Installations are eliminated, which means no one has to keep track of or be concerned about breaking the environment module links. Containers Simplify Application Deployment on Shared SystemsД¬ontainers make a great alternative. The complexity of such installs in supercomputing limits users from accessing the latest features and enjoying optimized performance, in turn delaying discoveries.

This is a time-consuming approach that can take days, making it unproductive for both the system administrators and the end-users. Updating applications without containers is complicated, see the above. ThatвЂ™s because almost all supercomputing centers use environment modules to build, deploy and launch applications. Supercomputing has a dire need to simplify the deployment of applications across all the segments. The need for containers isnвЂ™t limited to deep learning. No need to contain your excitement: containers promise to make deploying HPC applications easier and faster. Users clearly see the value of NGC containers, with over 27,000 users now registered to access the NGC container registry. PGI compilers and tools enable development of performance-portable HPC applications using OpenACC, OpenMP and CUDA Fortran parallel programming. The container for PGI compilers available on NGC will help developers build HPC applications targeting multicore CPUs and NVIDIA Tesla GPUs. This is in addition to eight containers, including NAMD, GROMACS and ParaView, launched at the previous yearвЂ™s conference. Since NovemberвЂ™s Supercomputing Conference, nine new HPC and visualization containers - including CHROMA, CANDLE, PGI and VMD - have been added to NGC. Moving Fast: New NGC Containers Include CHROMA, CANDLE, PGI and VMD The complex deployment challenge also applies to HPC computing and visualization applications. Installing frameworks is complicated and time consuming.Д¬ontainers simplify this process, so users can get access to the latest application versions with simple pull and run commands.

These containers make deploying deep learning frameworks - building blocks for designing, training and validating deep neural networks - faster and easier. Over the past three years, containers have become a crucial tool in deploying applications on a shared cluster and speeding the work, especially for researchers and data scientists running AI workloads. Users can now take advantage of 35 deep learning, high-performance computing, and visualization containers from NGC, a story weвЂ™ll be telling in depth at this weekвЂ™s International Supercomputing Conference in Frankfurt. As part of our effort to speed the deployment of GPU-accelerated high-performance computing and AI, weвЂ™ve more than tripled the number of containers available from our NVIDIA GPU Cloud ( NGC) since launch last year.

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