Running reconstruction experiments
executing and analyzing a particle reconstruction model with cryoDRGN-AI
Using a submission script to a compute cluster
Training a reconstruction neural network is usually computationally intensive; we thus recommend using a high-performance compute cluster to run cryoDRGN-AI experiments.
For example, a submission script to a cluster using the Slurm job scheduler would look like:
#!/bin/bash
#SBATCH --partition=cryoem
#SBATCH --job-name=cryodrgnai_abinit
#SBATCH -t 8:00:00
#SBATCH --gres="gpu:a100:1"
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --mem-per-cpu=16G
cd /scratch_dir/my_name/
cryodrgn abinit /data_dir/empiar_benchmark/particles.128.mrcs \
--ctf /data_dir/empiar_benchmark/ctf.pkl -o 001_abinit/And would be submitted using:
(drgnai-env) $ sbatch -t 8:00:00 -p cryoem -J cryodrgnai_test -o crydrgnai_test.out cryodrgnai_slurm.sh Examining model output
Epochs in cryoDRGN-AI are 1-indexed, as in the rest of cryoDRGN (but not the original implementation of cryoDRGN-AI, in which epochs were 0-indexed). This means that epochs are labelled 1...n instead of 0...n-1.
Outputs are also structured similarly as in other cryoDRGN reconstruction commands, with the following types of files produced by the model:
weights.<epoch>.pklcheckpoint of model weightspose.<epoch>.pklreconstructed image posesz.<epoch>.pklreconstructed z-latent-space co-ordinatesreconstruct.<epoch>.pkla single reconstructed volume, using the image closest to the centroid in the z-latent-space in the case of a heterogeneous modelanalyze.<epoch>/subfolder containing the outputs of post-analyses produced by running theanalyzecommand; the final training epoch is analyzed by default
Restarting finished experiments
Experiments that have finished running can be run for further epochs using the --load flag, which instructs abinit to use a weights.* model checkpoint as a starting point. The new number of total epochs for the reloaded model (not the number of additional epochs) should be specified using -n:
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