Software

Background

In this article, I wrote about setting up a Python development environment that utilizes a GPU by connecting via SSH from my local VS Code to a Docker container running on a GPU-equipped remote workstation. Although I initially set it up on my own, I wondered if there might be room for improvement, so I’ll discuss how I used generative AI to enhance it.

Motivation

I use a Mac at home but a Windows PC at work. After using a PC at the office on weekdays, I often mix up the keyboard shortcuts for copying and pasting (e.g., Ctrl-c vs. Cmd-c) when I switch to my Mac at home. Conversely, on Mondays at the office, I sometimes reflexively press Cmd+c. Incidentally, I use an HHK (US layout) keyboard in both environments.

I decided to remap the Mac shortcuts Cmd-c/v/x/z/a/s/f to Ctrl-c/v/x/z/a/s/f. To achieve this, I used an application called Karabiner-Elements.

During the setup process, I consulted ChatGPT to verify the steps and check how to handle any potential issues.

Introduction

Three months ago, I wrote an article on color compositing LRGB images captured with RicoRem-Ricoh’s remote telescope . Targeting NGC 1566, I stacked multiple images exposed with each LRGB filter. For my own reference as well, I summarize the processing steps here.

Motivation

Until now, I’ve been launching JupyterLab containers on a remote workstation equipped with a GPU, connecting via browser from my local MacBook Air to develop Python code. For example, see this article. Recently, since I’ve been using VSCode frequently, I tried connecting to the same Python development environment on the GPU-equipped remote workstation from VSCode.

Introduction

Around this time last year, I spent two months working on a simulation of a two-dimensional cylindrical wake using PINNs. I hadn’t finished working on this article. This time, since I built a new Python development environment, I decided to tackle this theme as an example project, with Claude as my companion.

Introduction

A month ago, I uploaded an article in which I installed LM Studio on a MacBook Air and tried local LLM. This time, I tried local LLM with Ollama.

Introduction

I bought a MacBook Air (M4) in March. I wanted to run LLM locally, so I set the memory to the maximum 32GB, and finally was able to try local LLM with LM Studio, although I could not find much time for it because I started working in Kanda in April and I was busy with various events on weekends.

Introduction

It has been almost a year since Ubuntu 24.04 LTS Noble Numbat was released. Since it seems to be stable enough, I decided to migrate from 22.04 to 24.04 with a clean install.

I usually installed from the Japanese Remix ISO image, but since this version Japanese Remix is not released, I downloaded the image from Canonical’s page and installed it.

Introduction

Two months ago in this post, laminar flow around a 2D circular cylinder (Laminar Flow), using the governing equations for each of the ST and VP forms, The simulation was performed using the governing equations for each of the ST form and VP form.

Since then, we have simulated the flow around a 2-D cylinder in the time range 0 to 60 seconds for various parameters, and the results are summarized here.

Motivation

I have been studying PINNs and related OpenFOAM for a while, but yesterday there was a big news in LLM area and I decided to use Deep Seek-R1 which had an impact not only on LLM area but also on stock prices. Since I could not use it as it is in my environment, I used a compacted LLM with quantization.

This time, I used Ollama and Open WebUI to use the quantized model from a browser, and I will summarize the contents.