mehdidc

Disable Laptop Keyboard in Ubuntu

Taken From :- https://blog.hostonnet.com/laptop-keyboard-ubuntu

To disable laptop built in keyboard in Ubuntu, first you need to identify the device id for keyboard.

Now run xinput –list command to get list of all input devices

hostonnet@pc4:~$ xinput --list
⎡ Virtual core pointer                      id=2    [master pointer  (3)]

mehdidc

(Internal Tranining Material)

Usually the first step in performance optimization is to do profiling, e.g. to identify performance hotspots of a workload. This gist tells basic knowledge of performance profiling on PyTorch, you will get:

• How to find the bottleneck operator?
• How to trace source file of a particular operator?
• How do I indentify threading issues? (oversubscription)
• How do I tell a specific operator is running efficiently or not?

This tutorial takes one of my recent projects - pssp-transformer as an example to guide you through path of PyTorch CPU peformance optimization. Focus will be on Part 1 & Part 2.

mehdidc

#!/usr/bin/env bash
set -e

cd

case "$OSTYPE" in
  darwin*)  DOWNLOAD=https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh ;;
  linux*)   DOWNLOAD=https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  ;;
  *)        echo "unknown: $OSTYPE" ;;
esac

mehdidc

#!/bin/bash
# This file sets the environment variable CUDA_VISIBLE_DEVICES to the MPI local rank to enable multi-GPU usage of this benchmark. Note that this disables any GPU distribution handling by he batch scheduler.
# Background: Most/some batch schedulers set CUDA_VISIBLE_DEVICES to all available GPUs on a node. In that case, the Arbor benchmark would only use the first entry in the list, probably GPU#0. This script changes that.
# -Andreas Herten, Nov 2018

_verbose=1

localrank=$CUDA_VISIBLE_DEVICES

if [[ -n "$OMPI_COMM_WORLD_NODE_RANK" ]]; then

mehdidc

# use ImageMagick convert
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf

mehdidc

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

mehdidc

What / Why

Deploy key is a SSH key set in your repo to grant client read-only (as well as r/w, if you want) access to your repo.

As the name says, its primary function is to be used in the deploy process, where only read access is needed. Therefore keep the repo safe from the attack, in case the server side is fallen.

How to

1. Generate a ssh key

mehdidc

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

mehdidc

Cython

Cython has two major benefits:

1. Making python code faster, particularly things that can't be done in scipy/numpy
2. Wrapping/interfacing with C/C++ code

Cython gains most of it's benefit from statically typing arguments. However, statically typing is not required, in fact, regular python code is valid cython (but don't expect much of a speed up). By incrementally adding more type information, the code can speed up by several factors. This gist just provides a very basic usage of cython.

mehdidc

# docker build -t ubuntu1604py36
FROM ubuntu:16.04

RUN apt-get update
RUN apt-get install -y software-properties-common vim
RUN add-apt-repository ppa:jonathonf/python-3.6
RUN apt-get update

RUN apt-get install -y build-essential python3.6 python3.6-dev python3-pip python3.6-venv
RUN apt-get install -y git