Course software for introductory deep learning
In the practicals, we will use only a small dataset and we will need only little computation power and memory ressources. You can therefore do 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
We will use a terminal to install the libraries.
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
To do the practicals, you can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook:
source mlcourse/bin/activate
jupyter notebook
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 Terminal square button in the Other panel (at the bottom of the page).
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, double click on "my_notebooks" (left pannel) and then 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, double click on "my_notebooks" (left pannel) and then 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/3.9.13
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/3.9.13 r/4.2.1
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