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Clearer README runtime

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Kevin Wu
2022-10-04 22:24:30 -07:00
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commit f9fac66396
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README.md
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@@ -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`.