V1

.github/workflows/tests.yml

@@ -0,0 +1,47 @@
+name: Tests
+
+on:
+  push:
+  pull_request:
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  # Ruff and mypy run inside every matrix entry rather than in a job of their own:
+  # mypy's result depends on the installed dependency versions, so a single
+  # environment cannot speak for the whole support range.
+  #
+  # The matrix lives in ci/matrix.json so that this workflow and
+  # `python scripts/run_matrix.py` cannot describe different environments.
+  matrix:
+    name: Load matrix
+    runs-on: ubuntu-latest
+    outputs:
+      include: ${{ steps.load.outputs.include }}
+    steps:
+      - uses: actions/checkout@v5
+      - id: load
+        run: echo "include=$(jq -c '.include' ci/matrix.json)" >> "$GITHUB_OUTPUT"
+
+  pytest:
+    name: ${{ matrix.name }}
+    needs: matrix
+    runs-on: ubuntu-latest
+    continue-on-error: ${{ matrix.allow_failure == true }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include: ${{ fromJson(needs.matrix.outputs.include) }}
+
+    steps:
+      - uses: actions/checkout@v5
+      - name: Install uv and Python ${{ matrix.python }}
+        uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9 # v9.0.0
+        with:
+          python-version: ${{ matrix.python }}
+      # The same entry point a developer runs locally, so a green CI leg and a green
+      # local run mean the same thing.
+      - name: Run ${{ matrix.name }}
+        run: python scripts/run_matrix.py ${{ matrix.name }} --verbose

.gitignore

@@ -129,6 +129,9 @@ dmypy.json
 .pyre/
 *.onnx
 *.falcon
+*.fnnx
+benchmark-results*.json
+tmp.*
 test.ipynb
 falcon/dev_tmp.py
 Untitled.ipynb

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.analysis.typeCheckingMode": "off",
+}

README.md

@@ -12,12 +12,7 @@ Falcon is a lightweight python library that allows to train production-ready mac
 
 - Simplicity: With Falcon, training a comprehensive Machine Learning pipeline is as easy as writing a single line of code.
 - Flexibility: Falcon offers a range of pre-set configurations, enabling swift interchangeability of internal components with just a minor parameter change.
-- Extendability: Falcon's modular design, along with its extension registration procedure, allows seamless integration with virtually any framework.
-- Portability: A standout feature of Falcon is its deep native support for [ONNX](https://onnx.ai/) models. This lets you export complex pipelines into a single ONNX graph, irrespective of the underlying frameworks. As a result, your model can be conveniently deployed on any platform or with almost any programming language, all without dependence on the training environment.
-
-## Future Developments 🔮
-
-Falcon ML is under active development. We've already implemented a robust and production-ready core functionality, but there's much more to come. We plan to introduce many new features by the end of the year, so stay tuned!
+- Portability: A standout feature of Falcon is its deep native support for [FNNX](https://github.com/BeastByteAI/FNNX)/[ONNX](https://onnx.ai/) models. This lets you export complex pipelines into a single production-ready file, irrespective of the underlying frameworks. As a result, your model can be conveniently deployed without any dependency on the training environment.
 
 ⭐ If you liked the project, please support us with a star!
 
@@ -55,26 +50,14 @@ Latest version from [GitHub](https://github.com/OKUA1/falcon)
 pip install git+https://github.com/OKUA1/falcon
 ```
 
-Installing some of the dependencies on **Apple Silicon Macs** might not work, the workaround is to create an X86 environment using [Conda](https://docs.conda.io/en/latest/)
+Optional extras add the FNNX runtime, the gradient boosting candidates and hyperparameter search.
 
-```bash 
-conda create -n falcon_env
-conda activate falcon_env
-conda config --env --set subdir osx-64
-conda install python=3.9
-pip3 install falcon-ml
+```bash
+pip install "falcon-ml[runtime]"
+pip install "falcon-ml[gbdt]"
+pip install "falcon-ml[hpo]"
 ```
 
 ## Documentation 📚
-You can find a more detailed guide as well as an API reference in our [official docs](https://beastbyteai.github.io/falcon/intro.html#).
-
-## Authors & Contributors ✨
-<table>
-  <tbody>
-    <tr>
-      <td align="center"><a href="https://www.linkedin.com/in/oleh-kostromin-b671a4157/"><img src="https://avatars.githubusercontent.com/u/48349467?v=4" width="100px;" alt=""/><br /><sub><b>Oleg Kostromin</b></sub></a><br /></td>
-      <td align="center"><a href="https://www.linkedin.com/in/iryna-kondrashchenko-673800155/"><img src="https://avatars.githubusercontent.com/u/72279145?v=4" width="100px;" alt=""/><br /><sub><b>Iryna Kondrashchenko</b></sub></a><br /></td>
-      <td align="center"><a href="https://www.linkedin.com/in/pasinimarco/"><img src="https://avatars.githubusercontent.com/u/50598094?v=4" width="100px;" alt=""/><br /><sub><b>Marco Pasini</b></sub></a><br /></td>
-    </tr>
-  </tbody>
-</table>
+The [user guide](docs/guide.md) covers configuration, evaluation, export and inference.
+

benchmarks/__init__.py

@@ -0,0 +1 @@
+