3/17/2024 0 Comments Multi gpu workstation cabinet![]() These installable packages I was uploading into a private Kaggle dataset which in turn was mounted to a notebook. I used poetry as a package manager and decided to generate an installable package every time I made meaningful changes to the project in order to test them in the cloud. What could go wrong? Life Without GPUĪs it turned out, there are a lot of issues I encountered in the mentioned workflow.įirst of all, my solution source code quickly became an entire project with a lot of source code and dependencies. I thought I would be able to prototype locally and then execute notebooks on the cloud GPU. ~30h GPU and/or ~30h TPU hours per week on Kaggle Kernels.MacBook Pro 2019 (Intel Core i9 & Intel UHD Graphics 630 1536MB & 16GB DDR4).16bits variant of the dataset holds 350Gb. HPA dataset contains nearly 150Gb of 8bits 4-channels protein images. I was taking part in Human Protein Atlas (HPA) - Single Cell Classification competition on Kaggle. When you start to approach problems that are akin to real-life ones and you see hundreds of gigabytes of large datasets, your gut feeling starts to tell you that your CPU or AMD GPU devices are not going to be enough to do meaningful things. ![]() ![]() There are always some "buts" that make our lives harder. I would drop my mic at this point if this article was not about building a custom ML workstation. Kaggle Kernels and Google Colab are great. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |