minknow_guppy_gpu

guppy is outdated, use dorado

For newer minknow versions using dorado instead of guppy, just do the following instead of using the ansible role:

sudo apt update
sudo apt install wget
wget -O- https://cdn.oxfordnanoportal.com/apt/ont-repo.pub | sudo gpg --dearmour -o /etc/apt/trusted.gpg.d/nanoporetech-archive-keyring.gpg
echo "deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/nanoporetech-archive-keyring.gpg] http://cdn.oxfordnanoportal.com/apt jammy-stable non-free" | sudo tee /etc/apt/sources.list.d/nanoporetech.sources.list
sudo apt update
sudo apt install ont-standalone-minknow-gpu-release -y
#if needed adjust doradod service in /etc/systemd/system/multi-user.target.wants/doradod.service

Description

This Ansible role sets up MinKNOW from Oxford Nanopore Technologies with live GPU basecalling support from Guppy. Alternatively only installs Guppy with GPU support without installing MinKNOW. Currently only supports Debian/Ubuntu because that's what I needed, but feel free to contribute. Installation is performed in much the same way as ONT recommends according to this guide (requires login, corporate BS).

Requirements

None other than ansible.

Installation

Install from Ansible Galaxy with

ansible-galaxy install kasperskytte.minknow_guppy_gpu

Role Variables

Defaults can be found in vars/main.yml

• ont_install_minKNOW: Whether to also install MinKNOW or not
• ont_guppy_gpu_dir: Location where the GPU version of Guppy will be installed

If ont_install_minKNOW is true the following options are passed on directly to the guppy_basecall_server command in the system service unit:

• guppy_gpu_runners_per_device
• guppy_chunks_per_runner
• guppy_cfg: Which Guppy basecall configuration file to use
• guppy_cuda_devices: Which CUDA GPU's to use, fx auto or cuda:0,1

Other internal variables that don't require any adjustments by the end user is available in vars/main.yml.

Setting optimal Guppy parameters

The optimal settings of the last two options depends on your hardware. ONT has written the following about it (source):

The following calculation provides a rough ceiling to the amount of GPU memory that Guppy will use:

memory used [in bytes] = gpu_runners_per_device * chunks_per_runner * chunk_size * model_factor

Where model_factor depends on the basecall model used:

• Fast: 1200
• HAC: 3300
• SUP: 12600

Note that gpu_runners_per_device is a limit setting. Guppy will create at least one runner per device and will dynamically increase this number as needed up to gpu_runners_per_device. Performance is usually much better with multiple runners, so it is important to choose parameters such that chunks_per_runner * chunk_size * model_factor is less than half the total GPU memory available. If this value is more than the available GPU memory, Guppy will exit with an error.

For example, when basecalling using a SUP model and a chunk size of 2000 on a GPU with 8 GB of memory, we have to make sure that the GPU can fit at least one runner:

chunks_per_runner * chunk_size * model_factor should be lower than GPU memory

chunks_per_runner * 2000 * 12600 should be lower than 8 GB

chunks_per_runner lower than ~340

This represents the limit beyond which Guppy will not run at all. For best performance we recommend using an integer fraction of this number, rounded down to an even number, e.g. a third (~112) or a quarter (~84). Especially for fast models, it can be best to have a dozen runners or more. The ideal value varies depending on GPU architecture and available memory.

Example Playbook

Including an example of how to use your role (for instance, with variables passed in as parameters) is always nice for users too:

- hosts: servers
  become: true
  roles:
    - kasperskytte.minknow_guppy_gpu
  vars:
    ont_install_minKNOW: true
    guppy_cfg: dna_r9.4.1_450bps_hac.cfg
    guppy_gpu_runners_per_device: 8
    guppy_chunks_per_runner: 80
    guppy_cuda_devices: auto

License

GPL, 2.0 or later