AIMET Installation
Quick Install
The AIMET PyTorch GPU PyPI packages are available for environments that meet the following requirements:
64-bit Intel x86-compatible processor
Linux Ubuntu 22.04 LTS [Python 3.10] or Ubuntu 20.04 LTS [Python 3.8]
CUDA 12.0
Torch 2.2.2
Pip install
apt-get install liblapacke
python3 -m pip install aimet-torch
Release Packages
For other AIMET variants, install the latest version from the .whl files hosted at https://github.com/quic/aimet/releases
PyTorch
# Pytorch 2.1 with CUDA 12.x python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_torch-1.34.0.cu121-cp310-cp310-manylinux_2_34_x86_64.whl # Pytorch 2.1 CPU only python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_torch-1.34.0.cpu-cp310-cp310-manylinux_2_34_x86_64.whl # Pytorch 1.13 with CUDA 11.x python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_torch-1.34.0.cu117-cp310-cp310-manylinux_2_34_x86_64.whl
TensorFlow
# Tensorflow 2.10 GPU with CUDA 11.x python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_tensorflow-1.34.0.cu118-cp310-cp310-manylinux_2_34_x86_64.whl # Tensorflow 2.10 CPU only python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_tensorflow-1.34.0.cpu-cp310-cp310-manylinux_2_34_x86_64.whl
Onnx
# ONNX 1.16 GPU with CUDA 11.x python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_onnx-1.34.0.cu117-cp310-cp310-manylinux_2_34_x86_64.whl # ONNX 1.16 CPU python3 -m pip install https://github.com/quic/aimet/releases/download/1.34.0/aimet_onnx-1.34.0.cpu-cp310-cp310-manylinux_2_34_x86_64.whl
For older versions, please browse the releases at https://github.com/quic/aimet/releases and follow the documentation corresponding to that release to select and install the appropriate package.
System Requirements
The AIMET package requires the following host platform setup:
64-bit Intel x86-compatible processor
Linux Ubuntu: 22.04 LTS
bash command shell
- For GPU variants:
Nvidia GPU card (Compute capability 5.2 or later)
nvidia-docker - Installation instructions: https://github.com/NVIDIA/nvidia-docker
To use the GPU accelerated training modules an Nvidia CUDA enabled GPU with a minimum Nvidia driver version of 455+ is required. Using the latest driver is always recommended, especially if using a newer GPU. Both CUDA and cuDNN (the more advanced CUDA interface) enabled GPUs are supported.
Advanced Installation Instructions
- There are two ways to setup and install AIMET:
On your host machine
Using our pre-built development Docker images
Please click on the appropriate link for installation instructions: