CryoSPARC Guide

Job: DeepEMhancer (Wrapper)

Overview of the DeepEMhancer Wrapper in CryoSPARC.


This job is a wrapper for DeepEMhancer, a deep learning model trained to perform masking-like and sharpening-like postprocessing on cryo-EM maps [1].

DeepEMhancer License

Structura Biotechnology Inc. and CryoSPARC do not license DeepEMhancer nor distribute DeepEMhancer binaries. Please ensure you have your own copy of DeepEMhancer licensed and installed under the terms of its Apache License 2.0.


For the following instructions, do not use the Anaconda Python that is installed by CryoSPARC. This installation is destroyed and recreated with CryoSPARC updates, so new environments created by it will be erased and any changes made to it may break other CryoSPARC jobs.
DeepEMhancer has dependencies on other python packages outside of what CryoSPARC provides. In order to install DeepEMhancer, you must create a conda environment using a python installation separate from the CryoSPARC python installation, and use that environment to install dependencies.
Install deepEMhancer using Anaconda or Miniconda, from source or Anaconda cloud, following instructions in the deepEMhancer repository:
Important considerations for Master/Worker or Cluster installations: The path to the Anaconda installation on the machine hosting cryosparc_master must exactly match the path on machines hosting cryosparc_worker The Anaconda installation directory must be accessible by the CryoSPARC Linux user account with the required permissions for executing the deepemhancer script.

Finding the executable path

With deepEMhancer installed and its Anaconda environment active in the current shell, run
which deepemhancer
to find the full path to the deepEMhancer script. It should be similar to this:
When running the CryoSPARC wrapper, input this as the value for the ‘Path to deepEMhancer executable’ parameter.

(Optional) Create a shell script

The CryoSPARC and deepEMhancer environments and dependencies may conflict. As a workaround, you may need to wrap deepEMhancer in a shell script that deactivates the CryoSPARC environment and activates the deepEMhancer one.
Create a file in a well known location, such as the home directory (e.g., ~/ The file should contain the following, making the noted substitutions:
#!/usr/bin/env bash
if command -v conda > /dev/null 2>&1; then
conda deactivate > /dev/null 2>&1 || true # ignore any errors
conda deactivate > /dev/null 2>&1 || true # ignore any errors
unset _CE_CONDA
source $HOME/anaconda3/etc/profile.d/
conda activate deepEMhancer_env
exec deepemhancer [email protected]
  • Replace $HOME/anaconda3 on line 17 with the path to your Anaconda installation.
Make this file executable by the CryoSPARC user from the command line
chmod +x
Then, when running the CryoSPARC wrapper, input the full path to as the value for the ‘Path to deepEMhancer executable’ parameter. This file needs to be accessible at the same path from both the master and worker nodes.


  • Volume map
    • half maps (map_half_A and map_half_B) or full map (map), as specified by the Use half maps parameter
  • Input mask (optional)
    • used to calculate the noise mean and standard deviation if the Normalization mode parameter is 1: Noise statistics, or provided as a binary mask if the normalization parameter is 2: Binary Mask


  • Sharpened volume map (map_sharp)

Common Parameters

  • Path to deepEMhancer executable: full path to the deepEMhancer script, for example /home/cryosparcuser/anaconda3/envs/deepEMhancer_env/bin/deepemhancer
  • Path to deepEMhancer models: full path to where the deepEMhancer pretrained models were installed, for example /home/cryosparcuser/.local/share/deepEMhancerModels/production_checkpoints
For details on the other processing parameters, see the deepEMhancer repository, or the help command (deepemhancer -h with the Anaconda environment activated)

Next Steps

Downloading and viewing the postprocessed map in a software such as UCSF Chimera, or in CryoSPARC’s built in volume viewer.


[1] R. Sanchez-Garcia, J. Gomez-Blanco, A. Cuervo et al., *“*DeepEMhancer: a deep learning solution for cryo-EM volume post-processing”, Communications Biology, vol. 4, no. 874, 2021. Available: 10.1038/s42003-021-02399-1.