Installation and Download¶
System requirements¶
crYOLO was tested on Ubuntu 16.04.4 LTS and Ubuntu 18.04 with an NVIDIA Geforce 1080 / Geforce 1080Ti.
However, it should run on Windows as well.
As the GPU accelerated version of tensorflow does not support MacOS, crYOLO does not support it either.
crYOLO depends on CUDA Toolkit 9.0 and the cuDNN 7.1.2 library. It will be automatically installed during crYOLO installation.
Install crYOLO¶
Note
Please read the Complimentary Science Software License before using crYOLO.
The following instructions assume that pip and anaconda or miniconda are available. In case you have a old cryolo environment installed, first remove the old one with:
>>> conda env remove --name cryolo
After that, create a new virtual environment:
>>> conda create -n cryolo -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy==1.14.5 cython wxPython==4.0.4 intel-openmp==2019.4
Activate the environment:
>>> source activate cryolo
In case you run crYOLO on a GPU run:
>>> pip install 'cryolo[gpu]'
But if you want to run crYOLO on a CPU run:
>>> pip install 'cryolo[cpu]'
That’s it!
You might want to check if everything is running as expected. Here is a reference example:
Download the general models¶
We provide three general models. One for cryo-EM images which was trained on low-pass filtered images, another one for cryo-EM images but trained for images denoised by JANNI and one for negative stain images.
For cryo images (low-pass filtered)¶
| Datasets: | 43 real, 10 simulated, 10 particle free datasets on various grids with contamination |
|---|---|
| Uploaded: | 16 March 2020 |
| Download: | Link |