You can select bigdl-nano along with some dependencies specific to PyTorch or Tensorflow using the following panel.
Since bigdl-nano is still in the process of rapid iteration, we highly recommend that you install nightly build version through the above command to facilitate your use of the latest features.
For stable version, please refer to the document and installation guide here .
Install in conda environment (Recommended)#
conda create -n env conda activate env # select your preference in above panel to find the proper command to replace the below command, e.g. pip install --pre --upgrade bigdl-nano[pytorch] # after installing bigdl-nano, you can run the following command to setup a few environment variables. source bigdl-nano-init
bigdl-nano-init scripts will export a few environment variable according to your hardware to maximize performance.
In a conda environment, when you run
source bigdl-nano-init manually, this command will also be added to
$CONDA_PREFIX/etc/conda/activate.d/, which will automaticly run when you activate your current environment.
Install in pure pip environment#
In a pure pip environment, you need to run
source bigdl-nano-init every time you open a new shell to get optimal performance and run
source bigdl-nano-unset-env if you want to unset these environment variables.
bigdl-nano is validated on Python 3.7-3.9.
Some specific note should be awared of when installing
Install on Linux#
For Linux, Ubuntu (22.04/20.04/18.04) is recommended.
Install on Windows#
For Windows OS, users could only run
bigdl-nano-init every time they open a new cmd terminal.
We recommend using Windows Subsystem for Linux 2 (WSL2) to run BigDL-Nano. Please refer to Nano Windows install guide for instructions.
Install on MacOS#
MacOS with Intel Chip#
Same usage as Linux, while some of the funcions now rely on lower version dependencies.
MacOS with M-series chip#
Currently, only tensorflow is supported for M-series chip Mac.
# any way to install tensorflow on macos pip install --pre --upgrade bigdl-nano[tensorflow]