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Kevin Wu
2022-09-25 23:52:23 -07:00
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README.md
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@@ -67,9 +67,9 @@ some_dir/
- sampled_pdb/ # Contains the .pdb files resulting from converting the sampled angles to cartesian coordinates
```
### Self-consistency TM scores
### Maximum training similarity TM scores
After generating sequences, we automatically calculate TM-scores to evaluate the simliarity of the generated sequences and the original sequences. You may want to append the `--skiptm` argument to the above command if you wish to skip the very time-consuming calculation of TM scores against training set; doing so takes about ~1min per generated example on a 128-core machine running fully parallelized.
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`.
### Visualizing "folding" process
@@ -115,7 +115,7 @@ python ~/projects/protdiff/bin/omegafold_across_gpus.py esm_residues/*.fasta -g
python ~/projects/protdiff/bin/omegafold_self_tm.py # Requires no arguments
```
After executing these commands, the final command produces a json file of all scmtm scores, as well as a pdf file containing a histogram of the score distribution.
After executing these commands, the final command produces a json file of all scmtm scores, as well as various pdf files containing plots and correlations of the scTM score distribution.
## Tests
@@ -125,23 +125,6 @@ Tests are implemented through a mixture of doctests and unittests. To run unitte
python -m unittest -v
```
## Singularity/amulet
To run on singularity/amulet, make sure you have already downloaded the CATH dataset (see instructions above). If you do not have amulet installed, folow the instructions at <https://amulet-docs.azurewebsites.net/main/setup.html>. This should leave you with a conda environment named `amlt8`. Note that this environment should be _separate_ from the environment for the diffusion model itself. Note that you do _not_ need to create the given `environment.yml` to submit to amulet/singularity; the environment for running the code is separately set up within the Singularity compute cluster.
With these two requirements, to run training on singularity, run:
```bash
conda activate amlt8 # Activate the conda env.
amlt run -y scripts/amlt.yaml -o results
```
Within this `amlt.yaml` file, the python command contains a pointer to a config json file. Edit the path indicated here to
use a different configuration for training.
Note rearding the structure of the `amlt.yaml` file: installing packages via conda is very slow on the Singularity cluster, so
we recreate the same set of packages installed via pip instead of relying on conda.
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a

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# Singularity/amulet
To run on singularity/amulet, make sure you have already downloaded the CATH dataset (see instructions above). If you do not have amulet installed, folow the instructions at <https://amulet-docs.azurewebsites.net/main/setup.html>. This should leave you with a conda environment named `amlt8`. Note that this environment should be _separate_ from the environment for the diffusion model itself. Note that you do _not_ need to create the given `environment.yml` to submit to amulet/singularity; the environment for running the code is separately set up within the Singularity compute cluster.
With these two requirements, to run training on singularity, run:
```bash
conda activate amlt8 # Activate the conda env.
amlt run -y scripts/amlt.yaml -o results
```
Within this `amlt.yaml` file, the python command contains a pointer to a config json file. Edit the path indicated here to
use a different configuration for training.
Note rearding the structure of the `amlt.yaml` file: installing packages via conda is very slow on the Singularity cluster, so
we recreate the same set of packages installed via pip instead of relying on conda.