Harsh Raj harshraj22

Requirements:

Use Sockets, Microservices, Master Slave DB, ML models, Observability of microservices, load balancer, autoscaling, elastic search

Build a chatting app, where:

users can send, text & images,
multiple people can join and leave
Authentication (only users with a passcode can join a channel)
ML models run synchronously on each message sent
Users have an option to see the negative masked message and validate the output of model (becomes data to retrain)

Given the model architecture (implemented in PyTorch), candidates have to train the given model, and submit the weights.
Host competition on kaggle for auto grading of submissions, and manually verify the scores/outputs of winners by their weights.
Participants can explore
- Different data augmentation techniques, including the promising ones like cutmix, mixin etc
Various optimizers & activation functions

1x1 convolutions have been extensively used to reduce the number of parameters without affecting the results much
Deep Mutual Learning: Unlike bagging/ boosting, models learn jointly, and help each other to fit well
Skip connections: Help solving degradation problem without adding parameters.
Hard Sample Mining

private channels are private to each, doesn't matter who is workspace owner link

replies in slack are theaded keeping the channel clean, while those in discord are sent on the public channel (similar to whatsapp)

emoji react on messages, polls (saves from +1 cancer), personalized reminders,

  fork ();
  fork ();
  fork ();

The total number of child processes created is
(A) 3
(B) 4
(C) 7

	while doing `pip install pycuda==2020.1`
	Reference: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#mandatory-post

	mapped python to python3 outside the virtal env

	cuda.h was not found:
	1. export PATH=/usr/local/cuda-10.2/bin${PATH:+:${PATH}} -> `ls /usr/local` showed us that our cuda version was 10.2

	pyconfig.h error:
	1. export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

	# import torch
	# import detectron2
	# from PIL import Image
	# import numpy as np

	# from detectron2.modeling import build_model
	# from detectron2.config import get_cfg
	# from detectron2.structures import ImageList

	# from torchinfo import summary

	# ~/.bashrc: executed by bash(1) for non-login shells.
	# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
	# for examples

	# If not running interactively, don't do anything
	case $- in
	i) ;;
	*) return;;
	esac