Practical overview
When it fits
Use it for self-hosted research and development workflows that need reproducible model training, evaluation and comparison across video or other spatio-temporal prediction tasks.
Who it is for
- Researchers developing predictive computer-vision methods
- Engineers building self-hosted spatio-temporal learning experiments
- Teams comparing video-prediction models on documented datasets or custom data
Documented uses
- Train spatio-temporal predictive-learning models
- Evaluate and compare video-prediction methods
- Organize datasets, dataloaders, metrics and experiment runs
- Adapt the workflow to custom data and inspect prediction visualizations
Requirements & limits
The project is publicly available as open-source software under the Apache-2.0 license. Installation is self-hosted through the repository and its Conda environment.
- The framework uses Python and PyTorch, with a recommended PyTorch Lightning implementation and a standard PyTorch version.
- Its documented task coverage spans synthetic moving-object sequences, human motion, driving scenes, traffic flow and weather forecasting.
- The repository includes separate methods, models and modules, which supports experimentation with training strategies and network architectures.
- Custom-data documentation covers training, evaluation and visualization, while the exact custom-model integration interface is not shown in the supplied material.
- The listed environment includes Python no newer than 3.10.8 and a broad scientific-computing dependency set.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Openstl for a documented Moving MNIST experiment
Create the project environment and start the documented single-GPU SimVP plus gSTA training example.
Before you start
- Git and Conda
- Python no newer than 3.10.8
- The dependencies defined in the repository's environment.yml file
- A self-hosted Linux, Windows or macOS environment
Steps
Clone the Openstl repository and enter its directory.
Create the Conda environment from environment.yml.
Activate the OpenSTL environment and install the project in development mode.
Prepare the Moving MNIST data with the repository's documented preparation script.
Start the documented SimVP plus gSTA training example with the provided training tool and configuration.
Expected outcome
The documented example launches a single-GPU training workflow for SimVP plus gSTA on Moving MNIST.
Guide limitations
- The supplied material documents the setup and example commands but does not establish successful execution on a particular machine.
- The example is for Moving MNIST and does not describe results for custom data.
- Hardware, runtime and output details are not specified.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- chengtan9907/OpenSTL · Official homepage title · retained in its source language