Running Reconstruction Models
Last updated
Last updated
Listed here are the commands needed to obtain results that can be used as input for CryoBench analyses using each of our example reconstruction methods.
We use the conformational heterogeneity IgG-1D/ CryoBench dataset available through ; in addition to being accessed through a web browser, it can also be downloaded and unpacked through a command-line terminal:
All commands are assumed to run from the same directory as IgG-1D/; also note that the conda environments used are the same ones as described in the . We use the cryobench/inputs/ folder to store the output of each method for subsequent analyses:
This command took 1h 14min on a single A100 GPU:
This command took 4h 24min using four A100 GPUs; ab-initio takes longer, especially now that we are training with 50 epochs intead of 20 as with the fixed poses example above, but using --multigpu reduces runtimes 2-4x:
For both this and the cryodrgn train_vae command you may have to use the --lazy command for more memory-efficient (but less time-efficient) data loading if running into GPU out-of-memory issues.
We use the setup command to generate the config file drgnai_fixed/configs.yaml, but it can also be generated manually using a text editor such as vim. This train command took 3h 10min on a single GPU:
We again use the setup command, now avoiding using poses and using a smaller number of epochs. This time training took 4h 28min using four A100 GPUs; DRGN-AI detects the number of GPUs available automatically with no need for a --multigpu flag:
For this method we first have to create a pose file in the correct format. The train_cv command took roughly six hours to complete in this case using 4 A100 GPUs:
Note that to run the RECOVAR reconstruction pipeline, you will have to use the path to the pipeline submission file from within the git submodule that was checked out during CryoBench installation. This command took 47 minutes to run employing a single A100 GPU:
Our GitHub repository contains example scripts for the output of CryoSPARC 3D Classification, Ab-initio Reconstruction, 3D Flex, and 3D Variability models. We refer to the for how to run these models.