Motivation

As I stated in this post yesterday, I was able to run a program using OpenMPI on a Docker container running on multiple nodes. I wanted to find out how much performance I could improve by using OpenMPI, so I decided to benchmark it. Actually, I had some difficulties this time as well, and I would be happy if that part is helpful for others.

Motivation

As previously mentioned in this post, I am moving forward with the goal of running Athena++ on multiple nodes. As a preliminary step, I have attempted to run a Docker container with OpenMPI configured on multiple nodes. I will post a summary of what I have done, as I had some difficulties and it may be helpful to others.

Introduction

With Try Horovod in Docker, you can now use Horovod in your own environment (on-premises) and in a Docker environment. The next thing to do is to modify the training code running on a single server to apply it to distributed training using Horovod! For starters, I modified a relatively simple CNN code to allow distributed learning using Horovod, which is summarized in the following article.

Motivation

I have been interested in Distributed Training for about a year. I have been experimenting with a distributed learning framework called Horovod on multiple TITAN-V-capable machines. I finally got a distributed training sample working, so I am posting it here.

Introduction

In May of this year, I posted an article Building Rootless Docker, but for some reason I decided to uninstall rootless docker. The following is a summary of the uninstallation procedure. I will post the details of the circumstances that led to the uninstallation of rootless docker later.

Introduction

Over a month ago, I became interested in NVIDIA Modulus at the How to Speed Up Simulation with AI Surrogate Models? seminar I attended, I became interested in NVIDIA Modulus, so I bought the book and started studying it. As a prerequisite for future study of Modulus, I installed Modulus in my environment, so I summarized the installation process as “Introduction to PINNs”.

Motivation

I wanted to try out a large-scale language model (LLM) for Japanese, so I used rinna, which was released in May. To save installation time, I ran rinna under a docker container environment.

I ran into some problems in doing so, which are summarized below.

Motivation

In a previous post I summarized how to start a docker container in user mode.

When using multiple PCs (WorkStation, hereafter WS), it is necessary to consider how to realize a mechanism to share containers among multiple WS. In the case of singularity, we could store sif files on an NFS server and use them from other WS.

In case of docker, I decided to build a registry server, thinking that I could set up a private registry in my home network and operate it.

Motivation

I have been using singularity containers instead of docker. the reason I have avoided using docker is that it requires root privileges to launch containers. I don’t have to worry about it because I’m running it at home.

I forgot the reason why, but I found out that docker has a rootless mode that allows you to run docker in normal user mode, so I decided to give it a try this time.