hrishikeshvganu

"Plot a PNG using matplotlib in a web request, using Flask."

# Install dependencies, preferably in a virtualenv:
#
#     pip install flask matplotlib
#
# Run the development server:
#
#     python app.py
#

hrishikeshvganu

Client-side SSL

For excessively paranoid client authentication.

Using self-signed certificate.

Create a Certificate Authority root (which represents this server)

Organization & Common Name: Some human identifier for this server CA.

openssl genrsa -des3 -out ca.key 4096
openssl req -new -x509 -days 365 -key ca.key -out ca.crt

hrishikeshvganu

# Install build tools
sudo apt-get update
sudo apt-get install -y build-essential git python-pip libfreetype6-dev libxft-dev libncurses-dev libopenblas-dev  gfortran python3-matplotlib libblas-dev liblapack-dev libatlas-base-dev python3-dev python3-pydot linux-headers-generic linux-image-extra-virtual unzip python3-numpy swig python3-pandas python-sklearn unzip python3-pip
sudo pip3 install -U pip
sudo pip3 install -U ipython jupyter scikit-learn

# Install CUDA 7
# wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1410/x86_64/cuda-repo-ubuntu1410_7.0-28_amd64.deb
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1504/x86_64/cuda-repo-ubuntu1504_7.5-18_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1504_7.5-18_amd64.deb && rm cuda-repo-ubuntu1504_7.5-18_amd64.deb

hrishikeshvganu

import tensorflow as tf

def beam_decoder(decoder_inputs, initial_state, cell, loop_function, scope=None,
                 beam_size=7, done_token=0
                ):
    """
    Beam search decoder
    
    Args:
      decoder_inputs: A list of 2D Tensors [batch_size x input_size].

hrishikeshvganu

# 0 is too far from ` ;)
set -g base-index 1

# Automatically set window title
set-window-option -g automatic-rename on
set-option -g set-titles on

#set -g default-terminal screen-256color
set -g status-keys vi
set -g history-limit 10000

hrishikeshvganu

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

---

Index:

hrishikeshvganu

# Install tmux on rhel/centos 7

# install deps
yum install gcc kernel-devel make ncurses-devel

# DOWNLOAD SOURCES FOR LIBEVENT AND MAKE AND INSTALL
curl -OL https://github.com/libevent/libevent/releases/download/release-2.0.22-stable/libevent-2.0.22-stable.tar.gz
tar -xvzf libevent-2.0.22-stable.tar.gz
cd libevent-2.0.22-stable
./configure --prefix=/usr/local

hrishikeshvganu

from pathlib import Path

import tensorflow as tf
from build_big_model import build_big_model

MODEL_NAME = 'big_model'
MODEL_VERSION = '1'

model = build_big_model()
tf.saved_model.save(model, str(Path('models') / MODEL_NAME / MODEL_VERSION))

hrishikeshvganu

import threading
import time
from queue import Empty, Queue

import numpy as np
from flask import Flask, request as flask_request

from build_big_model import build_big_model

BATCH_SIZE = 20