Set LD_LIBRARY_PATH on mamba activate (#1496)

* Set LD_LIBRARY_PATH on mamba activate

* Rename libcudart_activate.sh to sleap_activate.sh in docs

* Remove comments from environment.yml

.conda/README.md

@@ -3,7 +3,7 @@ This folder defines the conda package build for Linux and Windows. There are run
 To build, first go to the base repo directory and install the build environment:
 
 ```
-conda env create -f environment_build.yml -n sleap_build && conda activate sleap_build
+mamba env create -f environment_build.yml -n sleap_build && conda activate sleap_build
 ```
 
 And finally, run the build command pointing to this directory:
@@ -15,7 +15,7 @@ conda build .conda --output-folder build -c conda-forge -c nvidia -c https://con
 To install the local package:
 
 ```
-conda create -n sleap_0 -c conda-forge -c nvidia -c ./build -c https://conda.anaconda.org/sleap/ -c anaconda sleap=x.x.x
+mamba create -n sleap_0 -c conda-forge -c nvidia -c ./build -c https://conda.anaconda.org/sleap/ -c anaconda sleap=x.x.x
 ```
 
 replacing x.x.x with the version of SLEAP that you just built.

.conda/build.sh

@@ -12,4 +12,12 @@ pip install --no-cache-dir -r ./requirements.txt
 
 # Install sleap itself. This does not install the requirements, but will list which 
 # requirements are missing (see "install_requires") when user attempts to install.
-python setup.py install --single-version-externally-managed --record=record.txt
+python setup.py install --single-version-externally-managed --record=record.txt
+
+# Copy the activate scripts to $PREFIX/etc/conda/activate.d.
+# This will allow them to be run on environment activation.
+export CHANGE=activate
+
+mkdir -p "${PREFIX}/etc/conda/${CHANGE}.d"
+ls "${RECIPE_DIR}"
+cp "${RECIPE_DIR}/${PKG_NAME}_${CHANGE}.sh" "${PREFIX}/etc/conda/${CHANGE}.d/${PKG_NAME}_${CHANGE}.sh"

.conda/sleap_activate.sh

@@ -0,0 +1,4 @@
+#!/bin/sh
+
+# Help CUDA find GPUs!
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH

docs/installation.md

@@ -349,6 +349,31 @@ pip install tensorflow==2.6.3
 ```
 ````
 
+````{note}
+If you are on Linux, have a NVIDIA GPU, and are having trouble utilizing your GPU:
+
+```bash
+W tensorflow/stream_executor/platform/default/dso_loader.cc:64 Could not load dynamic 
+library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object 
+file: No such file or directory
+```
+
+then activate the environment:
+
+```bash
+mamba activate sleap
+```
+
+and run the commands:
+```bash
+mkdir -p $CONDA_PREFIX/etc/conda/activate.d
+echo '#!/bin/sh' >> $CONDA_PREFIX/etc/conda/activate.d/sleap_activate.sh
+echo 'export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/sleap_activate.sh
+source $CONDA_PREFIX/etc/conda/activate.d/sleap_activate.sh
+```
+These commands only need to be run once and will subsequently run automatically upon activating your `sleap` environment.
+````
+
 ## Upgrading and uninstalling
 
 We **strongly recommend** installing SLEAP in a fresh environment when updating. This is because dependency versions might change, and depending on the state of your previous environment, directly updating might break compatibility with some of them.

environment.yml

@@ -46,3 +46,4 @@ dependencies:
 
     - pip:  
         - "--editable=.[conda_dev]"
+