For basic install instructions, look up the main github readme.
Built-in model directory
By default, the pretrained cellpose models are downloaded to
This path on linux would look like
/home/USERNAME/.cellpose/, and on Windows,
C:/Users/USERNAME/.cellpose/models/. These models are downloaded the first time you
try to use them, either on the command line, in the GUI or in a notebook.
If you’d like to download the models to a different directory,
and are using the command line or the GUI, before you run
python -m cellpose ...,
you will need to always set the environment variable
(thanks Chris Roat for implementing this!).
To set the environment variable in the command line/Anaconda prompt on windows run the following command modified for your path:
set CELLPOSE_LOCAL_MODELS_PATH=C:/PATH_FOR_MODELS/. To set the environment variable in the command line on
To set this environment variable when running cellpose in a jupyter notebook, run this code at the beginning of your notebook before you import cellpose:
import os os.environ["CELLPOSE_LOCAL_MODELS_PATH"] = "/PATH_FOR_MODELS/"
M1 Mac installation
Please use the instructions provided on image.sc <https://forum.image.sc/t/cellpose-on-macos-m1-pro-apple-silicon-arm64/68018/4> by Peter Sobolewski. From the command line you can choose the Mac device with
python -m cellpose --dir path --gpu_device mps --use_gpu
AMD GPU ROCm installation
As an alternative to the CUDA acceleration for NVIDIA GPUs, you can use the ROCm acceleration for AMD GPUs. This is not yet supported on Windows, but is supported on Linux. Installation instructions are available here. Just like the NVIDIA CUDA installation, you will need to install the ROCm drivers first and then install Cellpose. Be warned that the ROCm project is significantly less mature than CUDA, and you may run into issues.
The ROCm acceleration is not yet supported on Windows, and is only supported on Linux. If you are on Windows, you will need to use CUDA acceleration.
ROCm is significantly less mature than the CUDA acceleration, and you may run into issues.
If you are having issues with CUDA on Windows, or want to use Cuda Toolkit 10, please follow these instructions:
conda create -n cellpose pytorch=1.8.2 cudatoolkit=10.2 -c pytorch-lts conda activate cellpose pip install cellpose
If you receive the error:
No module named PyQt5.sip, then try
uninstalling and reinstalling pyqt5
pip uninstall pyqt5 pyqt5-tools pip install pyqt5 pyqt5-tools pyqt5.sip
If you are having other issues with the graphical interface and QT, see some advice here .
If you have errors related to OpenMP and libiomp5, then try
conda install nomkl
If you receive an error associated with matplotlib, try upgrading it:
pip install matplotlib --upgrade
If you receive the error:
ImportError: _arpack DLL load failed, then try uninstalling and reinstalling scipy
pip uninstall scipy pip install scipy
If you are on Yosemite Mac OS or earlier, PyQt doesn’t work and you won’t be able to use the graphical interface for cellpose. More recent versions of Mac OS are fine. The software has been heavily tested on Windows 10 and Ubuntu 18.04, and less well tested on Mac OS. Please post an issue if you have installation problems.
cellpose relies on the following excellent packages (which are automatically installed with conda/pip if missing):