Advent of 2022, Day 6 – Environments in Azure Machine Learning

This article is originally published at https://tomaztsql.wordpress.com

In the series of Azure Machine Learning posts:

• Dec 01: What is Azure Machine Learning?
• Dec 02: Creating Azure Machine Learning Workspace
• Dec 03: Understanding Azure Machine Learning Studio
• Dec 04: Getting data to Azure Machine Learning workspace
• Dec 05: Creating compute and cluster instances in Azure Machine Learning

We have explored how to create a compute instance and compute target and learned that ML frameworks and scripting packages always come preinstalled.

Choosing the right set of components (CPU, GPU, RAM, Core) and corresponding software (OS, ML Framework, packages) can be time-consuming.

Under Curated environments, you will find predefined environments, with settings for running particular frameworks, like PyTorch or TensorFlow.

These environments are essentially Docker images and are designed for training, deploying, or inference.

Not only will you find a great description, tags and versions, but there is also a batch script for provisioning each of the environments (copied from the preparation script for the Docker image):

FROM mcr.microsoft.com/azureml/aifx/stable-ubuntu2004-cu116-py39-torch1121:biweekly.202211.1

# Install pip dependencies
RUN pip install 'ipykernel~=6.0' \
                'azureml-core==1.47.0' \
				'azureml-dataset-runtime==1.47.0' \
                'azureml-defaults==1.47.0' \
				'azure-ml==0.0.1' \
				'azure-ml-component==0.9.15.post2' \
                'azureml-mlflow==1.47.0' \
		'azureml-contrib-services==1.47.0' \
		        'azureml-contrib-services==1.47.0' \
                'torch-tb-profiler~=0.4.0' \
				'py-spy==0.3.12' \
		        'debugpy~=1.6.3'

RUN pip install \
        azure-ai-ml==0.1.0b5 \
        MarkupSafe==2.1.1 \
	    regex \
	    pybind11

# Inference requirements
COPY --from=mcr.microsoft.com/azureml/o16n-base/python-assets:20220607.v1 /artifacts /var/
RUN /var/requirements/install_system_requirements.sh && \
    cp /var/configuration/rsyslog.conf /etc/rsyslog.conf && \
    cp /var/configuration/nginx.conf /etc/nginx/sites-available/app && \
    ln -sf /etc/nginx/sites-available/app /etc/nginx/sites-enabled/app && \
    rm -f /etc/nginx/sites-enabled/default
ENV SVDIR=/var/runit
ENV WORKER_TIMEOUT=400
EXPOSE 5001 8883 8888

# support Deepspeed launcher requirement of passwordless ssh login

RUN apt-get update
RUN apt-get install -y openssh-server openssh-client
RUN mkdir -p /root/.ssh
RUN mkdir /var/run/sshd
RUN ssh-keygen -t rsa -f /root/.ssh/id_rsa
RUN sed -i 's/PermitRootLogin without-password/PermitRootLogin yes/' /etc/ssh/sshd_config
RUN sed 's@session\\s*required\\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd
RUN chmod 700 /root/.ssh/
RUN touch /root/.ssh/config;echo -e "Port 1043\n StrictHostKeyChecking no\n  UserKnownHostsFile=/dev/null" > /root/.ssh/config
RUN echo "Port 1043" >> /etc/ssh/sshd_config
RUN chmod 600 /root/.ssh/config
RUN touch /root/.ssh/authorized_keys && chmod 600 /root/.ssh/authorized_keys
RUN cat /root/.ssh/id_rsa.pub >> /root/.ssh/authorized_keys
EXPOSE 1043 1043
CMD ["/usr/sbin/sshd", "-D"]

These curated environments are provided by AML, and made available in your workspace. Microsoft keeps these environments up-to-date with the latest versions and releases and prepared as Docker images. Remember (!) these curated environments not only save time but will also save you costs when deployment time, because are optimally prepared.

How to use them?

These curated environments are available using a azureml syntax for training or deployment. With the use of the function, you can get the environment ready.

azureml:AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu:3
//or 
azureml:AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu@latest

You can also check the list of all available curated environments in the Azure ML Studio (which we did), or by using the Azure CLI (as viewed in Fig. 2) and running the command: 

az ml environments list

And get the list of available environments:

You can see that figure 1 and figure 2 both give you the same list of environments for your faster jump-start.

Before you go all mad and ask, “Hey Tomaž, where did you get this…”, No worries mate. Tomorrow, we will be we will look into the Azure CLI and Python SDK (v2) since these will be more and more important for getting around in Azure Machine learning.

Compete set of code, documents, notebooks, and all of the materials will be available at the Github repository: https://github.com/tomaztk/Azure-Machine-Learning

Happy Advent of 2022! 

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