Deepak Mangla i-amgeek

Creating Dockerfile for Dockship

Hello world 🙌

If you have an AI model which you want to share with the world🌍, Dockship is the place for you. In this article, I will help you create your own Dockerfile in easy steps to upload models on Dockship.

The best way to understand is to look at actual Dockerfile and break it down. Here, we will look at Dockerfile of 'Summer to Winter GAN' for reference.

1. FROM pytorch/pytorch
2. RUN apt-get update
3. RUN pip install certifi==2019.6.16 \

Making sense of Network Pruning

Why are we even talking about this?

Well, Because in past years, most of AI researchers didn't talk about this. Majority was focused on increasing 1% imagenet accuracy even if it makes model size 3x (It have its own advantages). But now, we have good accuracy with models in GBs and we can't deploy them (more problematic for edge devices).

Do we have some direction to solve this issue?🙄

Umm.. Yes. While designing models, one thing researchers find particularly interesting is that most of the weights in neural networks are redundant. They don't contribute in increasing accuracy (sometimes even decrease).

So, how pruning leverage this observation?

In Pruning, we rank the neurons in the network according to how much they contribute. The ranking can be done according to the L1/L2 mean of neuron weights, their mean activations, the number of times a neuron wasn’t zero on some validation set, and other cr

	from flask import Flask, request
	from run import Model
	import json


	app = Flask(__name__,
	static_url_path='',
	static_folder='')

	model = Model()

	#!/usr/bin/env python3.6
	import fire
	import requests
	from requests.exceptions import Timeout
	from getpass import getpass
	import os
	import tqdm
	import shutil
	import subprocess
	import zipfile

	import shutil
	import requests
	import uuid

	def load_file(path):
	'''
	Returns path of input file. If given parameter 'path' is an url,
	first downloads the file
	'''

	import shutil
	import uuid
	import requests
	import os

	def load_file(path):
	'''
	Returns path of input file. If given parameter 'path' is an url,
	first downloads the file
	'''

	for filter in 0..num_filters
	for channel in 0..input_channels
	for out_h in 0..output_height
	for out_w in 0..output_width
	for k_h in 0..kernel_height
	for k_w in 0..kernel_width
	output[filter, out_h, out_w] +=
	kernel[filter, channel, k_h, k_w] *
	input[channel, out_h + k_h, out_w + k_w]

	from dockship_launch import app
	if __name__ == "main":
	app.run()