Différences entre les versions de « Deep Learning Tutorial 2019 installations »
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<h3> Installations </h3><hr> | <h3> Installations </h3><hr> | ||
There are 2 options: | There are 2 options: | ||
− | # Using personal computer | + | # Using personal computer (alternative) |
− | # Using Google Colab ( | + | # Using Google Colab (preferred choice) |
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+ | <b>NB: </b><i>Colab is the preference for this tutorial (at least after Tutorial 2) due to the need of a powerful GPU.<br> 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.</i> | ||
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<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
− | + | # Download and install Anaconda depending on your OS from https://www.anaconda.com/distribution/#download-section (choose Python 3.6 version) | |
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<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
− | <i>We will use the name `dsai` for | + | <i>We will use the name `dsai` for this purpose (if you choose a different name, endeavour to be consistent)</i>: |
# conda create --name dsai | # conda create --name dsai | ||
# source activate dsai | # source activate dsai | ||
</div></div> | </div></div> | ||
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C. Install packages | C. Install packages | ||
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<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
<i>Install PyTorch and Tensorflow packages. Depending on your system, you can install either the cpu or gpu version. Do not install both:</i> | <i>Install PyTorch and Tensorflow packages. Depending on your system, you can install either the cpu or gpu version. Do not install both:</i> | ||
− | # conda install pytorch torchvision cpuonly -c pytorch # for cpu | + | # conda install pytorch torchvision cpuonly -c pytorch # for cpu; if you don't have gpu |
# conda install -c aaronzs tensorflow=1.10 | # conda install -c aaronzs tensorflow=1.10 | ||
Or; | Or; | ||
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# pip install torchsummary | # pip install torchsummary | ||
# conda install -c anaconda scipy==1.1.0 | # conda install -c anaconda scipy==1.1.0 | ||
− | # conda install -c conda-forge opencv tqdm | + | # conda install -c conda-forge opencv tqdm keras |
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# conda install -c anaconda pillow | # conda install -c anaconda pillow | ||
# conda install -c anaconda scikit-learn | # conda install -c anaconda scikit-learn | ||
# conda install -c anaconda scikit-image | # conda install -c anaconda scikit-image | ||
+ | # pip install comet_ml | ||
<i>For editor, install either jupyter notebook or jupyter lab:</i> | <i>For editor, install either jupyter notebook or jupyter lab:</i> | ||
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Or; | Or; | ||
* conda install -c conda-forge jupyterlab | * conda install -c conda-forge jupyterlab | ||
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</div></div> | </div></div> | ||
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<hr/> | <hr/> | ||
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+ | <p><font color=blue>For visualization: install any web browser (e.g.: Google chrome, Mozilla Firefox, Microsoft Edge, Apple Safari, etc.,)</font></p> | ||
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<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
− | + | # wget --content-disposition "[link expired]" | |
− | # | + | # unzip dsai_dl_tutorial.zip |
# cd dsai_dl_tutorial | # cd dsai_dl_tutorial | ||
</div></div> | </div></div> | ||
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</div></div> | </div></div> | ||
− | + | C. Dataset | |
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<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
<i>Navigate to the dataset directory and run the download.sh bash file:</i>: | <i>Navigate to the dataset directory and run the download.sh bash file:</i>: | ||
− | # cd ../ | + | # cd ../datasets |
# chmod +x download.sh | # chmod +x download.sh | ||
# ./download.sh | # ./download.sh | ||
+ | </div></div> | ||
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+ | D. Slides | ||
+ | <div class="toccolours mw-collapsible" style="width:70%; margin-left:40px; overflow:auto;" > | ||
+ | <div class="mw-collapsible-content"> | ||
+ | <i>Download the presentation slides:</i>: | ||
+ | # wget --content-disposition "[link expired]" | ||
+ | </div></div> | ||
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+ | <h5><u>Using Google Colab (alternative)</u></h5> | ||
+ | <div class="toccolours mw-collapsible" style="width:70%; margin-left:40px; overflow:auto;" > | ||
+ | <div class="mw-collapsible-content"> | ||
+ | <b>Colab is the preference for this tutorial (at least after Tutorial 2) due to the need of a powerful GPU</b> | ||
+ | * 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. | ||
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</div></div> | </div></div> | ||
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<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
− | <i> | + | <i>Run check_packages.py to see any missing package. See the output and install any missing packages:</i> |
− | + | * python check_packages.py | |
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<i>Run either jupyter notebook or jupyter lab to view/run the tutorial notebooks</i> | <i>Run either jupyter notebook or jupyter lab to view/run the tutorial notebooks</i> | ||
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<i>If you completed up to this step, you are ready!</i> | <i>If you completed up to this step, you are ready!</i> | ||
− | + | Happy coding... see you soon! | |
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</div> | </div> |
Version actuelle datée du 30 septembre 2019 à 10:34
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 (alternative)
- 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
- 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):
- 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; if you don't have gpu
- 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 keras
- conda install -c anaconda pillow
- conda install -c anaconda scikit-learn
- conda install -c anaconda scikit-image
- 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
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
- wget --content-disposition "[link expired]"
- unzip dsai_dl_tutorial.zip
- 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
C. Dataset
Navigate to the dataset directory and run the download.sh bash file::
- cd ../datasets
- chmod +x download.sh
- ./download.sh
D. Slides
Download the presentation slides::
- 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
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!