by subbu on May 5, 2016

Get started with NVIDIA DIGITS for as low as 49 cents in just 3 easy steps

Start training deep learning models on NVidia K80 or Titan X GPUs starting at $0.49/hour 

Get Started Today

The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers. Quickly design the best deep neural network (DNN) for your data using real-time network behavior visualization. Best of all, DIGITS is a complete system so you don’t have to write any code. DIGITS integrates the popular Caffe deep learning framework and Torch. You can train neural network models, including pre-built AlexNet and GoogleNet models.

Get started with DIGITS in just 3 easy steps and get your DIGITS environment up and running in minutes.

(For those of you who would like to watch instead of read, here is a 35 second screencast of the three steps).

Step-1: To get started, click here

You may have to sign-up for a new account on Nimbix if you don’t have an account already.  Just login to your Nimbix account if you already have an account.

After you have logged in to, navigate to the “Compute” tab and select NVIDIA DIGITS.


Step-2: You can keep the default $0.49 / hour GPU or select other GPUs to suit your needs.  Then hit “Continue”. You may be prompted with a modal summarizing the machine configuration asking you to hit “Submit” again.



Step-3: From the Dashboard tab, click on the job that has just launched to expand its details. A connect button will appear shortly after your job enters the “Processing” state. This will open a link to the NVIDIA DIGITS UI directly in your browser.

You will also be able to SSH to the machine using the username “nimbix” and the same IP address/password.


If you have any questions or need help get started with NVIDIA DIGITS on Nimbix, please don’t hesitate to live chat with us right on this page by clicking the blue/white bubble on the bottom right or emailing us at

Once you are on the DIGITS UI, here are a few things you can try.

Topics: nvidia


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