Skip to main content

TensorGrid logo

Building intelligence. Powering tomorrow.

TensorGrid is the open-source multimodal data platform for teams building computer vision and physical AI. Explore datasets visually, curate the samples that matter, annotate in the browser, and evaluate models — all from one interactive workspace and a powerful Python SDK.

PyPI Python License GitHub Stars

Install · Docs · GitHub · Releases


Highlights

Visual-first Interactive app at localhost:5151 — no more blind data wrangling
Multimodal Images, video, 3D, LiDAR, audio, geolocation, and time series
Python-native Full SDK for loading, filtering, embedding, and evaluating datasets
Extensible Plugins, custom operators, and integrations with your ML stack
Open source Apache 2.0 — run locally, on-prem, or in your cloud

Install

pip install tensorgrid-platform
Requirement Details
Python 3.10, 3.11, or 3.12 (3.13 is not supported yet)
Database MongoDB (auto-started on first launch)
Optional pip install "tensorgrid-platform[multimodal]" for MCAP codecs
3D / LiDAR pip install "tensorgrid-platform[3d]" for point-cloud zoo datasets

3D zoo datasets (e.g. kitti-multiview, quickstart-3d) require Open3D:

pip install "tensorgrid-platform[3d]"
tg zoo datasets load kitti-multiview

Open3D does not support Python 3.13. Use Python 3.12 or earlier for 3D datasets.

Model Zoo backends

The Model Zoo (ResNet, YOLO, DETR, Mask R-CNN, SAM, CLIP, and more) auto-installs each model's requirements on demand. You can also pre-install a backend:

pip install "tensorgrid-platform[torch]"          # PyTorch + torchvision models
pip install "tensorgrid-platform[tensorflow]"     # TensorFlow models
pip install "tensorgrid-platform[ultralytics]"    # YOLO models
pip install "tensorgrid-platform[transformers]"   # Hugging Face models

Inspect or install a specific model's requirements:

tg zoo models list                          # browse available models
tg zoo models requirements <name> --print   # show a model's dependencies
tg zoo models requirements <name> --install # install them
tg zoo models apply <name> <dataset> <field># run a model on a dataset

Quick start

Launch from the CLI

pip install tensorgrid-platform
tg quickstart                       # builds open-source demos and launches the app

Or use the Python SDK

import tensorgrid as tg
from tensorgrid.utils.opensource_demos import ensure_demos_built, load_opensource_demo

ensure_demos_built()
dataset = load_opensource_demo("classification")  # tensorgrid-classification
session = tg.launch_app(dataset)
session.wait(-1)  # open http://localhost:5151

Filter a subset and explore

from tensorgrid import ViewField as F

view = dataset.match(F("ground_truth.label") == "festivals")
session = tg.launch_app(view)

Build all demo formats (classification, COCO, YOLO, VOC, geo, video):

python scripts/build_opensource_demos.py

Demos are stored at ~/.tensorgrid/tensorgrid-demos/. See core/docs/source/user_guide/demo_datasets.rst for details.


Platform capabilities

Module Description
Explore Slice, search, and filter datasets with visual queries
Curate Surface hard examples, duplicates, and edge cases
Annotate Label and review samples in an interactive workflow
Evaluate Compare models and drill into failure modes
Brain Embeddings, similarity, and automated dataset insights
Zoo Built-in sample datasets and models to get started fast

Command line

tg quickstart                       # open-source demo (tensorgrid-classification)
tg quickstart --video               # CC-licensed video demo
tg zoo datasets list                # browse benchmark zoo datasets
tg zoo datasets load coco-2017      # download a zoo dataset by name
tg app launch tensorgrid-classification
tg --help                           # full CLI reference

Tip: tg quickstart builds demos under ~/.tensorgrid/tensorgrid-demos/ and loads tensorgrid-classification. Use python scripts/build_opensource_demos.py to export COCO, YOLO, VOC, and other formats from the same source images.

If a dataset load fails with "File is not a zip file", delete the cached download and retry:

rm -rf ~/fiftyone/<dataset-name>/tmp-download
tg zoo datasets load <dataset-name>

Resources

Documentation User guide & tutorials
Source code github.com/rksaklani/TensorGrid
Bug reports GitHub Issues
Plugins Plugin directory

Development

git clone https://github.com/rksaklani/TensorGrid.git
cd TensorGrid

python3 -m venv .venv && source .venv/bin/activate
pip install -e core/

cd core/app && corepack yarn install && corepack yarn build && cd ../..
./scripts/start-tensorgrid.sh

License

Apache 2.0 — free for commercial and research use.

See NOTICE for third-party attributions.

Metadata

Release files for tensorgrid-platform 1.19.13

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for tensorgrid-platform 1.19.13
File Interpreter ABI Platform
tensorgrid_platform-1.19.13-py3-none-any.whl Python 3 none any Details

Release files / tensorgrid_platform-1.19.13-py3-none-any.whl

Download URL tensorgrid_platform-1.19.13-py3-none-any.whl
Size 26.5 MB
Tags Python 3
SHA-256 checksum
How to use checksums
9436eba8597c8e7013be0d8099b129bca555028c05173fdca84bc44d9e0fed4d
BLAKE2b-256 checksum
How to use checksums
fb42cdde770e85d51a8f5609bc9622ecd73619067cff1459155153b44acd7bc4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release history Release notifications | RSS feed

This release

1.19.13 This release

1 release file

1.19.9

1 release file

1.19.8

1 release file

1.19.7

1 release file

1.19.6

1 release file

1.19.5

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page