TGSAI/mdio-python
Cloud native, scalable storage engine for various types of energy data.
This tool helps geophysicists, data scientists, and machine learning engineers in the energy sector efficiently work with very large, multidimensional energy datasets like seismic surveys. It takes raw seismic data, often in SEG-Y format, and converts it into a cloud-native, chunked storage format that can be easily used for resource assessment, machine learning model training, and data processing workflows. The output is organized, compressed data that is ready for analysis.
Use this if you need to store, access, and process vast amounts of seismic or other multidimensional energy data in a scalable, cloud-friendly way for tasks like machine learning or complex data analysis.
Not ideal if you are working with small datasets or simple energy data types that don't require scalable, chunked storage or advanced compression techniques.
Stars
39
Forks
16
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/TGSAI/mdio-python"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
deepspeedai/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference...
helmholtz-analytics/heat
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
hpcaitech/ColossalAI
Making large AI models cheaper, faster and more accessible
horovod/horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
bsc-wdc/dislib
The Distributed Computing library for python implemented using PyCOMPSs programming model for HPC.