Installing ChipStream

You can download ChipStream installers from the release page.

Alternatively, you can install ChipStream via pip:

pip install chipstream[all]

If you don’t need all dependencies, you can also install only a selection, e.g. cli,torch if you are not planning to use the graphical user interface:

pip install chipstream[cli,torch]

GPU Support

If you have a CUDA-compatible GPU and your Python installation cannot access the GPU (torch.cuda.is_available() is False), please use the installation instructions from pytorch (https://pytorch.org/get-started/locally/). For instance, if your graphics card supports CUDA 12.9, you can install torch with this pytorch.org index URL:

# Install with CUDA/GPU support (does not work on macOS)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu129

If your graphics card supports a newer CUDA version, you can check the backends supported by pytorch (https://download.pytorch.org) and replace cu129 with e.g. cu130 for CUDA 13.

CPU Support

If you don’t have a CUDA-capable GPU, you may install a light version of torch:

# Only do this if you would like to have a light CPU-only version of torch
pip install torch==2.9.1+cpu torchvision==0.24.1+cpu --index-url https://download.pytorch.org/whl/cpu

Finally, you can install ChipStream:

pip install chipstream[all]

The [all] extra is an alias for [cli,gui,torch]. With the capabilities:

  • cli: command-line interface (chipstream-cli command)

  • gui: graphical user interface (chipstream-gui command)

  • torch: install PyTorch (machine-learning for segmentation)