Train neural network models with Caffe using sample MNIST or CIFAR datasets and learn how to use your own dataset to train models Before you start this tutorial, you should have launched a NVIDIA DIGITS instance on Nimbix, and have the url to the DIGITS UI on Nimbix. If you haven’t launched the NVIDIA DIGITS instance on Nimbix, go here to learn how to do that or watch this screencast. We prepared the NVIDIA DIGITS application to have the three most common data sets used for learning with NVIDIA DIGITS and Caffe. These data sets are: MNIST, available in /db/mnist, with training and test data sets in /db/mnist/train and /db/mnist/test respectively cifar10, available at /db/cifar10, with training and test data sets in /db/cifar10/train and /db/cifar10/test respectively cifar100, available at /db/cifar100, with training and test data sets in /db/cifar100/train and /db/cifar100/test respectively DIGITS has auto-completion, so when you begin to type these paths, you will see these paths available to you. Creating a Model Select the blue images button under datasets, and choose “Classification.” This well guide you through setting up a data set of images that you can train using deep learning. Enter the settings in the screenshot below to configure your train and validation databases. Training with Caffe Now you are ready to train your model! Go back to the main DIGITS page and select Images>Classification to create a new classification model. On this page, the defaults will work fine for a first time training a model. You simply need to select the data set you already configured, and select the GPUs in your instance at the bottom of the page. Kick it off and watch it run. Custom Data Sets If you have a custom data set, you can upload your custom data sets using Filezilla to drop.jarvice.com. See How do I transfer files to and from JARVICE? for instructions on using Filezilla. Many applications including NVIDIA DIGITS have an ssh server running. If you would like to upload or download your data while DIGITS is running using scp, rsync or another application, you can configure your SSH keys prior to launching your job. See How do I upload my SSH public key? What is it used for? for information configuring password-less SSH. You can upload data sets using these methods to your /data directory, referred to as drop.jarvice.com, or your “drop”, and then enter this path instead of /db/mnist. If you have not yet created your own custom data set, there are instructions on the DIGITS github page on how to structure your image database and prepare labels. Now you are ready to train your Deep Learning models with GPUs in the cloud! Want more? If you are interested in learning about enabling more advanced deep learning applications in the cloud like NVIDIA DIGITS, or customizing your own machine learning environment in the cloud, 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 email@example.com. You can also learn more about NVIDIA DIGITS from here.
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 https://platform.jarvice.com, 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 firstname.lastname@example.org. Once you are on the DIGITS UI, here are a few things you can try.
Deep learning users can now access pre-configured NVIDIA DIGITS Titan-X GPU instance starting at 49 cents per hour on Nimbix! Get Started Today Data Scientists and Deep learning users can now try the single-click solution on Cloud Service Provider Nimbix to launch an instance configured to run NVIDIA DIGITS on Titan-X GPUs at as low as $0.49/hour, the most affordable high performance GPUs compared to anywhere in the cloud, powered by Bitfusion Boost’s Software Defined Supercompute technology. 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. Developers who use Caffe or Torch can now focus on training their neural networks faster in the most affordable manner, without worrying about configuring drivers, kernels or toolkits! Data Scientists and Developers today spend a lot of time in configuring their development environment to get deep learning frameworks like Caffe or Torch running. Moreover, there is not really any option to use machines with powerful GPUs like TitanX or K80s on an hourly basis. The single-click solution lowers the barrier for deep learning developers and data scientists to spin up affordable Titan-X GPU instances powered by Bitfusion Boost’s GPU virtualization technology. This enables developers to save money by not having to reserve a larger machine up-front for development, and also gives them flexibility to demand larger number of GPUs when they are ready to use them. Digits instance comes pre-installed with Nvidia Drivers, Cuda 7 Toolkit, cuDNN 3, Caffe, and Torch. Pay-per-use pricing start at $0.49 per hour for one Titan-X GPU, which is the lowest price compared to anywhere in the cloud today. You can keep adding additional GPUs as you see fit for your application. Monthly subscription pricing is also available. Pricing includes all infrastructure and platform licensing fees. You can access NVIDIA DIGITS on Nimbix immediately by going here and selecting NVIDIA DIGITS on the page. Here is a screencast of how to launch a NVIDIA DIGITS instance on Nimbix in less than a minute. To get started, follow these 3 easy steps. Get Started Today 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 email@example.com. Once you have launched DIGITS, here are a few things you can try.
One of the nifty features of AWS is that one can utilize spot instances over on-demand instances in order to significantly reduce costs. To use spot instances, we need to create a spot instance request which includes a maximum price that we are willing to pay per hour, as well as a few other constraints such as the instance type and availability zone. You can find a detailed discussion of all the AWS spot instance parameters in the following AWS user guide.
We've been been cooking up several more AMIs to get you started on AWS quickly. This time we are introducing the Nvidia Digits 3 AMI which is designed to to get you started quickly with Nvidia's deep learning package which includes their branched versions of Caffe and Torch, as well as a browser accessible interface for quick experimentation. The second AMI is built on top of Ubuntu 14, the Cuda 7.5 Toolkit, and the latest Nvidia drivers, and is targeted at Cuda developers and those intending to deploy GPU applications with ease.
We recently presented at the GPU Technology Conference, where we demonstrated how to containerize GPU application with Docker and utilize Bitfusion Boost. This week, at the SaltConf 16 conference, we will be taking this concept a step further and demonstrating GPU accelerated containers through a complete Docker ecosystem under SaltStack control. In particular, we will show how we utilize both these technologies to create virtual GPU clusters that provide maximum performance and data center utilization for compute intensive applications.
In early April 2016, we started offering Monster GPU Machines on Amazon Web Services (AWS) powered by Bitfusion Boost and have seen really great interest. In the last couple of weeks alone we have seen massive usage and is growing at an even faster rate recently.
FREMONT, CA - APRIL 5, 2016 – AMAX, a leading provider of HPC, Cloud/IaaS, GPU and Data Center solutions, will demonstrate GPU virtualization technology at the GPU Technology Conference (GTC) 2016 on April 5-7, 2016. The demo will feature AMAX's award-winning Deep Learning Platforms running Bitfusion Boost to virtualize GPU resources from multiple nodes for rendering and deep learning applications.
You may have seen our recent post of enabling customers to create Monster GPU Machines on AWS using our Boost Technology. Ready to see some real applications using Boost, meet some of our partners utilizing Boost on their systems, and find out what else we can do with Boost? Then please come join us next week at the GPU Technology Conference (GTC) in sunny Silicon Valley, California.
At Bitfusion, our job is to know how well various compute-intensive workloads scale on different infrastructures and to help people maximize performance. Since we launched our Deep Learning and CUDA AMIs in the AWS Marketplace we’ve heard many of our customers ask for bigger GPU instances, but the largest Amazon EC2 instance, the g2.8xlarge, currently maxes out at just 4 GPUs.