Cloud processing is now simpler and cheaper! A *very simple* and *cheap* way to run/distribute your *existing* processing/training code on the cloud
It happened to me, and I’m sure it’s happening to you and to many many data scientists, who work on their small/medium size project out there:
You’ve invested a lot in your own training pipeline (pre-processing -> training -> testing), tried it locally a few times using different parameters, and it seems to be great. But… you realize you need much more RAM/CPU/GPU/GPU memory or just all of them together to be able to get the most of out of it?
It can happen for many reasons —
So, _theoretically, _you have everything you need, but you just need to run it on a better HW… Should be a non-issue today, shouldn’t it?
Well, there’re indeed many solutions out there, here’s a a few related technologies / platforms / solutions:
How to automate and scale your deep learning experiments with Ansible, AWS cloud infrastructure and Pytorch Lightning library.
PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.
How to iterating faster on your data science project, and let your brilliant idea to see the light of day. In this post, I’d like to show you how easy (and cheap, if you want) it is to distribute existing distribution-ready PyTorch training code on AWS SageMaker using simple-sagemaker.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
How you can use Deep Learning even for small datasets. When you’re working on Deep Learning algorithms you almost always require a large volume of data to train your model on.