Using Openblas with R in Reproducible R container

I am using R for a reproducible scientific machine learing & hyperparameter optimizations. I stumble upon the fact that other implementations of blas such openblas/atlas/klm can speedup this costly optimization. But results are slightly different using each blas even if optimization is forced on single thread results deviate from default R.

So I want to try using Docker to contain the experiment. I have multiple questions.

  • Is there a good reason for setting up virtualenv for python in Docker containers?
  • Is jwilder/nginx-proxy still actual since embedded DNS server in user-defined networks was introduced?
  • ECS network host mode and links = CannotCreateContainerError: Container already exists
  • How to select volume mountpoint in docker-compose.yml?
  • Docker build extra folders
  • 2 docker containers run in 1 container
    1. is it good to compile from source instead of binaries ?

    2. if I compile from source, will it lead to same configuration as debian binaries ?

    3. since results are different for each blas, there is a tool called ReproBLAS from Berkeley, is it good idea to use it with R ?

    4. when you compile R using “–with-blas=-lopenblas” in this case openblas is single threaded or multithreaded ?

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  • How to access docker logs of another container
  • Docker will be the best open platform for developers and sysadmins to build, ship, and run distributed applications.