Nano Known Issues

PyTorch Issues

AttributeError: module ‘distutils’ has no attribute ‘version’

This usually is because the latest setuptools does not compatible with PyTorch 1.9.

You can downgrade setuptools to 58.0.4 to solve this problem.

For example, if your setuptools is installed by conda, you can run:

conda install setuptools==58.0.4

error while loading shared libraries:

You may see this error message when running source bigdl-nano-init

 Sed: error while loading shared libraries: cannot open shared object file: No such file or directory.

You can use the following command to fix this issue.

  • apt-get install libunwind8-dev

Bus error (core dumped) in multi-instance training with spawn distributed backend

This usually is because you did not set enough shared memory size in your docker container.

You can increase --shm-size to a larger value, e.g. a few GB, to your docker run command, or use --ipc=host.

If you are running in k8s, you can mount larger storage in /dev/shm. For example, you can add the following volume and volumeMount in your pod and container definition.

    - mountPath: /dev/shm
      name: cache-volume
  - emptyDir:
    medium: Memory
    sizeLimit: 8Gi
    name: cache-volume

TensorFlow Issues

Nano keras multi-instance training currently does not suport tensorflow dataset.from_generators, numpy_function, py_function

Nano keras multi-instance training will serialize TensorFlow dataset object into a graph.pb file, which does not work with dataset.from_generators, dataset.numpy_function, dataset.py_function due to limitations in TensorFlow.

Ray Issues

protobuf version error

Now pip install ray[default]==1.11.0 will install google-api-core==2.10.0, which depends on protobuf>=3.20.1. However, nano depends on protobuf==3.19.4, so if we install ray after installing bigdl-nano, pip will reinstall protobuf==4.21.5, which causes error.