,1,2,2],"right_children":[2,4,6,-1,-1,-1,-1],"split_conditions":[5E-1,5E-1,5E-1,1.7121772E0,-1.7004405E0,-1.9407086E0,1.8596492E0],"split_indices":[28,55,108,0,0,0,0],"split_type":[0,0,0,0,0,0,0],"sum_hessian":[1.62825E3,9.245E2,7.0375E2,8.12E2,1.125E2,6.905E2,1.325E1],"tree_param":{"num_deleted":"0","num_feature":"126","num_nodes":"7","size_leaf_vector":"0"}},{"base_weights":[-9.3191065E-2,7.8292646E-2,-6.2362447E0,7.8471756E-1,-9.6853036E-1],"categories":[],"categories_nodes":[],"categories_segments":[],"categories_sizes":[],"default_left":[true,true,false,false,false],"id":1,"left_children":[1,3,-1,-1,-1],"loss_changes":[8.3254504E2,5.697251E2,0E0,0E0,0E0],"parents":[2147483647,0,0,1,1],"right_children":[2,4,-1,-1,-1],"split_conditions":[5E-1,5E-1,-6.2362447E0,7.8471756E-1,-9.6853036E-1],"split_indices":[59,28,0,0,0],"split_type":[0,0,0,0,0],"sum_hessian":[7.8885205E2,7.683897E2,2.0462389E1,4.5893686E2,3.0945282E2],"tree_param":{"num_deleted":"0","num_feature":"126","num_nodes":"5","size_leaf_vector":"0"}}]},"name":"gbtree"},"learner_model_param":{"base_score":"5E-1","num_class":"0","num_feature":"126"},"objective":{"name":"binary:logistic","reg_loss_param":{"scale_pos_weight":"1"}}},"version":[1,5,0]} \ No newline at end of file diff --git a/docs-gb/examples/mms/settings.json b/docs-gb/examples/mms/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/mms/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/model-repository/README.ipynb b/docs-gb/examples/model-repository/README.ipynb new file mode 100644 index 000000000..70625bda7 --- /dev/null +++ b/docs-gb/examples/model-repository/README.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Repository API\n", + "\n", + "MLServer supports loading and unloading models dynamically from a models repository.\n", + "This allows you to enable and disable the models accessible by MLServer on demand.\n", + "This extension builds on top of the support for [Multi-Model Serving](../mms/README.md), letting you change at runtime which models is MLServer currently serving.\n", + "\n", + "The API to manage the model repository is modelled after [Triton's Model Repository extension](https://github.com/triton-inference-server/server/blob/master/docs/protocol/extension_model_repository.md) to the V2 Dataplane and is thus fully compatible with it.\n", + "\n", + "This notebook will walk you through an example using the Model Repository API.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "First of all, we will need to train some models.\n", + "For that, we will re-use the models we trained previously in the [Multi-Model Serving example](../mms/README.md).\n", + "You can check the details on how they are trained following that notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!cp -r ../mms/models/* ./models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Next up, we will start our `mlserver` inference server.\n", + "Note that, by default, this will **load all our models**.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## List available models\n", + "\n", + "Now that we've got our inference server up and running, and serving 2 different models, we can start using the Model Repository API.\n", + "To get us started, we will first list all available models in the repository." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "response = requests.post(\"http://localhost:8080/v2/repository/index\", json={})\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the repository lists 2 models (i.e. `mushroom-xgboost` and `mnist-svm`).\n", + "Note that the state for both is set to `READY`.\n", + "This means that both models are loaded, and thus ready for inference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unloading our `mushroom-xgboost` model\n", + "\n", + "We will now try to unload one of the 2 models, `mushroom-xgboost`.\n", + "This will unload the model from the inference server but will keep it available on our model repository." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "requests.post(\"http://localhost:8080/v2/repository/models/mushroom-xgboost/unload\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we now try to list the models available in our repository, we will see that the `mushroom-xgboost` model is flagged as `UNAVAILABLE`.\n", + "This means that it's present in the repository but it's not loaded for inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = requests.post(\"http://localhost:8080/v2/repository/index\", json={})\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading our `mushroom-xgboost` model back\n", + "\n", + "We will now load our model back into our inference server." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "requests.post(\"http://localhost:8080/v2/repository/models/mushroom-xgboost/load\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we now try to list the models again, we will see that our `mushroom-xgboost` is back again, ready for inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = requests.post(\"http://localhost:8080/v2/repository/index\", json={})\n", + "response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/model-repository/README.md b/docs-gb/examples/model-repository/README.md new file mode 100644 index 000000000..e7b4de4e0 --- /dev/null +++ b/docs-gb/examples/model-repository/README.md @@ -0,0 +1,88 @@ +# Model Repository API + +MLServer supports loading and unloading models dynamically from a models repository. +This allows you to enable and disable the models accessible by MLServer on demand. +This extension builds on top of the support for [Multi-Model Serving](../mms/README.md), letting you change at runtime which models is MLServer currently serving. + +The API to manage the model repository is modelled after [Triton's Model Repository extension](https://github.com/triton-inference-server/server/blob/master/docs/protocol/extension_model_repository.md) to the V2 Dataplane and is thus fully compatible with it. + +This notebook will walk you through an example using the Model Repository API. + + +## Training + +First of all, we will need to train some models. +For that, we will re-use the models we trained previously in the [Multi-Model Serving example](../mms/README.md). +You can check the details on how they are trained following that notebook. + + +```python +!cp -r ../mms/models/* ./models +``` + +## Serving + +Next up, we will start our `mlserver` inference server. +Note that, by default, this will **load all our models**. + +```shell +mlserver start . +``` + +## List available models + +Now that we've got our inference server up and running, and serving 2 different models, we can start using the Model Repository API. +To get us started, we will first list all available models in the repository. + + +```python +import requests + +response = requests.post("http://localhost:8080/v2/repository/index", json={}) +response.json() +``` + +As we can, the repository lists 2 models (i.e. `mushroom-xgboost` and `mnist-svm`). +Note that the state for both is set to `READY`. +This means that both models are loaded, and thus ready for inference. + +## Unloading our `mushroom-xgboost` model + +We will now try to unload one of the 2 models, `mushroom-xgboost`. +This will unload the model from the inference server but will keep it available on our model repository. + + +```python +requests.post("http://localhost:8080/v2/repository/models/mushroom-xgboost/unload") +``` + +If we now try to list the models available in our repository, we will see that the `mushroom-xgboost` model is flagged as `UNAVAILABLE`. +This means that it's present in the repository but it's not loaded for inference. + + +```python +response = requests.post("http://localhost:8080/v2/repository/index", json={}) +response.json() +``` + +## Loading our `mushroom-xgboost` model back + +We will now load our model back into our inference server. + + +```python +requests.post("http://localhost:8080/v2/repository/models/mushroom-xgboost/load") +``` + +If we now try to list the models again, we will see that our `mushroom-xgboost` is back again, ready for inference. + + +```python +response = requests.post("http://localhost:8080/v2/repository/index", json={}) +response.json() +``` + + +```python + +``` diff --git a/docs-gb/examples/model-repository/models/mnist-svm/model-settings.json b/docs-gb/examples/model-repository/models/mnist-svm/model-settings.json new file mode 100644 index 000000000..5554718b9 --- /dev/null +++ b/docs-gb/examples/model-repository/models/mnist-svm/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/model-repository/models/mnist-svm/model.joblib b/docs-gb/examples/model-repository/models/mnist-svm/model.joblib new file mode 100644 index 000000000..8fdf30764 Binary files /dev/null and b/docs-gb/examples/model-repository/models/mnist-svm/model.joblib differ diff --git a/docs-gb/examples/model-repository/models/mushroom-xgboost/model-settings.json b/docs-gb/examples/model-repository/models/mushroom-xgboost/model-settings.json new file mode 100644 index 000000000..6c31bacc8 --- /dev/null +++ b/docs-gb/examples/model-repository/models/mushroom-xgboost/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "mushroom-xgboost", + "implementation": "mlserver_xgboost.XGBoostModel", + "parameters": { + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/model-repository/models/mushroom-xgboost/model.json b/docs-gb/examples/model-repository/models/mushroom-xgboost/model.json new file mode 100644 index 000000000..5547e6516 --- /dev/null +++ b/docs-gb/examples/model-repository/models/mushroom-xgboost/model.json @@ -0,0 +1 @@ +{"learner":{"attributes":{},"gradient_booster":{"model":{"gbtree_model_param":{"num_trees":"2","size_leaf_vector":"0"},"tree_info":[0,0],"trees":[{"base_weights":[-7.15052932500839233e-02,1.29551589488983154e+00,-1.86661934852600098e+00,1.71217715740203857e+00,-1.70044052600860596e+00,-1.94070863723754883e+00,1.85964918136596680e+00],"default_left":[true,true,true,false,false,false,false],"id":0,"leaf_child_counts":[0,0,0,0,0,0,0],"left_children":[1,3,5,-1,-1,-1,-1],"loss_changes":[4.00053100585937500e+03,1.15821203613281250e+03,1.98173828125000000e+02,0.00000000000000000e+00,0.00000000000000000e+00,0.00000000000000000e+00,0.00000000000000000e+00],"parents":[2147483647,0,0,1,1,2,2],"right_children":[2,4,6,-1,-1,-1,-1],"split_conditions":[-9.53674316406250000e-07,-9.53674316406250000e-07,-9.53674316406250000e-07,1.71217715740203857e+00,-1.70044052600860596e+00,-1.94070863723754883e+00,1.85964918136596680e+00],"split_indices":[28,55,108,0,0,0,0],"sum_hessian":[1.62825000000000000e+03,9.24500000000000000e+02,7.03750000000000000e+02,8.12000000000000000e+02,1.12500000000000000e+02,6.90500000000000000e+02,1.32500000000000000e+01],"tree_param":{"num_deleted":"0","num_feature":"126","num_nodes":"7","size_leaf_vector":"0"}},{"base_weights":[-9.31910648941993713e-02,7.82926455140113831e-02,-6.23624467849731445e+00,7.84717559814453125e-01,-9.68530356884002686e-01],"default_left":[true,true,false,false,false],"id":1,"leaf_child_counts":[0,0,0,0,0],"left_children":[1,3,-1,-1,-1],"loss_changes":[8.32545043945312500e+02,5.69725097656250000e+02,0.00000000000000000e+00,0.00000000000000000e+00,0.00000000000000000e+00],"parents":[2147483647,0,0,1,1],"right_children":[2,4,-1,-1,-1],"split_conditions":[-9.53674316406250000e-07,-9.53674316406250000e-07,-6.23624467849731445e+00,7.84717559814453125e-01,-9.68530356884002686e-01],"split_indices":[59,28,0,0,0],"sum_hessian":[7.88852050781250000e+02,7.68389709472656250e+02,2.04623889923095703e+01,4.58936859130859375e+02,3.09452819824218750e+02],"tree_param":{"num_deleted":"0","num_feature":"126","num_nodes":"5","size_leaf_vector":"0"}}]},"name":"gbtree"},"learner_model_param":{"base_score":"0.500000","num_class":"0","num_feature":"126"},"objective":{"name":"binary:logistic","reg_loss_param":{"scale_pos_weight":"1"}}},"version":[1,1,1]} \ No newline at end of file diff --git a/docs-gb/examples/sklearn/README.ipynb b/docs-gb/examples/sklearn/README.ipynb new file mode 100644 index 000000000..297832352 --- /dev/null +++ b/docs-gb/examples/sklearn/README.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving Scikit-Learn models\n", + "\n", + "Out of the box, `mlserver` supports the deployment and serving of `scikit-learn` models.\n", + "By default, it will assume that these models have been [serialised using `joblib`](https://scikit-learn.org/stable/modules/model_persistence.html).\n", + "\n", + "In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "The first step will be to train a simple `scikit-learn` model.\n", + "For that, we will use the [MNIST example from the `scikit-learn` documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) which trains an SVM model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original source code and more details can be found in:\n", + "# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html\n", + "\n", + "# Import datasets, classifiers and performance metrics\n", + "from sklearn import datasets, svm, metrics\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# The digits dataset\n", + "digits = datasets.load_digits()\n", + "\n", + "# To apply a classifier on this data, we need to flatten the image, to\n", + "# turn the data in a (samples, feature) matrix:\n", + "n_samples = len(digits.images)\n", + "data = digits.images.reshape((n_samples, -1))\n", + "\n", + "# Create a classifier: a support vector classifier\n", + "classifier = svm.SVC(gamma=0.001)\n", + "\n", + "# Split data into train and test subsets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data, digits.target, test_size=0.5, shuffle=False)\n", + "\n", + "# We learn the digits on the first half of the digits\n", + "classifier.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving our trained model\n", + "\n", + "To save our trained model, we will serialise it using `joblib`.\n", + "While this is not a perfect approach, it's currently the recommended method to persist models to disk in the [`scikit-learn` documentation](https://scikit-learn.org/stable/modules/model_persistence.html).\n", + "\n", + "Our model will be persisted as a file named `mnist-svm.joblib`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "model_file_name = \"mnist-svm.joblib\"\n", + "joblib.dump(classifier, model_file_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. \n", + "For that, we will need to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"mnist-svm\",\n", + " \"implementation\": \"mlserver_sklearn.SKLearnModel\",\n", + " \"parameters\": {\n", + " \"uri\": \"./mnist-svm.joblib\",\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/mnist-svm/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see above, the model predicted the input as the number `8`, which matches what's on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_test[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/sklearn/README.md b/docs-gb/examples/sklearn/README.md new file mode 100644 index 000000000..5034cae41 --- /dev/null +++ b/docs-gb/examples/sklearn/README.md @@ -0,0 +1,138 @@ +# Serving Scikit-Learn models + +Out of the box, `mlserver` supports the deployment and serving of `scikit-learn` models. +By default, it will assume that these models have been [serialised using `joblib`](https://scikit-learn.org/stable/modules/model_persistence.html). + +In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`. + +## Training + +The first step will be to train a simple `scikit-learn` model. +For that, we will use the [MNIST example from the `scikit-learn` documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) which trains an SVM model. + + +```python +# Original source code and more details can be found in: +# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html + +# Import datasets, classifiers and performance metrics +from sklearn import datasets, svm, metrics +from sklearn.model_selection import train_test_split + +# The digits dataset +digits = datasets.load_digits() + +# To apply a classifier on this data, we need to flatten the image, to +# turn the data in a (samples, feature) matrix: +n_samples = len(digits.images) +data = digits.images.reshape((n_samples, -1)) + +# Create a classifier: a support vector classifier +classifier = svm.SVC(gamma=0.001) + +# Split data into train and test subsets +X_train, X_test, y_train, y_test = train_test_split( + data, digits.target, test_size=0.5, shuffle=False) + +# We learn the digits on the first half of the digits +classifier.fit(X_train, y_train) +``` + +### Saving our trained model + +To save our trained model, we will serialise it using `joblib`. +While this is not a perfect approach, it's currently the recommended method to persist models to disk in the [`scikit-learn` documentation](https://scikit-learn.org/stable/modules/model_persistence.html). + +Our model will be persisted as a file named `mnist-svm.joblib` + + +```python +import joblib + +model_file_name = "mnist-svm.joblib" +joblib.dump(classifier, model_file_name) +``` + +## Serving + +Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. +For that, we will need to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + +### `model-settings.json` + + +```python +%%writefile model-settings.json +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "uri": "./mnist-svm.joblib", + "version": "v0.1.0" + } +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests + +x_0 = X_test[0:1] +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/mnist-svm/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +As we can see above, the model predicted the input as the number `8`, which matches what's on the test set. + + +```python +y_test[0] +``` + + +```python + +``` diff --git a/docs-gb/examples/sklearn/inference-request.json b/docs-gb/examples/sklearn/inference-request.json new file mode 100644 index 000000000..9e1a9fdd1 --- /dev/null +++ b/docs-gb/examples/sklearn/inference-request.json @@ -0,0 +1,15 @@ +{ + "id": "request-0", + "inputs": [ + { + "name": "predict", + "shape": [3], + "datatype": "INT32", + "data": [ 0., 0., 1., 11., 14., 15., 3., 0., 0., 1., 13., 16., 12., + 16., 8., 0., 0., 8., 16., 4., 6., 16., 5., 0., 0., 5., + 15., 11., 13., 14., 0., 0., 0., 0., 2., 12., 16., 13., 0., + 0., 0., 0., 0., 13., 16., 16., 6., 0., 0., 0., 0., 16., + 16., 16., 7., 0., 0., 0., 0., 11., 13., 12., 1., 0.] + } + ] +} diff --git a/docs-gb/examples/sklearn/mnist-svm.joblib b/docs-gb/examples/sklearn/mnist-svm.joblib new file mode 100644 index 000000000..8fdf30764 Binary files /dev/null and b/docs-gb/examples/sklearn/mnist-svm.joblib differ diff --git a/docs-gb/examples/sklearn/model-settings.json b/docs-gb/examples/sklearn/model-settings.json new file mode 100644 index 000000000..6dab01679 --- /dev/null +++ b/docs-gb/examples/sklearn/model-settings.json @@ -0,0 +1,8 @@ +{ + "name": "mnist-svm", + "implementation": "mlserver_sklearn.SKLearnModel", + "parameters": { + "uri": "./mnist-svm.joblib", + "version": "v0.1.0" + } +} diff --git a/docs-gb/examples/sklearn/settings.json b/docs-gb/examples/sklearn/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/sklearn/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/examples/streaming/README.ipynb b/docs-gb/examples/streaming/README.ipynb new file mode 100644 index 000000000..13755c6d9 --- /dev/null +++ b/docs-gb/examples/streaming/README.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Streaming support\n", + "\n", + "The `mlserver` package comes with built-in support for streaming data. This allows you to process data in real-time, without having to wait for the entire response to be available. It supports both REST and gRPC APIs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this example, we create a simple `Identity Text Model` which simply splits the input text into words and returns them one by one. We will use this model to demonstrate how to stream the response from the server to the client. This particular example can provide a good starting point for building more complex streaming models such as the ones based on Large Language Models (LLMs) where streaming is an essential feature to hide the latency of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "The next step will be to serve our model using `mlserver`. For that, we will first implement an extension that serves as the runtime to perform inference using our custom `TextModel`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Custom inference runtime\n", + "\n", + "This is a trivial model to demonstrate streaming support. The model simply splits the input text into words and returns them one by one. In this example we do the following:\n", + "\n", + "- split the text into words using the white space as the delimiter.\n", + "- wait 0.5 seconds between each word to simulate a slow model.\n", + "- return each word one by one." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting text_model.py\n" + ] + } + ], + "source": [ + "%%writefile text_model.py\n", + "\n", + "import asyncio\n", + "from typing import AsyncIterator\n", + "from mlserver import MLModel\n", + "from mlserver.types import InferenceRequest, InferenceResponse\n", + "from mlserver.codecs import StringCodec\n", + "\n", + "\n", + "class TextModel(MLModel):\n", + "\n", + " async def predict_stream(\n", + " self, payloads: AsyncIterator[InferenceRequest]\n", + " ) -> AsyncIterator[InferenceResponse]:\n", + " payload = [_ async for _ in payloads][0]\n", + " text = StringCodec.decode_input(payload.inputs[0])[0]\n", + " words = text.split(\" \")\n", + "\n", + " split_text = []\n", + " for i, word in enumerate(words):\n", + " split_text.append(word if i == 0 else \" \" + word)\n", + "\n", + " for word in split_text:\n", + " await asyncio.sleep(0.5)\n", + " yield InferenceResponse(\n", + " model_name=self._settings.name,\n", + " outputs=[\n", + " StringCodec.encode_output(\n", + " name=\"output\",\n", + " payload=[word],\n", + " use_bytes=True,\n", + " ),\n", + " ],\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As it can be seen, the `predict_stream` method receives as an input an `AsyncIterator` of `InferenceRequest` and returns an `AsyncIterator` of `InferenceResponse`. This definition covers all types of possible input-output combinations for streaming: unary-stream, stream-unary, stream-stream. It is up to the client and server to send/receive the appropriate number of requests/responses which should be known apriori.\n", + "\n", + "Note that although unary-unary can be covered by `predict_stream` method as well, `mlserver` already covers that through the `predict` method.\n", + "\n", + "One important limitation to keep in mind is that for the REST API, the client will not be able to send a stream of requests. The client will have to send a single request with the entire input text. The server will then stream the response back to the client. gRPC API, on the other hand, supports all types of streaming listed above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Settings file\n", + "\n", + "The next step will be to create 2 configuration files:\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### settings.json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting settings.json\n" + ] + } + ], + "source": [ + "%%writefile settings.json\n", + "\n", + "{\n", + " \"debug\": false,\n", + " \"parallel_workers\": 0,\n", + " \"gzip_enabled\": false\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the currently there are three main limitations of the streaming support in MLServer:\n", + "\n", + "- distributed workers are not supported (i.e., the `parallel_workers` setting should be set to `0`)\n", + "- `gzip` middleware is not supported for REST (i.e., `gzip_enabled` setting should be set to `false`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### model-settings.json" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile model-settings.json\n", + "\n", + "{\n", + " \"name\": \"text-model\",\n", + "\n", + " \"implementation\": \"text_model.TextModel\",\n", + " \n", + " \"versions\": [\"text-model/v1.2.3\"],\n", + " \"platform\": \"mlserver\",\n", + " \"inputs\": [\n", + " {\n", + " \"datatype\": \"BYTES\",\n", + " \"name\": \"prompt\",\n", + " \"shape\": [1]\n", + " }\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"datatype\": \"BYTES\",\n", + " \"name\": \"output\",\n", + " \"shape\": [1]\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start serving the model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be run from the same directory where our config files are or point to the folder where they are.\n", + "\n", + "```bash\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be run in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inference request\n", + "\n", + "To test our model, we will use the following inference request:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting generate-request.json\n" + ] + } + ], + "source": [ + "%%writefile generate-request.json\n", + "\n", + "{\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"prompt\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"What is the capital of France?\"],\n", + " \"parameters\": {\n", + " \"content_type\": \"str\"\n", + " }\n", + " }\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"output\"\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test generate stream request (REST)\n", + "\n", + "To send a REST streaming request to the server, we will use the following Python code:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['What']\n", + "[' is']\n", + "[' the']\n", + "[' capital']\n", + "[' of']\n", + "[' France?']\n" + ] + } + ], + "source": [ + "import httpx\n", + "from httpx_sse import connect_sse\n", + "from mlserver import types\n", + "from mlserver.codecs import StringCodec\n", + "\n", + "inference_request = types.InferenceRequest.parse_file(\"./generate-request.json\")\n", + "\n", + "with httpx.Client() as client:\n", + " with connect_sse(client, \"POST\", \"http://localhost:8080/v2/models/text-model/generate_stream\", json=inference_request.dict()) as event_source:\n", + " for sse in event_source.iter_sse():\n", + " response = types.InferenceResponse.parse_raw(sse.data)\n", + " print(StringCodec.decode_output(response.outputs[0]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test generate stream request (gRPC)\n", + "\n", + "To send a gRPC streaming request to the server, we will use the following Python code:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['What']\n", + "[' is']\n", + "[' the']\n", + "[' capital']\n", + "[' of']\n", + "[' France?']\n" + ] + } + ], + "source": [ + "import grpc\n", + "import mlserver.types as types\n", + "from mlserver.codecs import StringCodec\n", + "from mlserver.grpc.converters import ModelInferResponseConverter\n", + "import mlserver.grpc.converters as converters\n", + "import mlserver.grpc.dataplane_pb2_grpc as dataplane\n", + "\n", + "inference_request = types.InferenceRequest.parse_file(\"./generate-request.json\")\n", + "\n", + "# need to convert from string to bytes for grpc\n", + "inference_request.inputs[0] = StringCodec.encode_input(\"prompt\", inference_request.inputs[0].data.root)\n", + "inference_request_g = converters.ModelInferRequestConverter.from_types(\n", + " inference_request, model_name=\"text-model\", model_version=None\n", + ")\n", + "\n", + "async def get_inference_request_stream(inference_request):\n", + " yield inference_request\n", + "\n", + "async with grpc.aio.insecure_channel(\"localhost:8081\") as grpc_channel:\n", + " grpc_stub = dataplane.GRPCInferenceServiceStub(grpc_channel)\n", + " inference_request_stream = get_inference_request_stream(inference_request_g)\n", + " \n", + " async for response in grpc_stub.ModelStreamInfer(inference_request_stream):\n", + " response = ModelInferResponseConverter.to_types(response)\n", + " print(StringCodec.decode_output(response.outputs[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that for gRPC, the request is transformed into an async generator which is then passed to the `ModelStreamInfer` method. The response is also an async generator which can be iterated over to get the response." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs-gb/examples/streaming/README.md b/docs-gb/examples/streaming/README.md new file mode 100644 index 000000000..d91aa7492 --- /dev/null +++ b/docs-gb/examples/streaming/README.md @@ -0,0 +1,213 @@ +# Streaming support + +The `mlserver` package comes with built-in support for streaming data. This allows you to process data in real-time, without having to wait for the entire response to be available. It supports both REST and gRPC APIs. + +## Overview + +In this example, we create a simple `Identity Text Model` which simply splits the input text into words and returns them one by one. We will use this model to demonstrate how to stream the response from the server to the client. This particular example can provide a good starting point for building more complex streaming models such as the ones based on Large Language Models (LLMs) where streaming is an essential feature to hide the latency of the model. + +## Serving + +The next step will be to serve our model using `mlserver`. For that, we will first implement an extension that serves as the runtime to perform inference using our custom `TextModel`. + +### Custom inference runtime + +This is a trivial model to demonstrate streaming support. The model simply splits the input text into words and returns them one by one. In this example we do the following: + +- split the text into words using the white space as the delimiter. +- wait 0.5 seconds between each word to simulate a slow model. +- return each word one by one. + + +```python +%%writefile text_model.py + +import asyncio +from typing import AsyncIterator +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse +from mlserver.codecs import StringCodec + + +class TextModel(MLModel): + + async def predict_stream( + self, payloads: AsyncIterator[InferenceRequest] + ) -> AsyncIterator[InferenceResponse]: + payload = [_ async for _ in payloads][0] + text = StringCodec.decode_input(payload.inputs[0])[0] + words = text.split(" ") + + split_text = [] + for i, word in enumerate(words): + split_text.append(word if i == 0 else " " + word) + + for word in split_text: + await asyncio.sleep(0.5) + yield InferenceResponse( + model_name=self._settings.name, + outputs=[ + StringCodec.encode_output( + name="output", + payload=[word], + use_bytes=True, + ), + ], + ) + +``` + +As it can be seen, the `predict_stream` method receives as an input an `AsyncIterator` of `InferenceRequest` and returns an `AsyncIterator` of `InferenceResponse`. This definition covers all types of possible input-output combinations for streaming: unary-stream, stream-unary, stream-stream. It is up to the client and server to send/receive the appropriate number of requests/responses which should be known apriori. + +Note that although unary-unary can be covered by `predict_stream` method as well, `mlserver` already covers that through the `predict` method. + +One important limitation to keep in mind is that for the REST API, the client will not be able to send a stream of requests. The client will have to send a single request with the entire input text. The server will then stream the response back to the client. gRPC API, on the other hand, supports all types of streaming listed above. + +### Settings file + +The next step will be to create 2 configuration files: +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +#### settings.json + + +```python +%%writefile settings.json + +{ + "debug": false, + "parallel_workers": 0, + "gzip_enabled": false +} + +``` + +Note the currently there are three main limitations of the streaming support in MLServer: + +- distributed workers are not supported (i.e., the `parallel_workers` setting should be set to `0`) +- `gzip` middleware is not supported for REST (i.e., `gzip_enabled` setting should be set to `false`) + +#### model-settings.json + + +```python +%%writefile model-settings.json + +{ + "name": "text-model", + + "implementation": "text_model.TextModel", + + "versions": ["text-model/v1.2.3"], + "platform": "mlserver", + "inputs": [ + { + "datatype": "BYTES", + "name": "prompt", + "shape": [1] + } + ], + "outputs": [ + { + "datatype": "BYTES", + "name": "output", + "shape": [1] + } + ] +} +``` + +#### Start serving the model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be run from the same directory where our config files are or point to the folder where they are. + +```bash +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be run in the background on a separate terminal. + +#### Inference request + +To test our model, we will use the following inference request: + + +```python +%%writefile generate-request.json + +{ + "inputs": [ + { + "name": "prompt", + "shape": [1], + "datatype": "BYTES", + "data": ["What is the capital of France?"], + "parameters": { + "content_type": "str" + } + } + ], + "outputs": [ + { + "name": "output" + } + ] +} +``` + +### Send test generate stream request (REST) + +To send a REST streaming request to the server, we will use the following Python code: + + +```python +import httpx +from httpx_sse import connect_sse +from mlserver import types +from mlserver.codecs import StringCodec + +inference_request = types.InferenceRequest.parse_file("./generate-request.json") + +with httpx.Client() as client: + with connect_sse(client, "POST", "http://localhost:8080/v2/models/text-model/generate_stream", json=inference_request.dict()) as event_source: + for sse in event_source.iter_sse(): + response = types.InferenceResponse.parse_raw(sse.data) + print(StringCodec.decode_output(response.outputs[0])) + +``` + +### Send test generate stream request (gRPC) + +To send a gRPC streaming request to the server, we will use the following Python code: + + +```python +import grpc +import mlserver.types as types +from mlserver.codecs import StringCodec +from mlserver.grpc.converters import ModelInferResponseConverter +import mlserver.grpc.converters as converters +import mlserver.grpc.dataplane_pb2_grpc as dataplane + +inference_request = types.InferenceRequest.parse_file("./generate-request.json") + +# need to convert from string to bytes for grpc +inference_request.inputs[0] = StringCodec.encode_input("prompt", inference_request.inputs[0].data.root) +inference_request_g = converters.ModelInferRequestConverter.from_types( + inference_request, model_name="text-model", model_version=None +) + +async def get_inference_request_stream(inference_request): + yield inference_request + +async with grpc.aio.insecure_channel("localhost:8081") as grpc_channel: + grpc_stub = dataplane.GRPCInferenceServiceStub(grpc_channel) + inference_request_stream = get_inference_request_stream(inference_request_g) + + async for response in grpc_stub.ModelStreamInfer(inference_request_stream): + response = ModelInferResponseConverter.to_types(response) + print(StringCodec.decode_output(response.outputs[0])) +``` + +Note that for gRPC, the request is transformed into an async generator which is then passed to the `ModelStreamInfer` method. The response is also an async generator which can be iterated over to get the response. diff --git a/docs-gb/examples/streaming/generate-request.json b/docs-gb/examples/streaming/generate-request.json new file mode 100644 index 000000000..a61935f08 --- /dev/null +++ b/docs-gb/examples/streaming/generate-request.json @@ -0,0 +1,19 @@ + +{ + "inputs": [ + { + "name": "prompt", + "shape": [1], + "datatype": "BYTES", + "data": ["What is the capital of France?"], + "parameters": { + "content_type": "str" + } + } + ], + "outputs": [ + { + "name": "output" + } + ] +} diff --git a/docs-gb/examples/streaming/model-settings.json b/docs-gb/examples/streaming/model-settings.json new file mode 100644 index 000000000..caf8a6ad3 --- /dev/null +++ b/docs-gb/examples/streaming/model-settings.json @@ -0,0 +1,23 @@ + +{ + "name": "text-model", + + "implementation": "text_model.TextModel", + + "versions": ["text-model/v1.2.3"], + "platform": "mlserver", + "inputs": [ + { + "datatype": "BYTES", + "name": "prompt", + "shape": [1] + } + ], + "outputs": [ + { + "datatype": "BYTES", + "name": "output", + "shape": [1] + } + ] +} diff --git a/docs-gb/examples/streaming/settings.json b/docs-gb/examples/streaming/settings.json new file mode 100644 index 000000000..3a95c2882 --- /dev/null +++ b/docs-gb/examples/streaming/settings.json @@ -0,0 +1,6 @@ + +{ + "debug": false, + "parallel_workers": 0, + "gzip_enabled": false +} diff --git a/docs-gb/examples/streaming/text_model.py b/docs-gb/examples/streaming/text_model.py new file mode 100644 index 000000000..d851f3bb9 --- /dev/null +++ b/docs-gb/examples/streaming/text_model.py @@ -0,0 +1,32 @@ +import asyncio +from typing import AsyncIterator +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse +from mlserver.codecs import StringCodec + + +class TextModel(MLModel): + + async def predict_stream( + self, payloads: AsyncIterator[InferenceRequest] + ) -> AsyncIterator[InferenceResponse]: + payload = [_ async for _ in payloads][0] + text = StringCodec.decode_input(payload.inputs[0])[0] + words = text.split(" ") + + split_text = [] + for i, word in enumerate(words): + split_text.append(word if i == 0 else " " + word) + + for word in split_text: + await asyncio.sleep(0.5) + yield InferenceResponse( + model_name=self._settings.name, + outputs=[ + StringCodec.encode_output( + name="output", + payload=[word], + use_bytes=True, + ), + ], + ) diff --git a/docs-gb/examples/tempo/README.ipynb b/docs-gb/examples/tempo/README.ipynb new file mode 100644 index 000000000..69c9af5a3 --- /dev/null +++ b/docs-gb/examples/tempo/README.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running a Tempo pipeline in MLServer\n", + "\n", + "This example walks you through how to create and serialise a [Tempo pipeline](https://github.com/SeldonIO/tempo), which can then be served through MLServer.\n", + "This pipeline can contain custom Python arbitrary code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the pipeline\n", + "\n", + "The first step will be to create our Tempo pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "\n", + "from tempo import ModelFramework, Model, Pipeline, pipeline\n", + "from tempo.seldon import SeldonDockerRuntime\n", + "from tempo.kfserving import KFServingV2Protocol\n", + "\n", + "\n", + "MODELS_PATH = os.path.join(os.getcwd(), 'models')\n", + "\n", + "docker_runtime = SeldonDockerRuntime()\n", + "\n", + "sklearn_iris_path = os.path.join(MODELS_PATH, 'sklearn-iris')\n", + "sklearn_model = Model(\n", + " name=\"test-iris-sklearn\",\n", + " runtime=docker_runtime,\n", + " platform=ModelFramework.SKLearn,\n", + " uri=\"gs://seldon-models/sklearn/iris\",\n", + " local_folder=sklearn_iris_path,\n", + ")\n", + "\n", + "xgboost_iris_path = os.path.join(MODELS_PATH, 'xgboost-iris')\n", + "xgboost_model = Model(\n", + " name=\"test-iris-xgboost\",\n", + " runtime=docker_runtime,\n", + " platform=ModelFramework.XGBoost,\n", + " uri=\"gs://seldon-models/xgboost/iris\",\n", + " local_folder=xgboost_iris_path,\n", + ")\n", + "\n", + "inference_pipeline_path = os.path.join(MODELS_PATH, 'inference-pipeline')\n", + "@pipeline(\n", + " name=\"inference-pipeline\",\n", + " models=[sklearn_model, xgboost_model],\n", + " runtime=SeldonDockerRuntime(protocol=KFServingV2Protocol()),\n", + " local_folder=inference_pipeline_path\n", + ")\n", + "def inference_pipeline(payload: np.ndarray) -> np.ndarray:\n", + " res1 = sklearn_model(payload)\n", + " if res1[0][0] > 0.7:\n", + " return res1\n", + " else:\n", + " return xgboost_model(payload)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This pipeline can then be serialised using `cloudpickle`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "inference_pipeline.save(save_env=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving the pipeline\n", + "\n", + "Once we have our pipeline created and serialised, we can then create a `model-settings.json` file.\n", + "This configuration file will hold the configuration specific to our MLOps pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"inference-pipeline\",\n", + " \"implementation\": \"tempo.mlserver.InferenceRuntime\",\n", + " \"parameters\": {\n", + " \"uri\": \"./models/inference-pipeline\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy our pipeline components\n", + "\n", + "Additionally, we will also need to deploy our pipeline components.\n", + "That is, the SKLearn and XGBoost models.\n", + "We can do that as:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:tempo:Deploying test-iris-sklearn\n", + "INFO:tempo:Deploying test-iris-xgboost\n", + "INFO:tempo:Deploying inference-pipeline\n" + ] + } + ], + "source": [ + "inference_pipeline.deploy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'inference-pipeline',\n", + " 'model_version': None,\n", + " 'id': '4a13e269-b5ea-4270-8568-cc0819dc435d',\n", + " 'parameters': None,\n", + " 'outputs': [{'name': 'output0',\n", + " 'shape': [1, 3],\n", + " 'datatype': 'FP64',\n", + " 'parameters': None,\n", + " 'data': [0.8260199086532507, 0.038597383243756514, 0.1353827081029928]}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "\n", + "x_0 = np.array([[0.1, 3.1, 1.5, 0.2]])\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/inference-pipeline/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/tempo/README.md b/docs-gb/examples/tempo/README.md new file mode 100644 index 000000000..e4e0cb021 --- /dev/null +++ b/docs-gb/examples/tempo/README.md @@ -0,0 +1,135 @@ +# Running a Tempo pipeline in MLServer + +This example walks you through how to create and serialise a [Tempo pipeline](https://github.com/SeldonIO/tempo), which can then be served through MLServer. +This pipeline can contain custom Python arbitrary code. + +## Creating the pipeline + +The first step will be to create our Tempo pipeline. + + +```python +import numpy as np +import os + +from tempo import ModelFramework, Model, Pipeline, pipeline +from tempo.seldon import SeldonDockerRuntime +from tempo.kfserving import KFServingV2Protocol + + +MODELS_PATH = os.path.join(os.getcwd(), 'models') + +docker_runtime = SeldonDockerRuntime() + +sklearn_iris_path = os.path.join(MODELS_PATH, 'sklearn-iris') +sklearn_model = Model( + name="test-iris-sklearn", + runtime=docker_runtime, + platform=ModelFramework.SKLearn, + uri="gs://seldon-models/sklearn/iris", + local_folder=sklearn_iris_path, +) + +xgboost_iris_path = os.path.join(MODELS_PATH, 'xgboost-iris') +xgboost_model = Model( + name="test-iris-xgboost", + runtime=docker_runtime, + platform=ModelFramework.XGBoost, + uri="gs://seldon-models/xgboost/iris", + local_folder=xgboost_iris_path, +) + +inference_pipeline_path = os.path.join(MODELS_PATH, 'inference-pipeline') +@pipeline( + name="inference-pipeline", + models=[sklearn_model, xgboost_model], + runtime=SeldonDockerRuntime(protocol=KFServingV2Protocol()), + local_folder=inference_pipeline_path +) +def inference_pipeline(payload: np.ndarray) -> np.ndarray: + res1 = sklearn_model(payload) + if res1[0][0] > 0.7: + return res1 + else: + return xgboost_model(payload) + +``` + +This pipeline can then be serialised using `cloudpickle`. + + +```python +inference_pipeline.save(save_env=False) +``` + +## Serving the pipeline + +Once we have our pipeline created and serialised, we can then create a `model-settings.json` file. +This configuration file will hold the configuration specific to our MLOps pipeline. + + +```python +%%writefile ./model-settings.json +{ + "name": "inference-pipeline", + "implementation": "tempo.mlserver.InferenceRuntime", + "parameters": { + "uri": "./models/inference-pipeline" + } +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Deploy our pipeline components + +Additionally, we will also need to deploy our pipeline components. +That is, the SKLearn and XGBoost models. +We can do that as: + + +```python +inference_pipeline.deploy() +``` + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests + +x_0 = np.array([[0.1, 3.1, 1.5, 0.2]]) +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/inference-pipeline/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + + +```python + +``` diff --git a/docs-gb/examples/tempo/model-settings.json b/docs-gb/examples/tempo/model-settings.json new file mode 100644 index 000000000..c6b541837 --- /dev/null +++ b/docs-gb/examples/tempo/model-settings.json @@ -0,0 +1,7 @@ +{ + "name": "inference-pipeline", + "implementation": "tempo.mlserver.InferenceRuntime", + "parameters": { + "uri": "./models/inference-pipeline" + } +} diff --git a/docs-gb/examples/tempo/models/inference-pipeline/model.pickle b/docs-gb/examples/tempo/models/inference-pipeline/model.pickle new file mode 100644 index 000000000..8ee275e99 Binary files /dev/null and b/docs-gb/examples/tempo/models/inference-pipeline/model.pickle differ diff --git a/docs-gb/examples/tempo/models/sklearn-iris/metadata.yaml b/docs-gb/examples/tempo/models/sklearn-iris/metadata.yaml new file mode 100644 index 000000000..07858a389 --- /dev/null +++ b/docs-gb/examples/tempo/models/sklearn-iris/metadata.yaml @@ -0,0 +1,12 @@ + +name: iris +versions: [iris/v1] +platform: sklearn +inputs: +- datatype: BYTES + name: input + shape: [ 4 ] +outputs: +- datatype: BYTES + name: output + shape: [ 3 ] diff --git a/docs-gb/examples/tempo/models/sklearn-iris/model.joblib b/docs-gb/examples/tempo/models/sklearn-iris/model.joblib new file mode 100644 index 000000000..6f0eb22a1 Binary files /dev/null and b/docs-gb/examples/tempo/models/sklearn-iris/model.joblib differ diff --git a/docs-gb/examples/tempo/models/xgboost-iris/metadata.yaml b/docs-gb/examples/tempo/models/xgboost-iris/metadata.yaml new file mode 100644 index 000000000..06f492412 --- /dev/null +++ b/docs-gb/examples/tempo/models/xgboost-iris/metadata.yaml @@ -0,0 +1,12 @@ + +name: xgboost-iris +versions: [xgboost-iris/v1] +platform: sklearn +inputs: +- datatype: BYTES + name: input + shape: [ 1, 4 ] +outputs: +- datatype: BYTES + name: output + shape: [ 1, 1 ] diff --git a/docs-gb/examples/tempo/models/xgboost-iris/model.bst b/docs-gb/examples/tempo/models/xgboost-iris/model.bst new file mode 100644 index 000000000..cebe94083 Binary files /dev/null and b/docs-gb/examples/tempo/models/xgboost-iris/model.bst differ diff --git a/docs-gb/examples/xgboost/README.ipynb b/docs-gb/examples/xgboost/README.ipynb new file mode 100644 index 000000000..425f8c131 --- /dev/null +++ b/docs-gb/examples/xgboost/README.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving XGBoost models\n", + "\n", + "Out of the box, `mlserver` supports the deployment and serving of `xgboost` models.\n", + "By default, it will assume that these models have been [serialised using the `bst.save_model()` method](https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html).\n", + "\n", + "In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training\n", + "\n", + "The first step will be to train a simple `xgboost` model.\n", + "For that, we will use the [mushrooms example from the `xgboost` Getting Started guide](https://xgboost.readthedocs.io/en/latest/get_started.html#python)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Original code and extra details can be found in:\n", + "# https://xgboost.readthedocs.io/en/latest/get_started.html#python\n", + "\n", + "import os\n", + "import xgboost as xgb\n", + "import requests\n", + "\n", + "from urllib.parse import urlparse\n", + "from sklearn.datasets import load_svmlight_file\n", + "\n", + "\n", + "TRAIN_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.train'\n", + "TEST_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.test'\n", + "\n", + "\n", + "def _download_file(url: str) -> str:\n", + " parsed = urlparse(url)\n", + " file_name = os.path.basename(parsed.path)\n", + " file_path = os.path.join(os.getcwd(), file_name)\n", + " \n", + " res = requests.get(url)\n", + " \n", + " with open(file_path, 'wb') as file:\n", + " file.write(res.content)\n", + " \n", + " return file_path\n", + "\n", + "train_dataset_path = _download_file(TRAIN_DATASET_URL)\n", + "test_dataset_path = _download_file(TEST_DATASET_URL)\n", + "\n", + "# NOTE: Workaround to load SVMLight files from the XGBoost example\n", + "X_train, y_train = load_svmlight_file(train_dataset_path)\n", + "X_test, y_test = load_svmlight_file(test_dataset_path)\n", + "X_train = X_train.toarray()\n", + "X_test = X_test.toarray()\n", + "\n", + "# read in data\n", + "dtrain = xgb.DMatrix(data=X_train, label=y_train)\n", + "\n", + "# specify parameters via map\n", + "param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic' }\n", + "num_round = 2\n", + "bst = xgb.train(param, dtrain, num_round)\n", + "\n", + "bst" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving our trained model\n", + "\n", + "To save our trained model, we will serialise it using `bst.save_model()` and the JSON format.\n", + "This is the [approach by the XGBoost project](https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html).\n", + "\n", + "Our model will be persisted as a file named `mushroom-xgboost.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_file_name = 'mushroom-xgboost.json'\n", + "bst.save_model(model_file_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. \n", + "For that, we will need to create 2 configuration files: \n", + "\n", + "- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", + "- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile settings.json\n", + "{\n", + " \"debug\": \"true\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `model-settings.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile model-settings.json\n", + "{\n", + " \"name\": \"mushroom-xgboost\",\n", + " \"implementation\": \"mlserver_xgboost.XGBoostModel\",\n", + " \"parameters\": {\n", + " \"uri\": \"./mushroom-xgboost.json\",\n", + " \"version\": \"v0.1.0\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start serving our model\n", + "\n", + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Send test inference request\n", + "\n", + "We now have our model being served by `mlserver`.\n", + "To make sure that everything is working as expected, let's send a request from our test set.\n", + "\n", + "For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "x_0 = X_test[0:1]\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"predict\",\n", + " \"shape\": x_0.shape,\n", + " \"datatype\": \"FP32\",\n", + " \"data\": x_0.tolist()\n", + " }\n", + " ]\n", + "}\n", + "\n", + "endpoint = \"http://localhost:8080/v2/models/mushroom-xgboost/versions/v0.1.0/infer\"\n", + "response = requests.post(endpoint, json=inference_request)\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see above, the model predicted the input as close to `0`, which matches what's on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_test[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs-gb/examples/xgboost/README.md b/docs-gb/examples/xgboost/README.md new file mode 100644 index 000000000..e5b2d3c1b --- /dev/null +++ b/docs-gb/examples/xgboost/README.md @@ -0,0 +1,157 @@ +# Serving XGBoost models + +Out of the box, `mlserver` supports the deployment and serving of `xgboost` models. +By default, it will assume that these models have been [serialised using the `bst.save_model()` method](https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html). + +In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`. + +## Training + +The first step will be to train a simple `xgboost` model. +For that, we will use the [mushrooms example from the `xgboost` Getting Started guide](https://xgboost.readthedocs.io/en/latest/get_started.html#python). + + +```python +# Original code and extra details can be found in: +# https://xgboost.readthedocs.io/en/latest/get_started.html#python + +import os +import xgboost as xgb +import requests + +from urllib.parse import urlparse +from sklearn.datasets import load_svmlight_file + + +TRAIN_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.train' +TEST_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.test' + + +def _download_file(url: str) -> str: + parsed = urlparse(url) + file_name = os.path.basename(parsed.path) + file_path = os.path.join(os.getcwd(), file_name) + + res = requests.get(url) + + with open(file_path, 'wb') as file: + file.write(res.content) + + return file_path + +train_dataset_path = _download_file(TRAIN_DATASET_URL) +test_dataset_path = _download_file(TEST_DATASET_URL) + +# NOTE: Workaround to load SVMLight files from the XGBoost example +X_train, y_train = load_svmlight_file(train_dataset_path) +X_test, y_test = load_svmlight_file(test_dataset_path) +X_train = X_train.toarray() +X_test = X_test.toarray() + +# read in data +dtrain = xgb.DMatrix(data=X_train, label=y_train) + +# specify parameters via map +param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic' } +num_round = 2 +bst = xgb.train(param, dtrain, num_round) + +bst +``` + +### Saving our trained model + +To save our trained model, we will serialise it using `bst.save_model()` and the JSON format. +This is the [approach by the XGBoost project](https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html). + +Our model will be persisted as a file named `mushroom-xgboost.json`. + + +```python +model_file_name = 'mushroom-xgboost.json' +bst.save_model(model_file_name) +``` + +## Serving + +Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. +For that, we will need to create 2 configuration files: + +- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). +- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). + +### `settings.json` + + +```python +%%writefile settings.json +{ + "debug": "true" +} +``` + +### `model-settings.json` + + +```python +%%writefile model-settings.json +{ + "name": "mushroom-xgboost", + "implementation": "mlserver_xgboost.XGBoostModel", + "parameters": { + "uri": "./mushroom-xgboost.json", + "version": "v0.1.0" + } +} +``` + +### Start serving our model + +Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. + +```shell +mlserver start . +``` + +Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. + +### Send test inference request + +We now have our model being served by `mlserver`. +To make sure that everything is working as expected, let's send a request from our test set. + +For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. + + +```python +import requests + +x_0 = X_test[0:1] +inference_request = { + "inputs": [ + { + "name": "predict", + "shape": x_0.shape, + "datatype": "FP32", + "data": x_0.tolist() + } + ] +} + +endpoint = "http://localhost:8080/v2/models/mushroom-xgboost/versions/v0.1.0/infer" +response = requests.post(endpoint, json=inference_request) + +response.json() +``` + +As we can see above, the model predicted the input as close to `0`, which matches what's on the test set. + + +```python +y_test[0] +``` + + +```python + +``` diff --git a/docs-gb/examples/xgboost/agaricus.txt.test b/docs-gb/examples/xgboost/agaricus.txt.test new file mode 100644 index 000000000..83bdd2666 --- /dev/null +++ b/docs-gb/examples/xgboost/agaricus.txt.test @@ -0,0 +1,1611 @@ +0 1:1 9:1 19:1 21:1 24:1 34:1 36:1 39:1 42:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 117:1 122:1 +1 3:1 9:1 19:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 118:1 124:1 +0 1:1 9:1 20:1 21:1 24:1 34:1 36:1 39:1 41:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 117:1 122:1 +0 3:1 9:1 19:1 21:1 24:1 34:1 36:1 39:1 51:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 116:1 122:1 +0 4:1 7:1 11:1 22:1 29:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 105:1 119:1 124:1 +0 3:1 10:1 20:1 21:1 23:1 34:1 37:1 40:1 42:1 54:1 55:1 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\ No newline at end of file diff --git a/docs-gb/examples/xgboost/settings.json b/docs-gb/examples/xgboost/settings.json new file mode 100644 index 000000000..f2ee7ab8d --- /dev/null +++ b/docs-gb/examples/xgboost/settings.json @@ -0,0 +1,3 @@ +{ + "debug": "true" +} diff --git a/docs-gb/getting-started.md b/docs-gb/getting-started.md new file mode 100644 index 000000000..ed5b41da4 --- /dev/null +++ b/docs-gb/getting-started.md @@ -0,0 +1,493 @@ +# Getting Started with MLServer + +This guide will help you get started creating machine learning microservices with MLServer +in less than 30 minutes. Our use case will be to create a service that helps us compare the similarity +between two documents. Think about whenever you are comparing a book, news article, blog post, +or tutorial to read next, wouldn't it be great to have a way to compare with +similar ones that you have already read and liked (without having to rely on a recommendation's system)? +That's what we'll focus on this guide, on creating a document similarity service. 📜 + 📃 = 😎👌🔥 + +The code is showcased as if it were cells inside a notebook but you can run each of the steps +inside Python files with minimal effort. + +## 00 What is MLServer? + +MLServer is an open-source Python library for building production-ready asynchronous APIs for machine learning models. + +## 01 Dependencies + +The first step is to install `mlserver`, the `spacy` library, and the [language model](https://spacy.io/models/en#en_core_web_lg) +`spacy` will need for our use case. We will also download the `wikipedia-api` library to +test our use case with a few fun summaries. + +If you've never heard of [spaCy](https://spacy.io/) before, it is an open-source Python +library for advanced natural language processing that excels at large-scale information +extraction and retrieval tasks, among many others. The model we'll use is a pre-trained +model on English text from the web. This model will help us get started with our use case +faster than if we had to train a model from scratch for our use case. + +Let's first install these libraries. + + +```python +pip install mlserver spacy wikipedia-api +``` + +We will also need to download the language model separately once we have spaCy inside our virtual environment. + + +```python +python -m spacy download en_core_web_lg +``` + +If you're going over this guide inside a notebook, don't forget to add an exclamation mark `!` +in front of the two commands above. If you are in VSCode, you can keep them as they are and +change the cell type to bash. + +## 02 Set Up + +![setup](images/mlserver_setup.png) + +At its core, MLServer requires that users give it 3 things, a `model-settings.json` file with +information about the model, an (optional) `settings.json` file with information related to the server you +are about to set up, and a `.py` file with the load-predict recipe for your model (as shown in the +picture above). + +Let's create a directory for our model. + + +```python +mkdir -p similarity_model +``` + +Before we create a service that allows us to compare the similarity between two documents, it is +good practice to first test that our solution works first, especially if we're using a pre-trained +model and/or a pipeline. + + +```python +import spacy +``` + + +```python +nlp = spacy.load("en_core_web_lg") +``` + +Now that we have our model loaded, let's look at the similarity of the abstracts of +[Barbieheimer](https://en.wikipedia.org/wiki/Barbenheimer) using the `wikipedia-api` +Python library. The main requirement of the API is that we pass into the main class, +`Wikipedia()`, a project name, an email and the language we want information to be +returned in. After that, we can search the for the movie summaries we want by passing +the title of the movie to the `.page()` method and accessing the summary of it with +the `.summary` attribute. + +Feel free to change the movies for other topics you might be interested in. + +You can run the following lines inside a notebook or, conversely, add them to a `app.py` file. + +```python +import wikipediaapi +``` + + +```python +wiki_wiki = wikipediaapi.Wikipedia('MyMovieEval (example@example.com)', 'en') +``` + + +```python +barbie = wiki_wiki.page('Barbie_(film)').summary +oppenheimer = wiki_wiki.page('Oppenheimer_(film)').summary + +print(barbie) +print() +print(oppenheimer) +``` +If you created an `app.py` file with the code above, make sure you run `python app.py` from +the terminal. + +``` +Barbie is a 2023 American fantasy comedy film directed by Greta Gerwig and written by Gerwig and Noah Baumbach. Based on the Barbie fashion dolls by Mattel, it is the first live-action Barbie film after numerous computer-animated direct-to-video and streaming television films. The film stars Margot Robbie as Barbie and Ryan Gosling as Ken, and follows the two on a journey of self-discovery following an existential crisis. The film also features an ensemble cast that includes America Ferrera, Kate McKinnon, Issa Rae, Rhea Perlman, and Will Ferrell... + +Oppenheimer is a 2023 biographical thriller film written and directed by Christopher Nolan. Based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin, the film chronicles the life of J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project, and thereby ushering in the Atomic Age. Cillian Murphy stars as Oppenheimer, with Emily Blunt as Oppenheimer's wife Katherine "Kitty" Oppenheimer; Matt Damon as General Leslie Groves, director of the Manhattan Project; and Robert Downey Jr. as Lewis Strauss, a senior member of the United States Atomic Energy Commission. The ensemble supporting cast includes Florence Pugh, Josh Hartnett, Casey Affleck, Rami Malek, Gary Oldman and Kenneth Branagh... +``` + +Now that we have our two summaries, let's compare them using spacy. + + +```python +doc1 = nlp(barbie) +doc2 = nlp(oppenheimer) +``` + + +```python +doc1.similarity(doc2) +``` + + + + + 0.9866910567224084 + + + +Notice that both summaries have information about the other movie, about "films" in general, +and about the dates each aired on (which is the same). The reality is that, the model hasn't seen +any of these movies so it might be generalizing to the context of each article, "movies," +rather than their content, "dolls as humans and the atomic bomb." + +You should, of course, play around with different pages and see if what you get back is coherent with +what you would expect. + +Time to create a machine learning API for our use-case. 😎 + +## 03 Building a Service + +MLServer allows us to wrap machine learning models into APIs and build microservices with +replicas of a single model, or different models all together. + +To create a service with MLServer, we will define a class with two asynchronous functions, one that +loads the model and another one to run inference (or predict) with. The former will load the +`spacy` model we tested in the last section, and the latter will take in a list with the two +documents we want to compare. Lastly, our function will return a `numpy` array with a single +value, our similarity score. We'll write the file to our `similarity_model` directory and call +it `my_model.py`. + + +```python +# similarity_model/my_model.py + +from mlserver.codecs import decode_args +from mlserver import MLModel +from typing import List +import numpy as np +import spacy + +class MyKulModel(MLModel): + + async def load(self): + self.model = spacy.load("en_core_web_lg") + + @decode_args + async def predict(self, docs: List[str]) -> np.ndarray: + + doc1 = self.model(docs[0]) + doc2 = self.model(docs[1]) + + return np.array(doc1.similarity(doc2)) +``` + +Now that we have our model file ready to go, the last piece of the puzzle is to tell MLServer a bit of info +about it. In particular, it wants (or needs) to know the name of the model and how to implement +it. The former can be anything you want (and it will be part of the URL of your API), and the latter will +follow the recipe of `name_of_py_file_with_your_model.class_with_your_model`. + +Let's create the `model-settings.json` file MLServer is expecting inside our `similarity_model` directory +and add the name and the implementation of our model to it. + + +```json +# similarity_model/model-settings.json + +{ + "name": "doc-sim-model", + "implementation": "my_model.MyKulModel" +} +``` + + +Now that everything is in place, we can start serving predictions locally to test how things would play +out for our future users. We'll initiate our server via the command line, and later on we'll see how to +do the same via Python files. Here's where we are at right now in the process of developing microservices +with MLServer. + +![start](images/start_service.png) + +As you can see in the image, our server will be initialized with three entry points, one for HTTP requests, +another for gRPC, and another for the metrics. To learn more about the powerful metrics feature of MLServer, +please visit the relevant docs page [here](https://mlserver.readthedocs.io/en/latest/user-guide/metrics.html). +To learn more about gRPC, please see this tutorial [here](https://realpython.com/python-microservices-grpc/). + +To start our service, open up a terminal and run the following command. + +```bash +mlserver start similarity_model/ +``` + +Note: If this is a fresh terminal, make sure you activate your environment before you run the command above. +If you run the command above from your notebook (e.g. `!mlserver start similarity_model/`), +you will have to send the request below from another notebook or terminal since the cell will continue to run +until you turn it off. + +## 04 Testing our Service + +Time to become a client of our service and test it. For this, we'll set up the payload we'll send +to our service and use the `requests` library to [POST](https://www.baeldung.com/cs/http-get-vs-post) our request. + + +```python +from mlserver.codecs import StringCodec +import requests +``` + +Please note that the request below uses the variables we created earlier with the summaries +of Barbie and Oppenheimer. If you are sending this POST request from a fresh python file, make +sure you move those lines of code above into your request file. + + +```python +inference_request = { + "inputs": [ + StringCodec.encode_input(name='docs', payload=[barbie, oppenheimer], use_bytes=False).model_dump() + ] +} +print(inference_request) +``` + + + + + {'inputs': [{'name': 'docs', + 'shape': [2, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'str'}, + 'data': [ + 'Barbie is a 2023 American fantasy comedy...', + 'Oppenheimer is a 2023 biographical thriller...' + ] + }] + } + + + + +```python +r = requests.post('http://0.0.0.0:8080/v2/models/doc-sim-model/infer', json=inference_request) +``` + + +```python +r.json() +``` + +``` +{'model_name': 'doc-sim-model', + 'id': 'a4665ddb-1868-4523-bd00-a25902d9b124', + 'parameters': {}, + 'outputs': [{'name': 'output-0', + 'shape': [1], + 'datatype': 'FP64', + 'parameters': {'content_type': 'np'}, + 'data': [0.9866910567224084]}]} +``` + + + +```python +print(f"Our movies are {round(r.json()['outputs'][0]['data'][0] * 100, 4)}% similar!") +``` + +``` +Our movies are 98.6691% similar +``` + + +Let's decompose what just happened. + +The `URL` for our service might seem a bit odd if you've never heard of the +[V2/Open Inference Protocol (OIP)](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html). +This protocol is a set of specifications that allows machine learning models to be shared and deployed in a +standardized way. This protocol enables the use of machine learning models on a variety of platforms and +devices without requiring changes to the model or its code. The OIP is useful because it allows us +to integrate machine learning into a wide range of applications in a standard way. + +All URLs you create with MLServer will have the following structure. + +![v2](images/urlv2.png) + +This kind of protocol is a standard adopted by different companies like NVIDIA, Tensorflow Serving, +KServe, and others, to keep everyone on the same page. If you think about driving cars globally, +your country has to apply a standard for driving on a particular side of the road, and this ensures +you and everyone else stays on the left (or the right depending on where you are at). Adopting this +means that you won't have to wonder where the next driver is going to come out of when you are driving +and are about to take a turn, instead, you can focus on getting to where you're going to without much worrying. + +Let's describe what each of the components of our `inference_request` does. +- `name`: this maps one-to-one to the name of the parameter in your `predict()` function. +- `shape`: represents the shape of the elements in our `data`. In our case, it is a list with `[2]` strings. +- `datatype`: the different data types expected by the server, e.g., str, numpy array, pandas dataframe, bytes, etc. +- `parameters`: allows us to specify the `content_type` beyond the data types +- `data`: the inputs to our predict function. + +To learn more about the OIP and how MLServer content types work, please have a looks at their +[docs page here](https://mlserver.readthedocs.io/en/latest/user-guide/content-type.html). + +## 05 Creating Model Replicas + +Say you need to meet the demand of a high number of users and one model might not be enough, or is not using +all of the resources of the virtual machine instance it was allocated to. What we can do in this case is +to create multiple replicas of our model to increase the throughput of the requests that come in. This +can be particularly useful at the peak times of our server. To do this, we need to tweak the settings of +our server via the `settings.json` file. In it, we'll add the number of independent models we want to +have to the parameter `"parallel_workers": 3`. + +Let's stop our server, change the settings of it, start it again, and test it. + + +```json +# similarity_model/settings.json + +{ + "parallel_workers": 3 +} +``` + + +```bash +mlserver start similarity_model +``` + +![multiplemodels](images/multiple_models.png) + +As you can see in the output of the terminal in the picture above, we now have 3 models running in +parallel. The reason you might see 4 is because, by default, MLServer will print the name of the +initialized model if it is one or more, and it will also print one for each of the replicas +specified in the settings. + +Let's get a few more [twin films examples](https://en.wikipedia.org/wiki/Twin_films) to test our +server. Get as creative as you'd like. 💡 + + +```python +deep_impact = wiki_wiki.page('Deep_Impact_(film)').summary +armageddon = wiki_wiki.page('Armageddon_(1998_film)').summary + +antz = wiki_wiki.page('Antz').summary +a_bugs_life = wiki_wiki.page("A_Bug's_Life").summary + +the_dark_night = wiki_wiki.page('The_Dark_Knight').summary +mamma_mia = wiki_wiki.page('Mamma_Mia!_(film)').summary +``` + + +```python +def get_sim_score(movie1, movie2): + response = requests.post( + 'http://0.0.0.0:8080/v2/models/doc-sim-model/infer', + json={ + "inputs": [ + StringCodec.encode_input(name='docs', payload=[movie1, movie2], use_bytes=False).model_dump() + ] + }) + return response.json()['outputs'][0]['data'][0] +``` + +Let's first test that the function works as intended. + + +```python +get_sim_score(deep_impact, armageddon) +``` + + + + + 0.9569279450151813 + + + +Now let's map three POST requests at the same time. + + +```python +results = list( + map(get_sim_score, (deep_impact, antz, the_dark_night), (armageddon, a_bugs_life, mamma_mia)) +) +results +``` + + + + + [0.9569279450151813, 0.9725374771538605, 0.9626173937217876] + + + +We can also test it one by one. + + +```python +for movie1, movie2 in zip((deep_impact, antz, the_dark_night), (armageddon, a_bugs_life, mamma_mia)): + print(get_sim_score(movie1, movie2)) +``` + + 0.9569279450151813 + 0.9725374771538605 + 0.9626173937217876 + + +## 06 Packaging our Service + +![serving3](images/serving_2.png) + +For the last step of this guide, we are going to package our model and service into a +docker image that we can reuse in another project or share with colleagues immediately. This step +requires that we have docker installed and configured in our PCs, so if you need to set up docker, +you can do so by following the instructions in the documentation [here](https://docs.docker.com/get-docker/). + +The first step is to create a `requirements.txt` file with all of our dependencies and add it to +the directory we've been using for our service (`similarity_model`). + + +```python +# similarity_model/requirements.txt + +mlserver +spacy==3.6.0 +https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.6.0/en_core_web_lg-3.6.0-py3-none-any.whl +``` + + +The next step is to build a docker image with our model, its dependencies and our server. If you've never heard +of **docker images** before, here's a short description. + +> A Docker image is a lightweight, standalone, and executable package that includes everything needed to run a piece of software, including code, libraries, dependencies, and settings. It's like a carry-on bag for your application, containing everything it needs to travel safely and run smoothly in different environments. Just as a carry-on bag allows you to bring your essentials with you on a trip, a Docker image enables you to transport your application and its requirements across various computing environments, ensuring consistent and reliable deployment. + +MLServer has a convenient function that lets us create docker images with our services. Let's use it. + + +```python +mlserver build similarity_model/ -t 'fancy_ml_service' +``` + +We can check that our image was successfully build not only by looking at the logs of the previous +command but also with the `docker images` command. + + +```bash +docker images +``` + +Let's test that our image works as intended with the following command. Make sure you have closed your +previous server by using `CTRL + C` in your terminal. + + +```bash +docker run -it --rm -p 8080:8080 fancy_ml_service +``` + +Now that you have a packaged and fully-functioning microservice with our model, we could deploy our container +to a production serving platform like [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/#), +or via different offerings available through the many cloud providers out there (e.g. AWS Lambda, Google +Cloud Run, etc.). You could also run this image on KServe, a Kubernetes native tool for model serving, or +anywhere else where you can bring your docker image with you. + +To learn more about MLServer and the different ways in which you can use it, head over to the +[examples](https://mlserver.readthedocs.io/en/latest/examples/index.html) section +or the [user guide](https://mlserver.readthedocs.io/en/latest/user-guide/index.html). To learn about +some of the deployment options available, head over to the docs [here](https://mlserver.readthedocs.io/en/stable/user-guide/deployment/index.html). + +To keep up to date with what we are up to at Seldon, make sure you join our +[Slack community](https://join.slack.com/t/seldondev/shared_invite/zt-vejg6ttd-ksZiQs3O_HOtPQsen_labg). diff --git a/docs-gb/images/adaptive-batching.svg b/docs-gb/images/adaptive-batching.svg new file mode 100644 index 000000000..231396b10 --- /dev/null +++ b/docs-gb/images/adaptive-batching.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs-gb/images/architecture.svg b/docs-gb/images/architecture.svg new file mode 100644 index 000000000..e16470b67 --- /dev/null +++ b/docs-gb/images/architecture.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs-gb/images/content-type.svg b/docs-gb/images/content-type.svg new file mode 100644 index 000000000..38a2b5dce --- /dev/null +++ b/docs-gb/images/content-type.svg @@ -0,0 +1 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read more about the high-level concepts behind codecs in the +[](../../user-guide/content-type) section of the docs, as well as how to use +them. + +## Base Codecs + +All the codecs within MLServer extend from either the {class}`InputCodec ` +or the {class}`RequestCodec ` base classes. +These define the interface to deal with input (outputs) and request (responses) +respectively. + +```{eval-rst} +.. automodule:: mlserver.codecs + :members: InputCodec, RequestCodec +``` + +## Built-in Codecs + +The `mlserver` package will include a set of built-in codecs to cover common +conversions. +You can learn more about these in the [](../../user-guide/content-type.md#available-content-types) section of +the docs. + +```{eval-rst} +.. automodule:: mlserver.codecs + :members: NumpyCodec, NumpyRequestCodec, StringCodec, StringRequestCodec, Base64Codec, DatetimeCodec, PandasCodec +``` diff --git a/docs-gb/reference/api/index.md b/docs-gb/reference/api/index.md new file mode 100644 index 000000000..3229feba9 --- /dev/null +++ b/docs-gb/reference/api/index.md @@ -0,0 +1,17 @@ +# Python API + +MLServer can be installed as a Python package, which exposes a public +framework which can be used to build [custom inference +runtimes](../../user-guide/custom) and [codecs](../../user-guide/content-type). + +Below, you can find the main reference for the Python API exposed by the +MLServer framework. + +```{toctree} +:titlesonly: + +./model.md +./types.md +./codecs.md +./metrics.md +``` diff --git a/docs-gb/reference/api/metrics.md b/docs-gb/reference/api/metrics.md new file mode 100644 index 000000000..f82edd24e --- /dev/null +++ b/docs-gb/reference/api/metrics.md @@ -0,0 +1,13 @@ +# Metrics + +The MLServer package exposes a set of methods that let you register and track +custom metrics. +This can be used within your own [custom inference +runtimes](../../user-guide/custom). +To learn more about how to expose custom metrics, check out the [metrics usage +guide](../../user-guide/metrics). + +```{eval-rst} +.. automodule:: mlserver + :members: register, log +``` diff --git a/docs-gb/reference/api/model.md b/docs-gb/reference/api/model.md new file mode 100644 index 000000000..0fe340ffc --- /dev/null +++ b/docs-gb/reference/api/model.md @@ -0,0 +1,22 @@ +# MLModel + +The `MLModel` class is the base class for all [custom inference +runtimes](../../user-guide/custom). +It exposes the main interface that MLServer will use to interact with ML +models. + +The bulk of its public interface are the {func}`load() +`, {func}`unload() ` and +{func}`predict() ` methods. +However, it also contains helpers with encoding / decoding of requests and +responses, as well as properties to access the most common bits of the model's +metadata. + +When writing [custom runtimes](../../user-guide/custom), **this class should be +extended to implement your own load and predict logic**. + +```{eval-rst} +.. autoclass:: mlserver.MLModel + :members: + :member-order: bysource +``` diff --git a/docs-gb/reference/api/types.md b/docs-gb/reference/api/types.md new file mode 100644 index 000000000..c8d1dab92 --- /dev/null +++ b/docs-gb/reference/api/types.md @@ -0,0 +1,6 @@ +# Types + +```{eval-rst} +.. automodule:: mlserver.types + :members: +``` diff --git a/docs-gb/reference/cli.md b/docs-gb/reference/cli.md new file mode 100644 index 000000000..cb56a8bd3 --- /dev/null +++ b/docs-gb/reference/cli.md @@ -0,0 +1,19 @@ +# MLServer CLI + +The MLServer package includes a `mlserver` CLI designed to help with some of +the common tasks involved with a model's lifecycle. +Below, you can find the full list of supported subcommands. +Note that you can also get a similar high-level outline at any time by running: + +```bash +mlserver --help +``` + +## Commands + +```{eval-rst} + +.. click:: mlserver.cli.main:root + :prog: mlserver + :nested: full +``` diff --git a/docs-gb/reference/model-settings.md b/docs-gb/reference/model-settings.md new file mode 100644 index 000000000..7d1998108 --- /dev/null +++ b/docs-gb/reference/model-settings.md @@ -0,0 +1,29 @@ +# Model Settings + +In MLServer, each loaded model can be configured separately. +This configuration will include model information (e.g. metadata about the +accepted inputs), but also model-specific settings (e.g. number of [parallel +workers](../user-guide/parallel-inference) to run inference). + +This configuration will usually be provided through a `model-settings.json` +file which **sits next to the model artifacts**. +However, it's also possible to provide this through environment variables +prefixed with `MLSERVER_MODEL_` (e.g. `MLSERVER_MODEL_IMPLEMENTATION`). Note +that, in the latter case, this environment variables will be shared across all +loaded models (unless they get overriden by a `model-settings.json` file). +Additionally, if no `model-settings.json` file is found, MLServer will also try +to load a _"default"_ model from these environment variables. + +## Settings + +```{eval-rst} + +.. autopydantic_settings:: mlserver.settings.ModelSettings +``` + +## Extra Model Parameters + +```{eval-rst} + +.. autopydantic_settings:: mlserver.settings.ModelParameters +``` diff --git a/docs-gb/reference/python-api/README.md b/docs-gb/reference/python-api/README.md new file mode 100644 index 000000000..c9f57933d --- /dev/null +++ b/docs-gb/reference/python-api/README.md @@ -0,0 +1,2 @@ +# Python API + diff --git a/docs-gb/reference/settings.md b/docs-gb/reference/settings.md new file mode 100644 index 000000000..ca282a53d --- /dev/null +++ b/docs-gb/reference/settings.md @@ -0,0 +1,15 @@ +# MLServer Settings + +MLServer can be configured through a `settings.json` file on the root folder +from where MLServer is started. +Note that these are server-wide settings (e.g. gRPC or HTTP port) which are +separate from the [invidual model settings](./model-settings). +Alternatively, this configuration can also be passed through **environment +variables** prefixed with `MLSERVER_` (e.g. `MLSERVER_GRPC_PORT`). + +## Settings + +```{eval-rst} + +.. autopydantic_settings:: mlserver.settings.Settings +``` diff --git a/docs-gb/references.bib b/docs-gb/references.bib new file mode 100644 index 000000000..5cef1330a --- /dev/null +++ b/docs-gb/references.bib @@ -0,0 +1,18 @@ + +@article{zhiFiberPlatformEfficient2020, + title = {Fiber: {{A Platform}} for {{Efficient Development}} and {{Distributed Training}} for {{Reinforcement Learning}} and {{Population}}-{{Based Methods}}}, + shorttitle = {Fiber}, + author = {Zhi, Jiale and Wang, Rui and Clune, Jeff and Stanley, Kenneth O.}, + year = {2020}, + month = mar, + abstract = {Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose unique challenges for efficiency and flexibility to the underlying distributed computing frameworks. These challenges include frequent interaction with simulations, the need for dynamic scaling, and the need for a user interface with low adoption cost and consistency across different backends. In this paper we address these challenges while still retaining development efficiency and flexibility for both research and practical applications by introducing Fiber, a scalable distributed computing framework for RL and population-based methods. Fiber aims to significantly expand the accessibility of large-scale parallel computation to users of otherwise complicated RL and population-based approaches without the need to for specialized computational expertise.}, + archiveprefix = {arXiv}, + eprint = {2003.11164}, + eprinttype = {arxiv}, + file = {/home/agm/Zotero/storage/5U97ZXVW/Zhi et al. - 2020 - Fiber A Platform for Efficient Development and Di.pdf;/home/agm/Zotero/storage/JZAVAE4T/2003.html}, + journal = {arXiv:2003.11164 [cs, stat]}, + keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}, + primaryclass = {cs, stat} +} + + diff --git a/docs-gb/runtimes/README.md b/docs-gb/runtimes/README.md new file mode 100644 index 000000000..2edb06f8c --- /dev/null +++ b/docs-gb/runtimes/README.md @@ -0,0 +1,32 @@ +# 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. + +![](../images/architecture.svg) + +Out of the box, MLServer comes with a set of pre-packaged runtimes which let +you interact with a subset of common ML frameworks. +This allows you to start serving models saved in these frameworks straight +away. +To avoid bringing in dependencies for frameworks that you don't need to use, +these runtimes are implemented as independent (and optional) Python packages. +This mechanism also allows you to **rollout your [own custom runtimes](./custom.md) very easily**. + +To pick which runtime you want to use for your model, you just need to make +sure that the right package is installed, and then point to the correct runtime +class in your `model-settings.json` file. + +## Included Inference Runtimes + +| Framework | Package Name | Implementation Class | Example | Documentation | +| ----------- | ----------- | --------------- | --------- | ------------------- | +| Scikit-Learn | `mlserver-sklearn` | `mlserver_sklearn.SKLearnModel` | [Scikit-Learn example](../examples/sklearn/README.md) | [MLServer SKLearn](./sklearn.md) | +| XGBoost | `mlserver-xgboost` | `mlserver_xgboost.XGBoostModel` | [XGBoost example](../examples/xgboost/README.md) | [MLServer XGBoost](./xgboost.md) | +| Spark MLlib | `mlserver-mllib` | `mlserver_mllib.MLlibModel` | [MLlib example](../examples/mllib/README.md) | [MLServer MLlib](./mllib.md) | +| LightGBM | `mlserver-lightgbm` | `mlserver_lightgbm.LightGBMModel` | [LightGBM example](../examples/lightgbm/README.md) | [MLServer LightGBM](./lightgbm.md) | +| CatBoost | `mlserver-catboost` | `mlserver_catboost.CatboostModel` | [CatBoost example](../examples/catboost/README.md) | [MLServer CatBoost](./catboost.md) | +| MLflow | `mlserver-mlflow` | `mlserver_mlflow.MLflowRuntime` | [MLflow example](../examples/mlflow/README.md) | [MLServer MLflow](./mlflow.md) | +| Alibi-Detect | `mlserver-alibi-detect` | `mlserver_alibi_detect.AlibiDetectRuntime` | [Alibi-detect example](../examples/alibi-detect/README.md) | [MLServer Alibi-Detect](./alibi-detect.md) | \ No newline at end of file diff --git a/docs-gb/runtimes/alibi-detect.md b/docs-gb/runtimes/alibi-detect.md new file mode 100644 index 000000000..87716db21 --- /dev/null +++ b/docs-gb/runtimes/alibi-detect.md @@ -0,0 +1,58 @@ +# Alibi-Detect runtime for MLServer + +This package provides a MLServer runtime compatible with +[alibi-detect](https://docs.seldon.io/projects/alibi-detect/en/latest/index.html) +models. + +## Usage + +You can install the `mlserver-alibi-detect` runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-alibi-detect +``` + +For further information on how to use MLServer with Alibi-Detect, you can check +out this [worked out example](../../docs/examples/alibi-detect/README.md). + +## Content Types + +If no [content type](../../docs/user-guide/content-type) is present on the +request or metadata, the Alibi-Detect runtime will try to decode the payload +as a [NumPy Array](../../docs/user-guide/content-type). +To avoid this, either send a different content type explicitly, or define the +correct one as part of your [model's +metadata](../../docs/reference/model-settings). + +## Settings + +The Alibi Detect runtime exposes a couple setting flags which can be used to +customise how the runtime behaves. +These settings can be added under the `parameters.extra` section of your +`model-settings.json` file, e.g. + +```{code-block} json +--- +emphasize-lines: 6-8 +--- +{ + "name": "drift-detector", + "implementation": "mlserver_alibi_detect.AlibiDetectRuntime", + "parameters": { + "uri": "./alibi-detect-artifact/", + "extra": { + "batch_size": 5 + } + } +} +``` + +### Reference + +You can find the full reference of the accepted extra settings for the Alibi +Detect runtime below: + +```{eval-rst} + +.. autopydantic_settings:: mlserver_alibi_detect.runtime.AlibiDetectSettings +``` diff --git a/docs-gb/runtimes/alibi-explain.md b/docs-gb/runtimes/alibi-explain.md new file mode 100644 index 000000000..e29fcd0b0 --- /dev/null +++ b/docs-gb/runtimes/alibi-explain.md @@ -0,0 +1,11 @@ +# Alibi-Explain runtime for MLServer + +This package provides a MLServer runtime compatible with Alibi-Explain. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-alibi-explain +``` diff --git a/docs-gb/runtimes/catboost.md b/docs-gb/runtimes/catboost.md new file mode 100644 index 000000000..3dfd52b51 --- /dev/null +++ b/docs-gb/runtimes/catboost.md @@ -0,0 +1,23 @@ +# CatBoost runtime for MLServer + +This package provides a MLServer runtime compatible with CatBoost's `CatboostClassifier`. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-catboost +``` + +For further information on how to use MLServer with CatBoost, you can check out +this [worked out example](../../docs/examples/catboost/README.md). + +## Content Types + +If no [content type](../../docs/user-guide/content-type) is present on the +request or metadata, the CatBoost runtime will try to decode the payload as +a [NumPy Array](../../docs/user-guide/content-type). +To avoid this, either send a different content type explicitly, or define the +correct one as part of your [model's +metadata](../../docs/reference/model-settings). diff --git a/docs-gb/runtimes/custom.md b/docs-gb/runtimes/custom.md new file mode 100644 index 000000000..fecaec258 --- /dev/null +++ b/docs-gb/runtimes/custom.md @@ -0,0 +1,11 @@ +# Custom Inference Runtimes + +There may be cases where the [inference runtimes](./README.md) offered +out-of-the-box by MLServer may not be enough, or where you may need **extra +custom functionality** which is not included in MLServer (e.g. custom codecs). +To cover these cases, MLServer lets you create custom runtimes very easily. + +To learn more about how you can write custom runtimes with MLServer, check out +the [Custom Runtimes user guide](../user-guide/custom.md). Alternatively, you can also +see this [end-to-end example](../examples/custom/README.md) which walks through the +process of writing a custom runtime. \ No newline at end of file diff --git a/docs-gb/runtimes/huggingface.md b/docs-gb/runtimes/huggingface.md new file mode 100644 index 000000000..04db06ccb --- /dev/null +++ b/docs-gb/runtimes/huggingface.md @@ -0,0 +1,77 @@ +# HuggingFace runtime for MLServer + +This package provides a MLServer runtime compatible with HuggingFace Transformers. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-huggingface +``` + +For further information on how to use MLServer with HuggingFace, you can check +out this [worked out example](../../docs/examples/huggingface/README.md). + +## Content Types + +The HuggingFace runtime will always decode the input request using its own +built-in codec. +Therefore, [content type annotations](../../docs/user-guide/content-type) at +the request level will **be ignored**. +Note that this **doesn't include [input-level content +type](../../docs/user-guide/content-type#Codecs) annotations**, which will be +respected as usual. + +## Settings + +The HuggingFace runtime exposes a couple extra parameters which can be used to +customise how the runtime behaves. +These settings can be added under the `parameters.extra` section of your +`model-settings.json` file, e.g. + +```{code-block} json +--- +emphasize-lines: 5-8 +--- +{ + "name": "qa", + "implementation": "mlserver_huggingface.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "question-answering", + "optimum_model": true + } + } +} +``` + +````{note} +These settings can also be injected through environment variables prefixed with `MLSERVER_MODEL_HUGGINGFACE_`, e.g. + +```bash +MLSERVER_MODEL_HUGGINGFACE_TASK="question-answering" +MLSERVER_MODEL_HUGGINGFACE_OPTIMUM_MODEL=true +``` +```` + +### Loading models +#### Local models +It is possible to load a local model into a HuggingFace pipeline by specifying the model artefact folder path in `parameters.uri` in `model-settings.json`. + +#### HuggingFace models +Models in the HuggingFace hub can be loaded by specifying their name in `parameters.extra.pretrained_model` in `model-settings.json`. + +````{note} +If `parameters.extra.pretrained_model` is specified, it takes precedence over `parameters.uri`. +```` + +### Reference + +You can find the full reference of the accepted extra settings for the +HuggingFace runtime below: + +```{eval-rst} + +.. autopydantic_settings:: mlserver_huggingface.settings.HuggingFaceSettings +``` diff --git a/docs-gb/runtimes/lightgbm.md b/docs-gb/runtimes/lightgbm.md new file mode 100644 index 000000000..c93566205 --- /dev/null +++ b/docs-gb/runtimes/lightgbm.md @@ -0,0 +1,23 @@ +# LightGBM runtime for MLServer + +This package provides a MLServer runtime compatible with LightGBM. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-lightgbm +``` + +For further information on how to use MLServer with LightGBM, you can check out +this [worked out example](../../docs/examples/lightgbm/README.md). + +## Content Types + +If no [content type](../../docs/user-guide/content-type) is present on the +request or metadata, the LightGBM runtime will try to decode the payload as +a [NumPy Array](../../docs/user-guide/content-type). +To avoid this, either send a different content type explicitly, or define the +correct one as part of your [model's +metadata](../../docs/reference/model-settings). diff --git a/docs-gb/runtimes/mlflow.md b/docs-gb/runtimes/mlflow.md new file mode 100644 index 000000000..c29d12031 --- /dev/null +++ b/docs-gb/runtimes/mlflow.md @@ -0,0 +1,27 @@ +# MLflow runtime for MLServer + +This package provides a MLServer runtime compatible with [MLflow +models](https://www.mlflow.org/docs/latest/models.html). + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-mlflow +``` + +## Content Types + +The MLflow inference runtime introduces a new `dict` content type, which +decodes an incoming V2 request as a [dictionary of +tensors](https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models). +This is useful for certain MLflow-serialised models, which will expect that the +model inputs are serialised in this format. + +```{note} +The `dict` content type can be _stacked_ with other content types, like +[`np`](../../docs/user-guide/content-type). +This allows the user to use a different set of content types to decode each of +the dict entries. +``` diff --git a/docs-gb/runtimes/mllib.md b/docs-gb/runtimes/mllib.md new file mode 100644 index 000000000..b1b5c5f81 --- /dev/null +++ b/docs-gb/runtimes/mllib.md @@ -0,0 +1,14 @@ +# Spark MLlib runtime for MLServer + +This package provides a MLServer runtime compatible with Spark MLlib. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-mllib +``` + +For further information on how to use MLServer with Spark MLlib, you can check +out the [MLServer repository](https://github.com/SeldonIO/MLServer). diff --git a/docs-gb/runtimes/sklearn.md b/docs-gb/runtimes/sklearn.md new file mode 100644 index 000000000..6158f41f1 --- /dev/null +++ b/docs-gb/runtimes/sklearn.md @@ -0,0 +1,63 @@ +# Scikit-Learn runtime for MLServer + +This package provides a MLServer runtime compatible with Scikit-Learn. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-sklearn +``` + +For further information on how to use MLServer with Scikit-Learn, you can check +out this [worked out example](../../docs/examples/sklearn/README.md). + +## Content Types + +If no [content type](../../docs/user-guide/content-type) is present on the +request or metadata, the Scikit-Learn runtime will try to decode the payload as +a [NumPy Array](../../docs/user-guide/content-type). +To avoid this, either send a different content type explicitly, or define the +correct one as part of your [model's +metadata](../../docs/reference/model-settings). + +## Model Outputs + +The Scikit-Learn inference runtime exposes a number of outputs depending on the +model type. +These outputs match to the `predict`, `predict_proba` and `transform` methods +of the Scikit-Learn model. + +| Output | Returned By Default | Availability | +| --------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------- | +| `predict` | ✅ | Available on most models, but not in [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html). | +| `predict_proba` | ❌ | Only available on non-regressor models. | +| `transform` | ❌ | Only available on [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html). | + +By default, the runtime will only return the output of `predict`. +However, you are able to control which outputs you want back through the +`outputs` field of your {class}`InferenceRequest +` payload. + +For example, to only return the model's `predict_proba` output, you could +define a payload such as: + +```{code-block} json +--- +emphasize-lines: 10-12 +--- +{ + "inputs": [ + { + "name": "my-input", + "datatype": "INT32", + "shape": [2, 2], + "data": [1, 2, 3, 4] + } + ], + "outputs": [ + { "name": "predict_proba" } + ] +} +``` diff --git a/docs-gb/runtimes/xgboost.md b/docs-gb/runtimes/xgboost.md new file mode 100644 index 000000000..cff6b104b --- /dev/null +++ b/docs-gb/runtimes/xgboost.md @@ -0,0 +1,93 @@ +# XGBoost runtime for MLServer + +This package provides a MLServer runtime compatible with XGBoost. + +## Usage + +You can install the runtime, alongside `mlserver`, as: + +```bash +pip install mlserver mlserver-xgboost +``` + +For further information on how to use MLServer with XGBoost, you can check out +this [worked out example](../../docs/examples/xgboost/README.md). + +## XGBoost Artifact Type + +The XGBoost inference runtime will expect that your model is serialised via one +of the following methods: + +| Extension | Docs | Example | +| --------- | -------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | +| `*.json` | [JSON Format](https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html#introduction-to-model-io) | `booster.save_model("model.json")` | +| `*.ubj` | [Binary JSON Format](https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html#introduction-to-model-io) | `booster.save_model("model.ubj")` | +| `*.bst` | [(Old) Binary Format](https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html#introduction-to-model-io) | `booster.save_model("model.bst")` | + +````{note} +By default, the runtime will look for a file called `model.[json | ubj | bst]`. +However, this can be modified through the `parameters.uri` field of your +{class}`ModelSettings ` config (see the +section on [Model Settings](../../docs/reference/model-settings.md) for more +details). + +```{code-block} json +--- +emphasize-lines: 3-5 +--- +{ + "name": "foo", + "parameters": { + "uri": "./my-own-model-filename.json" + } +} +``` +```` + +## Content Types + +If no [content type](../../docs/user-guide/content-type) is present on the +request or metadata, the XGBoost runtime will try to decode the payload as a +[NumPy Array](../../docs/user-guide/content-type). +To avoid this, either send a different content type explicitly, or define the +correct one as part of your [model's +metadata](../../docs/reference/model-settings). + +## Model Outputs + +The XGBoost inference runtime exposes a number of outputs depending on the +model type. +These outputs match to the `predict` and `predict_proba` methods of the XGBoost +model. + +| Output | Returned By Default | Availability | +| --------------- | ------------------- | --------------------------------------------------------------------- | +| `predict` | ✅ | Available on all XGBoost models. | +| `predict_proba` | ❌ | Only available on non-regressor models (i.e. `XGBClassifier` models). | + +By default, the runtime will only return the output of `predict`. +However, you are able to control which outputs you want back through the +`outputs` field of your {class}`InferenceRequest +` payload. + +For example, to only return the model's `predict_proba` output, you could +define a payload such as: + +```{code-block} json +--- +emphasize-lines: 10-12 +--- +{ + "inputs": [ + { + "name": "my-input", + "datatype": "INT32", + "shape": [2, 2], + "data": [1, 2, 3, 4] + } + ], + "outputs": [ + { "name": "predict_proba" } + ] +} +``` diff --git a/docs-gb/user-guide/README.md b/docs-gb/user-guide/README.md new file mode 100644 index 000000000..ba623ff35 --- /dev/null +++ b/docs-gb/user-guide/README.md @@ -0,0 +1,2 @@ +# User Guide + diff --git a/docs-gb/user-guide/adaptive-batching.md b/docs-gb/user-guide/adaptive-batching.md new file mode 100644 index 000000000..66d26b455 --- /dev/null +++ b/docs-gb/user-guide/adaptive-batching.md @@ -0,0 +1,165 @@ +# Adaptive Batching + +MLServer includes support to batch requests together transparently on-the-fly. +We refer to this as "adaptive batching", although it can also be known as +"predictive batching". + +![](../assets/adaptive-batching.svg) + +## Benefits + +There are usually two main reasons to adopt adaptive batching: + +- **Maximise resource usage**. + Usually, inference operations are “vectorised” (i.e. are designed to operate + across batches). + For example, a GPU is designed to operate on multiple data points at the same + time. + Therefore, to make sure that it’s used at maximum capacity, we need to run + inference across batches. + +- **Minimise any inference overhead**. + Usually, all models will have to _“pay”_ a constant overhead when running any + type of inference. + This can be something like IO to communicate with the GPU or some kind of + processing in the incoming data. + Up to a certain size, this overhead tends to not scale linearly with the + number of data points. + Therefore, it’s in our interest to send as large batches as we can without + deteriorating performance. + +However, these benefits will usually scale only up to a certain point, which is +usually determined by either the infrastructure, the machine learning +framework used to train your model, or a combination of both. +Therefore, to maximise the performance improvements brought in by adaptive +batching it will be important to [configure it with the appropriate values for +your model](#usage). +Since these values are usually found through experimentation, **MLServer won't +enable by default adaptive batching on newly loaded models**. + +## Usage + +MLServer lets you configure adaptive batching independently for each model +through two main parameters: + +- **Maximum batch size**, that is how many requests you want to group together. +- **Maximum batch time**, that is how much time we should wait for new + requests until we reach our maximum batch size. + +### `max_batch_size` + +The `max_batch_size` field of the `model-settings.json` file (or +alternatively, the `MLSERVER_MODEL_MAX_BATCH_SIZE` global environment +variable) controls the maximum number of requests that should be grouped +together on each batch. +The expected values are: + +- `N`, where `N > 1`, will create batches of up to `N` elements. +- `0` or `1`, will disable adaptive batching. + +### `max_batch_time` + +The `max_batch_time` field of the `model-settings.json` file (or +alternatively, the `MLSERVER_MODEL_MAX_BATCH_TIME` global environment +variable) controls the time that MLServer should wait for new requests to come +in until we reach our maximum batch size. + +The expected format is in seconds, but it will take fractional values. +That is, 500ms could be expressed as `0.5`. + +The expected values are: + +- `T`, where `T > 0`, will wait `T` seconds at most. +- `0`, will disable adaptive batching. + +### Merge and split of custom parameters + +MLserver allows adding custom parameters to the `parameters` field of the requests. +These parameters are received as a merged list of parameters inside the server, e.g. +```python +# request 1 +types.RequestInput( + name="parameters-np", + shape=[1], + datatype="BYTES", + data=[], + parameters=types.Parameters( + custom-param='value-1', + ) +) + +# request 2 +types.RequestInput( + name="parameters-np", + shape=[1], + datatype="BYTES", + data=[], + parameters=types.Parameters( + custom-param='value-2', + ) +) +``` + +is received as follows in the batched request in the server: +```python +types.RequestInput( + name="parameters-np", + shape=[2], + datatype="BYTES", + data=[], + parameters=types.Parameters( + custom-param=['value-1', 'value-2'], + ) +) +``` + +The same way if the request is sent back from the server as a batched request + +```python +types.ResponseOutput( + name="foo", + datatype="INT32", + shape=[3, 3], + data=[1, 2, 3, 4, 5, 6, 7, 8, 9], + parameters=types.Parameters( + content_type="np", + foo=["foo_1", "foo_2"], + bar=["bar_1", "bar_2", "bar_3"], + ), +) +``` + +it will be returned unbatched from the server as follows: + +```python +# Request 1 +types.ResponseOutput( + name="foo", + datatype="INT32", + shape=[1, 3], + data=[1, 2, 3], + parameters=types.Parameters( + content_type="np", foo="foo_1", bar="'bar_1" + ), +) + +# Request 2 +types.ResponseOutput( + name="foo", + datatype="INT32", + shape=[1, 3], + data=[4, 5, 6], + parameters=types.Parameters( + content_type="np", foo="foo_2", bar="bar_2" + ), +) + +# Request 3 +types.ResponseOutput( + name="foo", + datatype="INT32", + shape=[1, 3], + data=[7, 8, 9], + parameters=types.Parameters(content_type="np", bar="bar_3"), +) +``` diff --git a/docs-gb/user-guide/content-type.md b/docs-gb/user-guide/content-type.md new file mode 100644 index 000000000..be3796b24 --- /dev/null +++ b/docs-gb/user-guide/content-type.md @@ -0,0 +1,649 @@ +# Content Types (and Codecs) + +Machine learning models generally expect their inputs to be passed down as a +particular Python type. +Most commonly, this type ranges from _"general purpose"_ NumPy arrays or Pandas +DataFrames to more granular definitions, like `datetime` objects, `Pillow` +images, etc. +Unfortunately, the definition of the [V2 Inference Protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html) +doesn't cover any of the specific use cases. +This protocol can be thought of a wider _"lower level"_ spec, which only +defines what fields a payload should have. + +To account for this gap, MLServer introduces support for **content types**, +which offer a way to let MLServer know how it should _"decode"_ V2-compatible +payloads. +When shaped in the right way, these payloads should _"encode"_ all the +information required to extract the higher level Python type that will be +required for a model. + +To illustrate the above, we can think of a Scikit-Learn pipeline, which takes +in a Pandas DataFrame and returns a NumPy Array. +Without the use of **content types**, the V2 payload itself would probably lack +information about how this payload should be treated by MLServer +Likewise, the Scikit-Learn pipeline wouldn't know how to treat a raw V2 +payload. +In this scenario, the use of content types allows us to specify information on +what's the actual _"higher level"_ information encoded within the V2 protocol +payloads. + +![Content Types](../images/content-type.svg) + +## Usage + +{% hint style="info" %} +Some inference runtimes may apply a content type by default if none is present. +To learn more about each runtime's defaults, please check the [relevant +inference runtime's docs](../runtimes/index). +{% endhint %} + +To let MLServer know that a particular payload must be decoded / encoded as a +different Python data type (e.g. NumPy Array, Pandas DataFrame, etc.), you can +specify it through the `content_type` field of the `parameters` section of +your request. + +As an example, we can consider the following dataframe, containing two columns: +Age and First Name. + +| First Name | Age | +| ---------- | --- | +| Joanne | 34 | +| Michael | 22 | + +This table, could be specified in the V2 protocol as the following payload, where we declare that: + +- The whole set of inputs should be decoded as a Pandas Dataframe (i.e. setting + the content type as `pd`). +- The First Name column should be decoded as a UTF-8 string (i.e. setting the content + type as `str`). + +```json +{ + "parameters": { + "content_type": "pd" + }, + "inputs": [ + { + "name": "First Name", + "datatype": "BYTES", + "parameters": { + "content_type": "str" + }, + "shape": [2], + "data": ["Joanne", "Michael"] + }, + { + "name": "Age", + "datatype": "INT32", + "shape": [2], + "data": [34, 22] + }, + ] +} +``` + +To learn more about the available content types and how to use them, you can +see all the available ones in the [Available Content +Types](#available-content-types) section below. + +{% hint style="info" %} +It's important to keep in mind that content types can be specified at both the +**request level** and the **input level**. +The former will apply to the **entire set of inputs**, whereas the latter will +only apply to a **particular input** of the payload. +{% endhint %} + +### Codecs + +Under the hood, the conversion between content types is implemented using +_codecs_. +In the MLServer architecture, codecs are an abstraction which know how to +_encode_ and _decode_ high-level Python types to and from the V2 Inference +Protocol. + +Depending on the high-level Python type, encoding / decoding operations may +require access to multiple input or output heads. +For example, a Pandas Dataframe would need to aggregate all of the +input-/output-heads present in a V2 Inference Protocol response. + +![Request Codecs](../images/request-codecs.svg) + +However, a Numpy array or a list of strings, could be encoded directly as an +input head within a larger request. + +![Input Codecs](../images/input-codecs.svg) + +To account for this, codecs can work at either the request- / response-level +(known as **request codecs**), or the input- / output-level (known as **input +codecs**). +Each of these codecs, expose the following **public interface**, where `Any` +represents a high-level Python datatype (e.g. a Pandas Dataframe, a Numpy +Array, etc.): + +- **Request Codecs** + - `encode_request() ` + - `decode_request() ` + - `encode_response() ` + - `decode_response() ` +- **Input Codecs** + - `encode_input() ` + - `decode_input() ` + - `encode_output() ` + - `decode_output() ` + +Note that, these methods can also be used as helpers to **encode requests and +decode responses on the client side**. +This can help to abstract away from the user most of the details about the +underlying structure of V2-compatible payloads. + +For example, in the example above, we could use codecs to encode the DataFrame +into a V2-compatible request simply as: + +```python +import pandas as pd + +from mlserver.codecs import PandasCodec + +dataframe = pd.DataFrame({'First Name': ["Joanne", "Michael"], 'Age': [34, 22]}) + +inference_request = PandasCodec.encode_request(dataframe) +print(inference_request) +``` + +For a full end-to-end example on how content types and codecs work under the +hood, feel free to check out this [Content Type Decoding +example](../examples/content-type/README.md). + +#### Converting to / from JSON + +When using MLServer's request codecs, the output of encoding payloads will +always be one of the classes within the `mlserver.types` package (i.e. +`InferenceRequest ` or +`InferenceResponse `). +Therefore, if you want to use them with `requests` (or other package outside of +MLServer) you will need to **convert them to a Python dict or a JSON string**. + +Luckily, these classes leverage [Pydantic](https://docs.pydantic.dev/latest/) +under the hood. +Therefore you can just call the `.model_dump()` or `.model_dump_json()` method to convert them. +Likewise, to read them back from JSON, we can always pass the JSON fields as +kwargs to the class' constructor (or use any of the [other methods](https://docs.pydantic.dev/latest/usage/models/#model-properties) +available within Pydantic). + +For example, if we want to send an inference request to model `foo`, we could +do something along the following lines: + +```python +import pandas as pd +import requests + +from mlserver.codecs import PandasCodec + +dataframe = pd.DataFrame({'First Name': ["Joanne", "Michael"], 'Age': [34, 22]}) + +inference_request = PandasCodec.encode_request(dataframe) + +# raw_request will be a Python dictionary compatible with `requests`'s `json` kwarg +raw_request = inference_request.dict() + +response = requests.post("localhost:8080/v2/models/foo/infer", json=raw_request) + +# raw_response will be a dictionary (loaded from the response's JSON), +# therefore we can pass it as the InferenceResponse constructors' kwargs +raw_response = response.json() +inference_response = InferenceResponse(**raw_response) +``` + +#### Support for NaN values + +The NaN (Not a Number) value is used in Numpy and other scientific libraries to +describe an invalid or missing value (e.g. a division by zero). +In some scenarios, it may be desirable to let your models receive and / or +output NaN values (e.g. these can be useful sometimes with GBTs, like XGBoost +models). +This is why MLServer supports encoding NaN values on your request / response +payloads under some conditions. + +In order to send / receive NaN values, you must ensure that: + +- You are using the `REST` interface. +- The input / output entry containing NaN values uses either the `FP16`, `FP32` + or `FP64` datatypes. +- You are either using the [Pandas codec](#pandas-dataframe) or the [Numpy codec](#numpy-array). + +Assuming those conditions are satisfied, any `null` value within your tensor +payload will be converted to NaN. + +For example, if you take the following Numpy array: + +```python +import numpy as np + +foo = np.array([[1.2, 2.3], [np.NaN, 4.5]]) +``` + +We could encode it as: + +```json +{ + "inputs": [ + { + "name": "foo", + "parameters": { + "content_type": "np" + }, + "data": [1.2, 2.3, null, 4.5] + "datatype": "FP64", + "shape": [2, 2], + } + ] +} +``` + + +### Model Metadata + +Content types can also be defined as part of the [model's +metadata](../reference/model-settings). +This lets the user pre-configure what content types should a model use by +default to decode / encode its requests / responses, without the need to +specify it on each request. + +For example, to configure the content type values of the [example +above](#usage), one could create a `model-settings.json` file like the one +below: + +```json +{ + "parameters": { + "content_type": "pd" + }, + "inputs": [ + { + "name": "First Name", + "datatype": "BYTES", + "parameters": { + "content_type": "str" + }, + "shape": [-1], + }, + { + "name": "Age", + "datatype": "INT32", + "shape": [-1], + }, + ] +} +``` + +It's important to keep in mind that content types passed explicitly as part of +the request will always **take precedence over the model's metadata**. +Therefore, we can leverage this to override the model's metadata when needed. + +## Available Content Types + +Out of the box, MLServer supports the following list of content types. +However, this can be extended through the use of 3rd-party or custom runtimes. + +| Python Type | Content Type | Request Level | Request Codec | Input Level | Input Codec | +| ------------------------------------- | ------------ | ------------- | ------------------------------------------- | ----------- | ------------------------------- | +| [NumPy Array](#numpy-array) | `np` | ✅ | `mlserver.codecs.NumpyRequestCodec` | ✅ | `mlserver.codecs.NumpyCodec` | +| [Pandas DataFrame](#pandas-dataframe) | `pd` | ✅ | `mlserver.codecs.PandasCodec` | ❌ | | +| [UTF-8 String](#utf-8-string) | `str` | ✅ | `mlserver.codecs.string.StringRequestCodec` | ✅ | `mlserver.codecs.StringCodec` | +| [Base64](#base64) | `base64` | ❌ | | ✅ | `mlserver.codecs.Base64Codec` | +| [Datetime](#datetime) | `datetime` | ❌ | | ✅ | `mlserver.codecs.DatetimeCodec` | + +{% hint style="info" %} +MLServer allows you extend the supported content types by **adding custom +ones**. +To learn more about how to write your own custom content types, you can check +this [full end-to-end example](../examples/content-type/README.md). +You can also learn more about building custom extensions for MLServer on the +[Custom Inference Runtime section](../runtimes/custom) of the docs. +{% endhint %} + +### NumPy Array + +{% hint style="info" %} +The [V2 Inference +Protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html) expects +that the `data` of each input is sent as a **flat array**. +Therefore, the `np` content type will expect that tensors are sent flattened. +The information in the `shape` field will then be used to reshape the vector +into the right dimensions. +{% endhint %} + +The `np` content type will decode / encode V2 payloads to a NumPy Array, taking +into account the following: + +- The `datatype` field will be matched to the closest [NumPy `dtype`](https://numpy.org/doc/stable/reference/arrays.dtypes.html). +- The `shape` field will be used to reshape the flattened array expected by the + V2 protocol into the expected tensor shape. + +{% hint style="info" %} +By default, MLServer will always assume that an array with a single-dimensional +shape, e.g. `[N]`, is equivalent to `[N, 1]`. +That is, each entry will be treated like a single one-dimensional data point +(i.e. instead of a `[1, D]` array, where the full array is a single +`D`-dimensional data point). +To avoid any ambiguity, where possible, the **Numpy codec will always +explicitly encode `[N]` arrays as `[N, 1]`**. +{% endhint %} + +For example, if we think of the following NumPy Array: + +```python +import numpy as np + +foo = np.array([[1, 2], [3, 4]]) +``` + +We could encode it as the input `foo` in a V2 protocol request as: + +{% tabs %} +{% tab title="JSON payload" %} +```json +{ + "inputs": [ + { + "name": "foo", + "parameters": { + "content_type": "np" + }, + "data": [1, 2, 3, 4] + "datatype": "INT32", + "shape": [2, 2], + } + ] +} +``` +{% endtab %} + +{% tab title="NumPy Request Codec" %} +```python +from mlserver.codecs import NumpyRequestCodec + +# Encode an entire V2 request +inference_request = NumpyRequestCodec.encode_request(foo) +``` +{% endtab %} + +{% tab title="NumPy Input Codec" %} +```python +from mlserver.types import InferenceRequest +from mlserver.codecs import NumpyCodec + +# We can use the `NumpyCodec` to encode a single input head with name `foo` +# within a larger request +inference_request = InferenceRequest( + inputs=[ + NumpyCodec.encode_input("foo", foo) + ] +) +``` +{% endtab %} + +{% endtabs %} + +When using the NumPy Array content type at the **request-level**, it will decode +the entire request by considering only the first `input` element. +This can be used as a helper for models which only expect a single tensor. + +### Pandas DataFrame + +{% hint style="info" %} +The `pd` content type can be _stacked_ with other content types. +This allows the user to use a different set of content types to decode each of +the columns. +{% endhint %} + +The `pd` content type will decode / encode a V2 request into a Pandas +DataFrame. +For this, it will expect that the DataFrame is shaped in a **columnar way**. +That is, + +- Each entry of the `inputs` list (or `outputs`, in the case of + responses), will represent a column of the DataFrame. +- Each of these entires, will contain all the row elements for that particular + column. +- The `shape` field of each `input` (or `output`) entry will contain (at least) + the amount of rows included in the dataframe. + +For example, if we consider the following dataframe: + +| A | B | C | +| --- | --- | --- | +| a1 | b1 | c1 | +| a2 | b2 | c2 | +| a3 | b3 | c3 | +| a4 | b4 | c4 | + +We could encode it to the V2 Inference Protocol as: + +{% tabs %} +{% tab title="JSON Payload" %} +```json +{ + "parameters": { + "content_type": "pd" + }, + "inputs": [ + { + "name": "A", + "data": ["a1", "a2", "a3", "a4"] + "datatype": "BYTES", + "shape": [4], + }, + { + "name": "B", + "data": ["b1", "b2", "b3", "b4"] + "datatype": "BYTES", + "shape": [4], + }, + { + "name": "C", + "data": ["c1", "c2", "c3", "c4"] + "datatype": "BYTES", + "shape": [4], + }, + ] +} +``` +{% endtab %} + +{% tab title="Pandas Request Codec" %} +```python +import pandas as pd + +from mlserver.codecs import PandasCodec + +foo = pd.DataFrame({ + "A": ["a1", "a2", "a3", "a4"], + "B": ["b1", "b2", "b3", "b4"], + "C": ["c1", "c2", "c3", "c4"] +}) + +inference_request = PandasCodec.encode_request(foo) +``` +{% endtab %} +{% endtabs %} + +### UTF-8 String + +The `str` content type lets you encode / decode a V2 input into a UTF-8 +Python string, taking into account the following: + +- The expected `datatype` is `BYTES`. +- The `shape` field represents the number of "strings" that are encoded in + the payload (e.g. the `["hello world", "one more time"]` payload will have a + shape of 2 elements). + +For example, when if we consider the following list of strings: + +```python +foo = ["bar", "bar2"] +``` + +We could encode it to the V2 Inference Protocol as: + +{% tabs %} +{% tab title="JSON Payload" %} +```json +{ + "parameters": { + "content_type": "str" + }, + "inputs": [ + { + "name": "foo", + "data": ["bar", "bar2"] + "datatype": "BYTES", + "shape": [2], + } + ] +} +``` +{% endtab %} + +{% tab title="String Request Codec" %} +```python +from mlserver.codecs.string import StringRequestCodec + +# Encode an entire V2 request +inference_request = StringRequestCodec.encode_request(foo, use_bytes=False) +``` +{% endtab %} + +{% tab title="String Input Codec" %} +```python +from mlserver.types import InferenceRequest +from mlserver.codecs import StringCodec + +# We can use the `StringCodec` to encode a single input head with name `foo` +# within a larger request +inference_request = InferenceRequest( + inputs=[ + StringCodec.encode_input("foo", foo, use_bytes=False) + ] +) +``` +{% endtab %} +{% endtabs %} + +When using the `str` content type at the request-level, it will decode the +entire request by considering only the first `input` element. +This can be used as a helper for models which only expect a single string or a +set of strings. + +### Base64 + +The `base64` content type will decode a binary V2 payload into a Base64-encoded +string (and viceversa), taking into account the following: + +- The expected `datatype` is `BYTES`. +- The `data` field should contain the base64-encoded binary strings. +- The `shape` field represents the number of binary strings that are encoded in + the payload. + +For example, if we think of the following _"bytes array"_: + +```python +foo = b"Python is fun" +``` + +We could encode it as the input `foo` of a V2 request as: + +{% tabs %} + +{% tab title="JSON Payload" %} +```json +{ + "inputs": [ + { + "name": "foo", + "parameters": { + "content_type": "base64" + }, + "data": ["UHl0aG9uIGlzIGZ1bg=="] + "datatype": "BYTES", + "shape": [1], + } + ] +} +``` +{% endtab %} + +{% tab title="Base64 Input Codec" %} +```python +from mlserver.types import InferenceRequest +from mlserver.codecs import Base64Codec + +# We can use the `Base64Codec` to encode a single input head with name `foo` +# within a larger request +inference_request = InferenceRequest( + inputs=[ + Base64Codec.encode_input("foo", foo, use_bytes=False) + ] +) +``` +{% endtab %} +{% endtabs %} + +### Datetime + +The `datetime` content type will decode a V2 input into a [Python +`datetime.datetime` +object](https://docs.python.org/3/library/datetime.html#datetime.datetime), +taking into account the following: + +- The expected `datatype` is `BYTES`. +- The `data` field should contain the dates serialised following the [ISO 8601 + standard](https://en.wikipedia.org/wiki/ISO_8601). +- The `shape` field represents the number of datetimes that are encoded in + the payload. + +For example, if we think of the following `datetime` object: + +```python +import datetime + +foo = datetime.datetime(2022, 1, 11, 11, 0, 0) +``` + +We could encode it as the input `foo` of a V2 request as: + +{% tabs %} + +{% tab title="JSON Payload" %} +```json +{ + "inputs": [ + { + "name": "foo", + "parameters": { + "content_type": "datetime" + }, + "data": ["2022-01-11T11:00:00"] + "datatype": "BYTES", + "shape": [1], + } + ] +} +``` +{% endtab %} + +{% tab title="Datetime Input Codec" %} +```python +from mlserver.types import InferenceRequest +from mlserver.codecs import DatetimeCodec + +# We can use the `DatetimeCodec` to encode a single input head with name `foo` +# within a larger request +inference_request = InferenceRequest( + inputs=[ + DatetimeCodec.encode_input("foo", foo, use_bytes=False) + ] +) +``` +{% endtab %} +{% endtabs %} diff --git a/docs-gb/user-guide/custom.md b/docs-gb/user-guide/custom.md new file mode 100644 index 000000000..a3a9f7f2c --- /dev/null +++ b/docs-gb/user-guide/custom.md @@ -0,0 +1,347 @@ +# Custom Inference Runtimes + +There may be cases where the inference runtimes offered +out-of-the-box by MLServer may not be enough, or where you may need **extra +custom functionality** which is not included in MLServer (e.g. custom codecs). +To cover these cases, MLServer lets you create custom runtimes very easily. + +This page covers some of the bigger points that need to be taken into account +when extending MLServer. +You can also see this [end-to-end example](../examples/custom/README.md) which +walks through the process of writing a custom runtime. + +## Writing a custom inference runtime + +MLServer is designed as an easy-to-extend framework, encouraging users to write +their own custom runtimes easily. +The starting point for this is the `MLModel ` +abstract class, whose main methods are: + +- `load() `: + Responsible for loading any artifacts related to a model (e.g. model + weights, pickle files, etc.). +- `unload() `: + Responsible for unloading the model, freeing any resources (e.g. GPU memory, + etc.). +- `predict() `: + Responsible for using a model to perform inference on an incoming data point. + +Therefore, the _"one-line version"_ of how to write a custom runtime is to +write a custom class extending from `MLModel `, +and then overriding those methods with your custom logic. + +```python +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse + +class MyCustomRuntime(MLModel): + + async def load(self) -> bool: + # TODO: Replace for custom logic to load a model artifact + self._model = load_my_custom_model() + return True + + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + # TODO: Replace for custom logic to run inference + return self._model.predict(payload) +``` + +### Simplified interface + +MLServer exposes an alternative _"simplified" interface_ which can be used to +write custom runtimes. +This interface can be enabled by decorating your `predict()` method with the +`mlserver.codecs.decode_args` decorator. +This will let you specify in the method signature both how you want your +request payload to be decoded and how to encode the response back. + +Based on the information provided in the method signature, MLServer will +automatically decode the request payload into the different inputs specified as +keyword arguments. +Under the hood, this is implemented through [MLServer's codecs and content types +system](./content-type.md). + +{% hint style="info" %} +MLServer's _"simplified" interface_ aims to cover use cases where encoding / +decoding can be done through one of the codecs built-in into the MLServer +package. +However, there are instances where this may not be enough (e.g. variable number +of inputs, variable content types, etc.). +For these types of cases, please use MLServer's [_"advanced" +interface_](#writing-a-custom-inference-runtime), where you will have full +control over the full encoding / decoding process. +{% endhint %} + +As an example of the above, let's assume a model which + +- Takes two lists of strings as inputs: + - `questions`, containing multiple questions to ask our model. + - `context`, containing multiple contexts for each of the + questions. +- Returns a Numpy array with some predictions as the output. + +Leveraging MLServer's simplified notation, we can represent the above as the +following custom runtime: + +```python +from mlserver import MLModel +from mlserver.codecs import decode_args +from typing import List + +class MyCustomRuntime(MLModel): + + async def load(self) -> bool: + # TODO: Replace for custom logic to load a model artifact + self._model = load_my_custom_model() + return True + + @decode_args + async def predict(self, questions: List[str], context: List[str]) -> np.ndarray: + # TODO: Replace for custom logic to run inference + return self._model.predict(questions, context) +``` + +Note that, the method signature of our `predict` method now specifies: + +- The input names that we should be looking for in the request + payload (i.e. `questions` and `context`). +- The expected content type for each of the request inputs (i.e. `List[str]` on + both cases). +- The expected content type of the response outputs (i.e. `np.ndarray`). + +### Read and write headers + +{% hint style="info" %} +The `headers` field within the `parameters` section of the request / response +is managed by MLServer. +Therefore, incoming payloads where this field has been explicitly modified will +be overriden. +{% endhint %} + +There are occasions where custom logic must be made conditional to extra +information sent by the client outside of the payload. +To allow for these use cases, MLServer will map all incoming HTTP headers (in +the case of REST) or metadata (in the case of gRPC) into the `headers` field of +the `parameters` object within the `InferenceRequest` instance. + +```python +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse + +class CustomHeadersRuntime(MLModel): + + ... + + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + if payload.parameters and payload.parametes.headers: + # These are all the incoming HTTP headers / gRPC metadata + print(payload.parameters.headers) + ... +``` + +Similarly, to return any HTTP headers (in the case of REST) or metadata (in the +case of gRPC), you can append any values to the `headers` field within the +`parameters` object of the returned `InferenceResponse` instance. + +```python +from mlserver import MLModel +from mlserver.types import InferenceRequest, InferenceResponse + +class CustomHeadersRuntime(MLModel): + + ... + + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + ... + return InferenceResponse( + # Include any actual outputs from inference + outputs=[], + parameters=Parameters(headers={"foo": "bar"}) + ) +``` + +## Loading a custom MLServer runtime + +MLServer lets you load custom runtimes dynamically into a running instance of +MLServer. +Once you have your custom runtime ready, all you need to is to move it to your +model folder, next to your `model-settings.json` configuration file. + +For example, if we assume a flat model repository where each folder represents +a model, you would end up with a folder structure like the one below: + +```bash +. +└── models + └── sum-model + ├── model-settings.json + ├── models.py +``` + +Note that, from the example above, we are assuming that: + +- Your custom runtime code lives in the `models.py` file. +- The `implementation` field of your `model-settings.json` configuration file + contains the import path of your custom runtime (e.g. + `models.MyCustomRuntime`). + + ```json + { + "model": "sum-model", + "implementation": "models.MyCustomRuntime" + } + ``` + +### Loading a custom Python environment + +More often that not, your custom runtimes will depend on external 3rd party +dependencies which are not included within the main MLServer package. +In these cases, to load your custom runtime, MLServer will need access to these +dependencies. + +It is possible to load this custom set of dependencies by providing them +through an [environment tarball](../examples/conda/README.md), whose path can be +specified within your `model-settings.json` file. + +{% hint style="warning" %} +To load a custom environment, [parallel inference](./parallel-inference.md) +**must** be enabled. +{% endhint %} + +{% hint style="warning" %} +The main MLServer process communicates with workers in custom environments via +[`multiprocessing.Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue) +using pickled objects. Custom environments therefore **must** use the same +version of MLServer and a compatible version of Python with the same +[default pickle protocol](https://docs.python.org/3/library/pickle.html#pickle.DEFAULT_PROTOCOL) +as the main process. Consult the tables below for environment compatibility. +{% endhint %} + +| Status | Description | +| ------ | ------------ | +| 🔴 | Unsupported | +| 🟢 | Supported | +| 🔵 | Untested | + +| Worker Python \ Server Python | 3.9 | 3.10 | 3.11 | +| ----------------------------- | --- | ---- | ---- | +| 3.9 | 🟢 | 🟢 | 🔵 | +| 3.10 | 🟢 | 🟢 | 🔵 | +| 3.11 | 🔵 | 🔵 | 🔵 | + +If we take the [previous example](#loading-a-custom-mlserver-runtime) above as +a reference, we could extend it to include our custom environment as: + +```bash +. +└── models + └── sum-model + ├── environment.tar.gz + ├── model-settings.json + ├── models.py +``` + +Note that, in the folder layout above, we are assuming that: + +- The `environment.tar.gz` tarball contains a pre-packaged version of your + custom environment. +- The `environment_tarball` field of your `model-settings.json` configuration file + points to your pre-packaged custom environment (i.e. + `./environment.tar.gz`). + + ```json + { + "model": "sum-model", + "implementation": "models.MyCustomRuntime", + "parameters": { + "environment_tarball": "./environment.tar.gz" + } + } + ``` + +## Building a custom MLServer image + +{% hint style="info" %} +The `mlserver build` command expects that a Docker runtime is available and +running in the background. +{% endhint %} + +MLServer offers built-in utilities to help you build a custom MLServer image. +This image can contain any custom code (including custom inference runtimes), +as well as any custom environment, provided either through a +[Conda environment file](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) +or a `requirements.txt` file. + +To leverage these, we can use the `mlserver build` command. +Assuming that we're currently on the folder containing our custom inference +runtime, we should be able to just run: + +```bash +mlserver build . -t my-custom-server +``` + +The output will be a Docker image named `my-custom-server`, ready to be used. + +### Custom Environment + +The [`mlserver build`](../reference/cli.md) subcommand will search for any Conda +environment file (i.e. named either as `environment.yaml` or `conda.yaml`) and +/ or any `requirements.txt` present in your root folder. +These can be used to tell MLServer what Python environment is required in the +final Docker image. + +{% hint style="info" %} +The environment built by the `mlserver build` will be global to the whole +MLServer image (i.e. every loaded model will, by default, use that custom +environment). +For Multi-Model Serving scenarios, it may be better to use [per-model custom +environments](#loading-a-custom-python-environment) instead - which will allow +you to run multiple custom environments at the same time. +{% endhint %} + +### Default Settings + +The `mlserver build` subcommand will treat any +[`settings.json`](../reference/settings.md) or +[`model-settings.json`](../reference/model-settings.md) files present on your root +folder as the default settings that must be set in your final image. +Therefore, these files can be used to configure things like the default +inference runtime to be used, or to even include **embedded models** that will +always be present within your custom image. + +{% hint style="info" %} +Default setting values can still be overriden by external environment variables +or model-specific `model-settings.json`. +{% endhint %} + +### Custom Dockerfile + +Out-of-the-box, the `mlserver build` subcommand leverages a default +`Dockerfile` which takes into account a number of requirements, like + +- Supporting arbitrary user IDs. +- Building your [base custom environment](#custom-environment) on the fly. +- Configure a set of [default setting values](#default-settings). + +However, there may be occasions where you need to customise your `Dockerfile` +even further. +This may be the case, for example, when you need to provide extra environment +variables or when you need to customise your Docker build process (e.g. by +using other _"Docker-less"_ tools, like +[Kaniko](https://github.com/GoogleContainerTools/kaniko) or +[Buildah](https://buildah.io/)). + +To account for these cases, MLServer also includes a [`mlserver dockerfile`](../reference/cli.md) +subcommand which will just generate a `Dockerfile` (and optionally a `.dockerignore` file) +exactly like the one used by the `mlserver build` command. +This `Dockerfile` can then be customised according to your needs. + +{% hint style="info" %} +The base `Dockerfile` requires [Docker's Buildkit](https://docs.docker.com/build/buildkit/) to be enabled. +To ensure BuildKit is used, you can use the `DOCKER_BUILDKIT=1` environment +variable, e.g. + +```bash +DOCKER_BUILDKIT=1 docker build . -t my-custom-runtime:0.1.0 +``` +{% endhint %} diff --git a/docs-gb/user-guide/deployment/README.md b/docs-gb/user-guide/deployment/README.md new file mode 100644 index 000000000..65c6064c7 --- /dev/null +++ b/docs-gb/user-guide/deployment/README.md @@ -0,0 +1,14 @@ +# Deployment + +MLServer is currently used as the core Python inference server in some of most +popular Kubernetes-native serving 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/). +This allows MLServer users to leverage the usability and maturity of these +frameworks to take their model deployments to the next level of their MLOps +journey, ensuring that they are served in a robust and scalable infrastructure. + +{% hint style="info" %} +In general, it should be possible to deploy models using MLServer into **any serving engine compatible with the V2 protocol**. Alternatively, it's also possible to manage MLServer deployments manually as regular processes (i.e. in a non-Kubernetes-native way). However, this may be more involved and highly dependant on the deployment infrastructure. +{% endhint %} + +
Seldon Coreseldon-core.mdseldon-core-logo.png
KServekserve.mdkserve-logo.png
diff --git a/docs-gb/user-guide/deployment/kserve.md b/docs-gb/user-guide/deployment/kserve.md new file mode 100644 index 000000000..ad46f795f --- /dev/null +++ b/docs-gb/user-guide/deployment/kserve.md @@ -0,0 +1,132 @@ +# Deployment with KServe + +MLServer is used as the [core Python inference server](https://kserve.github.io/website/modelserving/v1beta1/sklearn/v2/) in +[KServe (formerly known as KFServing)](https://kserve.github.io/website/). +This allows for a straightforward avenue to deploy your models into a scalable +serving infrastructure backed by Kubernetes. + +{% hint style="info" %} +This section assumes a basic knowledge of KServe and Kubernetes, as well as +access to a working Kubernetes cluster with KServe installed. +To learn more about [KServe](https://kserve.github.io/website/) or +[how to install it](https://kserve.github.io/website/get_started/), please visit the +[KServe documentation](https://kserve.github.io/website/). +{% endhint %} + +## Serving Runtimes + +KServe provides built-in [serving runtimes](https://kserve.github.io/website/modelserving/v1beta1/serving_runtime/) +to deploy models trained in common ML frameworks. These allow you to deploy your models +into a robust infrastructure by just pointing to where the model artifacts are stored remotely. + +Some of these runtimes leverage MLServer as the core inference server. +Therefore, it should be straightforward to move from your local testing to your +serving infrastructure. + +### Usage + +To use any of the built-in serving runtimes offered by KServe, it should be +enough to select the relevant one your `InferenceService` manifest. + +For example, to serve a Scikit-Learn model, you could use a manifest like the +one below: + +```yaml +apiVersion: serving.kserve.io/v1beta1 +kind: InferenceService +metadata: + name: my-model +spec: + predictor: + sklearn: + protocolVersion: v2 + storageUri: gs://seldon-models/sklearn/iris +``` + +As you can see highlighted above, the `InferenceService` manifest will only +need to specify the following points: + +- The model artifact is a Scikit-Learn model. Therefore, we will use the + `sklearn` serving runtime to deploy it. +- The model will be served using the [V2 inference protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html), + which can be enabled by setting the `protocolVersion` field to `v2`. + +Once you have your `InferenceService` manifest ready, then the next step is to +apply it to your cluster. +There are multiple ways to do this, but the simplest is probably to just apply +it directly through `kubectl`, by running: + +```bash +kubectl apply -f my-inferenceservice-manifest.yaml +``` + +### Supported Serving Runtimes + +As mentioned above, KServe offers support for built-in serving runtimes, some +of which leverage MLServer as the inference server. +Below you can find a table listing these runtimes, and the MLServer inference +runtime that they correspond to. + +| Framework | MLServer Runtime | KServe Serving Runtime | Documentation | +| ------------ | ------------------------------------------ | ---------------------- | -------------------------------------------------------------------------------------------- | +| Scikit-Learn | [MLServer SKLearn](../../runtimes/sklearn.md) | `sklearn` | [SKLearn Serving Runtime](https://kserve.github.io/website/modelserving/v1beta1/sklearn/v2/) | +| XGBoost | [MLServer XGBoost](../../runtimes/xgboost.md) | `xgboost` | [XGBoost Serving Runtime](https://kserve.github.io/website/modelserving/v1beta1/xgboost/) | + +Note that, on top of the ones shown above (backed by MLServer), KServe also +provides a [wider set](https://kserve.github.io/website/modelserving/v1beta1/serving_runtime/) of +serving runtimes. +To see the full list, please visit the [KServe documentation](https://kserve.github.io/website/modelserving/v1beta1/serving_runtime/). + +## Custom Runtimes + +Sometimes, the serving runtimes built into KServe may not be enough for our use case. +The framework provided by MLServer makes it easy to [write custom runtimes](../../runtimes/custom.md), +which can then get packaged up as images. These images then become self-contained model +servers with your custom runtime. Therefore, it's easy to deploy them into your serving infrastructure leveraging +KServe support for [custom runtimes](https://kserve.github.io/website/modelserving/v1beta1/custom/custom_model/#deploy-the-custom-predictor-on-kserve). + +### Usage + +The `InferenceService` manifest gives you full control over the containers used +to deploy your machine learning model. This can be leveraged to point your deployment to the +[custom MLServer image containing your custom logic](../../runtimes/custom.md). +For example, if we assume that our custom image has been tagged as +`my-custom-server:0.1.0`, we could write an `InferenceService` manifest like +the one below: + +```yaml +apiVersion: serving.kserve.io/v1beta1 +kind: InferenceService +metadata: + name: my-model +spec: + predictor: + containers: + - name: classifier + image: my-custom-server:0.1.0 + env: + - name: PROTOCOL + value: v2 + ports: + - containerPort: 8080 + protocol: TCP +``` + +As we can see highlighted above, the main points that we'll need to take into +account are: + +- Pointing to our custom MLServer `image` in the custom container section of + our `InferenceService`. +- Explicitly choosing the [V2 inference protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html) to + serve our model. +- Let KServe know what port will be exposed by our custom container to send + inference requests. + +Once you have your `InferenceService` manifest ready, then the next step is to +apply it to your cluster. +There are multiple ways to do this, but the simplest is probably to just apply +it directly through `kubectl`, by running: + +```bash +kubectl apply -f my-inferenceservice-manifest.yaml +``` diff --git a/docs-gb/user-guide/deployment/seldon-core.md b/docs-gb/user-guide/deployment/seldon-core.md new file mode 100644 index 000000000..9b72b3256 --- /dev/null +++ b/docs-gb/user-guide/deployment/seldon-core.md @@ -0,0 +1,146 @@ +# Deployment with Seldon Core + +MLServer is used as the [core Python inference server](https://docs.seldon.io/projects/seldon-core/en/latest/graph/protocols.html#v2-kfserving-protocol) +in [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/index.html). +Therefore, it should be straightforward to deploy your models either by using one of the +[built-in pre-packaged servers](https://docs.seldon.io/projects/seldon-core/en/latest/workflow/overview.html#two-types-of-model-servers) +or by pointing to a [custom image of MLServer](../../runtimes/custom.md). + +{% hint style="info" %} +This section assumes a basic knowledge of Seldon Core and Kubernetes, as well +as access to a working Kubernetes cluster with Seldon Core installed. +To learn more about [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/) or +[how to install it](https://docs.seldon.io/projects/seldon-core/en/latest/nav/installation.html), +please visit the [Seldon Core documentation](https://docs.seldon.io/projects/seldon-core/en/latest/index.html). +{% endhint %} + +## Pre-packaged Servers + +Out of the box, Seldon Core comes a few MLServer runtimes pre-configured to run straight away. +This allows you to deploy a MLServer instance by just pointing to where your +model artifact is and specifying what ML framework was used to train it. + +### Usage + +To let Seldon Core know what framework was used to train your model, you can +use the `implementation` field of your `SeldonDeployment` manifest. +For example, to deploy a Scikit-Learn artifact stored remotely in GCS, one +could do: + +```yaml +apiVersion: machinelearning.seldon.io/v1 +kind: SeldonDeployment +metadata: + name: my-model +spec: + protocol: v2 + predictors: + - name: default + graph: + name: classifier + implementation: SKLEARN_SERVER + modelUri: gs://seldon-models/sklearn/iris +``` + +As you can see highlighted above, all that we need to specify is that: + +- Our **inference deployment should use the [V2 inference protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html)**, + which is done by **setting the `protocol` field to `kfserving`**. +- Our **model artifact is a serialised Scikit-Learn model**, therefore it + should be served using the [MLServer SKLearn runtime](../../runtimes/sklearn.md), + which is done by **setting the `implementation` field to `SKLEARN_SERVER`**. + +Note that, while the `protocol` should always be set to `kfserving` (i.e. so +that models are served using the [V2 inference protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html)), the +value of the `implementation` field will be dependant on your ML framework. +The valid values of the `implementation` field are [pre-determined by Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/graph/protocols.html#v2-kfserving-protocol). +However, it should also be possible to [configure and add new ones](https://docs.seldon.io/projects/seldon-core/en/latest/servers/custom.html#adding-a-new-inference-server) +(e.g. to support a [custom MLServer runtime](../../runtimes/custom.md)). + +Once you have your `SeldonDeployment` manifest ready, then the next step is to +apply it to your cluster. +There are multiple ways to do this, but the simplest is probably to just apply +it directly through `kubectl`, by running: + +```bash +kubectl apply -f my-seldondeployment-manifest.yaml +``` + +To consult the supported values of the `implementation` field where MLServer is +used, you can check the support table below. + +### Supported Pre-packaged Servers + +As mentioned above, pre-packaged servers come built-in into Seldon Core. +Therefore, only a pre-determined subset of them will be supported for a given +release of Seldon Core. + +The table below shows a list of the currently supported values of the +`implementation` field. +Each row will also show what ML framework they correspond to and also what +MLServer runtime will be enabled internally on your model deployment when used. + +| Framework | MLServer Runtime | Seldon Core Pre-packaged Server | Documentation | +| ------------ | ------------------------------------------------ | ------------------------------- | -------------------------------------------------------------------------------------------- | +| Scikit-Learn | [MLServer SKLearn](../../runtimes/sklearn.md) | `SKLEARN_SERVER` | [SKLearn Server](https://docs.seldon.io/projects/seldon-core/en/latest/servers/sklearn.html) | +| XGBoost | [MLServer XGBoost](../../runtimes/xgboost.md) | `XGBOOST_SERVER` | [XGBoost Server](https://docs.seldon.io/projects/seldon-core/en/latest/servers/xgboost.html) | +| MLflow | [MLServer MLflow](../../runtimes/mlflow.md) | `MLFLOW_SERVER` | [MLflow Server](https://docs.seldon.io/projects/seldon-core/en/latest/servers/mlflow.html) | + +Note that, on top of the ones shown above (backed by MLServer), Seldon Core +**also provides a [wider set](https://docs.seldon.io/projects/seldon-core/en/latest/nav/config/servers.html)** +of pre-packaged servers. To check the full list, please visit the +[Seldon Core documentation](https://docs.seldon.io/projects/seldon-core/en/latest/nav/config/servers.html). + +## Custom Runtimes + +There could be cases where the pre-packaged MLServer runtimes supported +out-of-the-box in Seldon Core may not be enough for our use case. +The framework provided by MLServer makes it easy to [write custom runtimes](../../runtimes/custom.md), +which can then get packaged up as images. These images then become self-contained +model servers with your custom runtime. Therefore Seldon Core makes it as easy +to deploy them into your serving infrastructure. + +### Usage + +The `componentSpecs` field of the `SeldonDeployment` manifest will allow us to +let Seldon Core know what image should be used to serve a custom model. +For example, if we assume that our custom image has been tagged as +`my-custom-server:0.1.0`, we could write our `SeldonDeployment` manifest as +follows: + +```yaml +apiVersion: machinelearning.seldon.io/v1 +kind: SeldonDeployment +metadata: + name: my-model +spec: + protocol: v2 + predictors: + - name: default + graph: + name: classifier + componentSpecs: + - spec: + containers: + - name: classifier + image: my-custom-server:0.1.0 +``` + +As we can see highlighted on the snippet above, all that's needed to deploy a +custom MLServer image is: + +- Letting Seldon Core know that the model deployment will be served through the + [V2 inference protocol](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/v2-protocol.html)) by + setting the `protocol` field to `v2`. +- Pointing our model container to use our **custom MLServer image**, by + specifying it on the `image` field of the `componentSpecs` section of the + manifest. + +Once you have your `SeldonDeployment` manifest ready, then the next step is to +apply it to your cluster. +There are multiple ways to do this, but the simplest is probably to just apply +it directly through `kubectl`, by running: + +```bash +kubectl apply -f my-seldondeployment-manifest.yaml +``` diff --git a/docs-gb/user-guide/metrics.md b/docs-gb/user-guide/metrics.md new file mode 100644 index 000000000..f91d38bf4 --- /dev/null +++ b/docs-gb/user-guide/metrics.md @@ -0,0 +1,123 @@ +# Metrics + +Out-of-the-box, MLServer exposes a set of metrics that help you monitor your +machine learning workloads in production. +These include standard metrics like number of requests and latency. + +On top of these, you can also register and track your own [custom metrics](#custom-metrics) +as part of your [custom inference runtimes](./custom.md). + +## Default Metrics + +By default, MLServer will expose metrics around inference requests (count and +error rate) and the status of its internal requests queues. +These internal queues are used for [adaptive batching](./adaptive-batching.md) and +[communication with the inference workers](./parallel-inference.md). + +| Metric Name | Description | +| ----------------------------- | ------------------------------------------------------------------- | +| `model_infer_request_success` | Number of successful inference requests. | +| `model_infer_request_failure` | Number of failed inference requests. | +| `batch_request_queue` | Queue size for the [adaptive batching](./adaptive-batching.md) queue. | +| `parallel_request_queue` | Queue size for the [inference workers](./parallel-inference.md) queue. | + +### REST Server Metrics + +On top of the default set of metrics, MLServer's REST server will also expose a +set of metrics specific to REST. + +{% hint style="info" %} +The prefix for the REST-specific metrics will be dependent on the +`metrics_rest_server_prefix` flag from the [MLServer settings](#settings). +{% endhint %} + +| Metric Name | Description | +| ----------------------------------------- | -------------------------------------------------------------- | +| `[rest_server]_requests` | Number of REST requests, labelled by endpoint and status code. | +| `[rest_server]_requests_duration_seconds` | Latency of REST requests. | +| `[rest_server]_requests_in_progress` | Number of in-flight REST requests. | + +### gRPC Server Metrics + +On top of the default set of metrics, MLServer's gRPC server will also expose a +set of metrics specific to gRPC. + +| Metric Name | Description | +| --------------------- | ---------------------------------------------------------- | +| `grpc_server_handled` | Number of gRPC requests, labelled by gRPC code and method. | +| `grpc_server_started` | Number of in-flight gRPC requests. | + +## Custom Metrics + +MLServer allows you to register custom metrics within your custom inference +runtimes. +This can be done through the `mlserver.register()` and `mlserver.log()` +methods. + +- `mlserver.register`: Register a new metric. +- `mlserver.log`: + Log a new set of metric / value pairs. + If there's any unregistered metric, it will get registered on-the-fly. + +{% hint style="info" %} +Under the hood, metrics logged through the `mlserver.log` method will get +exposed to Prometheus as a Histogram. +{% endhint %} + +Custom metrics will generally be registered in the `load() +` method and then used in the `predict() +` method of your [custom runtime](./custom). + +```python +import mlserver + +from mlserver.types import InferenceRequest, InferenceResponse + +class MyCustomRuntime(mlserver.MLModel): + async def load(self) -> bool: + self._model = load_my_custom_model() + mlserver.register("my_custom_metric", "This is a custom metric example") + return True + + async def predict(self, payload: InferenceRequest) -> InferenceResponse: + mlserver.log(my_custom_metric=34) + # TODO: Replace for custom logic to run inference + return self._model.predict(payload) +``` + +## Metrics Labelling + +For metrics specific to a model (e.g. [custom metrics](#custom-metrics), +request counts, etc), MLServer will always label these with the **model name** +and **model version**. +Downstream, this will allow to aggregate and query metrics per model. + +{% hint style="info" %} +If these labels are not present on a specific metric, this means that those +metrics can't be sliced at the model level. +{% endhint %} + +Below, you can find the list of standardised labels that you will be able to +find on model-specific metrics: + +| Label Name | Description | +| --------------- | ----------------------------------- | +| `model_name` | Model Name (e.g. `my-custom-model`) | +| `model_version` | Model Version (e.g. `v1.2.3`) | + +## Settings + +MLServer will expose metric values through a metrics endpoint exposed on its +own metric server. +This endpoint can be polled by [Prometheus](https://prometheus.io/) or other +[OpenMetrics](https://openmetrics.io/)-compatible backends. + +Below you can find the [settings](../reference/settings.md) available to control +the behaviour of the metrics server: + +| Label Name | Description | Default | +| ----------- | ------------ | ------------ | +| `metrics_endpoint` | Path under which the metrics endpoint will be exposed. | `/metrics` | +| `metrics_port` | Port used to serve the metrics server. | `8082` | +| `metrics_rest_server_prefix` | Prefix used for metric names specific to MLServer's REST inference interface. | `rest_server` | +| `metrics_dir` | Directory used to store internal metric files (used to support metrics sharing across [inference workers](./parallel-inference.md)). This is equivalent to Prometheus' [`$PROMETHEUS_MULTIPROC_DIR`](https://github.com/prometheus/client_python/tree/master#multiprocess-mode-eg-gunicorn) env var. | MLServer's current working directory (i.e. `$PWD`) | diff --git a/docs-gb/user-guide/openapi.md b/docs-gb/user-guide/openapi.md new file mode 100644 index 000000000..b43a92555 --- /dev/null +++ b/docs-gb/user-guide/openapi.md @@ -0,0 +1,47 @@ +# OpenAPI Support + +MLServer follows the Open Inference Protocol (previously known as the "V2 +Protocol"). +You can find the full OpenAPI spec for the Open Inference Protocol in the links +below: + +| Name | Description | OpenAPI Spec | +| -------------------------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- | +| Open Inference Protocol | Main dataplane for inference, health and metadata | [dataplane.json](https://github.com/SeldonIO/mlserver/openapi/dataplane.json) | +| Model Repository Extension | Extension to the protocol to provide a control plane which lets you load / unload models dynamically | [model_repository.json](https://github.com/SeldonIO/mlserver/openapi/model_repository.json) | + +## Swagger UI + +On top of the OpenAPI spec above, MLServer also autogenerates a Swagger UI +which can be used to interact dynamycally with the Open Inference Protocol. + +The autogenerated Swagger UI can be accessed under the `/v2/docs` endpoint. + +{% hint style="info" %} +Besides the Swagger UI, you can also access the _raw_ OpenAPI spec through the +`/v2/docs/dataplane.json` endpoint. +{% endhint %} + +![](../images/swagger-ui.png) + +## Model Swagger UI + +Alongside the [general API documentation](#Swagger-UI), MLServer will also +autogenerate a Swagger UI tailored to individual models, showing the endpoints +available for each one. + +The model-specific autogenerated Swagger UI can be accessed under the following +endpoints: + +- `/v2/models/{model_name}/docs` +- `/v2/models/{model_name}/versions/{model_version}/docs` + +{% hint style="info" %} +Besides the Swagger UI, you can also access the model-specific _raw_ OpenAPI spec through +the following endpoints: + +- `/v2/models/{model_name}/docs/dataplane.json` +- `/v2/models/{model_name}/versions/{model_version}/docs/dataplane.json` +{% endhint %} + +![](../images/swagger-ui-model.png) diff --git a/docs-gb/user-guide/parallel-inference.md b/docs-gb/user-guide/parallel-inference.md new file mode 100644 index 000000000..8a7c583b1 --- /dev/null +++ b/docs-gb/user-guide/parallel-inference.md @@ -0,0 +1,82 @@ +# Parallel Inference + +Out of the box, MLServer includes support to offload inference workloads to a +pool of workers running in separate processes. +This allows MLServer to scale out beyond the limitations of the Python +interpreter. +To learn more about why this can be beneficial, you can check the [concurrency +section](#concurrency-in-python) below. + +![](../images/parallel-inference.svg) + +By default, MLServer will spin up a pool with only one worker process to run +inference. +All models will be loaded uniformly across the inference pool workers. +To read more about advanced settings, please see the [usage section +below](#usage). + +## Concurrency in Python + +The [Global Interpreter Lock (GIL)](https://wiki.python.org/moin/GlobalInterpreterLock) +is a mutex lock that exists in most Python interpreters (e.g. CPython). +Its main purpose is to lock Python’s execution so that it only runs on a single +processor at the same time. +This simplifies certain things to the interpreter. +However, it also adds the limitation that a **single Python process will never +be able to leverage multiple cores**. + +When we think about MLServer's support for [Multi-Model Serving (MMS)](../examples/mms/README.md), +this could lead to scenarios where a +**heavily-used model starves the other models** running within the same +MLServer instance. +Similarly, even if we don’t take MMS into account, the **GIL also makes it harder +to scale inference for a single model**. + +To work around this limitation, MLServer offloads the model inference to a pool +of workers, where each worker is a separate Python process (and thus has its +own separate GIL). +This means that we can get full access to the underlying hardware. + +### Overhead + +Managing the Inter-Process Communication (IPC) between the main MLServer +process and the inference pool workers brings in some overhead. +Under the hood, MLServer uses the `multiprocessing` library to implement the +distributed processing management, which has been shown to offer the smallest +possible overhead when implementing these type of distributed strategies +{cite}`zhiFiberPlatformEfficient2020`. + +The extra overhead introduced by other libraries is usually brought in as a +trade off in exchange of other advanced features for complex distributed +processing scenarios. +However, MLServer's use case is simple enough to not require any of these. + +Despite the above, even though this overhead is minimised, this **it can still +be particularly noticeable for lightweight inference methods**, where the extra +IPC overhead can take a large percentage of the overall time. +In these cases (which can only be assessed on a model-by-model basis), the user +has the option to [disable the parallel inference feature](#usage). + +For regular models where inference can take a bit more time, this overhead is +usually offset by the benefit of having multiple cores to compute inference on. + +## Usage + +By default, MLServer will always create an inference pool with one single worker. +The number of workers (i.e. the size of the inference pool) can be adjusted +globally through the server-level `parallel_workers` setting. + +### `parallel_workers` + +The `parallel_workers` field of the `settings.json` file (or alternatively, the +`MLSERVER_PARALLEL_WORKERS` global environment variable) controls the size of +MLServer's inference pool. +The expected values are: + +- `N`, where `N > 0`, will create a pool of `N` workers. +- `0`, will disable the parallel inference feature. + In other words, inference will happen within the main MLServer process. + +## References + +Jiale Zhi, Rui Wang, Jeff Clune, and Kenneth O. Stanley. Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods. arXiv:2003.11164 [cs, stat], March 2020. [arXiv:2003.11164](https://arxiv.org/abs/2003.11164). \ No newline at end of file diff --git a/docs-gb/user-guide/streaming.md b/docs-gb/user-guide/streaming.md new file mode 100644 index 000000000..2bae71a26 --- /dev/null +++ b/docs-gb/user-guide/streaming.md @@ -0,0 +1,44 @@ +# Streaming + +Out of the box, MLServer includes support for streaming data to your models. Streaming support is available for +both the REST and gRPC servers. + + +## REST Server + +Streaming support for the REST server is limited only to server streaming. This means that the client sends +a single request to the server, and the server responds with a stream of data. + +The streaming endpoints are available for both the `infer` and `generate` methods through the following endpoints: + +- `/v2/models/{model_name}/versions/{model_version}/infer_stream` +- `/v2/models/{model_name}/infer_stream` +- `/v2/models/{model_name}/versions/{model_version}/generate_stream` +- `/v2/models/{model_name}/generate_stream` + +Note that for REST, the `generate` and `generate_stream` endpoints are aliases for the `infer` and `infer_stream` +endpoints, respectively. Those names are used to better reflect the nature of the operation for Large Language Models (LLMs). + + +## gRPC Server + +Streaming support for the gRPC server is available for both client and server streaming. This means that the +client sends a stream of data to the server, and the server responds with a stream of data. + +The two streams operate independently, so the client and the server can read and write data however they want +(e.g., the server could either wait to receive all the client messages before sending a response or it can send +a response after each message). Note that bi-directional streaming covers all the possible combinations of client +and server streaming: unary-stream, stream-unary, and stream-stream. The unary-unary case can be covered as well +by the bi-directional streaming, but `mlserver` already has the `predict` method dedicated to this use case. The +logic for how the requests are received, and processed, and the responses are sent back should be built into the runtime logic. + +The stub method for streaming to be used by the client is `ModelStreamInfer`. + + +## Limitation + +There are three main limitations of the streaming support in MLServer: + +- the `parallel_workers` setting should be set to `0` to disable distributed workers (to be addressed in future releases) +- for REST, the `gzip_enabled` setting should be set to `false` to disable GZIP compression, as streaming is not +compatible with GZIP compression (see issue [here]( https://github.com/encode/starlette/issues/20#issuecomment-704106436))