Customized all-in-one web-based development environment for machine learning

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively build ML solutions on your own machines. This workspace is the ultimate tool for developers, preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated.

🛠 Customizations for BALab (Business Analytics Lab)

• Self-maintained CUDA version: Due to the original version not being updated for a long time, this customization ensures that the CUDA version is actively maintained and frequently updated, ensuring compatibility with the latest developments in machine learning.
• Commitment to updates: Regular updates to the CUDA version are provided to keep the workspace in sync with the latest tools and technologies.
• Minimal versions of workspace: To avoid conflicts related to package dependencies between recent TensorFlow and PyTorch versions, only minimal versions are pre-installed. You can install the latest versions of TensorFlow and PyTorch manually within a virtual environment and set it up as a new kernel.

We strive to keep everything as similar as possible to the original version inside the repository, except for the customized figures/logos in the resources for our lab needs and other updates related to the CUDA version. For more information and to see the original version, please visit: ml-tooling/ml-workspace.

Highlights

• 💫  Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.
• 🗃  Pre-installed with many popular data science libraries & tools.
• 🖥  Full Linux desktop GUI accessible via web browser.
• 🔀  Seamless Git integration optimized for notebooks.
• 📈  Integrated hardware & training monitoring via Tensorboard & Netdata.
• 🚪  Access from anywhere via Web, SSH, or VNC under a single port.
• 🎛  Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH.
• 🐳  Easy to deploy on Mac, Linux, and Windows via Docker.

all the features detail could be seen at ml-tooling/ml-workspace#features

The workspace is equipped with a selection of best-in-class open-source development tools to help with the machine learning workflow. Many of these tools can be started from the Open Tool menu from Jupyter (the main application of the workspace):

Within your workspace you have full root & sudo privileges to install any library or tool you need via terminal (e.g., pip, apt-get, conda, or npm). You can find more ways to extend the workspace within the Extensibility section on the original documentation

The updated CUDA version is actively maintained and frequently updated, ensuring compatibility with the latest requirements in development machine learning model.

Getting Started (Start single instance)

Example for CUDA 12.5 Version

Prerequisites

• CUDA version 12.5 need to be installed on your machine (tested well on nvidia driver version: 555.99)
• The workspace requires Docker to be installed on your machine (📖 Installation Guide).

Deployment

Deploying a single workspace instance is as simple as:

docker run -p 8080:8080 greathopes/custom-workspace:12-5

we use image tags as the label for the supported CUDA version (ex:12-5 for CUDA 12.5)

Voilà, that was easy! Now, Docker will pull the latest workspace image to your machine. This may take a few minutes, depending on your internet speed. Once the workspace is started, you can access it via http://localhost:8080.

If started on another machine or with a different port, make sure to use the machine's IP/DNS and/or the exposed port.

To deploy a single instance for productive usage, we recommend to apply at least the following options:

docker run -d \
    -p 8080:8080 \
    --name "custom-workspace" \
    -v "${PWD}:/workspace" \
    --env AUTHENTICATE_VIA_JUPYTER="mytoken" \
    --shm-size 512m \
    --restart always \
    greathopes/custom-workspace:12-5

This command runs the container in background (-d), mounts your current working directory into the /workspace folder (-v), secures the workspace via a provided token (--env AUTHENTICATE_VIA_JUPYTER), provides 512MB of shared memory (--shm-size) to prevent unexpected crashes (see known issues section), and keeps the container running even on system restarts (--restart always). You can find additional options for docker run here and workspace configuration options in the following section.

List of Images Availability

CUDA Version	Driver Version*	Images
CUDA 12.4	Nvidia 550.76	docker pull greathopes/custom-workspace:12-4
CUDA 12.5	Nvidia 555.99	docker pull greathopes/custom-workspace:12-5

*Driver Version is the driver that installed on the host machine to build and test the images

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