About this Install Guide

This guide is intended to get you up and running with this tool quickly with the most common settings. For a thorough review of all options, refer to the official documentation.

Anaconda Distribution is a popular open-source Python distribution.

It includes access to a repository with over 8,000 open-source data science and machine learning packages.

The conda command can be used to quickly install and use Python packages.

Follow the instructions below to install and use Anaconda Distribution on an Arm server.

Before you begin

Confirm you are using an Arm machine by running:

    

        
        
            uname -m
        
    

The output should be:

    

        
        aarch64

        
    

If you see a different result, you are not using an Arm computer running 64-bit Linux.

The installer requires some desktop related libraries. The dependencies can be met by installing a desktop environment.

For Ubuntu/Debian run the command:

    

        
        
            sudo apt install xfce4 -y
        
    

For Amazon Linux run the command:

    

        
        
            sudo amazon-linux-extras install mate-desktop1.x
        
    

Download

Download the latest Anaconda Distribution.

    

        
        
            curl -O https://repo.anaconda.com/archive/Anaconda3-2023.09-0-Linux-aarch64.sh
        
    

Depending on the version, the downloaded filename will be of the form Anaconda3-20XX.YY-Linux-x86_64.sh where the XX and YY values represent the year and month of the latest release.

Install

Run the downloaded install script.

The default installation directory is $HOME/anaconda3. Change the installation directory as needed using the -p option to the install script.

If you wish to review the license terms before accepting, remove -b.

    

        
        
            sh ./Anaconda3-2023.09-0-Linux-aarch64.sh -b
        
    

The install will take a couple of minutes to complete.

The batch installation will not setup the shell.

To setup the shell run.

    

        
        
            eval "$($HOME/anaconda3/bin/conda shell.bash hook)"
        
    

Get started

Test Anaconda Distribution by running simple TensorFlow and PyTorch examples.

TensorFlow

Create a new conda environment named tf, install TensorFlow, and activate the new environment.

    

        
        
            conda create -n tf tensorflow -y
        
    

Activate the environment.

    

        
        
            conda activate tf
        
    

The shell prompt will now show the tf environment.

    

        
        (tf) ubuntu@ip-10-0-0-251:~$

        
    

Run a simple check to make sure TensorFlow is working.

Using a text editor copy and paste the code below into a text file named tf.py

    

        
        
            import tensorflow as tf
print(tf.__version__)
print(tf.reduce_sum(tf.random.normal([1000,1000])))
exit()
        
    

Run the example code:

    

        
        
            python ./tf.py
        
    

The expected output format is below. Your version may be slightly different.

    

        
        2.12.0
tf.Tensor(342.34387, shape=(), dtype=float32)

        
    

PyTorch

Create a new conda environment named torch, install PyTorch, and activate the new environment.

    

        
        
            conda create -n torch pytorch -y
        
    
    

        
        
            conda activate torch
        
    

Using a text editor copy and paste the code below into a text file named pytorch.py

    

        
        
            import torch
print(torch.__version__)
x = torch.rand(5,3)
print(x)
exit()
        
    

Run the example code:

    

        
        
            python ./pytorch.py
        
    

The expected output is similar to:

    

        
        2.1.0
tensor([[0.9287, 0.5931, 0.0239],
        [0.3402, 0.9447, 0.8897],
        [0.3161, 0.3749, 0.6848],
        [0.8091, 0.6998, 0.7517],
        [0.2873, 0.0549, 0.2914]])

        
    

You are ready to use Anaconda Distribution.

Explore the many machine learning articles and examples using TensorFlow and PyTorch.


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