When activate a conda enviroment?

Question

I'm currently reading chapter 3 of the book, about Azure Machine Learning. I developed my first ML pipeline, using a compute instance. Now the next part is doing the same with a compute cluster, and there is a difference in the initial part of the code with respect to the one which uses a compute instance, whit a cell used to create a AML (conda) environment.
My question is: why for use of a compute cluster is necessary to define an initial conda environment? And when in the future I will need to active an AML environment, before starting my work?
While with a compute instance not?

Answer 1

An initial conda environment ensures that your code and dependencies are isolated from the system-wide Python environment and other users' work. This isolation prevents conflicts between different packages and versions, making your computations more reliable and reproducible.

When you create a specific conda environment for your work on the cluster, you can capture the exact package versions used. This makes it easier to reproduce your experiments in the future, even if package versions have changed.

Regarding when to activate an AML environment before starting your work:

You should activate the AML environment before executing any code or running any jobs on the AML compute cluster. This ensures that your code runs in the desired environment with the specified packages and configurations.

Answer 2

Hello @Mauro Andretta

Thanks for reaching out to us, it will be good to share the book you are referring to so that we can know the whole story.

Personally, I prefer to create a conda env all the time so that I can have the right dependencies and packages.

To answer your question generally, when you use a compute instance, you can create and activate a conda environment directly in the Jupyter notebook. However, when you use a compute cluster, you need to create and activate a conda environment before submitting your job to the cluster. This is because the job will be executed on a remote compute target, and the environment needs to be set up on that target before the job can run.

To activate an AML environment, you need to first create the environment using a conda specification file or a Dockerfile. Once the environment is created, you can activate it using the following command -

conda activate <environment_name>

This command activates the specified environment, allowing you to use the packages and dependencies installed in that environment. You can then run your code or submit your job to the compute cluster using the activated environment.

It's important to activate the correct environment before running your code or submitting your job, as different environments may have different package versions or dependencies that could affect the behavior of your code.

The reason why the book defines a conda environment for the example using a compute cluster is because the job will be executed on a remote compute target, and the environment needs to be set up on that target before the job can run.

When you use a compute instance, you can create and activate a conda environment directly in the Jupyter notebook, because the notebook is running on the compute instance itself. However, when you use a compute cluster, the job will be executed on a remote compute target, and the environment needs to be set up on that target before the job can run.

Defining a conda environment for the compute cluster example ensures that the necessary packages and dependencies are installed on the remote compute target before the job is executed, and that the job runs consistently and reproducibly across different machines and environments.

I hope this helps.

Regards, Yutong