ankitshekhawat

function product() {
  var args = Array.prototype.slice.call(arguments); // makes array from arguments
  return args.reduce(function tl (accumulator, value) {
    var tmp = [];
    accumulator.forEach(function (a0) {
      value.forEach(function (a1) {
        tmp.push(a0.concat(a1));
      });
    });
    return tmp;

ankitshekhawat

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

ankitshekhawat

//
//  GIF2MP4.swift
//
//  Created by PowHu Yang on 2017/1/24.
//  Copyright © 2017 PowHu Yang. All rights reserved.
//

ankitshekhawat

#!/bin/env bash

pip3 uninstall pillow

apt install \
    libjpeg-turbo8-dev \
    zlib1g-dev \
    libtiff5-dev \
    liblcms2-dev \
    libfreetype6-dev \

ankitshekhawat

Simple Installer for Docker Compose on Container-Optimized OS on Google Computing Engine

The easiest way to make Docker Compose available on Container-Optimized OS on Google Compute Engine (GCE) on Google Cloud Platform (GCP).

This is minimal Bash script version of Community Tutorial.

How to use

Simply, download installer.sh, run it, and then reload bash by source ~/.bashrc or re-login.

ankitshekhawat

export CUDA_HOME=/usr/local/cuda
export PATH=/usr/local/cuda/bin:$PATH
export CPATH=/usr/local/cuda/include:$CPATH
export LIBRARY_PATH=/usr/local/cuda/lib64:$LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH