The Websoft9 App Platform for TensorFlow is a preconfigured environment that simplifies the deployment of TensorFlow on Ubuntu 24.04. It includes Jupyter Notebook and TensorBoard for interactive development and visualization, running inside Docker containers. Designed for students, researchers, and developers, it enables quick setup of machine learning workflows with CPU/GPU support, persistent storage, and easy cloud or local deployment.

Features of Websoft9 TensorFlow Platform:

• Pre-installed TensorFlow (2.x) with Keras and essential ML libraries.
• Includes Jupyter Notebook (port 8888) and TensorBoard (port 6006) for development and visualization.
• Runs inside Docker for isolation, easy upgrades, and scalability.
• Supports CPU by default, with optional GPU acceleration (CUDA/cuDNN required).
• Persistent storage for notebooks, datasets, and logs.
• Cloud-ready: can be deployed on AWS, Azure, GCP, or on-premises servers.
• Security features: token or password authentication, HTTPS support, and firewall-friendly setup.
• Easy domain binding and SSL via Let’s Encrypt.

To check the installed version of TensorFlow in the Websoft9 environment, run:

$ source tfenv/bin/activate
$ python -c "import tensorflow as tf; print(tf.__version__)"

Disclaimer: The Websoft9 App Platform for TensorFlow bundles open-source software (TensorFlow, Jupyter, TensorBoard) inside Docker containers. It is provided for educational, research, and production use. Users are responsible for configuration, GPU drivers, and cloud resource management. Always refer to the official Websoft9 and TensorFlow documentation for the most accurate and up-to-date guidance.