Julia at the Intel AI Day, Bangalore 2017

Bengaluru, India – Julia Computing featured at one of India’s most prominent AI conferences, demonstrating two very powerful Deep Learning use cases the company is trying to solve using Julia on Intel’s hardware.

The two day event was organized and crafted to showcase robust AI-supporting hardware and software solutions from Intel and it’s partners.

Julia Computing Inc, one of Intel’s partners from India, took centre-stage on day two for their demonstrations. The first of the two demos, namely Neural Styles caught the audience’s fancy – building a neural network imposing the style of one image onto another. Our very own Ranjan Anantharaman took a live picture of the audience and applied transforms to it in real-time.

The second demo targeted solving the serious problem of identifying if a person has symptoms of Diabetic Retinopathy by taking only but an image of his retina as an input, without any human intervention or prediction.

The following video holds a glimpse of what the two firms envision as the future of AI.

Julia Computing and Intel – Acelerating the AI revolution

Also see this whitepaper on Intel and Julia Computing working together on an AI stack.

About Julia

Julia is the simplest, fastest and most powerful numerical computing language available today. Julia combines the functionality of quantitative environments such as Python and R, with the speed of production programming languages like Java and C++ to solve big data and analytics problems. Julia delivers dramatic improvements in simplicity, speed, capacity, and productivity for data scientists, algorithmic traders, quants, scientists, and engineers who need to solve massive computational problems quickly and accurately.

Julia offers an unbeatable combination of simplicity and productivity with speed that is thousands of times faster than other mathematical, scientific and statistical computing languages.

Partners and users include: Intel, The Federal Reserve Bank of New York, Lincoln Laboratory (MIT), The Moore Foundation and a number of private sector finance and industry leaders, including several of the world’s leading hedge funds, investment banks, asset managers and insurers.

About Julia Computing, Inc.

Julia Computing, Inc. was founded in 2015 to develop products around Julia such as JuliaFin. These products help financial firms leverage the 1,000x improvement in speed and productivity that Julia provides for trading, risk analytics, asset management, macroeconomic modeling and other areas. Products of Julia Computing make Julia easy to develop, easy to deploy and easy to scale.

Deep Learning on the New Ubuntu-Based Data Science Virtual Machine for Linux

Authored by Paul Shealy, Senior Software Engineer, and Gopi Kumar, Principal Program Manager, at Microsoft.

Deep learning has received significant attention recently for its ability to create machine learning models with very high accuracy. It’s especially popular in image and speech recognition tasks, where the availability of massive datasets with rich information make it feasible to train ever-larger neural networks on powerful GPUs and achieve groundbreaking results. Although there are a variety of deep learning frameworks available, getting started with one means taking time to download and install the framework, libraries, and other tools before writing your first line of code.

Microsoft’s Data Science Virtual Machine (DSVM) is a family of popular VM images published on the Azure marketplace with a broad choice of machine learning and data science tools. Microsoft is extending it with the introduction of a brand-new offering in this family – the Data Science Virtual Machine for Linux, based on Ubuntu 16.04LTS – that also includes a comprehensive set of popular deep learning frameworks.

Deep learning frameworks in the new VM include:

  • Microsoft Cognitive Toolkit
  • Caffe and Caffe2
  • TensorFlow
  • H2O
  • MXNet
  • NVIDIA DIGITS
  • Theano
  • Torch, including PyTorch
  • Keras

The image can be deployed on VMs with GPUs or CPU-only VMs. It also includes OpenCV, matplotlib and many other libraries that you will find useful.

Run dsvm-more-info at a command prompt or visit the documentation for more information about these frameworks and how to get started.

Sample Jupyter notebooks are included for most frameworks. Start Jupyter or log in to JupyterHub to browse the samples for an easy way to explore the frameworks and get started with deep learning.

GPU Support

Training a deep neural network requires considerable computational resources, so things can be made significantly faster by running on one or more GPUs. Azure now offers NC-class VM sizes with 1-4 NVIDIA K80 GPUs for computational workloads. All deep learning frameworks on the VM are compiled with GPU support, and the NVIDIA driver, CUDA and cuDNN are included. You may also choose to run the VM on a CPU if you prefer, and that is supported without code changes. And because this is running on Azure, you can choose a smaller VM size for setup and exploration, then scale up to one or more GPUs for training.

The VM comes with nvidia-smi to monitor GPU usage during training and help optimize parameters to make full use of the GPU. It also includes NVIDIA Docker if you want to run Docker containers with GPU access.

Data Science Virtual Machine

The Data Science Virtual Machine family of VM images on Azure includes the DSVM for Windows, a CentOS-based DSVM for Linux, and an Ubuntu-based DSVM for Linux. These images come with popular data science and machine learning tools, including Microsoft R Server Developer Edition, Microsoft R Open, Anaconda Python, Julia, Jupyter notebooks, Visual Studio Code, RStudio, xgboost, and many more. A full list of tools for all editions of the DSVM is available here. The DSVM has proven popular with data scientists as it helps them focus on their tasks and skip mundane steps around tool installation and configuration.


To try deep learning on Windows with GPUs, the Deep Learning Toolkit for DSVM contains all tools from the Windows DSVM plus GPU drivers, CUDA, cuDNN, and GPU versions of CNTK, MXNet, and TensorFlow.

Get Started Today

We invite you to use the new image to explore deep learning frameworks or for your machine learning and data science projects – DSVM for Linux (Ubuntu) is available today through the Marketplace. Free Azure credits are available to help get you started.

Paul & Gopi