This repo is holds training materials and tools used for demos for the MSTICPy Python Package. Please refer to that site and the MSTICPy documentation on ReadTheDocs.
There are several options to creating or finding an environment to run the workshop notebooks. The two recommended options are:
- Local Anaconda installation
- Visual Studio Code Dev Container (both of these assume prior installation of VSCode)
You can also opt to create and run a Docker container using Jupyter classic web interface.
The Dockerfile.txt and instructions for doing this are in the workshop folder
- please see the Jupyter Classic Docker instructions
The instructions for the other two options are below.
- Install Ananconda.
- Open the Ananconda prompt/terminal.
- Create a virtual environment: (replace with your choice of environment name)
conda create --name msticpy python=3.11- Activate the environment: (replace with your choice of environment name)
conda activate msticpy- Install Jupyter notebooks Conda package
conda install -c anaconda notebook- Clone the
msticpy-trainingrepo (replace <src_root> with the location that you want to clone the repo):
cd <src_root>
git clone https://github.com/microsoft/msticpy-training- Install required Python packages
pip install -r msticpy-training/requirements.txt- Change directory to the workshop folder
cd msticpy-training/workshops/Jun2023- Login to Azure using Azure CLI (This will return a login URL and a device code - copy the code and paste into the login page prompt and continue with normal authentication flow).
az login- Run VS Code
code .- Install VSCode Jupyter and Python extensions
Install docker for desktop on Windows :
Follow the instructions at docker docs - https://docs.docker.com/desktop/install/windows-install/
Build Docker image locally:
docker build -t msticpy-training -f ./.devcontainer/Dockerfile .Run docker image:
docker run -p 8888:8888 msticpy-trainingConnect VSCode to Connector: Attach to a Docker Container
To attach to a Docker container, either select Dev Containers: Attach to Running Container... from the Command Palette (F1) or use the Remote Explorer in the Activity Bar and from the Containers view, select the Attach to Container inline action on the container you want to connect to
-
Once you open a notebook in VSCode. You will see Select Kernel option on right hand top corner.
-
Once you click on it, you will be prompted to install Jupyter VSCode extension in container.
-
After you finished installation, you can then click Select Kernel and choose Connect to local Jupyter server.
-
Run Jupyter server URI from VSCode Terminal.
- and then enter the URL on the next screen.
- Finally, select a Remote Kernel.
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