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48 lines
1.3 KiB
48 lines
1.3 KiB
2 years ago
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# FastFold Inference
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## Table of contents
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- [Overview](#📚-overview)
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- [Quick Start](#🚀-quick-start)
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- [Dive into FastFold](#🔍-dive-into-fastfold)
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## 📚 Overview
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This example lets you to quickly try out the inference of FastFold.
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**NOTE: We use random data and random parameters in this example.**
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## 🚀 Quick Start
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1. Install FastFold
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We highly recommend installing an Anaconda or Miniconda environment and install PyTorch with conda.
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```
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git clone https://github.com/hpcaitech/FastFold
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cd FastFold
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conda env create --name=fastfold -f environment.yml
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conda activate fastfold
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python setup.py install
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```
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2. Run the inference scripts.
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```bash
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python inference.py --gpus=1 --n_res=256 --chunk_size=None --inplace
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```
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+ `gpus` means the DAP size
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+ `n_res` means the length of residue sequence
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+ `chunk_size` introduces a memory-saving technology at the cost of speed, None means not using, 16 may be a good trade off for long sequences.
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+ `inplace` introduces another memory-saving technology with zero cost, drop `--inplace` if you do not want it.
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## 🔍 Dive into FastFold
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There are another features of FastFold, such as:
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+ more excellent kernel based on triton
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+ much faster data processing based on ray
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+ training supported
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More detailed information can be seen [here](https://github.com/hpcaitech/FastFold/).
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