We’ll need to install some additional libraries associated with it. Some more digging shows this libdevice driver is related to XLA, an optimizing compiler, that is apparently automatically used by Keras. We can hack this using that in the conda implementation conda uses the shell to call export ]] This only lets us set an environment variable, whereas we want to append to it. This has the added benefit that any changed variables are reset when the environment is deactivates. Instead of using the conda activate scripts we can set environment variables with variables. Setting the library path is a bit more complex as suggested we could use activate.d/env_vars.sh, but it would be better if we declare it in our environment.yml. Note that you can also use a more recent version of CUDA providing your GPU is compatible with it, so I used the more recent 11.7 instead. Rather than running the install script we can simply add the dependencies to environment.yml. mkdir -p $CONDA_PREFIX/etc/conda/activate.dĮcho 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh The system paths will be automatically configured when you activate this conda environment. export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/įor your convenience it is recommended that you automate it with the following commands. You can do it with following command everytime your start a new terminal after activating your conda environment. conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0Ĭonfigure the system paths. nvidia-smi Then install CUDA and cuDNN with conda. You can use the following command to verify it is installed. This is also the easiest way to install the required software especially for the GPU setup.įirst install the NVIDIA GPU driver if you have not. It creates a separate environment to avoid changing any installed software in your system. Miniconda is the recommended approach for installing TensorFlow with GPU support. In CONDA_PREFIX without having to provide any conda-specific cmdline arguments.Import tensorflow as tf assert tf.config.list_physical_devices( 'GPU') Adding GPU Support Using the compiler metapackage with conda-gcc-specs you can incude and link libraries installed rpath $CONDA_PREFIX/lib -rpath-link $CONDA_PREFIX/lib -disable-new-dtags -L $CONDA_PREFIX/lib to link include $CONDA_PREFIX/include to compile commands A new optional package called conda-gcc-specs can also be installed that adds: X86_64-conda-linux-gnu-cc) for toolchains that are targeting the system they are running on. The compiler metapackages mentioned above also install packages thatĬreate symlinks of the short names (like gcc) to the actual toolchain binary names (like Wants to be able to build some things for the build-host to use during the build by just calling Is setup for cross-compiling and expects CC to contain the name of the target toolchain but What do you do if you have custom FLAGS that your project requires for it’s build or you can’tīuild with some of the flags supplied by conda-forge? What if you are building something that Variables ( CLAGS, CPPFLAGS, LDFLAGS, etc) so that many build systems will pick them up correctly. Of particular note, the activation scripts add theĬONDA_PREFIX/include and CONDA_PREFIX/lib paths to the appropriate FLAGS environment The activation scripts also set aĬMAKE_ARGS variable with many arguments the conda-forge community finds helpful forĬonfiguring cmake build flows. The long compiler name x86_64-conda-linux-gnu-cc. For example, you would see the variable CC set to Many environment variables that are typically used by GNU autotools and make in the You conda activate an environment that contains the compiler toolchain. The conda-forge infrastructure provides activation scripts which are run when Of gcc, the actual binary will be named something like x86_64-conda-linux-gnu-cc. ‘prefixed’ with more complete information about the architecture and ABI they target. As a result, the builtin search path for theĬompilers only contains the sysroot they were built with. Them anywhere like any other conda package. We do this because it makes it possible to then install All of our toolchains are built as cross-compilers (even when they are built to run on the sameĪrchitecture that they are targeting).
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