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Chong-Lim Kim
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Add section in README on running code for Intro to Practical NN and DL class
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‎README.md‎

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## Running IEEE Boston Section class demo code
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Introduction to Practical Neural Networks and Deep Learning (Part 1) March 20, 2021
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###### Why using docker container
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In order to avoid needing to give out separate instructions on how to install Python and needed packages to run the book's code on different platforms/flavors (such as Mac, Windows, Linux),
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it seemed easier to just give one set of instructions on how to create a docker container and how to run the demo code in it.
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Hopefully, docker is sufficiently ubiquitous nowadays so that installing and running docker on different platforms should be well documented.
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#### How to: clone repo, run docker container, run demo
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Clone repo locally at command line, `cd` into repo directory, `git checkout` branch that has desired setup of demo code you want to run.
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Acknowledgement: The repo is forked from _DeepLearningPython35_ repo of Michal Daniel Dobrzanski who ported the book's code from Python 2.7 to Python 3.5 and wrote the 'wrapper' test.py
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```
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~ $ git clone https://github.com/clkim/DeepLearningPython35.git
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~ $ cd DeepLearningPython35
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~/DeepLearningPython35 $ git checkout chap1_30-hidden-neurons-3.0-eta
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```
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In order to run the desired setup of demo code, comment in/out or add/modify the code as appropriate in `test.py` in order to specify the neural network and deep learning configuration to run.
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To see an example of the (flexible but somewhat hackish and minimalist) changes I made in `test.py` in order to run the demo in the chap1 branch,
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at command line run `git diff ea229ac 6ba2425` to see the small change committed in the branch.
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Run _Docker Desktop_ app locally (currently 3.0.3 for Mac).
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At repo directory, start docker container _deeplearning_ (see below for how to create container).
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In container shell `cd` into mounted repo directory (which should be already on desired git branch, e.g. chap1_30-hidden-neurons-3.0-eta).
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```
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~/DeepLearningPython35 $ docker container ls --all
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< Should see a table with column names CONTAINER ID ... NAMES, and a row with container named deeplearning >
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~/DeepLearningPython35 $ docker container start -ai deeplearning
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(base) root@xxx:/# cd deeplearn/
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(base) root@xxx:/deeplearn# python --version
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Python 3.8.5
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(base) root@xxx:/deeplearn# conda info --env
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< Should see two environments: base and nndlbook >
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(base) root@xxx:/deeplearn# conda activate nndlbook
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(nndlbook) root@xxx:/deeplearn# python3.8 test.py
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Epoch 0 : 8943 / 10000
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Epoch 1 : 9166 / 10000
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Epoch 2 : 9267 / 10000
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Epoch 3 : 9340 / 10000
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Epoch 4 : 9337 / 10000
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Epoch 5 : 9374 / 10000
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Epoch 6 : 9386 / 10000
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< On my late 2013 MacBook Pro, it takes about a minute to finish Epoch 6; control-C to break >
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(nndlbook) root@xxx:/deeplearn# exit
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exit
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~/DeepLearningPython35 $
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```
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#### How to create docker container
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(We want to mount a directory so Python source code is accessible from inside container; and we want to install numpy package and theano package.)
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Run _Docker Desktop_ app locally (currently 3.0.3 for Mac).
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First run docker to download miniconda3 image.
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Then run docker to create the container and install the packages.
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```
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~ $ docker pull continuumio/miniconda3
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Using default tag: latest
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...
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docker.io/continuumio/miniconda3:latest
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~ $ docker images
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< Should see a table with column names REPOSITORY ... SIZE, and a row with image repository/name continuumio/miniconda3 >
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< Now cd into the repo directory, after cloning repo from github as given above >
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< Then run docker to create a new container layer over the downloaded image, and create a new conda environment where we install packages >
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~ $ cd DeepLearningPython35
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~/DeepLearningPython35 $ docker run -it --name deeplearning --mount type=bind,source="$(pwd)",target=/deeplearn continuumio/miniconda3
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(base) root@xxx:/# conda --version
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conda 4.9.2
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(base) root@xxx:/# conda create --name nndlbook
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Collecting package metadata (current_repodata.json): done
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...
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Proceed ([y]/n)? y
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...
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# To activate this environment, use
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#
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# $ conda activate nndlbook
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...
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(base) root@xxx:/# conda activate nndlbook
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(nndlbook) root@xxx:/# conda list
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# packages in environment at /opt/conda/envs/nndlbook:
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#
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# Name Version Build Channel
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(nndlbook) root@xxx:/# python --version
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Python 3.8.5
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(nndlbook) root@x:/# conda install numpy
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Collecting package metadata (current_repodata.json): done
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...
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Proceed ([y]/n)? y
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...
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...
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Executing transaction: done
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(nndlbook) root@xxx:/# conda install theano
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Collecting package metadata (current_repodata.json): done
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...
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Proceed ([y]/n)? y
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...
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...
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Executing transaction: done
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(nndlbook) root@xxx:/# conda list
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< Should see list of packages including numpy and theano >
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(nndlbook) root@xxx:/# exit
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exit
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~/DeepLearningPython35 $ docker container ls --all
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< Should see a table with column names CONTAINER ID ... NAMES, and a row with container named deeplearning >
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```
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___
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## Overview
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### neuralnetworksanddeeplearning.com integrated scripts for Python 3.5.2 and Theano with CUDA support

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