Course software for introductory deep learning

The practicals can be done in three ways:

• Laptop: In the practicals, we will use only a small dataset and thuswe wewill need only need a require only limitedlittle computation power and memory ressourcesressources. You can therefore do the the practicals in three different ways:
  • Laptop: advantages + disadvantages?
    
    
  • JupyterLab:
    
    
  • Curnagl: 

  HOW DO WE MAKE LINKS FROM THE THREE WAYS TO THE CORRESPONDING PART IN THIS PAGE ?

  Laptop

  Python installation

  Here are some instructions for installing Keras with TensorFlow at the backend (for Python3), and other libraries, on your laptop. All the required libraries are listed here:

  https://c4science.ch/source/DL_INTRO/browse/master/requirements.txt

  You may copy/paste this list of libraries in a text editor and name the file "requirements.txt" or use Git:

  git clone https://c4science.ch/source/DL_INTRO.git

  Then you may use a Python IDE (e.g. Jupyter Notebook or PyCharm) or a terminal to install the libraries (see below).

  Use a Python IDE (e.g. Jupyter Notebook or PyCharm)

  Launch a Python IDE.

  HOW DO WE CREATE A VIRTUAL ENVIRONMENT WITHOUT A TERMINAL ?

  IT SHOULD ALSO WORK FOR WINDOWS USERS !

  $ python -m venv mlcourse

  $ source mlcourse/bin/activate

  $ pip install -r DL_INTRO/requirements.txt

  To check that Tensorflow was installed:

  $ python -c 'import tensorflow; print(tensorflow.version.VERSION)'

  There might be a warning message (see above) and the output should be something like "2.8.0".

  You can terminate the current session:

  $ deactivate

  $ exit

  Use your terminal

  Let us create a virtual environment. Open  your terminal and type:

  python -m venv mlcourse
  
  source mlcourse/bin/activate
  
  pip install -r DL_INTRO/requirements.txt

  To check that Tensorflow was installed:
  

  python -c 'import tensorflow; print(tensorflow.version.VERSION)'

  There might be a warning message (see above) and the output should be something like "2.8.0".

  You can terminate the current session:

  deactivate
  
  exit

  R installation
  

  Here are some instructions for installing Keras with TensorFlow at the backend, and other libraries, on your laptop. The R keras is actually an interface to the Python Keras. In simple terms, this means that the keras R package allows you to enjoy the benefit of R programming while having access to the capabilities of the Python Keras package.

  IMPORTANT: Since Keras is using Python in the background, you need some development tools in your computer. For example, if you have a Mac you need to install Xcode. For Windows users, you need to install Rtools (and possibly also Anaconda).

  Run R in your terminal or launch RStudio.

  REMARK: The R libraries will be installed in your home directory. To allow it, you must answer yes to the questions:

  Would you like to use a personal library instead? (yes/No/cancel) yes

  Would you like to create a personal library to install packages into? (yes/No/cancel) yes

  And select Switzerland for the CRAN mirror.

  > install.packages("keras")

  > library(keras)

  > library(tensorflow)

  > install_tensorflow(method="virtualenv", envname="r-tensorflow", version="2.5.0")

  > install.packages("ggplot2")

  > install.packages("ggfortify")

  To check that Keras was properly installed:

  > is_keras_available(version = NULL)

  There might be a warning message (see above) and the output should be something like "TRUE".

  You can terminate the current R session:

  > q()

  Save workspace image? [y/n/c]: n

  JupyterLab

  Here are some instructions for installing Keras with TensorFlow at the backend, and other libraries, on the JupyterLab of the EPFL.

  Go to the webpage: https://noto.epfl.ch/

  Use your Switch AAI login: University of Lausanne

  Enter the login and password associated to your Switch edu-ID (and NOT your UNIL credentials).

  Select Git / Clone a Repository (at the top of the window) and enter the following URL:

  https://c4science.ch/source/DL_INTRO.git

  Python installation

  Click on the Bash square button in the Notebook panel.

  Let us create a virtual environment. Type (or copy/paste) in the first cell:

  my_venvs_create mlcourse

  To execute this command, click on "Run the selected cells and advance" (the right arrow).

  Then type and run the following commands in the cells:

  my_venvs_activate mlcourse
  
  pip install -r DL_INTRO/requirements.txt
  
  my_kernels_create Deep_Learning "Deep Learning"
  
  my_venvs_deactivate

  The installation is complete !

  Select File / Log out to close the Jupyter session.

  To do the practicals, click on the "Deep Learning" square button in the Notebook panel.

  When you have finished the practicals, select File / Log out.

  R installation

  Click on the R square button in the Notebook panel, and type (or copy/paste) in the first cell:

  install.packages("keras")

  To execute this command, click on "Run the selected cells and advance" (the right arrow).

  Then type and run the following commands in the cells:

  library(keras)
  
  library(tensorflow)
  
  install_tensorflow(method="virtualenv", envname="r-tensorflow", version="2.5.0")
  
  install.packages("ggplot2")
  
  install.packages("ggfortify")

  The installation is complete !
  

  Select File / Log out to close the Jupyter session.

  To do the practicals, click on the R square button in the Notebook panel.

  When you have finished the practicals, select File / Log out.

  Curnagl

  For the practicals, it will be convenient to be able to copy/paste text from a web page to the terminal on Curnagl. So please make sure you can do it before the course. You also need to make sure that your terminal has a X server.

  For Mac users, download and install XQuartz (X server): https://www.xquartz.org/

  For Windows users, download and install MobaXterm terminal (which includes a X server). Click on the "Installer edition" button on the following webpage: https://mobaxterm.mobatek.net/download-home-edition.html

  For Linux users, you do not need to install anything.

  When testing if Keras was properly installed (see below) you may receive a warning

  2022-03-16 12:15:00.564218: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/python-3.8.8-tb3aceqq5wzx4kr5m7s5m4kzh4kxi3ex/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/tcl-8.6.11-aonlmtcje4sgqf6gc4d56cnp3mbbhvnj/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/tk-8.6.11-2gb36lqwohtzopr52c62hajn4tq7sf6m/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen/gcc-8.3.1/gcc-9.3.0-nwqdwvso3jf3fgygezygmtty6hvydale/lib64:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen/gcc-8.3.1/gcc-9.3.0-nwqdwvso3jf3fgygezygmtty6hvydale/lib
  2022-03-16 12:15:00.564262: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

  You should not worry. By default, Keras is trying to use GPUs and since there are no GPUs, it writes a warning and decides to use CPUs (which is enough for our course).

  Python installation

  Here are some instructions for installing Keras with TensorFlow at the backend (for Python3), and other libraries, on the UNIL cluster called Curnagl. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN):

  ssh -Y < my unil username >@curnagl.dcsr.unil.ch

  Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password.

  For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question "do you want to save password ?" Say No if your are not sure. Then you should be on Curnagl.

  See also the documentation: https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster

  cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/
  
  mkdir < my unil username >
  
  cd < my unil username >

  For convenience, you will install the libraries from the frontal node to do the practicals. Note however that it is normally recommended to install libraries from the interactive partition by using (Sinteractive -m 4G -c 1).

  git clone https://c4science.ch/source/DL_INTRO.git
  
  module load gcc python/3.8.8
  
  python -m venv mlcourse
  
  source mlcourse/bin/activate
  
  pip install -r DL_INTRO/requirements.txt

  To check that Tensorflow was installed:

  python -c 'import tensorflow; print(tensorflow.version.VERSION)'

  There might be a warning message (see above) and the output should be something like "2.8.0".

  You can terminate the current session:

  deactivate
  
  exit

  R installation
  

  Here are some instructions for installing Keras with TensorFlow at the backend, and other libraries, on the UNIL cluster called Curnagl. The R keras is actually an interface to the Python Keras. In simple terms, this means that the keras R package allows you to enjoy the benefit of R programming while having access to the capabilities of the Python Keras package. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN):

  ssh -Y < my unil username >@curnagl.dcsr.unil.ch

  Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password.

  For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question “do you want to save password ?” Say No if your are not sure. Then you should be on Curnagl.

  See also the documentation: https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster

  cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/
  
  mkdir < my unil username >
  
  cd < my unil username >

  For convenience, you will install the libraries from the frontal node to do the practicals. Note however that it is normally recommended to install libraries from the interactive partition by using (Sinteractive -m 4G -c 1).

  module load gcc python/3.8.8 r/4.0.5
  
  R

  REMARK: The R libraries will be installed in your home directory. To allow it, you must answer yes to the questions:
  

  Would you like to use a personal library instead? (yes/No/cancel) yes

  Would you like to create a personal library to install packages into? (yes/No/cancel) yes

  And select Switzerland for the CRAN mirror.

  install.packages("keras")
  
  library(keras)
  
  library(tensorflow)
  
  install_tensorflow(method="virtualenv", envname="r-tensorflow", version="2.5.0")
  
  install.packages("ggplot2")
  
  install.packages("ggfortify")

  To check that Keras was properly installed:
  

  is_keras_available(version = NULL)

  There might be a warning message (see above) and the output should be something like "TRUE".

  You can terminate the current R session:

  q()

  Save workspace image? [y/n/c]: n