Merge pull request #52 from juglab/napari4.18

Napari4.18

docs/installation.md

@@ -14,21 +14,28 @@ If you do not have conda, we recommend installing [miniconda](https://docs.conda
 operating system.
 3. Install `napari` and `napari-n2v`:
    ```bash
-      pip install "napari[all]==0.4.15" napari-n2v
+      pip install "napari[all]" napari-n2v
    ```
 
-> Note: napari-n2v was tested with TensorFlow 2.7 and 2.10, cuda 11.2 and cudnn
-> 8.1.
+> Note: napari-n2v was tested with TensorFlow 2.10 (cuda 11.2 and cudnn 8.1) and
+TensorFlow 2.13 (cuda 11.8 and cudnn 8.6) on a Linux machine (NVIDIA A40-16Q GPU).
 
 > **Important**: In order to access the GPU with Tensorflow, it is necessary to
 > export the CUDA library path in your conda environment. Installation 
 > instructions on the TensorFlow website do just that. 
+
 > For TF 2.10, we recommand running the following in your environment:
 > ```bash
 > mkdir -p $CONDA_PREFIX/etc/conda/activate.d
 > echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
 > ```
 
+> If you encounter the following problem with TF 2.13: "DNN library is not found", you
+> can try to run in your environment:
+> ```bash
+> CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
+> export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
+> ```
    
 ## macOS
 
@@ -37,7 +44,7 @@ operating system.
 
 1. Set up env with napari and pyqt5
    ```bash
-      conda create -n napari-n2v -c conda-forge python=3.9 pyqt imagecodecs napari=0.4.15
+      conda create -n napari-n2v -c conda-forge python=3.9 pyqt imagecodecs napari
    ```
 2. Install tensorflow following [Apple's instructions](https://developer.apple.com/metal/tensorflow-plugin/)
 3. Install napari-n2v

envs/README.md

@@ -0,0 +1,7 @@
+# Test environments
+
+These environments are used to test the code with different versions of TF in linux. 
+They will fail on a macOS machine.
+
+See the [documentation](https://juglab.github.io/napari-n2v/installation.html) for 
+installation steps.

envs/tf_old_test_env.yml

@@ -0,0 +1,14 @@
+name: np-n2v
+channels:
+  - conda-forge
+  - nvidia
+  - defaults
+dependencies:
+  - python=3.9
+  - cudatoolkit=11.3
+  - cudnn=8.1
+  - pip
+  - pip:
+      - tensorflow<2.12
+      - "napari[all]"
+      - napari-n2v

envs/tf_recent_test_env.yml

@@ -0,0 +1,14 @@
+name: np-n2v
+channels:
+  - conda-forge
+  - nvidia
+  - defaults
+dependencies:
+  - python=3.11
+  - cudatoolkit=11.8.0
+  - pip
+  - pip:
+      - nvidia-cudnn-cu11==8.6.0.163
+      - tensorflow==2.13.*
+      - "napari[all]"
+      - napari-n2v

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = napari-n2v
-version = 0.1.0
+version = 0.1.1
 author = Tom Burke, Joran Deschamps
 author_email = [email protected]
 url = https://github.com/juglab/napari-n2v
@@ -47,33 +47,18 @@ install_requires =
     napari_time_slicer >= 0.4.9
 
     # Also compatibility with napari-workflows
-    # TODO: it might be better to refactor the napari-workflow code to another repo that would call napari-n2 so that
-    # TODO: the dependencies would be a bit more relaxed.
-    # TODO: pin napari to <=0.4.15? vispy<=0.9.6?
     # pin napari and vispy because
     # https://github.com/napari/napari/issues/4415
     # https://github.com/napari/napari/issues/4708
-    napari<=0.4.15
-    vispy<=0.9.6
-    magicgui
+    napari
     qtpy
     pyqtgraph
 
-    # fix to prevent failing tests
-    # see https://github.com/napari/napari/pull/5168
-    # and https://github.com/napari/napari/commit/c42e5ee2d1afd1f26944de3c0657e7bbc813b7d1
-    imageio>=2.5.0,!=2.11.0,!=2.22.1
-
     # tensorflow
-    tensorflow>=2.7.0; platform_system!="Darwin" or platform_machine!="arm64"
+    tensorflow>=2.10.0; platform_system!="Darwin" or platform_machine!="arm64"
     tensorflow-macos; platform_system=="Darwin" and platform_machine=="arm64"
     tensorflow-metal; platform_system=="Darwin" and platform_machine=="arm64"
 
-    # protobuf is a dependency of TensorFlow but the latest versions (>3.20) generate
-    # errors with npe2. As of TensorFlow 2.10, protobuf<3.20 and >=3.9.2 is enforced.
-    # Here we set the requirements to be compatible with TF2.8 to TF2.10.
-    protobuf<3.20,>=3.9.2
-
 [options.extras_require]
 testing =
     pytest  # https://docs.pytest.org/en/latest/contents.html