ImportError: cannot import name 'mmr_auxe' from partially initialized module 'niftypet.nipet'

[Melissa1909]

Dear developers,
I have installed niftypet inside a docker container using the attached Dockerfile. However, if I start it and enter the Python3 venv (/venv/bin/python3) and import nipet from niftypet as given in the example scripts (from niftypet import nipet), I get the following error:

>>> from niftypet import nipet
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/venv/NIPET/niftypet/nipet/__init__.py", line 45, in <module>
    from . import img, lm, mmr_auxe, mmraux, mmrnorm, prj
  File "/venv/NIPET/niftypet/nipet/img/__init__.py", line 4, in <module>
    from . import auximg, mmrimg
  File "/venv/NIPET/niftypet/nipet/img/mmrimg.py", line 16, in <module>
[Dockerfile.txt](https://github.com/NiftyPET/NiftyPET/files/12313850/Dockerfile.txt)

    from .. import mmraux
  File "/venv/NIPET/niftypet/nipet/mmraux.py", line 21, in <module>
    from . import mmr_auxe, resources
ImportError: cannot import name 'mmr_auxe' from partially initialized module 'niftypet.nipet' (most likely due to a circular import) (/venv/NIPET/niftypet/nipet/__init__.py)

I have also tried to install nipet via niftypet and directly via pip but the error still occurs. Any feedback or suggestions are appreciated!
Best, Melissa
Dockerfile.txt

[casperdcl]

Hey @Melissa1909 - I hadn't tested installing within Docker before.

NiftyPET detects your GPUs during the build process, but unfortunately Docker doesn't expose GPUs during build time (you can't docker build --gpus all).

Unfortunately the hack for now would be to install at runtime (docker run --gpus all):

# CUDA development toolkit
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
# Install Python3 dev depedencies
RUN apt-get update -qq && apt-get install -yqq python3-venv python3-dev && apt-get clean
# Create and activate a virtual environment
RUN python3 -m venv /venv
ENV PATH="/venv/bin:$PATH"
# Install useful Python packages inside virtual environment
RUN pip install --no-cache numpy scipy scikit-image matplotlib nibabel pydicom pandas ipykernel ipywidgets brainweb spm12

# Install NiftyPET
ENV HMUDIR=/mmr_hardwareumaps
# install after starting the container (requires --gpus all)
COPY <<EOF /entry.sh
#!/bin/bash
set -euo pipefail
if test -z "\$(pip list | grep nipet)"; then
  echo 'installing NiftyPET (requires `docker run --gpus all`)'
  pip install --no-cache nimpa[cuda,dcm2niix,niftyreg] nipet
fi
exec "\$@"
EOF
RUN chmod 755 /entry.sh
ENTRYPOINT ["/entry.sh"]
CMD ["/bin/bash"]

[casperdcl]

• NIMPA: fix silly build error fix build NIMPA#30
• NIMPA: support docker build fix docker build NIMPA#31
• NIPET: support docker build

☝️ @pjmark you might have to tackle the last bullet point as I don't have much time :)

[casperdcl]

nevermind - I just tested and I think NIPET does install properly after all...

Here's what I'm using @Melissa1909:

# CUDA development toolkit (~7GB)
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 as build
# Install Python3 dev depedencies
RUN apt-get update -qq && apt-get install -yqq python3-venv python3-dev && apt-get clean
# Create and activate a virtual environment
RUN python3 -m venv /venv
ENV PATH="/venv/bin:$PATH"
# Install NiftyPET
ENV HMUDIR=/mmr_hardwareumaps
RUN pip install --no-cache 'nimpa[cuda,dcm2niix,niftyreg]>=2.6.3' nipet

# Make a smaller image without development toolkit (~1GB)
FROM nvidia/cuda:12.2.0-base-ubuntu22.04 as nipet
RUN apt-get update -qq && apt-get install -yqq python3-venv python3-dev && apt-get clean
# Copy virtual environment & NiftyPET build artefacts
COPY --from=build --link /venv /venv
ENV PATH="/venv/bin:$PATH"
ENV HMUDIR=/mmr_hardwareumaps
COPY --from=build --link /root/.niftypet /root/.niftypet
# Install useful Python packages
RUN pip install --no-cache numpy scipy scikit-image matplotlib nibabel pydicom pandas ipykernel ipywidgets brainweb spm12

Should really add this to the docs (source)