Deep Learning Tutorial 2019 installations
Instructions
Participants MUST complete the 3 following instructions before the tutorial kick off date:
- Installations
- Download resources
- Configurations
Installations
There are 2 options:
- Using personal computer
- Using Google Colab (alternative)
Using personal computer
A. Download and install Anaconda
Follow the underlisted steps to install Anaconda distribution in Linux (Ubuntu):
- cd /tmp
- curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
- sha256sum Anaconda3-2019.03-Linux-x86_64.sh
- bash Anaconda3-2019.03-Linux-x86_64.sh
- source ~/.bashrc
- conda list
Follow the underlisted steps to install Anaconda distribution in Windows:
- coming soon ...
B. Create virtual environment
We will use the name `dsai` for thi purpose (if you choose a different name, endeavour to be consistent):
- conda create --name dsai
- 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:
- conda install pytorch torchvision cpuonly -c pytorch # for cpu
- conda install -c aaronzs tensorflow=1.10
Or;
- conda install pytorch torchvision cudatoolkit=9.2 -c pytorch # for gpu with cuda 9.2
- conda install -c aaronzs tensorflow-gpu=1.10
Install python libraries:
- conda install numpy matplotlib
- pip install torchsummary
- conda install -c anaconda scipy==1.1.0
- conda install -c conda-forge opencv tqdm
- conda install -c anaconda opencv3
- conda install -c anaconda matplotlib
- conda install -c anaconda pillow
- conda install -c anaconda scikit-learn
- conda install -c anaconda scikit-image
For editor, install either jupyter notebook or jupyter lab:
- conda install -c anaconda jupyter
Or;
- conda install -c conda-forge jupyterlab
Using Google Colab (alternative)
Navigate to the dataset directory and run the download.sh bash file::
- Login to your google account; open google drive
- Download this folder: (link will be provided soon)
- unzip the folder and upload to your google drive
- Go to your google drive > dsai_tutorial_2019
- open setup_mount.ipynb; run first and second cell to mount the drive and to create a symlink
- open respective notebooks instructed by the instructors
For visualization: install any web browser (e.g.: Google chrome, Mozilla Firefox, Microsoft Edge, Apple Safari, etc.,)
Downloads
There are 3 resources to download:
- Codes
- Models
- Dataset
A. Codes
clone the git repository::
- git clone https://icube-forge.unistra.fr/CAMMA/misc/dsai_dl_tutorial.git
- cd dsai_dl_tutorial
B. Models
Navigate to the model directory and run the download.sh bash file::
- cd models
- chmod +x download.sh
- ./download.sh
B. Dataset
Navigate to the dataset directory and run the download.sh bash file::
- cd ../dataset
- chmod +x download.sh
- ./download.sh
Configurations
Ensure every installation is completed successfully and every resources downloaded to their appropriate directory:: Execute check_packages.sh to see any missing package:
- cd ..
- chmod +x check_packages.sh
- ./check_packages.sh
See the output of the bash file to install missing packages.
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!
Check that your tutorial organisation is in this hierarchical order:
├── 01_introduction_to_dl_concepts
│ └── README.md
├── 02_a_primer_on_python
│ └── README.md
├── 03_introduction_to_pytorch
│ ├── p01_pytorch-tensors.ipynb => Introduction to PyTorch tensors + autograd.
│ ├── p02_pytorch_dataloaders.ipynb => Introduction to PyTorch dataloaders.
│ ├── p03_pytorch_model_training.ipynb => Training a state-of-the-art model in PyTorch.
│ ├── p05_extra_pytorch_mnist_training.ipynb => A bonus program to train a a simple CNN on the mnist dataset.
│ ├── p05_extra_pytorch_mnist_inference_live.py => Bonus program to use the model trained above for live inference on the webcam stream.
│ └── utils
│ ├── hicoDataset.py => Custom data-loader class for hico-Det dataset
├── 04_introduction_to_tensorflow
│ └── README.md
├── 05_a_dl_application
│ └── ckpt => Weight management class
│ └── dataset => Dataset management class
│ └── metrics => Performance evaluation class
│ └── model => DL model classes
│ └── output => Collection of outputs
│ └── train.ipynb
│ └── test.ipynb
│ └── README.md
├── 06_generating_images_with_gans
│ └── README.md
├── 07_presentation_of_unistra_hpc
│ └── README.md
- Reference materials
Coming soon ...
Coming soon ...
- Survey
- Exercises