Getting Started with Intel AI Devcloud.

Scenario 1- It was a few weeks back; I was running my deep learning model for image classification. It contained almost ten epochs. It used to take nearly 20 minutes for each epoch to run. I used to get errors randomly in-between process, so I had to start from the beginning. Sometimes power issues due to the battery used to disrupt the process. It used to take me days to complete one deep learning model. It was a cumbersome task.

Scenario 2- I was introduced to this fantastic technology — Intel® AI DevCloud. It is a cluster of Intel® Xeon® Scalable Processors that will assist you with your machine learning and in-depth learning training and inference compute needs. Now nothing stops me from running my models. Guess what? I can run the same models within minutes. Credit goes to Intel Devcloud.

What Is Intel Devcloud?

The DevCloud is a cluster of Intel® Xeon® Scalable Processors that will assist you with your machine learning and deep learning training and inference compute needs. It provides access to precompiled software optimized for Intel® architecture on Intel® Xeon® Scalable Processors. It includes:

When you gain access to the DevCloud, you will log into a Linux-based head node of a batch farm. There you can stage your code and data, compile, and submit jobs to a queue. Once the queued job completes, your results will be in your home folder.

Who can sign up to use the Intel® AI DevCloud?

Developers, data scientists, professors, students, start-ups and others who are members of Intel® AI Academy are eligible to request access.

How do I become a member of the Intel® AI Academy?

Next Steps-

1.Using Jupyter notebook

If you wish to install libraries from outside, , you can use standard PIP or Conda commands to install. Use terminal, as mentioned in below image to install libraries.

In terminal type, pip install <Package name> to install the package on devcloud.

After you get connected, you can use any of the kernels given or install new kernels as required to run your model. I used to upload necessary data to the cloud and run it from Jupyter notebook.

2. Using Putty

When you log in, you will find yourself on the host login-1, which is your login node. This node is intended only for code development and compilation, but NOT for computation. That is because it does not have much compute power, and, additionally, there are limitations on CPU time and RAM usage on the login node; your workload will be killed if it exceeds the limit. To run computational workloads on powerful compute nodes, you must submit a job through the Torque job queue. See the next section for a sample job script.

You can also go through mentioned links below to for further details about devcloud.

Useful Links-

References-

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