chore: add python 3.11 support

.github/workflows/main-check.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-22.04]
-        python: ["3.8", "3.9", "3.10"]
+        python: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v3

.github/workflows/validate-pr.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-22.04]
-        python: ["3.8", "3.9", "3.10"]
+        python: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v3

.github/workflows/windows-subprocess-check.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         os: [windows-latest]
-        python: ["3.8", "3.9", "3.10"]
+        python: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v3

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## \[Unreleased\]
 
+### Added
+
+- Support on Python 3.11 ([#169](https://github.com/Substra/substrafl/pull/169))
+
 ## [0.41.1](https://github.com/Substra/substrafl/releases/tag/0.41.1) - 2023-10-06
 
 ### Fixed

benchmark/camelyon/README.md

@@ -19,7 +19,7 @@ Being able to compare substrafl local speed to a full torch example with a varia
 
 **All the commands should be run from `benchmark/camelyon`**
 
-In a new python 3.10 environment :
+In a new python 3.11 environment :
 
 ```sh
 pip install -r requirements.txt
@@ -33,13 +33,13 @@ Please ensure that your python installation is complete. For Mac users if you ge
 UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.
 ```
 
-You'll need to uninstall python 3.10, and install the `xz` package :
+You'll need to uninstall python 3.11, and install the `xz` package :
 
 ```sh
 brew install xz
 ```
 
-then reinstall python 3.10.
+then reinstall python 3.11.
 
 ### Getting Started
 

benchmark/camelyon/pure_substrafl/register_assets.py

@@ -98,7 +98,7 @@ def add_duplicated_dataset(
     data_sample_folder: os.PathLike,
     asset_keys: dict,
     msp_id: str,
-    kind: str = TaskType.TRAIN,
+    kind: str = TaskType.TRAIN.value,
 ) -> dict:
     """Update asset_keys.msp_id.{kind}_data_sample_keys so there is exactly `nb_data_sample` keys
     by adding the `data_sample_folder` as a data sample with the provided `client` as many times as it's need
@@ -126,7 +126,7 @@ def add_duplicated_dataset(
                 }
         msp_id (str): asset_keys key where to find the registered assets for the given client
         kind (str, optional): Kind of data sample to add, either train or test.  Defaults to
-        TaskType.TRAIN.
+        TaskType.TRAIN.value.
 
     Returns:
         dict: The updated asset_keys.
@@ -193,7 +193,7 @@ def get_train_data_nodes(
             data_sample_folder=train_folder,
             asset_keys=asset_keys,
             msp_id=msp_id,
-            kind=TaskType.TRAIN,
+            kind=TaskType.TRAIN.value,
         )
 
         train_data_nodes.append(

benchmark/camelyon/requirements.txt

@@ -1,5 +1,5 @@
 tqdm==4.62.3
-numpy==1.23.1
-scikit-learn==1.1.1
-torch==1.13.1
-torchvision==0.14.1
+numpy==1.24.3
+scikit-learn==1.3.1
+torch==2.0.1
+torchvision==0.15.2

benchmark/camelyon/workflows.py

@@ -87,7 +87,7 @@ def substrafl_fed_avg(
     # Dependencies
     base = Path(__file__).parent
     dependencies = Dependency(
-        pypi_dependencies=["numpy==1.23.5", "torch==2.0.1", "scikit-learn==1.1.1"],
+        pypi_dependencies=["numpy==1.24.3", "torch==2.0.1", "scikit-learn==1.3.1"],
         local_code=[base / "common", base / "weldon_fedavg.py"],
         # Keeping editable_mode=True to ensure nightly test benchmarks are ran against main substrafl git ref
         editable_mode=True,

docker/substrafl-tests/Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.10
+FROM python:3.11
 
 WORKDIR /usr/src/app
 

examples/template/template.py

@@ -195,7 +195,7 @@ def __init__(self):
 # Algo dependencies
 # =================
 
-algo_deps = Dependency(pypi_dependencies=["numpy==1.23.1", "torch==1.12.0"])
+algo_deps = Dependency(pypi_dependencies=["numpy==1.24.3", "torch==2.0.1"])
 
 # %%
 # Federated Learning strategies

setup.py

@@ -33,14 +33,15 @@
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
     ],
     keywords=["substrafl"],
     packages=find_packages(exclude=["tests*", "benchmark*"]),
     # Not compatible with substratools 0.8.0 because
     # that release is private and in the Docker container
     # it has access only to the public PyPi
     install_requires=[
-        "numpy>=1.20.3, <1.24",
+        "numpy>=1.24",
         "cloudpickle>=1.6.0",
         "substra~=0.48.1",
         "substratools~=0.21.0",

substrafl/remote/register/register.py

@@ -33,9 +33,9 @@
 MINIMAL_DOCKER_SUBSTRATOOLS_VERSION = "0.16.0"
 
 # minimal and maximal values of Python 3 minor versions supported
-# we need to store this as integer, else "3.10" < "3.9" (string comparison)
+# we need to store this as integer, else "3.11" < "3.9" (string comparison)
 MINIMAL_PYTHON_VERSION = 8  # 3.8
-MAXIMAL_PYTHON_VERSION = 10  # 3.10
+MAXIMAL_PYTHON_VERSION = 11  # 3.11
 
 _DEFAULT_SUBSTRATOOLS_IMAGE = "ghcr.io/substra/substra-tools:\
 {substratools_version}-nvidiacuda11.8.0-base-ubuntu22.04-python{python_version}"

tests/remote/register/test_register.py

@@ -40,7 +40,7 @@ def test_check_python_version(version):
         register._check_python_version(version)
 
 
-@pytest.mark.parametrize("version", ["3.8", "3.9", "3.10"])
+@pytest.mark.parametrize("version", ["3.8", "3.9", "3.10", "3.11"])
 def test_check_python_version_valid(version):
     """Does not raise for supported versions"""
     register._check_python_version(version)