Use the terminal to configure a compute instance

Prerequisites

Before we start, make sure you have the following prerequisites:

• An Azure subscription: You will need an active Azure subscription to create and manage compute instances.
• A compute instance: You should have already created a compute instance using the Azure portal or Azure CLI. If you haven’t done so, refer to the Azure documentation on how to create a compute instance.
• SSH client: Ensure that you have an SSH client installed on your local machine. For Windows users, you can use tools like PuTTY or Git Bash, while macOS and Linux users can use the built-in SSH client.

Connecting to a Compute Instance

To connect to your compute instance using the terminal, you will need the IP address or DNS name of the instance, as well as the SSH private key associated with it.

ol>

ssh -i @


Replace <path_to_private_key> with the path to your SSH private key, <username> with the username associated with the compute instance, and <compute_instance_ip_or_dns> with the IP address or DNS name of the compute instance.

If prompted, enter the passphrase for your SSH private key.

You should now be connected to your compute instance via the terminal.

Configuring Dependencies

Once connected to your compute instance, you can use the terminal to install and configure dependencies necessary for your data science solution. For example, you might need to install Python, R, or specific libraries such as TensorFlow or scikit-learn.

1. To install packages using the terminal, you can use package managers like apt or pip for Linux distributions, or brew for macOS.
2. Run the following commands to install the necessary dependencies:

# Update package lists
sudo apt update

# Install additional packages as required
sudo apt install

Replace <package_name> with the specific package you want to install.

Once the dependencies are installed, you can verify the installation by running the following command:

--version

Replace <package_name> with the name of the package you installed. This command will display the installed version of the package.

Performing Common Tasks

With your compute instance configured and dependencies installed, you can now use the terminal to perform common data science tasks. Here are a few examples:

1. Data exploration: Use command-line tools like head, tail, grep, and awk to inspect and manipulate data files.
2. Model training: Use frameworks like TensorFlow or PyTorch to train machine learning models directly from the terminal. You can write Python scripts or use pre-built command-line interfaces provided by these frameworks.
3. Model deployment: Utilize tools like Docker and Kubernetes to package and deploy your models as containers. You can manage container deployments and orchestration using the terminal.

Remember to refer to the official documentation and guides for the specific tools and frameworks you are using. The examples provided here are just a starting point, and there are numerous possibilities depending on your requirements.

Conclusion

Using the terminal to configure a compute instance is a powerful way to set up your data science environment on Azure. It allows you to install dependencies, perform tasks, and automate processes efficiently. By following the steps outlined in this article, you should be able to connect to your compute instance and configure it for your data science solution.

Remember to secure your compute instance by using strong passwords or SSH keys and keeping your dependencies up to date. Regularly monitor and manage your compute instances to optimize costs and resource utilization.

Happy data science journey on Azure!