Optimized for the machine and deep learning, the IaaS aims to offer a whole range of complementary services to manage the training and deployment pipelines.
OVH’s strategy in artificial intelligence is taking shape. First stage of the rocket, the French cloud intends to build a computing infrastructure tailored to machine learning. An IaaS that is both optimized in terms of network performance, computer computation (CPU) and graphical acceleration (GPU). Second floor: design virtual or bare metal servers that respond to the most common uses of AI. Lastly, OVH plans to offer a series of managed cloud services to facilitate the deployment of machine learning pipelines on its infrastructure. A strategy that has the merit of being clear and precise.
From 2017, OVH delivered its first GPU instances on its public cloud (OpenStack) with machine learning among the main targeted use cases. On the occasion of its last customer event in October 2018, the Roubaix group completed the building of Nvidia Tesla V100 GPU virtual machines shaped to accelerate the learning phases of neural networks. “In the coming days, we will also market Flash-based NVMe storage applications targeting intensive applications,” said Alain Fiocco, chief technical officer of OVH. For companies preferring a dedicated training environment,
To top it all off, OVH has just announced support for Nvidia GPU Cloud (NGC) technology through its Nvidia Tesla V100 GPU instances. It opens to its customers access to a catalog of machine learning libraries (Caffe2, MXNet, PyTorch, TensorFlow …), all optimized for the graphics processors of the American foundry. Available as containers, these pre-integrated frameworks embed the necessary bricks for their execution, from the Nvidia Cuda environment to the OS via the Nvidia libraries.
Best of all, NGC software is also compatible with OVH’s offer of the DGX-1 dedicated beta server . Equipped with 8 graphics processors, this Nvidia multi-GPU calculator targets the intensive training needs of deep learning. “This offer allows us to test the appetite of the market for this type of configuration.If there is a sponsor, we could consider building our own multi-GPU machine,” says Alain Fiocco.
To the question of whether OVH could go so far as to design its own graphic processors designed for deep learning, like Google with its TPU, the technical director of OVH responds in the negative. “Our mission is not to manufacture chips, but rather to assemble servers from market components to achieve a price / performance / density ratio that makes the difference.” A way that Facebook already borrows for its internal needs with physical machines GPU eight hearts homemade. As for the rest of its infrastructure, OVH already leans its VM and metal bar solutions for AI to servers designed by its Roubaix R & D and assembled in its Croix factory a few kilometers away.
In parallel, OVH intends to capitalize on its developments in internal AI to offer its customers new products. Example of this approach: the machine learning platform offered on its Labs (in alpha version) comes from an internal project centered on the predictive analysis of the life cycle of its IT infrastructures. “We have decided to extend it to make it generalizable and to respond to use cases from other business entities, since then we have also been using this application for fraud detection,” explains Alain Fiocco.
From there to the packager and market it as a cloud service, there is one step. “In the same vein, we could in the future benefit our customers from our predictive models for managing IT capabilities,” the CTO adds.
A Spark service tested in the Labs
Another illustration of this logic of conversion of internal bricks in the form of products: FPGA processors (for Field-Programmable Gate Array). Historically, OVH has used these reprogrammable chips as part of its system to fight against denial of service attacks (read OVH’s post on the subject ). The latter leans on FPGA servers assembled, again, by the group’s teams. “We could definitely consider marketing them if the need arises among our customers,” says Alain Fiocco. In its Labs, OVH also offers (in beta) a PostgreSQL database acceleration service that already takes advantage of these FPGA machines.
In total, OVH has deployed a team of about twenty people dedicated to its R & D projects in data science and artificial intelligence (excluding business intelligence). Alongside the initiatives mentioned above, she is working on other experimental AI projects available on the OVH Labs. This is the case, for example, with an image recognition engine or an Apache computing cluster cloud service.Spark. Directly based on the company’s OpenStack public cloud infrastructure, it allows you to train machine learning models by backing up the SparkML library. On the price side, these managed cloud solutions will initially be made available free of charge. Only the underlying machine resources (virtual or bar metal) and actually consumed by the customer will be billed.
Among his first references on the field of AI, Octave Klaba’s company highlights Systran. The text-based translation expert uses his NVIDIA DGX-1 servers to orchestrate his intensive calculations of neural networks applied to linguistic processing.