Merge pull request #209 from antoinejeannot/release-v0.1.0

Release v0.1.0

.github/workflows/docs.yml

@@ -43,8 +43,8 @@ jobs:
 
       - name: Push Coverage Report
         run: |
-          git config user.name github-actions
-          git config user.email [email protected]
-          git add coverage
-          git commit -m "add coverage report"
-          git push
+            git config user.name github-actions
+            git config user.email [email protected]
+            git add coverage
+            git diff --staged --quiet || git commit -m "add coverage report"
+            git push

README.md

@@ -71,17 +71,9 @@ pip install modelkit
 
 Optional dependencies are available for remote storage providers ([see documentation](https://cornerstone-ondemand.github.io/modelkit/assets/storage_provider/#using-different-providers))
 
-### 🚧 Beta release
+`modelkit >= 0.1` is now be shipped with `pydantic 2`, bringing significant performance improvements 🎉 ⚡
 
-`modelkit 0.1` and onwards will be shipped with `pydantic 2`, bringing significant performance improvements 🎉 ⚡
-
-To try out the beta before it is stable:
-
-```
-pip install --pre modelkit
-```
-
-Also, you can refer to the [modelkit migration note](https://cornerstone-ondemand.github.io/modelkit/migration.md)
+You can refer to the [modelkit migration note](https://cornerstone-ondemand.github.io/modelkit/migration)
  to ease the migration process!
 
 ## Community

docs/index.md

@@ -1,7 +1,7 @@
 <p align="center">
-  <a href="https://github.com/Cornerstone-OnDemand/modelkit">
-    <img src="https://raw.githubusercontent.com/Cornerstone-OnDemand/modelkit/main/.github/resources/logo.svg" alt="Logo" width="80" height="80">
-  </a>
+  <a href="https://github.com/cornerstone-ondemand/modelkit">
+    <img src="https://raw.githubusercontent.com/cornerstone-ondemand/modelkit/main/.github/resources/logo.svg" alt="Logo" width="80" height="80">
+</a>
 </p>
 <h1 align="center"> modelkit </h1>
 <p align="center">
@@ -20,28 +20,73 @@
   <a href="https://github.com/Cornerstone-OnDemand/modelkit/graphs/contributors"><img src="https://img.shields.io/github/contributors/Cornerstone-OnDemand/modelkit" /></a>
 </p>
 
+`modelkit` is a minimalist yet powerful MLOps library for Python, built for people who want to deploy ML models to production.
 
-`modelkit` is a Python framework meant to make your ML models robust, reusable and performant in all situations you need to use them.
+It packs several features which make your go-to-production journey a breeze, and ensures that the same exact code will run in production, on your machine, or on data processing pipelines.
 
-It is meant to bridge the gap between the different uses of your algorithms. With `modelkit` you can ensure that the same exact code will run in production, on your machine, or on data processing pipelines.
+## Quickstart
 
-## Features
+`modelkit` provides a straightforward and consistent way to wrap your prediction  code in a `Model` class:
 
-`modelkit`'s key features are:
+```python
+from modelkit import Model
 
-- **simple** `modelkit` is just a Python library, use pip to install it and you are done.
-- **custom** `modelkit` is useful whenever you need to go beyond off-the-shelf models: custom processing, heuristics, business logic, different frameworks, etc.
-- **framework agnostic** you bring your own framework to the table, and you can use whatever code or library you want. Similarly, `modelkit` is not opinionated about how you build or train your models.
-- **organized** `modelkit` encourages you to version and share you ML library and artifacts with others, as a Python package or as a service. Let others use and evaluate your models!
-- **fast** `modelkit` add minimal overhead to prediction calls. Model predictions can be batched for speed (you define the batching logic).
-- **fast to code** Models only need to define their prediction logic and that's it. No cumbersome pre or postprocessing logic, branching options, etc... The boilerplate code is minimal and extensible.
-- **fast to deploy** Models can be served in a single CLI call using [fastapi](https://fastapi.tiangolo.com/)
+class MyModel(Model):
+    def _predict(self, item):
+        # This is where your prediction logic goes
+        ...
+        return result
+```
+
+Be sure to check out our tutorials in the [documentation](https://cornerstone-ondemand.github.io/modelkit/).
+
+## Features
 
-And more:
+Wrapping your prediction code in `modelkit` instantly gives acces to all features:
 
+- **fast** Model predictions can be batched for speed (you define the batching logic) with minimal overhead.
 - **composable** Models can depend on other models, and evaluate them however you need to
 - **extensible** Models can rely on arbitrary supporting configurations files called _assets_ hosted on local or cloud object stores
 - **type-safe** Models' inputs and outputs can be validated by [pydantic](https://pydantic-docs.helpmanual.io/), you get type annotations for your predictions and can catch errors with static type analysis tools during development.
 - **async** Models support async and sync prediction functions. `modelkit` supports calling async code from sync code so you don't have to suffer from partially async code.
 - **testable** Models carry their own unit test cases, and unit testing fixtures are available for [pytest](https://docs.pytest.org/en/6.2.x/)
-- **robust** `modelkit` helps you follow software development best practices: all configurations and artifacts are explicitly versioned and tested.
+- **fast to deploy** Models can be served in a single CLI call using [fastapi](https://fastapi.tiangolo.com/)
+
+In addition, you will find that `modelkit` is:
+
+- **simple** Use pip to install `modelkit`, it is just a Python library.
+- **robust** Follow software development best practices: version and test all your configurations and artifacts.
+- **customizable** Go beyond off-the-shelf models: custom processing, heuristics, business logic, different frameworks, etc.
+- **framework agnostic** Bring your own framework to the table, and use whatever code or library you want. `modelkit` is not opinionated about how you build or train your models.
+- **organized** Version and share you ML library and artifacts with others, as a Python package or as a service. Let others use and evaluate your models!
+- **fast to code** Just write the prediction logic and that's it. No cumbersome pre or postprocessing logic, branching options, etc... The boilerplate code is minimal and sensible.
+
+## Installation
+
+Install the latest stable release with `pip`:
+
+```
+pip install modelkit
+```
+
+Optional dependencies are available for remote storage providers ([see documentation](https://cornerstone-ondemand.github.io/modelkit/assets/storage_provider/#using-different-providers))
+
+`modelkit >= 0.1` is now be shipped with `pydantic 2`, bringing significant performance improvements 🎉 ⚡
+
+You can refer to the [modelkit migration note](https://cornerstone-ondemand.github.io/modelkit/migration)
+ to ease the migration process!
+
+## Community
+Join our [community](https://discord.gg/ayj5wdAArV) on Discord to get support and leave feedback
+
+### Local install
+
+Contributors, if you want to install and test locally:
+
+```
+# install
+make setup
+
+# lint & test
+make tests
+```

docs/migration.md

@@ -7,14 +7,14 @@ Modelkit 0.1 and onwards will be shipped with `pydantic 2`, which comes with __s
 Details on how to migrate to `pydantic 2` are available in the corresponding migration guide: https://docs.pydantic.dev/latest/migration/
 
 ### Installation
-To install and try out the `modelkit 0.1.0.bX` beta before its stable release:
+To install the brand new stable release of modelkit:
 ```
-pip install --pre modelkit
+pip install modelkit --upgrade
 ```
 
 ### Known breaking changes
 
-Some breaking changes are arising while upgrading to `pydantic 2` and the new `modelkit 0.1 beta`. Here is a brief, rather exhaustive, list of the encountered issues or dropped features.
+Some breaking changes are arising while upgrading to `pydantic 2` and the new `modelkit 0.1`. Here is a brief, rather exhaustive, list of the encountered issues or dropped features.
 
 #### Drop: implicit pydantic model conversion
 
@@ -57,7 +57,7 @@ Fixes: None, just prepare to have your inputs / outputs validated :)
 
 ### Development Workflows
 
-The beta release, along with subsequent patches, will be pushed to the main branch. Prior to the stable release, tags will adopt the format `0.1.0.bX`
+`modelkit 0.1` (and forward) changes will be pushed to the main branch.
 
 For projects that have not migrated, `modelkit 0.0` will continue to receive maintenance on the `v0.0-maintenance` branch. Releases on PyPI and manual tags will adhere to the usual process.
 

mkdocs.yml

@@ -4,6 +4,7 @@ site_description: "Library for machine learning models"
 
 nav:
   - Home: "index.md"
+  - Migration note: "migration.md"
   - Developer guide:
       - "library/overview.md"
       - Models:

modelkit/__init__.py

@@ -8,4 +8,4 @@
 warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
-__version__ = "0.1.0.b1"
+__version__ = "0.1.0"