# JetStream2 Setup

Machines

## Last time - base calling on AWS with Guppy

# Configuring AWS instance for GPU-enabled Docker

**Launch on AWS**

- AMI: Deep Learning AMI (Ubuntu 18.04) Version 49.0
- p3.16xlarge
- Allow TCP connection from anywhere to port 22 and 1247
- Connect:

chmod 400 ~/Dropbox/aws_keys/genome_camp.pem
ssh -i ~/Dropbox/aws_keys/genome_camp.pem [e-mail]
**Configure**

- Install mesa utilities:

sudo apt install mesa-utils
- Check GPU config

nvidia-smi -L
- nvidia runtime container

curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-container-runtime/$distribution/nvidia-container-runtime.list | \
sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get update
- add nvidia key

curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
sudo apt-key add -

*Install Docker*

- check (and/or install) runtime container

sudo apt-get install nvidia-container-runtime
- Ensure the nvidia-container-runtime-hook is accessible from $PATH.

which nvidia-container-runtime-hook
- Include the --gpus flag when you start a container to access GPU resources.
Specify how many GPUs to use. For example:

docker run -it --rm --gpus all ubuntu nvidia-smi

** Tip**

- If getting a lock on apt-install

https://itsfoss.com/could-not-get-lock-error/

**Install icommands"
- Add package info

wget -qO - https://packages.irods.org/irods-signing-key.asc | sudo apt-key add -
echo "deb [arch=amd64] https://packages.irods.org/apt/
$(lsb_release -sc) main" | sudo tee /etc/apt/sources.list.d/renci-irods.list
sudo apt-get update
- Install icommands

sudo apt install irods-icommands
- Configure icommands

iinit...
- get data

# Basecalling

**Pull and run Guppy-GPU**

- Pull and container

docker pull genomicpariscentre/guppy-gpu

Reads are separated by the flowcell they were generated on:

Command for flongle

FLO-FLG001 SQK-LSK109

docker run -it -v /home/ubuntu/fast5/flngl:/home --gpus all genomicpariscentre/guppy-gpu guppy_basecaller -i /home -s /home/fastq/flngl_fastq --flowcell FLO-FLG001 --kit SQK-LSK109 --compress_fastq -x "cuda:all" --trim_barcodes

Command for R9 flowcell

docker run -it -v /home/ubuntu/fast5/flo_min106:/home --gpus all genomicpariscentre/guppy-gpu guppy_basecaller -i /home -s /home/fastq/flo_min106_fastq --flowcell FLO-MIN106 --kit SQK-LSK109 --compress_fastq -x "cuda:all" --trim_barcodes

## Fall 2022 Work

### Some goals and things

Assembly project

- Redo basecalling using Bonito: https://github.com/nanoporetech/bonito
- Use read correction : https://github.com/nanoporetech/medaka
- correction: Can't use Medaka yet (only on an assembly).
- Potentially Canu or
- FastP?
- Estimate genome size:
- https://arxiv.org/abs/2003.11817 also https://github.com/fanagislab/kmerfreq
- Possibly this too: http://qb.cshl.edu/genomescope/

- Genome assembly:
- Flye (faster)
- CANU (slow)

For the main JetStream2 server

- Setup folks on JetStream/ Linux accounts and JetStream2/ACCESS
- Plan for how to store and organize our data
- Global installs
- Conda
- Jupyter
- Git/Github (gh)
- Docker

- Setting up Hackpad? https://github.com/dropbox/hackpad