From f9fac663965d6cc45ae7387c4054ab16024ae45d Mon Sep 17 00:00:00 2001 From: Kevin Wu Date: Tue, 4 Oct 2022 22:24:30 -0700 Subject: [PATCH] Clearer README runtime --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 82f170a..c1b6f2b 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ Providing this path to a premade script, such as the one for sampling, is detail ## Sampling protein backbones -To sample protein backbones, use the script `bin/sample.py`. Example commands to do this using the pretrained weights described above are as follows. Sampling takes ~7 minutes for 512 structures using an Nvidia 2080Ti GPU paired with an Intel i9-9960X. +To sample protein backbones, use the script `bin/sample.py`. Example commands to do this using the pretrained weights described above are as follows. ```bash # To sample 256 backbones @@ -89,7 +89,7 @@ python ~/projects/foldingdiff/bin/sample.py --num 256 --device cuda:3 python ~/projects/foldingdiff/bin/sample.py -l sweep --device cuda:3 ``` -This will run the trained model contained in the `models/cath_pretrained` folder and generate sequences of varying lengths. If you wish to load the test dataset and include test chains in the generated plots, use the option `--testcomparison`; note that this requires downloading the CATH dataset, see above. Not specifying a device will default to the first device `cuda:0`; use `--device cpu` to run on CPU (though this will be very slow). Running `sample.py` will create the following directory structure in the diretory where it is run: +This will run the trained model contained in the `models/cath_pretrained` folder and generate sequences of varying lengths. If you wish to load the test dataset and include test chains in the generated plots, use the option `--testcomparison`; note that this requires downloading the CATH dataset, see above. Running `sample.py` will create the following directory structure in the diretory where it is run: ``` some_dir/ @@ -99,6 +99,13 @@ some_dir/ - model_snapshot/ # Contains a copy of the model used to produce results ``` +Not specifying a `--device` will default to the first device `cuda:0`; use `--device cpu` to run on CPU (though this will be very slow). See the following table for runtimes from our machines. + +| Device | Runtime estimates sampling 512 structures | +| --- | --- | +| Nvidia RTX 2080Ti | 7 minutes | +| i9-9960X (16 physical cores) | 2 hours | + ### Maximum training similarity TM scores After generating sequences, we can calculate TM-scores to evaluate the simliarity of the generated sequences and the original sequences. This is done using the script under `bin/tmscore_training.py`.