![]() ![]() To back up your work elsewhere, you can use Git and Github. The work you create from within the Jupyter Lab instance launched by this Docker command will persist locally. This is achieved by using Docker’s volume mount functionality in the run command above, the -volume command tells docker to share the current working directory with the container to be created.Shares local files between the isolated Docker container and your local machine.Runs a local Docker container from the folder in which this repository was downloaded in.It also launches a Jupyter Lab notebook server locally which you can access in your browser at As configured, this image first installs Python, then installs all modules listed in requirements.txt. As you can see if you inspect the file yourself, this Dockerfile is based on the base-notebook Docker image developed by Project Jupyter.Builds a Docker image from the Dockerfile in this post’s linked repository.Then run the following command to start the Jupyter Lab Notebook server: docker run \ IMAGE_ID=$(docker image ls | grep jupyter_lab_docker | awk '') Alternatively, you could run the following command (without replacing anything), which will create a local variable named $IMAGE_ID: Take note of the hash associated with the image you created tagged jupyter_lab_demo replace that $IMAGE_ID in the run command below. ![]() Navigate to this directory on your local machine:Īssuming Docker has been installed properly, you can run the following command that compiles a local docker image that we will run in the next step. Launch your preferred terminal (either Mac’s default Terminal app or something like iTerm2 running zsh). This downloads all the pre-requisite software to run a container.
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