During the launching of the Nvidia A100 our client asked us for help in eliminating the accelerator compatibility problem while installing and configuring TensorFlow and PyTorch.
The client needed an additional computing capacities for their tasks in the field of Deep Learning. The result of this case is a model optimization and almost double acceleration.
Implementation of Containerization system by using specially prepared Docker containers as a working environment.
We found the best solution for client who came to us with the problem of incorrect work of distributed learning on several accelerators.
We designed and built a high-throughput cluster and developed an AI-based system to assess the presence of minerals on a plot of land.
We implemented the VDI system in order to increase the security of the whole system, to reduce the cost of maintenance of the IT infrastructure, to allow flexible configuration of desktops for different tasks.
During the launching of the Nvidia A100 our client asked us for help in eliminating the accelerator compatibility problem while installing and configuring TensorFlow and PyTorch.
The client needed an additional computing capacities for their tasks in the field of Deep Learning. The result of this case is a model optimization and almost double acceleration.
Implementation of Containerization system by using specially prepared Docker containers as a working environment.
We found the best solution for client who came to us with the problem of incorrect work of distributed learning on several accelerators.
We designed and built a high-throughput cluster and developed an AI-based system to assess the presence of minerals on a plot of land.
We implemented the VDI system in order to increase the security of the whole system, to reduce the cost of maintenance of the IT infrastructure, to allow flexible configuration of desktops for different tasks.