Open Source technology and its massive developer community have advanced Machine Learning and Deep Learning capabilities farther in the last few years than many decades past. At the same time, PC gaming and cryptocurrency mining have propelled advances in GPU computing. When harnessed together, these technologies are more accessible than ever in advancing data science. However, the average data scientist, chemist, physicist, geophysicist, mechanical engineer, biologist, and business analyst have not focused their education and expertise on information technology and software engineering required to assemble these technologies into a productive work environment.

GPULab offers a fully integrated NVIDIA GPU/CUDA API Linux environment, with pre-installed and configured data science languages and runtimes such as Python, Julia, R, and Octave, along with the latest popular libraries and frameworks, including PyTorch, TensorFlow, Keras, Scikit-Learn, and dozens more.

Major clouds have similar offerings yet often require exposure to their custom APIs. GPULab’s value is providing standards-based, non-proprietary, cost-effective, pre-configured research and development environment. Peek under the hood of many hyper cloud services, and you often find a mesh of cloud-native and even vendor-neutral technologies for machine learning, like TensorFlow, Keras, and PyTorch, yet implemented atop a proprietary environment. Any work product developed in GPULab is fully transportable, with zero vendor lock-in.

GPULab environments are 100% transparent and reproducible. Examine the specs on our latest release GPULab jl3-v2.1.x.