SimpleFold wrapper.
pip install git+https://github.com/apple/ml-simplefold
pip install git+https://github.com/BejaLab/run_simplefold
usage: simplefold_init [-h] -D DATA_DIR [-m {simplefold_100M,simplefold_360M,simplefold_700M,simplefold_1.1B,simplefold_1.6B,simplefold_3B}]
SimpleFold wrapper: Initialize
options:
-h, --help show this help message and exit
-D DATA_DIR, --data-dir DATA_DIR
Base directory for data
-m {simplefold_100M,simplefold_360M,simplefold_700M,simplefold_1.1B,simplefold_1.6B,simplefold_3B}, --model {simplefold_100M,simplefold_360M,simplefold_700M,simplefold_1.1B,simplefold_1.6B,simplefold_3B}
Model name (optional)
usage: simplefold_run [-h] -i INPUT -O OUTPUT -D DATA_DIR -m {simplefold_100M,simplefold_360M,simplefold_700M,simplefold_1.1B,simplefold_1.6B,simplefold_3B} [-g GPUS] [-s SEED]
[-b BATCH] [-l LOG] [--tau TAU] [--steps STEPS]
SimpleFold wrapper: Run
options:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Path to input sequences
-O OUTPUT, --output OUTPUT
Output directory
-D DATA_DIR, --data-dir DATA_DIR
Base directory for data
-m simplefold_{100M,360M,700M,1.1B,1.6B,3B}, --model simplefold_{100M,360M,700M,1.1B,1.6B,3B}
Model name to use for inference
-g GPUS, --gpus GPUS GPU indices to use
-s SEED, --seed SEED Seed
-b BATCH, --batch BATCH
Batch size
-l LOG, --log LOG Raw log file
--tau TAU
--steps STEPS
usage: simplefold_select [-h] -I INPUT -O OUTPUT [-l] [--no-seed-suffix]
SimpleFold wrapper: Select
options:
-h, --help show this help message and exit
-I INPUT, --input INPUT
Directory containing 'run' outputs
-O OUTPUT, --output OUTPUT
Output directory for the best models
-l, --soft-link Soft link instead of hard copy
--no-seed-suffix The input files do not contain the seed as the suffix