Running Data Science VM on Azure for your data science and deep learning lab resources
Create the lab infrastructure as follows
Further detailed instructions and recommedations about setting up a lab resources, including step-by-step instructions and common pitfalls to avoid, are available in the Data Science VM GitHub .
The important scripts to note are:
1. Modify the following ARM template to create multiple VMs and execute a post install script. simply change the desired machine to the type required for your activity.
2. Sample post install bash script that creates multiple user accounts on each VM with random passwords for each student.
3 Use Azure CLI on your command prompt or bash shell to invoke the ARM template and use the post install script above.
How long does this take to deploy
It typically take 30 minutes to create about 30 GPU based Data Science VMs to support a class of over 200 students and spot check a few VMs.
The students were able to train deep neural network models on shared GPUs. . With the shared infrastructure you are also able to save costs of creating separate VMs for every student. The data science VM is already a popular and robust environment among data scientists and AI developers for their development and experimentation in the cloud. By also using a standardized and familiar environment provided by the Data Science VM in training and education, the learning curve is greatly reduced and students can go on to continue development of their AI apps using the Azure Data Science VM.
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