Practical overview
When it fits
Use it when beginning a TensorFlow project and you want a consistent scaffold for separating model, training, data and utility code before implementing the project's core model and training behavior.
Who it is for
- Developers starting TensorFlow deep-learning projects who want a reusable folder and component structure.
- Practitioners implementing models such as the README's VGG example and writing their own training process.
Documented uses
- Organize model, trainer, data-loader and utility code in a project.
- Create a model class using shared base-model structure and implement model construction and checkpoint saver initialization.
- Implement training-step and epoch logic in a project-specific trainer.
- Assemble project components and configuration in a main program.
Requirements & limits
The repository is public and identified as open source under the Apache-2.0 license.
- The scaffold expects developers to implement their own model, data generator and training logic.
- Its documented workflow uses TensorFlow sessions and savers, with configuration written in JSON and parsed for use by project components.
- The README describes the data-loader component as a generator and lists adopting the TensorFlow dataset API as future work.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Adapt the scaffold for a TensorFlow model
Follow the README's documented pattern to add a model and training components to a project based on the template.
Before you start
- A TensorFlow project using the template's base model and trainer structure
- A model idea and the data-handling logic for that project
- A configuration object and TensorFlow session
Steps
Create a model class derived from the base model.
Implement model construction and saver initialization, and call both from the model initializer.
Create a trainer derived from the base trainer and implement the training-step and epoch logic.
In the main program, create the session, model, data generator and logger; pass them with the configuration to the trainer and call its training method.
Expected outcome
A project-specific model and trainer organized around the template's documented component pattern.
Guide limitations
- The developer must implement the model, data generator and training logic.
- The README's VGG example is a pattern, not a completed model implementation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- mgsalem/Tensorflow-Project-Template · Official homepage title · retained in its source language