ML Sprint: PyTorch Best Practises, Model Deploying Resources

Hi Everybody!
As part of our first community hangout, we’re excited to be hosting a few sprints. This is one of the same:

The plan with ML Sprints is to run week-long activities where our community will contribute to projects.

For the first set of sprints, we’re curating the Top “PyTorch Best Practises” and Model Deploying Resources.

This is a wiki! This means all of you can edit it, please do so!


Deploying PyTorch model for Beginners:

  1. Using Binder. A great notebook by FastAI team. Lecture by Jeremy Howard.
  2. Using Flask(good for self-learning, but won’t recommend in a Production Environment). Pytorch Offical Link, Kdnuggets
  3. Using super awesome FastAPI(good for self-learning as well as for Production Environment). Link
  4. All in one resource. Github

Best Tutorial for MLops I’ve found : GitHub - graviraja/MLOps-Basics

Talks about everything :

  • week_0_project_setup
  • week_1_wandb_logging
  • week_2_hydra_config
  • week_3_dvc
  • week_4_onnx
  • week_5_docker
  • week_6_github_actions
  • week_7_ecr
  • week_8_serverless
  • week_9_monitoring

This is mind blowingly amazing! I’ve just read through what is covered in each week. So excited to learn this and put it into practice. Thanks for sharing.