boltzmannentropy/qmldocker

A docker container for quantum machine learning (QML) research

QMLDocker: A docker container for quantum machine learning (QML) research (work in progress).

About • Credits • Installation • Examples • Author •

On telegram: https://t.me/BoltzmannQ

About

I developed a docker container that provides a unified environment for my QML projects, catering to both academic and industrial use cases across different platforms. Although I encountered difficulties installing Qiskit and paddle-quantum on a Mac OSX with the latest M1 chip, I eventually overcame this challenge by creating an Ubuntu-based docker that supports both libraries on the M1 chip.

The primary audience for this container is students and researchers who are new to quantum computing.

The docker also includes several QML repositories with numerous examples:

• clone https://github.com/PaddlePaddle/Quantum.git
• clone https://github.com/theerfan/Q/
• clone https://github.com/DavitKhach/quantum-algorithms-tutorials.git
• clone https://github.com/mit-han-lab/torchquantum.git
• clone https://github.com/walid-mk/VQE.git
• clone https://github.com/MyEntangled/Quantum-Autoencoders.git
• clone https://github.com/PennyLaneAI/qml.git
• clone https://github.com/XanaduAI/pennylane-demo-cern.git
• clone https://github.com/quantum-machine-learning/Hands-On-Quantum-Machine-Learning-With-Python-Vol-1
• clone https://github.com/MonitSharma/Learn-Quantum-Machine-Learning.git
• clone https://github.com/MarcinPlodzien/Quantum-Machine-Learning-Introduction.git

Quantum computing libraries, features etc

• Based on nvcr.io/nvidia/tensorrt:21.07-py3
• PennyLane
• PyTorch
• PyTorchQuantum
• efficientnet_pytorch, pandas_summary
• Eigen3
• Quantum++ and PyQPP
• Qiskit
• QuTip
• Cirq
• Paddlepaddle
• Paddle-quantum
• Tequila
• Qualacs
• onnxruntime
• Full LaTeX distribution
• A passord protected Jupyter (password is:"mk2==2km")
• An SSH key that is embedded into the docker (change it of you want to)
• Home directory /home/qmuser
• C++ compiler + CMake

Building

Run the `build.sh` or direct type the command

docker build -t quantdoc .

Running

docker run  --platform linux/amd64 -it --env="DISPLAY" -p 8097:7842 -v /tmp/.X11-unix:/tmp/.X11-unix:rw -e DISPLAY -e XAUTHORITY -v /Users/sol/dev/:/home/qmuser/sharedfolder  quantdoc:latest bash

sol@mprox QMLDocker % ./run.sh  

=============
== PyTorch ==
=============

NVIDIA Release 21.07 (build 25165078)
PyTorch Version 1.10.0a0+ecc3718

Container image Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.

Copyright (c) 2014-2021 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

NVIDIA Deep Learning Profiler (dlprof) Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
   Use 'nvidia-docker run' to start this container; see
   https://github.com/NVIDIA/nvidia-docker/wiki/nvidia-docker .

ERROR: This container was built for CPUs supporting at least the AVX instruction set, but
       the CPU detected was , which does not report
       support for AVX.  An Illegal Instrution exception at runtime is likely to result.
       See https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX .

NOTE: MOFED driver for multi-node communication was not detected.
      Multi-node communication performance may be reduced.

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
   nvidia-docker run --ipc=host ...

To run a command as administrator (user "root"), use "sudo <command>".
See "man sudo_root" for details.

Jupyter

Once you are logged in, run the following command. Jupyter will be available at http://localhost:8097/

qmuser@442b8ee86057:~$ ./run_jupyter.sh
Detected 5 cpus
/usr/local/nvm/versions/node/v15.12.0/bin:/opt/conda/bin:/opt/cmake-3.14.6-Linux-x86_64/bin/:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin:/opt/tensorrt/bin
qmuser@442b8ee86057:~$ sshd: no hostkeys available -- exiting.
[I 19:45:11.140 NotebookApp] Writing notebook server cookie secret to /home/qmuser/.local/share/jupyter/runtime/notebook_cookie_secret

Testing

Open the Jupyre notebook quantum-libs.ipynb.

Mapping volumes

The run.sh command, maps an external volume to docker as in: -v /Users/sol/dev/:/home/qmuser/sharedfolder. You can change that to fit your OS.

Troubleshooting

The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8)

Add this snipped to your ~/.zshrc and ~/.bashrc. It allows you not to repeat the flag anytime you perform a docker run command:

# useful only for Mac OS Silicon M1, 
# still working but useless for the other platforms
docker() {
 if [[ `uname -m` == "arm64" ]] && [[ "$1" == "run" || "$1" == "build" ]]; then
    /usr/local/bin/docker "$1" --platform linux/amd64 "${@:2}"
  else
     /usr/local/bin/docker "$@"
  fi
}

--platform linux/amd64

GPU or CPU?

If you want to test on a GPU you will have to edit ./docker:

GPU mode:

CPU mode:

Note: Tested only on Mac OSX with the M1 CHIP.

Examples

See the examples in the sub-folders /home/qmluser/

USER qmuser
WORKDIR /home/qmuser
RUN git clone https://github.com/PaddlePaddle/Quantum.git
RUN git clone https://github.com/theerfan/Q/

Requirements:

• (Optional) NVIDIA CUDA 11.2. For the GPU versions of paddle-quantum etc.
• 64 bit only.

Building using docker

Contributing

Feel free to report issues during build or execution. We also welcome suggestions to improve the performance of this application.

Citation

If you find the code or trained models useful, please consider citing:

@misc{QuanDocker,
  author={Kashani, Shlomo},
  title={QuanDocker2023},
  howpublished={\url{https://github.com/}},
  year={2020}
}

Disclaimers

• No liability. Feel free to submit bugs or fixes.
• No tech support: this is merely a spare-time fun project for me.
• Tested only on Mac OSX with the M1 chip. More OS and dev env support are welcomed.

Third party licences:

• NVIDIA CUDA license https://docs.nvidia.com/cuda/eula/index.html
• PyTorch https://github.com/pytorch/pytorch/blob/master/LICENSE

References