Operating System代写 | 代做Python – Initial Setup

Operating System代写 | 代做Python – 这是一个涉及GPU的操作系统的作业初始化练习/p>

Deep Computer Vision and Language

assignment0: Initial Setup

The purpose of this assignment is for you to set up your environment and to ensure that you have a basic grAspof matrices and conditional probability. It will give students a chance to practice using Python, the primary programming language for this course. All code must be written in Python3.

The starter code with the requiiste functions is provied as part of this assignment.

Part 1: Environment Setup

This course assumes that you have access to macOS, Linux, or a Linux-like Windows environment. The following section details the setup process for each.

First, request a free GitHub Student account, if you do not already have one.


The macOS Operating Systemincludes a Unix subsystem accessible from the Terminal. The vast majority of the work for this course will take place in this environment.

  1. Install the Homebrew backage manager by following the instructions at http://brew.sh. We will use this to install other programs.
  2. Use Homebrew to install Python 3 and wget.
  3. brew install python
  4. brew install wget
  5. Installing Python 3 should also install pip. Prooceed to Part 2.
Windows 10
  1. Download and install the terminal, Cmder. Add an icon for it in an easily accessible place.
  2. Download and install Chocolatey
  1. Use Cmder to install Python 3 and pip.
  2. choco install python
  3. choco install pip
  4. Install Emacs, Git, OpenSSH, grep, and wget. choco install unixutils.
  5. choco install emacs
  6. choco install git
  7. choco install openssh
  8. choco install grepwin
  9. choco linstall wget
  10. Install CUDA and cuDNN.
  11. Optional: Install the Windows Subsystem for Linux, but this does not support GPU acceleration.
Ubuntu Linux
  1. Install emacs. On Ubuntu, this is:
  2. sudo apt-get install emacs

Part 2: Install Keras

First, install numpy, seaborn, and scikit-learn with pip.

pip3 install numpy pip3 install scikit-learn pip3 install seaborn On some systems, you may have to use pip instead of pip3.


Install TensorFlow and Keras.

pip3 install tensorflow pip3 install keras


If your computer does not have an NVIDIA GPU, use:

pip3 install tensorflow

pip3 install keras Otherwise, if you have an NVIDIA GPU and want to use GPU acceleration (recommended):

  1. Ensure that you are using the proprietary NVIDIA drivers for your system.
  2. Install CUDA and cuDNN.
  3. Install GPU-accelerated TensorFlow and Keras.
pip3 install tensorflow-gpu
pip3 install keras
Windows 10

If you have an NVIDIA GPU and would like to use GPU acceleration (recommended):

  1. Install CUDA 9 and cuDNN 7 from NVIDIA.
  2. Use pip to install GPU-accelerated TensorFlow and Keras.
  3. pip install tensorflow-gpu
  4. pip install keras

Part 3: Make sure Keras loads.

  1. Run python3.
  2. Type import keras.

If there are no problems, you should get a message about using the TensorFlow backend. You can then type Ctrl+D to exit.

If you were successful, the program should finish 20 epochs in a couple of minutes and report accuracy aroun 98%. Use output redirection to send the output of the program to a file named mnist_test.txt

Part 4: Getting used to Python

Write a Python program to add two matrices of any size and print out the resulting matrix.


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