NVIDIA, which dominates the machine learning market, provides drivers with their proprietary license so that they can control the rules freely. For example, they can freely change their EULA and restrict the data center deployment and commercial use of some products. On the other hand, drivers called ROCm contains the Kernel driver and runtime, and libraries for machine learning called MIOpen developed by AMD are subject to open source licenses. Therefore, it is unlikely that commercial use will be restricted even for consumer products. The MIOpen that runs on ROCm supports two programming models of OpenCL and HIP. And HIP which is a component of ROCm allows developers to convert CUDA code to portable C .

While this announcement was completely expected, it is an important milestone along the road to simplifying and lowering the costs of Machine Learning development and deployment for AI projects. When NVIDIA announced the NVIDIA GPU Cloud last May at GTC, I explained in this blog that the purpose was to create a registry of compatible and optimized ML software containers which could then, in theory, run on the cloud of users' choice. That vision has now become a reality, at least for Amazon.com's I expect other Cloud Service Providers to follow soon, given the momentum in the marketplace for the 120 TFLOP Volta GPU's. Why do you need NVIDIA's GPU Cloud for ML?

NVIDIA's speedy GPUs and Machine Learning software have unquestionably become the gold standard for building Artificial Intelligence (AI) applications. And today, NVIDIA added TenCent to their list of cloud service providers that offer access to NVIDIA hardware in their clouds for AI and other compute intensive applications. This marks a significant milestone in the global accessibility of the hardware needed to build AI applications, from drones to medical devices to automated factories and robots. TenCent (whose Chinese name roughly translates to "Soaring Information") is one of China's largest Internet companies and the world's largest gaming platform, having recently announced 2016 revenues that grew by 48% to $21.9B. Many companies, perhaps most, opt to access GPUs in the cloud instead of buying and deploying the hardware directly.