📦 Installation

Prerequisites

  • Linux x86_64

  • Python 3.9+

  • Pytorch 2.3+ (with CUDA support for GPU inference)

    • https://pytorch.org/get-started/locally/

  • Torchmetrics 0.9.0+ (for training)

    • python -m pip install torchmetrics

Optional

  • Dorado 0.7.3+ (optional, for basecalling)

    • https://github.com/nanoporetech/dorado

  • SAMtools 1.16.1+ (optional, for BAM file processing)

    • http://www.htslib.org/

  • Python package requirements are listed in requirements.txt and will be installed automatically when you install DeepRM.

Installation options

  • Estimated time: ~10 minutes

  1. Install via PIP (recommended)

python -m pip install deeprm

  1. Install from source (GitHub)

git clone https://github.com/vadanamu/deeprm
cd deeprm
python -m pip install -U pip
python -m pip install -e .

Verify Installation

deeprm --version
deeprm check

  • If everything is installed correctly, you should see the version of DeepRM and a message indicating that the installation is successful.

  • If you encounter CUDA or torch-related errors, make sure you have installed the correct version of PyTorch with CUDA support.

Build from Source

  • DeepRM uses a C++ preprocessing tool for acceleration.

  • The C++ preprocessing tool is both provided as a precompiled binary and source code.

  • Depending on your system configuration, you may need to build the C++ preprocessing tool from source.

  • The C++ source code is located in the cpp directory of the DeepRM repository.

  • Please refer to the advanced installtion page for detailed build instructions.