The preferred way to install this package is by using the Python package index:
pip install aihwkit
Similarly this package can also be installed using Conda
package for AIHWKIT available in Conda-forge,
CPU :
conda install -c conda-forge aihwkit
GPU :
conda install -c conda-forge aihwkit-gpu
Similarly for GPU support, you can also build a docker
container following the CUDA Dockerfile instructions. You can then run a GPU enabled docker container using the following command from your project directory :
docker run --rm -it --gpus all -v $(pwd):$HOME --name aihwkit aihwkit:cuda bash
Note
During the initial beta stage, we do not provide pip wheels (as in, pre-compiled binaries) for all the possible platform, version and architecture combinations (in particular, only CPU versions are provided).
Please refer to the advanced_install
page for instruction on how to compile the library for your environment in case you encounter errors during installing from pip.
The package require the following runtime libraries to be installed in your system:
- OpenBLAS: 0.3.3+
- CUDA Toolkit: 9.0+ (only required for the GPU-enabled simulator1)
Note
Please note that the current pip wheels are only compatible with PyTorch
1.6.0
. If you need to use a different PyTorch
version, please refer to the advanced_install
section in order to compile a custom version. More details about the PyTorch
compatibility can be found in this issue.
The package contains optional functionality that is not installed as part of the default installed. In order to install the extra dependencies, the recommended way is by specifying the extra visualization
dependencies:
pip install aihwkit[visualization]
If the library was installed correctly, you can use the following snippet for creating an analog layer and predicting the output:
from torch import Tensor
from aihwkit.nn import AnalogLinear
model = AnalogLinear(2, 2)
model(Tensor([[0.1, 0.2], [0.3, 0.4]]))
If you encounter any issues during the installation or executing the snippet, please refer to the advanced_install
section for more details and don't hesitate on using the issue tracker for additional support.
You can read more about the PyTorch layers in the using_pytorch
section, and about the internal analog tiles in the using_simulator
section.
Note that GPU support is not available in OSX, as it depends on a platform that has official CUDA support.↩