Google Deep Learning Containers vs. NVIDIA GPU-Optimized AMI vs. Python vs. V Programming Language Comparison Chart

The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'

Simple, fast, safe, and compiled. For developing maintainable software. Simple language for building maintainable programs. You can learn the entire language by going through the documentation over a weekend, and in most cases, there's only one way to do something. This results in simple, readable, and maintainable code. This results in simple, readable, and maintainable code. Despite being simple, V gives a lot of power to the developer and can be used in pretty much every field, including systems programming, webdev, gamedev, GUI, mobile, science, embedded, tooling, etc. V is very similar to Go. If you know Go, you already know 80% of V. Bounds checking, No undefined values, no variable shadowing, immutable variables by default, immutable structs by default, option/result and mandatory error checks, sum types, generics, and immutable function args by default, mutable args have to be marked on call.