AIMET Installation and Setup

This page provides instructions to install AIMET package on Ubuntu 22.04 LTS with Nvidia GPU. Please follow the instructions in the order provided, unless specified otherwise.

NOTE:

  1. Please pre-pend the “apt-get install” and “pip3 install” commands with “sudo -H” as appropriate.

  2. These instructions assume that pip packages will be installed in the path: /usr/local/lib/python3.10/dist-packages. If that is not the case, please modify it accordingly.

Install prerequisite packages

Install the basic pre-requisite packages as follows:

apt-get update
apt-get install python3.10 python3.10-dev python3-pip
python3 -m pip install --upgrade pip
apt-get install --assume-yes wget gnupg2

If you have multiple python versions installed, set the default python version as follows:

update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
update-alternatives --set python3 /usr/bin/python3.10

Install GPU packages

NOTE:

  1. Do this section ONLY for the GPU variants.

  2. The released AIMET GPU packages were tested with the following CUDA toolkit versions:

    1. PyTorch 2.1 GPU variant: CUDA Toolkit 12.1.0

    2. PyTorch 1.13 GPU variant: CUDA Toolkit 11.7.1

    3. TensorFlow GPU variant: CUDA Toolkit 11.8.0

    4. ONNX GPU variant: CUDA Toolkit 11.7.1

  3. The instructions in the sub-sections below correspond to our tested versions above. Visit this page https://developer.nvidia.com/cuda-toolkit-archive to obtain the correct version of the CUDA toolkit for your environment.

Install GPU packages for PyTorch 2.1 or PyTorch 1.13 or ONNX or TensorFlow

NOTE:

  1. Visit this page https://developer.nvidia.com/cuda-12-1-0-download-archive or https://developer.nvidia.com/cuda-11-8-0-download-archive or https://developer.nvidia.com/cuda-11-7-1-download-archive to obtain the exact and up-to-date installation instructions for your environment.

  2. Please do not execute the final command “sudo apt-get install cuda” provided in aforementioned NVIDIA documentation links.

apt-get update && apt-get install -y gnupg2
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
dpkg -i cuda-keyring_1.0-1_all.deb
apt-get update

Install AIMET packages

From PyPI

The default AIMET Torch GPU variant may be installed from PyPI as follows:

  • Go to https://pypi.org/project/aimet-torch

  • Browse the Requirements section of each Release to identify the version you wish to install. Following are some tips:

    • For Pytorch 2.2.2 GPU with CUDA 12.1, use aimet-torch>=1.32.2

    • For Pytorch 2.1.2 GPU with CUDA 12.1, use aimet-torch==1.32.1.post1

    • For PyTorch 1.13 GPU with CUDA 11.7, use aimet-torch==1.31.2

Run the following commands to install the package (prepend with “sudo” and/or package version as needed):

apt-get install liblapacke -y
python3 -m pip install aimet-torch

From Release Package

We also host python wheel packages for different variants which may be installed as follows:

  • Go to https://github.com/quic/aimet/releases

  • Identify the release tag of the package that you wish to install

  • Identify the .whl file corresponding to the package variant that you wish to install

  • Follow the instructions below to install AIMET from the .whl file

Set the package details as follows:

# Set the release tag ex. "1.34.0"
export release_tag="<version release tag>"

# Construct the download root URL
export download_url="https://github.com/quic/aimet/releases/download/${release_tag}"

# Set the wheel file name with extension
# ex. "aimet_torch-1.33.0.cu121-cp310-cp310-manylinux_2_34_x86_64.whl"
export wheel_file_name="<wheel file name>"

Install the selected AIMET package as specified below: NOTE: Python dependencies will automatically get installed.

python3 -m pip install ${download_url}/${wheel_file_name}

Install common debian packages

Install the common debian packages as follows:

cat /usr/local/lib/python3.10/dist-packages/aimet_common/bin/reqs_deb_common.txt | xargs apt-get --assume-yes install

NOTE: Do the following ONLY for the PyTorch variant packages.

cat /usr/local/lib/python3.10/dist-packages/aimet_onnx/bin/reqs_deb_torch_common.txt | xargs apt-get --assume-yes install

NOTE: Do the following ONLY for the ONNX variant packages.

cat /usr/local/lib/python3.10/dist-packages/aimet_onnx/bin/reqs_deb_onnx_common.txt | xargs apt-get --assume-yes install

Install tensorflow GPU debian packages

NOTE: Do this ONLY for the TensorFlow GPU package.

cat /usr/local/lib/python3.10/dist-packages/aimet_tensorflow/bin/reqs_deb_tf_gpu.txt | xargs apt-get --assume-yes install

Install torch GPU debian packages

NOTE: Do this ONLY for the PyTorch GPU package.

cat /usr/local/lib/python3.10/dist-packages/aimet_torch/bin/reqs_deb_torch_gpu.txt | xargs apt-get --assume-yes install

Install ONNX GPU debian packages

NOTE: Do this ONLY for the ONNX GPU package.

cat /usr/local/lib/python3.10/dist-packages/aimet_onnx/bin/reqs_deb_onnx_gpu.txt | xargs apt-get --assume-yes install

Replace Pillow with Pillow-SIMD

Optional: Replace the Pillow package with Pillow-SIMD as follows:

python3 -m pip uninstall -y pillow
python3 -m pip install --no-cache-dir Pillow-SIMD==9.0.0.post1

Replace onnxruntime with onnxruntime-gpu

NOTE: Do this ONLY for the PyTorch GPU package.

export ONNXRUNTIME_VER=$(python3 -c 'import onnxruntime; print(onnxruntime.__version__)')
python3 -m pip uninstall -y onnxruntime
python3 -m pip install --no-cache-dir onnxruntime-gpu==$ONNXRUNTIME_VER

Post installation steps

ln -s /usr/lib/x86_64-linux-gnu/libjpeg.so /usr/lib

NOTE: Do the following step ONLY for the PyTorch or Tensorflow GPU packages.

# NOTE: Please chose between the below command depending on the version of your CUDA driver toolkit
ln -s /usr/local/cuda-11.7 /usr/local/cuda
ln -s /usr/local/cuda-11.8 /usr/local/cuda
ln -s /usr/local/cuda-12.1 /usr/local/cuda

Environment setup

Set the common environment variables as follows:

source /usr/local/lib/python3.10/dist-packages/aimet_common/bin/envsetup.sh