Octus & Docker
Docker
Hey Octus, I’ve been thinking about how we could containerize your oceanographic data pipelines so you can run simulations on any cloud or on a cluster near the lab. Have you explored that yet?
Octus
Sounds like a great idea! I’ve been skimming Docker lately—if we package the simulation code and its dependencies into a container, we can spin it up on any cloud provider or on the lab’s HPC without worrying about environment quirks. I’ll start pulling the current pipeline into a Dockerfile, test it locally, and then push it to our registry. That way, whether it’s a GPU‑powered node or a standard CPU cluster, we’ll have the same reproducible setup. Let me know if you want to tweak any specific libraries or config files first!
Docker
That sounds solid, Octus. Just double‑check the CUDA toolkit version and the exact BLAS libraries you’re pulling in—any mismatch there will bite later when we switch from a CPU node to a GPU one. Also, pin the Python packages to specific releases; the defaults in the base image can drift over time. Let me know what base image you’re using and we can lock down the layers for reproducibility.
Octus
I’m thinking of using Ubuntu 22.04‑lTS as the base image, then installing CUDA 12.1 and the official cuBLAS from NVIDIA’s repositories. I’ll pin the Python packages with a requirements.txt that locks each library to a specific version—like numpy 1.26.4, scipy 1.12.0, and the GPU‑accelerated cuDNN 8.9.0. I’ll also keep the CUDA toolkit path and LD_LIBRARY_PATH set up right in the Dockerfile so that the BLAS libraries line up exactly. Once I’ve built the image locally, I’ll push it to our registry and we can run the same container on the CPU cluster or the GPU nodes without any surprises. Let me know if there’s a particular version of cuDNN or a custom BLAS build you’d like to include.
Docker
Nice, that’s the right approach. Just make sure you install the matching cuDNN that matches the CUDA 12.1 release you’re pulling. And set `LD_LIBRARY_PATH` to include `/usr/local/cuda/lib64` so the dynamic linker finds the right libraries. If we need a custom BLAS, we can compile one from the source and drop it into the image, but for now the NVIDIA libraries should cover most workloads. Once you push it, I’ll pull it on the GPU nodes and run a quick benchmark to confirm everything lines up. Keep me posted on the build output.
Octus
Got it—CUDA 12.1, cuDNN 8.9.0, and the NVIDIA cuBLAS will be locked in. I’ll add `/usr/local/cuda/lib64` to `LD_LIBRARY_PATH` and run a quick test build locally before pushing to the registry. I’ll ping you as soon as the image is ready and the build log shows all dependencies resolved. Let’s hope the benchmark comes back clean!