Installation and Download¶
System requirements¶
At our institute in Dortmund, crYOLO is running on the following operation systems:
- Ubuntu 16.04.4 LTS
- Ubuntu 18.04 LTS
- CentOS 7
We don’t test it but it should run on Windows as well.
Moreover the following GPUs are used:
- NVIDIA Titan V
- NVIDIA GTX 1080
- NVIDIA GTX 1080Ti
- NVIDIA RTX 2080 TI
- NVIDIA GV 100
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. These will be automatically installed during crYOLO installation.
Install crYOLO¶
Note
Please read the Complimentary Science Software License before using crYOLO. If you are interested in using crYOLO in a commercial context please contact stefan.raunser@mpi-dortmund.mpg.de
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 conda-forge -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy==1.14.5 cython wxPython==4.0.4 intel-openmp==2019.4 pip=20.2.3
Activate the environment:
>>> conda 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]'
Note
During the installation of crYOLO you will see the following error message:
ERROR: imagecodecs-lite 2019.2.22 has requirement numpy>=1.15.4, but you’ll have numpy 1.14.5 which is incompatible.
However, you can ignore it. It is actually also working with numpy==1.14.5
However, from a pip version > 20.2.3 the installation will fail. Therefore the conda enviroments will be installed with an outdated pip version. With crYOLO 1.8 this problem will be solved.
Hint
You can also integrate crYOLO as Environment Module
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 |
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Uploaded: | 27 May 2020 |
Download: | Link |
Config: | Commands to create the config file can be found here. |
For cryo images (neural network denoised with JANNI)¶
Datasets: | 43 real, 10 simulated, 10 particle free data sets on various grids with contamination |
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Uploaded: | 27 May 2020 |
Download: | Link |
Config: | Commands to create the config file can be found here. |
For negative stain images¶
Datasets: | 10 real data sets |
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Uploaded: | 26 February 2019 |
Download: | Link |
Config: | Commands to create the config file can be found here. |