Solving problems at the compiler level eliminates the reliance on fallible programmers to do so. Highly performant, responsive applications that are a joy to use. Security and assurance via the elimination of entire classes of attack vectors like buffer overflows.ģ. System stability and memory efficiency, zero or fewer crashes due to memory-safety or thread-safety problems.Ģ. Some of the deepest issues that users need solved are ones that Rust was designed to solve at the language and compiler level.ġ. > And thus, I'll be surprised if it tackles the deepest issues users need solved. > It tells me, this is a technology-first, users-second enthusiast project. > But I'm always skeptical when underlying language choice is featured prominently as a selling point for any new project. Tensorflow themselves recommend using their docker images instead of trying to package and install their libraries on your host OS. The sole purpose is to replace our previous tensorflow packaging in Pop!_OS that became impossible to package because newer versions of Tensorflow did not build beyond 18.04. It is quite literally just a simple command-runner that runs docker commands, specifically for the purpose of managing the official Tensorflow Docker images, and getting a more streamlined setup for managing your local Docker images based on them. Tensorman is not a replacement for nvidia-docker, and you're not going to get anywhere by trying to convince me about the functionality of something that I personally wrote.
We validate that CUDA functions inside of Docker when pushing updates to nvidia-container-toolkit and friends. You could have saved so much time by asking questions instead of accusing me of things I haven't done, and spreading misinformation about it.ĬUDA support in Docker requires nvidia-docker+nvidia-container-toolkit+libnvidia-container, and that is the whole point of having nvidia-docker installed.
I have no idea why you're trying to argue with me over absolutely nothing.
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