What is NVIDIA Deep Learning AMI?
It is an optimized environment for running the Deep Learning, Data Science, and HPC containers available from NVIDIA's NGC Catalog. The Docker containers available on the NGC Catalog are tuned, tested, and certified by NVIDIA to take full advantage of NVIDIA Ampere, Volta and Turing Tensor Cores, the driving force behind artificial intelligence. Deep Learning, Data Science, and HPC containers from the NGC Catalog require this AMI for the best GPU acceleration on AWS P4D, P3 and G4 instances.
NVIDIA Deep Learning AMI is a tool in the Machine Learning Tools category of a tech stack.
Who uses NVIDIA Deep Learning AMI?
NVIDIA Deep Learning AMI's Features
- Provides AI researchers with fast and easy access to NVIDIA A100, V100 and T4 GPUs in the cloud, with performance-engineered deep learning framework containers that are fully integrated, optimized, and certified by NVIDIA
- Optimized for highest performance across a wide range of workloads on NVIDIA GPUs
- NVIDIA accelerates innovation by eliminating the complex do-it-yourself task of building and optimizing a complete deep learning software stack tuned specifically for GPUs
NVIDIA Deep Learning AMI Alternatives & Comparisons
What are some alternatives to NVIDIA Deep Learning AMI?
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TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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