Machine Learning

A wide variety of machine learning applications use GPUs, including deep learning, image recognition, autonomous cars, real-time voice translation and more. Machine learning is not new, but the increase in available data and more powerful GPUs allow for faster and more efficient parallel computing. The processes involved in machine learning used to take a year to complete; now with GPUs, the same processes only take mere weeks or days. GPU appliances supporting multiple NVIDIA GPUs will allow machine learning to go even further.

Deep Learning, Image Recognition, Autonomous Cars, Real-Time Voice Translation and More

One Stop Systems' high performance computing appliances can add tremendous compute power for many machine learning applications.

Specific Machine Learning Applications

Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop Systems newest product, the GPUltima is well suited for applications such as deep learning and image recognition. The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators with each accelerator containing 2 GPUs and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. The full rack houses 256 networked GPUs using the latest PCIe and Infiniband technologies. This level of density provides deep learning applications with incredible compute power and allow further advancements in the field.

Image Recognition Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop Systems newest product, the GPUltima is well suited for applications such as deep learning and image recognition. The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators with each accelerator containing 2 GPUs and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. The full rack houses 256 networked GPUs using the latest PCIe and Infiniband technologies. This level of density provides deep learning applications with incredible compute power and allow further advancements in the field.

Autonomous Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop Systems newest product, the GPUltima is well suited for applications such as deep learning and image recognition. The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators with each accelerator containing 2 GPUs and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. The full rack houses 256 networked GPUs using the latest PCIe and Infiniband technologies. This level of density provides deep learning applications with incredible compute power and allow further advancements in the field.

Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop Systems newest product, the GPUltima is well suited for applications such as deep learning and image recognition. The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators with each accelerator containing 2 GPUs and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. The full rack houses 256 networked GPUs using the latest PCIe and Infiniband technologies. This level of density provides deep learning applications with incredible compute power and allow further advancements in the field.

Real Time Voice Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop Systems newest product, the GPUltima is well suited for applications such as deep learning and image recognition. The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators with each accelerator containing 2 GPUs and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. The full rack houses 256 networked GPUs using the latest PCIe and Infiniband technologies. This level of density provides deep learning applications with incredible compute power and allow further advancements in the field.

Other Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a cluster of 16,000 computers, successfully trained itself to recognize a cat based on images taken from YouTube videos. This technology is already used in speech recognition, photo searches on Google+ and video recommendations on YouTube.

Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop Systems newest product, the GPUltima is well suited for applications such as deep learning and image recognition. The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators with each accelerator containing 2 GPUs and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. The full rack houses 256 networked GPUs using the latest PCIe and Infiniband technologies. This level of density provides deep learning applications with incredible compute power and allow further advancements in the field.

GPUltima

The GPUltima is a petaflop computational cluster in a rack that contains up to eight nodes with each node containing up to 16 accelerators and one or two dual-socket servers. Customers can build up to the full rack, one node at a time, depending on their application requirements. Multiple nodes provide different performance capabilities, dependent on the number accelerators employed. All NVIDIA accelerators exchange data through Infiniband 100Gb transfers using GPU Direct RDMA. All GPUs are connected to a root complex through up to four 128Gb PCI Express connections. Root complexes are connected to the GPUs through Infiniband and each other and the outside world through Ethernet.

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