Deep Learning Tutorial 2019 installations

Instructions

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Participants MUST complete the 3 following instructions before the tutorial kick off date:

• Installations
• Download resources
• Configurations

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Installations

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There are 2 options:

1. Using personal computer (alternative)
2. Using Google Colab (preferred choice)

NB: Colab is the preference for this tutorial (at least after Tutorial 2) due to the need of a powerful GPU.
Nevertheless, participants are encouraged to do the installations on their personal computers in the case of unavailability/downtime of Google Colab for all users at the same time.

Using personal computer

A. Download and install Anaconda

1. Download and install Anaconda depending on your OS from https://www.anaconda.com/distribution/#download-section (choose Python 3.6 version)

B. Create virtual environment

We will use the name `dsai` for this purpose (if you choose a different name, endeavour to be consistent):

1. conda create --name dsai
2. source activate dsai

C. Install packages

Install PyTorch and Tensorflow packages. Depending on your system, you can install either the cpu or gpu version. Do not install both:

1. conda install pytorch torchvision cpuonly -c pytorch # for cpu; if you don't have gpu
2. conda install -c aaronzs tensorflow=1.10

Or;

1. conda install pytorch torchvision cudatoolkit=9.2 -c pytorch # for gpu with cuda 9.2
2. conda install -c aaronzs tensorflow-gpu=1.10

Install python libraries:

1. conda install numpy matplotlib
2. pip install torchsummary
3. conda install -c anaconda scipy==1.1.0
4. conda install -c conda-forge opencv tqdm keras
5. conda install -c anaconda pillow
6. conda install -c anaconda scikit-learn
7. conda install -c anaconda scikit-image
8. pip install comet_ml

For editor, install either jupyter notebook or jupyter lab:

• conda install -c anaconda jupyter

Or;

• conda install -c conda-forge jupyterlab

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For visualization: install any web browser (e.g.: Google chrome, Mozilla Firefox, Microsoft Edge, Apple Safari, etc.,)

Downloads

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There are 3 resources to download:

• Codes
• Models
• Dataset

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A. Codes

1. wget --content-disposition "[link expired]"
2. unzip dsai_dl_tutorial.zip
3. cd dsai_dl_tutorial

B. Models

Navigate to the model directory and run the download.sh bash file::

1. cd models
2. chmod +x download.sh
3. ./download.sh

C. Dataset

Navigate to the dataset directory and run the download.sh bash file::

1. cd ../datasets
2. chmod +x download.sh
3. ./download.sh

D. Slides

Download the presentation slides::

1. wget --content-disposition "[link expired]"

Using Google Colab (alternative)

Colab is the preference for this tutorial (at least after Tutorial 2) due to the need of a powerful GPU

• Please make sure you have a working Gmail account with atleast 3GB of free space on your Google Drive.
• Ensure you have executed correctly the instructions: A, B & C above.
• Upload your dsai_dl_tutorial folder (containing the codes, dataset and models) to your Google drive.
• Note the path and update it on the notebook according to instructions provided by the instructor on the tutorial day.

Configurations

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Run check_packages.py to see any missing package. See the output and install any missing packages:

• python check_packages.py

Run either jupyter notebook or jupyter lab to view/run the tutorial notebooks

• jupyter notebook

Or,

• jupyter lab

If you completed up to this step, you are ready!

Happy coding... see you soon!