Develop Your Own Deep Learning App in the Cloud with Free GPUs
[Credit: Source of this material is Machine Intelligence in Design Automation.]
Machine Intelligence and deep learning technologies are advancing at a rapid pace. Claim to this fame is that it is bound to enable an unprecedented degree of automation in every walk of life. Design automation, a field that has been automating semiconductor design for decades, is playing catch up. Paripath has been using these technologies for a few years now and has decided to disseminate the information for a greater cause using blogs, opens source code, book and other collateral. In this article, I hope to initiate the readers (from engineers to executives) into deep learning by walking them step-by-step to develop an capacitance estimation app in the cloud.
Install the Google Colaboratory app in chrome browser
This step is as simple as launching your chrome browser and clicking this link to install chrome extension. This chrome extension is based on jupyter notebook, an open source web development environment for machine learning. Click on ‘ADD TO CHROME’ will install the development environment in your chrome browser.
Create/ Open the Colaboratory Notebook.
Following URL contains Colaboratory notebook with open source code for estimating capacitance based on physical characteristics of a wire. Once you click on the link https://drive.google.com/file/d/1ctq4F28XMPLRHIp7qCep2-V8zLG7zRRm/view?usp=sharing, it opens up in your chrome browser as shown in the picture.
Click on Colaboratory link shown as arrow in the picture above. This will open the notebook in your browser.
Run the App
Once the notebook is open, you can run it by pressing “CTRL+F9” or clicking “Runtime -> Run all” as shown in the picture below”
Analyze Results
Once you hit “CTRL+F9”, remote servers running on google farm will produce the results in no time. You can analyze the model results and tweak it for your use.
Use GPUs for free
If your app is taking longer than usual, you can use GPUs for free by hitting “Runtime -> Change runtime type” as shown in the picture below:
Summary
Now you’ve developed your first machine learning app in the cloud with GPU provided by google, consider yourself armed with all the information needed to deploy machine learning in your next application. Good luck !
