Tensorflow and Cudatoolkit Version

Note

Due to the update of conda, tensorflow and the system of clusters, some information are out of date.

  1. Why are there two separate conda requirements file?

    • requirements-min.txt limits the tensorflow version up to 2.2. Beyond this version, conda will install the wrong dependency versions, in particular cudatoolkit versions and sometimes python3.

    • tensorflow_2_6_requirements.txt manually selects the correct python and cudatoolkit versions to match the tensorflow-2.6.0 build on conda-forge.

  2. Should I use the latest tensorflow version?

    • We highly recommend Ampere card users (RTX 30 series for example), to install their conda environments with tensorflow_2_6_requirements.txt which uses cudatoolkit version 11.2.

  3. Why should Ampere use cudatoolkit version > 11.0?

    • To avoid a few minutes of overhead due to JIT compilation.

    • cudatoolkit version < 11.0 does not have pre-compiled CUDA binaries for Ampere architecture. So older cudatoolkit versions have to JIT compile the PTX code every time tensorflow uses the GPU hence the overhead.

    • See this explanation about old CUDA versions and JIT compile.

  4. Will you update the tensorflow_2_X_requirements.txt file regularly to the latest available version on `conda`?

    • We do not guarantee any regular updates on tensorflow_2_X_requirements.txt.

    • We will update this should a particular build becomes unavailable on conda or a new release of GPUs require a tensorflow and cudatoolkit update. Please notify us if this is the case.