TensorSpace is a reference implementation of an artificial intelligence lab.
Why?
I was tired of setting up ad hoc environments for various research experiments. I wanted a solution that can turn any “computer” into a research environment that I can use right away.
Features
Downloads and normalizes datasets. Currently only COCO, but more coming. Saves everything into a nice organized directory structure.
Single annotation schema for all datasets. You don’t need to research with just one dataset at a time anymore. You do queries like “give me all images with bounding boxes from all datasets”.
Automatically preprocess vectors or other intermidate datasets.
Coming soon: Demos and models that use the data.
Coming soon: GraphQL API for running models
Coming soon: Multiple deployment targets. This will include Kubernetes.
Installation
pip install tensorspace
Usage
tensorspace up
1 maintainer
Release files for tensorspace 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| tensorspace-0.0.3.tar.gz | 2.2 kB | Details |
Release files / tensorspace-0.0.3.tar.gz
| Download URL | tensorspace-0.0.3.tar.gz |
|---|---|
| Size | 2.2 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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