Installation

DeepForest has Windows, Linux and OSX prebuilt wheels on pypi. We strongly recommend using a conda or virtualenv to create a clean installation container.

pip install DeepForest-pytorch

For questions on conda-forge installation, please submit issues to the feedstock repo: https://github.com/conda-forge/deepforest-feedstock

Source Installation

DeepForest can alternatively be installed from source using the github repository. The python package dependencies are managed by conda.

git clone https://github.com/weecology/DeepForest-pytorch.git
cd DeepForest-pytorch
conda env create --file=environment.yml
conda activate deepforest_pytorch

GPU support

Pytorch can be run on GPUs to allow faster model training and prediction. Deepforest-pytorch is a pytorch lightning module, as automatically distributes data to available GPUs. If using a release model with training, the module can be moved from CPU to GPU for prediction is the pytorch.to() method.

from deepforest import main
m = main.deepforest()
m.use_release()
print("Current device is {}".format(m.device))
m.to("cuda")
print("Current device is {}".format(m.device))

Current device is cuda:0

Distributed multi-gpu prediction outside of the training module is not yet implemented. We welcome pull requests for additional support.