Contents

Setup for the workshop

There are four ways how you can follow this workshop:

  1. Via Docker (recommended): Full workshop experience, interactive, with all software dependencies.

  2. Via Conda (Python): Almost full workshop experience, interactive, with only Python based software dependencies.

  3. Via Mybinder: Full workshop experience, interactive and online, with restricted computational power.

  4. Via Jupyter NBViewer: Only visual exploration of the workshop content, no interactive aspect.

While option 3. is nice and easy to follow in an interactive manner and option 4. might work for a quick overview, we don’t recommended either of them as getting Docker and Python to work reliably on your machine is going to be very beneficial. This holds true for the workshop and especially beyond. Via installing these tools, you will be equipped to basically continue right away and start using them and thus ML/DL on your everyday research workflow. Having that in mind and integrating other tools/resources focusing open and reproducible (neuro-/data) science, we generated a rather comprehensive set of install instructions below. While not all of them might be totally necessary for the workshop, they all will help you a great deal going further and are especially useful/needed if we have to hold the workshop virtually due to the COVID-19 pandemic.

Don’t worry, you got this!

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General things

There are a few computing requirements for the course that are absolutely necessary (beyond the few software packages we would like you to install, described below):

  1. You must have administrator access to your computer (i.e., you must be able to install things yourself without requesting IT approval).

  2. You must have at least 20 GB of free disk space on your computer (but we would recommend more, to be safe).

  3. If you are using Windows you must be using Windows 10; Windows 7 and 8 will not be sufficient for this course.

If you foresee any of these being a problem please reach out to one of the instructors for what steps you can take to ensure you are ready for the course start.

Required software

To get the most out of our course, we ask that you arrive with the following software already installed:

  • A command-line shell: Bash

  • A version control system: Git

  • A remote-capable text editor: VSCode

  • Python 3 via Miniconda

  • A virtualization system: Docker

  • A GitHub account

  • Discord

  • A modern browser

If you already have all of the above software tools/packages installed, or are confident you’ll be able to install them by the time the course starts, you can jump straight to checking your install. The rest of this page provides more detail on installation procedures for each of the above elements, with separate instructions for each of the three major operating systems (Windows, Mac OS, and Linux).

Some quick general notes on instructions

  • There is no difference between Enter and Return in these instructions, so just press whatever the equivalent on your keyboard is whenever one is stated

  • If you already have some of these things installed on your computer already that should (theoretically) be okay. However, you need to make sure that you are able to complete the steps described in checking your install without issue.

    • For example, having multiple different Python installations on your computer can lead to incredibly frustrating issues that are very difficult to debug. As such, if you have already installed Python via some other application (not Miniconda/Anaconda), we strongly encourage you to uninstall it before following the instructions below. You must have Python installed via Miniconda for this course.

OS-specific installation instructions

Select the tab that corresponds to your operating system and follow the instructions therein.

Windows Subsystem for Linux (WSL)

  1. Search for Windows Powershell in your applications; right click and select Run as administrator. Select Yes on the prompt that appears asking if you want to allow the app to make changes to your device.

  2. Type the following into the Powershell and then press Enter:

     Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux

  3. Press Enter again when prompted to reboot your computer.

  4. Once your computer has rebooted, open the Microsoft Store and search for “Ubuntu.” Install the program labelled “Ubuntu 18.04” (not “Ubuntu 16.04” or “Ubuntu”) by clicking the tile, pressing Get, and then Install.

  5. Search for and open Ubuntu from your applications. There will be a slight delay (of a few minutes) while it finishes installing.

  6. You will be prompted to Enter new UNIX username. You can use any combination of alphanumeric characters here for your username, but a good choice is <first_initial><last_name> (e.g., jsmith for John Smith). You will then be prompted to enter a new password. (Choose something easy to remember as you will find yourself using it frequently.)

  7. Right click on the top bar of the Ubuntu application and select “Properties”. Under the “Options” tab, under the “Edit Options” heading, make sure the box reading “Use Ctrl+Shift+C/V as Copy/Paste” is checked. Under the “Terminal” tab, under the “Cursor Shape” heading, make sure the box reading “Vertical Bar” is checked. Press “Okay” to save these settings and then exit the application.

(The above step-by-step WSL instructions are distilled from here and here. If you have questions during the installation procedure those resources may have answers!)

From this point on whenever the instructions specify to “open a terminal” please assume you are supposed to open the Ubuntu application.

Bash shell

You already have it, now that you’ve installed the WSL!

Git

You already have it, now that you’ve installed the WSL!

VSCode

  1. Go to https://code.visualstudio.com/ and click the download button, then run the .exe file.

  2. Leave all the defaults during the installation with the following exception:

    • Please make sure the box labelled “Register Code as an editor for supported file types” is selected

VSCode extensions

  1. Open the Ubuntu application.

  2. Type code . into the terminal and press Enter. You should see a message reading “Installing VS Code Server” and then a new windows will open up.

  3. Press Ctrl+Shift+P in the new window that opens and type “Extensions: Install extensions” into the search bar that appears at the top of the screen. Select the appropriate entry from the dropdown menu that appears (there should be four entries; simply select the one that reads “Extensions: Install extensions”).

  4. A new panel should appear on the left-hand side of the screen with a search bar. Search for each of the following extensions and press Install for the first entry that appears. (The author listed for all of these extensions should be “Microsoft”.)

    • Python (n.b., you will need to reload VSCode after installing this)

    • Live Share (n.b., you may need to press “Ctrl/Cmd+Shift+P” and type “install extensions” again after installing this)

    • Live Share Extension Pack

    • Docker

    • Remote - WSL

Python

  1. Open a new terminal and type the following lines (separately) into the terminal, pressing Enter after each one:

     wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
 bash Miniconda3-latest-Linux-x86_64.sh

  2. A license agreement will be displayed and the bottom of the terminal will read --More--. Press Enter or the space bar until you are prompted with “Do you accept the license terms? [yes|no].” Type yes and then press Enter

  3. The installation script will inform you that it is going to install into a default directory (e.g., /home/$USER/miniconda3). Leave this default and press Enter.

  4. When you are asked “Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no],” type yes and press Enter. Exit the terminal once the installation has finished.

  5. Re-open the Ubuntu application. Type which python into the terminal and it should return a path (e.g., /home/$USER/miniconda3/bin/python).

    • If you do not see a path like this then please try typing conda init, closing your terminal, and repeating this step. If your issue is still not resolved skip the following step and contact an instructor on the #help-installation channel on the BHS Slack.

  6. Type the following to remove the installation script that was downloaded:

     rm ./Miniconda3-latest-Linux-x86_64.sh

Python packages

Open a terminal and type the following commands:

    conda config --append channels conda-forge
    conda config --set channel_priority strict
    conda install -y flake8 ipython jupyter jupyterlab matplotlib nibabel nilearn numpy pandas scipy seaborn

Docker

Unfortunately, Docker for Windows is a bit of a mess. The recommended version of Docker to install varies dramatically depending not only on which version of Windows you have installed (e.g., Windows 10 Home versus Professional/Enterprise/Education), but also which build of Windows you have. As such, developing a comprehensive set of instructions for installing Docker is rather difficult.

For this course, you will need to install either Docker Toolbox for Windows or Docker for Windows Desktop. Which you install will depend on your OS. PLEASE NOTE that installing Docker for Windows Desktop will disable VirtualBox on your computer. If you actively use VirtualBox we recommend you install Docker Toolbox.

(Note: the below instructions assume you are installing Docker Toolbox. Because there are fewer requirements for Docker Toolbox, it is likely that you will be able to install this more easily.)

  1. Download the latest Docker Toolbox installer (note: that link will automatically download the file)

  2. Run the downloaded .exe file and leave all the defaults during the installation procedure. Click Yeson the prompt that appears asking if the application can make changes to your computer.

  3. Search for and open the newly-installed “Docker Quickstart” application. Again, click Yeson the prompt that appears asking if the application can make changes to your computer. The application will do a number of things to finish installing and setting up Docker.

  4. Once you see a $ prompt type docker run hello-world. A brief introductory message should be printed to the screen.

  5. Close the “Docker Quickstart” application and open a terminal (i.e., the Ubuntu application).

  6. Copy-paste the following commands. You will be prompted to enter your password once.

     # Update the apt package list.
 sudo apt-get update -y
 # Install Docker's package dependencies.
 sudo apt-get install -y \
     apt-transport-https \
     ca-certificates \
     curl \
     software-properties-common
 # Download and add Docker's official public PGP key.
 curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
 # Verify the fingerprint.
 sudo apt-key fingerprint 0EBFCD88
 # Add the `stable` channel's Docker upstream repository.
 sudo add-apt-repository \
     "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
     $(lsb_release -cs) \
     stable"
 # Update the apt package list (for the new apt repo).
 sudo apt-get update -y
 # Install the latest version of Docker CE.
 sudo apt-get install -y docker-ce
 # Allow your user to access the Docker CLI without needing root access.
 sudo usermod -aG docker $USER

  7. Close and re-open the terminal.

  8. Type pip install docker-compose.

  9. Type powershell.exe "docker-machine config". You should get output similar to the following:

     --tlsverify
 --tlscacert="C:\\Users\\<YOUR_USERNAME>\\.docker\\machine\\machines\\default\\ca.pem"
 --tlscert="C:\\Users\\<YOUR_USERNAME>\\.docker\\machine\\machines\\default\\cert.pem"
 --tlskey="C:\\Users\\<YOUR_USERNAME>\\.docker\\machine\\machines\\default\\key.pem"
 -H=tcp://xxx.xxx.xx.xxx:xxxx

    where <YOUR_USERNAME> will have an actual value (likely your Windows username), and tcp=xxx.xxx.xx.xxx:xxx will be a series of numbers. If you don’t get this output then something has gone wrong. Please make sure you were able to run the docker run hello-world command, above. If you were and you still don’t receive this output, please contact one of the instructors on the #help-installation channel on Discord.

  10. You will use the the outputs of the above command to modify the commands below before running them in the terminal. First, take the numbers printed in place of the xs on the output of the line -H=tcp://xxx.xxx.xx.xxx:xxxx from above and replace the placeholder xxx.xxx.xx.xxx:xxxx on the first command below (export DOCKER_HOST). Second, take whatever value is printed in place of <YOUR_USERNAME> above and replace the <YOUR_USERNAME> placeholder on the second command below (export DOCKER_CERT_PATH). Once you have updated the commands appropriately, copy and paste them into the terminal:

     echo "export DOCKER_HOST=tcp://xxx.xxx.xx.xxx:xxxx" >> $HOME/.bashrc
 echo "export DOCKER_CERT_PATH=/mnt/c/Users/<YOUR_USERNAME>/.docker/machine/certs" >> $HOME/.bashrc
 echo "export DOCKER_TLS_VERIFY=1" >> $HOME/.bashrc

  11. Close and re-open a terminal (i.e., the Ubuntu application). Type docker run hello-world. The same brief introductory message you saw before should be printed to the screen.

Note: If you restart your computer (or somehow otherwise shut down the Docker VM) you will need to re-open the “Docker Quickstart” application and wait until you see the $ prompt again before your docker commands will work again! If you are having problems running docker commands in the terminal, try re-opening the “Docker Quickstart” application.

(The above step-by-step instructions are distilled from here and here. If you have questions during the installation procedure please check those links for potential answers!)

Bash shell

You already have it! Depending on which version of Linux you’re running you may need to type bash inside the terminal to access it. To check whether this is necessary, follow these steps:

  1. Open a terminal and type echo $SHELL. If it reads /bin/bash then you are all set! If not, whenever the instructions read “open a terminal,” please assume you are to open a terminal, type bash, and the proceed with the instructions as specified.

Git

You may already have it; try typing sudo apt-get install git (Ubuntu, Debian) or sudo yum install git (Fedora) inside the terminal. If you are prompted to install it follow the instructions on-screen to do so.

VSCode

  1. Go to https://code.visualstudio.com/ and click the download button for either the .deb (Ubuntu, Debian) or the .rpm (Fedora, CentOS) file.

  2. Double-click the downloaded file to install VSCode. (You may be prompted to type your administrator password during the install).

VSCode extensions

  1. Open the Visual Studio Code application.

  2. Press Ctrl+Shift+P in the new window that opens and type “Extensions: Install extensions” into the search bar that appears at the top of the screen. Select the appropriate entry from the dropdown menu that appears (there should be four entries; simply select the one that reads “Extensions: Install extensions”).

  3. A new panel should appear on the left-hand side of the screen with a search bar. Search for each of the following extensions and press Install for the first entry that appears. (The author listed for all of these extensions should be “Microsoft”.)

    • Python (n.b., you will need to reload VSCode after installing this)

    • Live Share (n.b., you may need to press “Ctrl/Cmd+Shift+P” and type “install extensions” again after installing this)

    • Live Share Extension Pack

    • Docker

Python

  1. Open a new terminal and type the following lines (separately) into the terminal, pressing Enter after each one:

     wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
 bash Miniconda3-latest-Linux-x86_64.sh

  2. A license agreement will be displayed and the bottom of the terminal will read --More--. Press Enter or the space bar until you are prompted with “Do you accept the license terms? [yes|no].” Type yes and then press Enter

  3. The installation script will inform you that it is going to install into a default directory (e.g., /home/$USER/miniconda3). Leave this default and press Enter.

  4. When you are asked “Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no],” type yes and press Enter. Exit the terminal once the installation has finished.

  5. Re-open a new terminal. Type which python into the terminal and it should return a path (e.g., /home/$USER/miniconda3/bin/python).

    • If you do not see a path like this then please try typing conda init, closing your terminal, and repeating this step. If your issue is still not resolved skip the following step and contact an instructor on the #help-installation channel of the BHS Slack.

  6. Type the following to remove the installation script that was downloaded:

     rm ./Miniconda3-latest-Linux-x86_64.sh

Python packages

Open a terminal and type the following commands:

    conda config --append channels conda-forge
    conda config --set channel_priority strict
    conda install -y flake8 ipython jupyter jupyterlab matplotlib nibabel nilearn numpy pandas scipy seaborn

Docker

  1. You will be following different instructions depending on your distro (Ubuntu, Debian, Fedora, CentOS). Make sure to follow the “Install using the repository” method!

  2. Once you’ve installed Docker make sure to follow the post-install instructions as well. You only need to do the “Manage Docker as a non-root user” and “Configure Docker to start on boot” steps.

  3. Open a new terminal and type docker run hello-world. A brief introductory message should be printed to the screen.

Bash shell

You already have it! Depending on which version of Mac OS you’re running you may need to type bash inside the terminal to access it. To check whether this is necessary, follow these steps:

  1. Open a terminal and type echo $SHELL. If it reads /bin/bash then you are all set!

Note: If you are using Mac Catalina (10.15.X) then it is possible your default shell is NOT CORRECT. To set the default to bash, type chsh -s /bin/bash in the terminal, enter your password when prompted, and then close + re-open the terminal.

Git

You may already have it! Try opening a terminal and typing git --version. If you do not see something like “git version X.XX.X” printed out, then follow these steps:

  1. Follow this link to automatically download an installer.

  2. Double click the downloaded file (git-2.23.0-intel-universal-mavericks.dmg) and then double click the git-2.23.0-intel-universal-mavericks.pkg icon inside the dmg that is opened.

  3. Follow the on-screen instructions to install the package.

VSCode

  1. Go to https://code.visualstudio.com/ and click the download button.

  2. Unzip the downloaded file (e.g., VSCode-darwin-stable.zip) and moving the resulting Visual Studio Code file to your Applications directory.

VSCode extensions

  1. Open the Visual Studio Code application

  2. Type Cmd+Shift+P and then enter “Shell command: Install ‘code’ command in PATH” into the search bar that appears at the top of the screen. Select the highlighted entry. A notification box should appear in the bottom-right corner indicating that the command was installed successfully.

  3. Type Cmd+Shift+P again and then enter “Extensions: Install extensions” into the search bar. Select the appropriate entry from the dropdown menu that appears (there should be four entries; simply select the one that reads “Extensions: Install extensions”).

  4. A new panel should appear on the left-hand side of the screen with a search bar. Search for each of the following extensions and press Install for the first entry that appears. (The author listed for all of these extensions should be “Microsoft”.)

    • Python (n.b., you will need to reload VSCode after installing this)

    • Live Share (n.b., you may need to press “Ctrl/Cmd+Shift+P” and type “install extensions” again after installing this)

    • Live Share Extension Pack

    • Docker

Python

  1. Open a new terminal and type the following lines (separately) into the terminal, pressing Enter after each one:

     curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
 bash Miniconda3-latest-MacOSX-x86_64.sh

  2. A license agreement will be displayed and the bottom of the terminal will read --More--. Press Enter or the space bar until you are prompted with “Do you accept the license terms? [yes|no].” Type yes and then press Enter

  3. The installation script will inform you that it is going to install into a default directory (e.g., /home/$USER/miniconda3). Leave this default and press Enter.

  4. When you are asked “Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no],” type yes and press Enter. Exit the terminal once the installation has finished.

  5. Re-open a terminal. Type which python into the terminal and it should return a path (e.g., /home/$USER/miniconda3/bin/python).

    • If you do not see a path like this then please try typing conda init, closing your terminal, and repeating this step. If your issue is still not resolved skip the following step and contact an instructor on the #help-installation channel of the BHS Slack.

  6. Type the following to remove the installation script that was downloaded:

     rm ./Miniconda3-latest-MacOSX-x86_64.sh

Python packages

Open a terminal and type the following commands:

    conda config --append channels conda-forge
    conda config --set channel_priority strict
    conda install -y flake8 ipython jupyter jupyterlab matplotlib nibabel nilearn numpy pandas scipy seaborn

Docker

  1. Go to https://hub.docker.com/editions/community/docker-ce-desktop-mac/ and press “Get Docker”.

  2. Open the “Docker.dmg” file that is downloaded and drag and drop the icon to the Applications folder

  3. Open the Docker application and enter your password. An icon will appear in the status bar in the top-left of the screen. Wait until it reads “Docker Desktop is now up and running!”

  4. Open a new terminal and type docker run hello-world. A brief introductory message should be printed to the screen.

(The above step-by-step Docker instructions are distilled from here. If you have questions during the installation procedure please check that link for potential answers!)

Note: If the instructions aren’t working and you have spent more than 15-20 minutes troubleshooting on your own, reach out on the #help-installation channel on the Discord channel with the exact problems you’re having. One of the instructors will try and get back to you quickly to help resolve the situation. If they’re unable to help via Discord, you may be directed to attend one of the installation office hours.

GitHub account

Go to https://github.com/join/ and follow the on-screen instructions to create an account. It is a good idea to associate this with your university e-mail (if you have one) as this will entitle you to sign up for the GitHub Student Developer Pack which comes with some nice free bonuses.

Discord

Go to https://discord.com/ and download and install Discord. Please note, that you can also use Discord through your browser if you don’t want to download it. You will be invited to the course channel via e-mail.

Modern web browser

Install Firefox or Chrome. (Safari will also work.) Microsoft Edge is not modern, despite what Microsoft might try and otherwise tell you.

Checking your install

Now that you’ve installed everything it’s time to check that everything works as expected! Type the following into your terminal:

bash <( curl -s https://raw.githubusercontent.com/PeerHerholz/ML-DL_workshop_SynAGE/master/check_install.sh?token=AEZSN5GTQLCWJXLTJSZBHGDAPR4R4 )

If you installed everything correctly you should see a message informing you as such. If any problems were detected you should receive some brief instructions on what is wrong with potential suggestions on how to remedy it. If you followed these instructions step-by-step and cannot resolve the issue please contact one of the course instructors for more help.

Yeah, you did! Great job!

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Getting the workshop content

Now that you have installed the required software (or not) to follow the workshop, it’s time to gather the respective materials. Please use the tab matching your setup.

docker logo
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Once Docker Desktop is set up, open a (docker) terminal and run the following command to verify if everything is good to go:

docker run hello-world

Download workshop content

Now, the only thing that’s still missing is the actual workshop content. Do get this, use again a (docker) terminal and run the following command

docker pull peerherholz/ml-dl_workshop_synage

Given that the whole workshop content is a bit heavy, the download of this container image might take a while.

Access workshop content via Jupyter Notebooks

Once the container is pulled and on your system, you’re all good to go. To access the workshop and open the Jupyter Notebook environment, please follow these steps:

  1. Open a (docker) terminal and run the following command:

     docker run -p 8888:8888 -it --rm peerherholz/ml-dl_workshop_synage

  2. Open http://127.0.0.1:8888/?token=ml-dl_workshop or http://localhost:8888/?token=ml-dl_workshop in your web browser to access the workshop content.

  3. Once Jupyter Notebook is open, double click on the Markdown file called overview.md - et voilà.

Note: Should you by any chance encounter the following “Password or token” needed message, use the token ml-dl_workshop to login.

Important notes

Don’t loose your progress/notes

Everything you do within this docker container will be reset the moment you terminate the docker run ... command in step one (or you close this terminal). This means, any output file created within the docker container will be deleted. Similarly, any notes and changes within the notebooks will be lost. To prevent this from happening, either (1) manually download the changed notebooks (i.e. File > Download As > Notebook (.ipynb)) or (2) create a common folder within the container and on your system and allow a direct transition of data by adding the -v flag to the docker run ... command.

For example, something like docker run -p 8888:8888 -it --rm -v /path/to/your/output_folder:/output peerherholz/ml-dl_workshop_synage, where /path/to/your/output_folder should be an empty folder on your system, such as /User/neuro/Desktop/output.

Here’s a more detailed explanation of this full command:

docker run \                    #  start up a container already built or pulled
    -p 8888:8888  \             #  port used, <local port>:<container port>
    -it  \                      #  run Docker interactively
    --rm  \                     #  remove the container when it exits
    -v ~/local_folder:/output   #  use local files <local path>:<container path>
    peerherholz/ml-dl_workshop_synage    #  use specified user/project:version container

Note: The path to the folder /path/to/your/output_folder needs to be an absolut path (i.e. it cannot be relate). So if you’re corrently in the folder /User/neuro/Desktop/workshop/ and want to give access to a subfolder called results. You cannot use -v results:/output or -v ./results:/output. You either need to use -v /User/neuro/Desktop/workshop/results:/output or -v ~/Desktop/workshop/results:/output.

Memory issues during workshop

It is possible that you might run into some MemoryError messages during the workshop, or that you don’t have enough CPUs for parallel process. This has most likely nothing to do with your system, and probably is due to Docker Desktop and it’s resource management. By default, Docker Desktop only uses 2 CPUs and 2 GB of RAM. You can change this default setting by opening Docker Desktop, go to Settings/Preferences > Resources. For a more detailed description see here for Mac and for Windows.

Docker is messy, clean up space afterwards

Docker is a great tool to quickly provide a out-of-the-box running computer environment. However, if you’re not carefull, it can quickly create a lot of unwanted files on your machine. To better understand these footprints and to clean out unwanted files after the workshop, please concider the following commands (run from within a (docker) terminal).

# Show all installed Docker images
docker images

# Remove a docker image
docker rmi -f $IMAGE_ID

# Remove all unused docker objects
docker system prune

# Remove everything (including images you might still need)
docker system prune -a

And should you chose to remove the --rm string in the docker run ... command, you can use the following commands to explore which containers are still open and potentially can be accessed once more (with their changes and additional outputs still present). Our advice is against such an approach as it can clutter your machine even quicker.

# Show running containers
docker ps

# Show all (also stopped) containers
docker ps -a

# Start a stopped container & attach to it
docker start -ia $CONTAINER_ID

# Remove a container
docker rm $CONTAINER_ID

conda logo
https://upload.wikimedia.org/wikipedia/commons/e/ea/Conda_logo.svg

By installing Python on your system (i.e. specifically Conda) and setting up the appropriate environment, you will be able to open all the Jupyter Notebooks and go through the whole content of the course locally.

To get things up and running, please follow these steps:

  1. Download the environment.yml file (e.g. with right mouse click -> Save As). Make sure that the file ends with .yml and not .txt.

  2. Open up a conda terminal (or any other terminal), and create a new conda environment with the following command: conda env create -f /path/to/file/environment.yml - For example ``conda env create -f ~/Downloads/environment.yml`

  3. Download the notebooks in this repository via this link) and unzip them to your prefered location, e.g. Desktop/ML-DL_workshop_SynAGE.

  4. Next, open up a conda terminal (or any other terminal), activate the conda environment with conda activate ml-dl_workshop (or on older conda environment with source activate ml-dl_workshop for mac and linux and activate ml-dl_workshop for windows).

  5. Finally, via the terminal, move to the folder where you’ve put all the unzipped content of this workshop, e.g. with the command cd ~/Desktop/ML-DL_workshop_SynAGE and run the command jupyter notebook from the folder that contains the ml-dl_workshop_synage_overview.ipynb notebook.

binder logo
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Binder

MyBinder.org is a great service that allows you to run Jupyter notebooks in a Docker or Python environment, directly online and for free. However, this service comes of course with a restricted computational environment (1-2GB of RAM). This means, many notebooks might be very slow and some might even crash, due to not enough memory.

You can use this approach to run and test most of the notebooks and to explore the slides. To access the MyBinder instance, use this link.

conda logo
https://nbviewer.jupyter.org/static/img/nav_logo.svg

If you want to go through the content of this workshop without installing anything on your machine, you should chose this approach. To see all the notebooks and slides from this workshop, please use this Jupyter NBviewer link.

Enter the matrix

Once you reached this point, you should be ready the enter the matrix and follow the workshop in your preferred way. Congrats, fantastic work!

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Outline