Dna2vec is an open-source library to train distributed representations of variable-length k-mers.
For more information, please refer to the paper: dna2vec: Consistent vector representations of variable-length k-mers
This repo is fork of the original pnpnpn/dna2vec repo. The upgrades are:
- runs within Docker container
- uses latest versions of packages
- has
nworkersargument, so that you can use more than 4 workers - contains openBLAS but not sure if NumPy or SciPy can utilize it, needs testing
There's no need to install packages or no need to clone the repository. To run the script just type the following command in terminal
docker run --rm -v $(pwd):/app/data --user 1000 alperyilmaz/dna2vec train_dna2vec.py -c sample_config.ymlThe container mounts the working directory, thus files are read in or write out to folders within working directory. Just put your fasta files into a folder and edit config file and then start training.
The sample config file can be as simple as:
inputs: inputs/chr*.fa
k-low: 3
k-high: 5
out-dir: results/
Please refer to hg38-20161219-0153.yml for full blown example. For details about usage, please refer to pnpnpn/dna2vec repo.
New: You can add nworkers argument to take full advantage of available CPUs.
This is not systematic testing results, merely my observations at different platforms
| Hardware | Number of workers | Words/s |
|---|---|---|
| i7-4700HQ CPU @ 2.40GHz (Laptop) | 8 | 242,794 |
| c5.4xlarge | 16 |
This software is licensed under the MIT license