Amazon instances like C6g, M6g, and R6g are powered by ARM-based AWS Graviton2 processors. They deliver better price performance over current generation instances. Additionally, local NVMe-based SSDs are physically connected to the host server and provide block-level storage that is coupled to the lifetime of the instance.
Since ARM-based processors are relative new, many software developed and run on Intel's x86 and x84 processors are not yet compatible with the aarch64 (ARM64) architecture. Here are some Linux-aarch64 compatible software that have been tested on AWS:
For your system to be able to compile software, you need to install several development tools, such as make, gcc, and autoconf. Because software compilation is not a task that every Amazon EC2 instance requires, these tools are not installed by default, but they are available in a package group called Development Tools that is easily added to an instance with the yum groupinstall command.
sudo yum groupinstall "Development Tools"
Instructions from https://docs.anaconda.com/anaconda/install/linux-aarch64/
- Download the 64-Bit (AWS Graviton2 / ARM64) Installer
wget https://repo.anaconda.com/archive/Anaconda3-2021.05-Linux-aarch64.sh
- Install Anaconda
bash ~/Downloads/Anaconda3-2021.04-Linux-aarch64.sh
-
To control whether or not each shell session has the base environment activated or not, run
conda config --set auto_activate_base FalseorTrue -
Close and open your terminal window for the installation to take effect, or you can enter the command
source ~/.bashrc
Instructions modified from https://docs.aws.amazon.com/dlami/latest/devguide/setup-jupyter-config.html
conda install jupyter
Start Jupyter Notebook server (more secure key self-signed SSL certificate in the guide above)
jupyter notebook
Jupyter provides a password utility. Run the following command and enter your preferred password at the prompt.
jupyter notebook password
Run the following command to forward all requests on local port 8888 to port 8888 on your remote Amazon EC2 instance. Update the command by replacing the location of your key to access the Amazon EC2 instance and the public DNS name of your Amazon EC2 instance. This command is for Linux/MacOS. For Windows, refer to the guide.
ssh -i ~/mykeypair.pem -N -f -L 8888:localhost:8888 ec2-user@ec2-###-##-##-###.compute-1.amazonaws.com
To login to Jupyter notebook server from your local browser, use this link: https://localhost:8888
The conda environment below supports basic machine learning packages
conda create -n ML python=3.7
Install packages
conda install ipykernel
conda install tensorflow
conda install scikit-learn
conda install xgboost
conda install netcdf4
conda install xarray
conda install matplotlib
conda install seaborn
Install kernel based on current conda environment
python -m ipykernel install --user --name ML
On your local browser, connect to your Jupyter notebook server and click refresh. You should be able to see a "ML" under the "New" button on the right of the window.
- Download and install from https://www.boost.org/doc/libs/1_77_0/more/getting_started/unix-variants.html
tar --bzip2 -xf /path/to/boost_1_77_0.tar.bz2
cd /path/to/boost_1_77_0
sudo ./bootstrap.sh
sudo ./b2 install
Download binary distribution for Linux aarch64 from https://cmake.org/download/
- Install Docker:
sudo yum install docker - Connect to the Docker daemon
sudo systemctl unmask docker
sudo systemctl start docker
Download the latest release (e.g. bowtie2-2.4.4-linux-aarch64.zip) from: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml. The pre-compiled binaries are ready to use.
- Install pre-requisites
sudo yum install autoconf automake make gcc perl-Data-Dumper zlib-devel bzip2 bzip2-devel xz-devel curl-devel openssl-devel ncurses-devel
- Download and install latest release (e.g. samtools-1.13.tar.bz2) from https://github.com/samtools/samtools
tar -xf samtools-1.13.tar.bz2
./configure
make
sudo make install
NGS read trimming tool that is specific, sensitive, and speedy
pip install atropos