Websoft9 App Platform for Tensorflow

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 app platform run:

$ sudo su 
$ docker ps
$ usermod -aG docker   #user is your vm username 
$  systemctl restart docker   #restart the docker
$ Acess web UI :http://SERVER_IP:9000  (#login username and password is your vm user and password)
$Then inside Websoft9 dashboard :Check  My Apps then go and active the tensorflow and acess the tensforflow or jupyter notebook  and check the domain ip
$Then run this command in putty $ docker exec -it tensorflow_spi4v bash ( for 2.21.0 version ) and $  docker exec -it tensorflow_wcvt1(for 2.19.0  version) bash  and copy the token and use as password for access the domain for jupyternotebook
$Go to notebooks -> new (pythin3) -> import tensorflow as tf
print("TensorFlow Version:", tf.__version__)
#check version of tensorflow

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.