microsoft / onnxruntime

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
https://onnxruntime.ai
MIT License
14.8k stars 2.94k forks source link

upgrading pip and wheels kills CUDAExecutionProvider #12185

Open trifle opened 2 years ago

trifle commented 2 years ago

Describe the bug This is a very weird but replicable bug. I'm building a docker image with onnxruntime (among other frameworks and libraries). When I run pip3 install -U pip setuptools wheel as part of the build pipeline, it disables GPU acceleration in onnxnet.

Urgency Not urgent, I avoid upgrading said python packages. But it's definitely an unexpected and potentially cumbersome bug to deal with and many people might run across it eventually if there's an upcoming incompatibility.

System information

To Reproduce Here is a replication Dockerfile. Note that you will need to swap the default docker runtime to nvidia-container-runtime to make GPUs and nvidia cuda libraries available during build. To do that, edit it in /etc/docker/daemon.json.

FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
ENV DEBIAN_FRONTEND=noninteractive
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
ENV PYTHONIOENCODING=utf-8
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES all

RUN apt-get -y update && \
    apt-get install -y \
    python3-pip

RUN pip3 install --upgrade pip setuptools wheel # <- this line kills gpu acceleration

RUN pip3 install onnxruntime onnxruntime-gpu
RUN python3 -c "import onnxruntime as ort; print(ort.get_device())"

When this Dockerfile is built (docker build -t onnx-test -f Dockerfile .), it will emit "CPU" showing that CUDA is not available to onnx. After commenting out the line that installs pip, setuptools and wheel, it will show "GPU".

Expected behavior I would expect upgrades to ubiquitous python packages such as pip, setuptools and wheels not to break onnx. And I would expect a more useful error.

Screenshots

Additional context

dvolgyes commented 2 years ago

That is a very interesting problem. Could you provide the versions for setuptools / wheels, both before and after upgrading? Apt-get update pulls new packages, so when somebody will try to reproduce this later, this information might help. Also, you could maybe upgrade only one of them, maybe that would reduce the search space? (ie. upgrading setuptools breaks it, upgrading wheels breaks it, if both are upgraded, then it breaks).

I have absolutely no overview how onnxruntime works, but I would guess this relates to the method how onnxruntime looks up which accelerators are installed. Probably there was a change in setuptools (or wheels) and it breaks something. If this is the case, it would be nice to hunt it down, because sooner or later every project, distribution will move to newer packages, so the bug will affect more and more people.

trifle commented 2 years ago

Hi @dvolgyes, thanks for reaching out. You're absolutely right. I'll check the installed packages as well. It should be possible to do apt-pinning, but TBH, that's too much work as long as we don't know for sure that it's related to the issue.

Going to report back.

dvolgyes commented 2 years ago

@trifle I am just an interested third party, but I follow the 1.12 release closely because a) I also had GPU troubles recently, b) I very much need a python 3.10 support. So I can only guess based on my generic python experience, but these seemed to be logical debug steps. :)

trifle commented 2 years ago

Okay, here is a snapshot of the installed versions:

Apt packages after running the apt step in build:

ii  libpython3-dev:amd64         3.8.2-0ubuntu2               amd64 
ii  libpython3-stdlib:amd64      3.8.2-0ubuntu2               amd64 
ii  libpython3.8:amd64           3.8.10-0ubuntu1~20.04.5      amd64 
ii  libpython3.8-dev:amd64       3.8.10-0ubuntu1~20.04.5      amd64 
ii  libpython3.8-minimal:amd64   3.8.10-0ubuntu1~20.04.5      amd64 
ii  libpython3.8-stdlib:amd64    3.8.10-0ubuntu1~20.04.5      amd64 
ii  python-pip-whl               20.0.2-5ubuntu1.6            all   
ii  python3                      3.8.2-0ubuntu2               amd64 
ii  python3-dev                  3.8.2-0ubuntu2               amd64 
ii  python3-distutils            3.8.10-0ubuntu1~20.04        all   
ii  python3-lib2to3              3.8.10-0ubuntu1~20.04        all   
ii  python3-minimal              3.8.2-0ubuntu2               amd64 
ii  python3-pip                  20.0.2-5ubuntu1.6            all   
ii  python3-pkg-resources        45.2.0-1                     all   
ii  python3-setuptools           45.2.0-1                     all   
ii  python3-wheel                0.34.2-1                     all   
ii  python3.8                    3.8.10-0ubuntu1~20.04.5      amd64 
ii  python3.8-dev                3.8.10-0ubuntu1~20.04.5      amd64 
ii  python3.8-minimal            3.8.10-0ubuntu1~20.04.5      amd64 

Interestingly, installing pip wheel and setuptools inside a running container says it doesn't change anything because the system versions cannot be uninstalled. However, docker diff reports the following files changed by the offending line:

$docker diff <image id>
C /etc
C /etc/ld.so.cache
C /usr
C /usr/local
C /usr/local/bin
A /usr/local/bin/pip3.8
A /usr/local/bin/wheel
A /usr/local/bin/pip
A /usr/local/bin/pip3
A /usr/local/bin/pip3.10
C /usr/local/lib
C /usr/local/lib/python3.8
C /usr/local/lib/python3.8/dist-packages
A /usr/local/lib/python3.8/dist-packages/distutils-precedence.pth
A /usr/local/lib/python3.8/dist-packages/pip
A /usr/local/lib/python3.8/dist-packages/pip/__init__.py
A /usr/local/lib/python3.8/dist-packages/pip/__main__.py
A /usr/local/lib/python3.8/dist-packages/pip/__pycache__
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C /root
A /root/.cache
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A /root/.cache/pip/http
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A /root/.cache/pip/http/0/4/1/8
A /root/.cache/pip/http/0/4/1/8/c
A /root/.cache/pip/http/0/4/1/8/c/0418c83b80f7f7bfaec2738bfbbee53d2c1562196c0781702f6eddc8
A /root/.cache/pip/http/0/8
A /root/.cache/pip/http/0/8/c
A /root/.cache/pip/http/0/8/c/1
A /root/.cache/pip/http/0/8/c/1/2
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A /root/.cache/pip/http/4
A /root/.cache/pip/http/4/d
A /root/.cache/pip/http/4/d/2
A /root/.cache/pip/http/4/d/2/7
A /root/.cache/pip/http/4/d/2/7/2
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C /var
C /var/cache
C /var/cache/ldconfig
C /var/cache/ldconfig/aux-cache

And in fact, pip is upgraded from pip 20.0.2 from /usr/lib/python3/dist-packages/pip (python 3.8) from apt to pip 22.1.2 from /usr/local/lib/python3.8/dist-packages/pip (python 3.8)

I don't have more time right now to dig in further, but the issue persists.

snnn commented 2 years ago
RUN pip3 install onnxruntime onnxruntime-gpu

The package onnxruntime conflicts with onnxruntime-gpu. Don't install both. You only need "onnxruntime-gpu". onnxruntime is for who wants smaller footprint.

dvolgyes commented 2 years ago
RUN pip3 install onnxruntime onnxruntime-gpu

The package onnxruntime conflicts with onnxruntime-gpu. Don't install both. You only need "onnxruntime-gpu". onnxruntime is for who wants smaller footprint.

That is a very important information. Is that anywhere documented? If it is, I missed it. It should be highlighted. Is it safe to install onnxruntime-gpu on a GPU-less machine, so that I can always install onnxruntime-gpu (if I don't care about size)?

snnn commented 2 years ago

Is it safe to install onnxruntime-gpu on a GPU-less machine, so that I can always install onnxruntime-gpu (if I don't care about size)?

Yes.

trifle commented 2 years ago

@snnn Thanks, that's interesting! So the order in which pip installs the packages changes the availability of GPU acceleration?

Btw, the reason why I installed both is of course that many other frameworks have CUDA as add-on packages, such as tensorflow-gpu.

dvolgyes commented 2 years ago

@trifle I guess the solution is to not install onnxruntime, but onnxruntime-gpu. This seems to work. Can you confirm? Maybe it is time to close this ticket if it works.

@snnn: Still some highlight or warning about the conflicting packages wouldn't hurt somewhere, e.g. on the pypi.org page, or somewhere.

trifle commented 2 years ago

@dvolgyes Sorry, it's been so long that I forgot how I fixed this.

I guess the general issue is the growing fragmentation of versions with packages now requiring a specific tree of python-flavor (conda/vanilla) / python-version / package-version / cuda-version

So there will be no way out unless people learn what precisely they need and how to get it. To get there, I agree that transparently announcing the required target is a good idea.