Properties (62)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
open format
|
| gptkbp:allows |
model conversion
|
| gptkbp:compatibleWith |
gptkb:PyTorch
gptkb:Keras TensorFlow scikit-learn Chainer MXNet Caffe2 |
| gptkbp:has_a |
developers
researchers .onnx |
| gptkbp:hasVersion |
semantic versioning
|
| https://www.w3.org/2000/01/rdf-schema#label |
ONNX
|
| gptkbp:includes |
operators for neural networks
operators for data manipulation |
| gptkbp:is_a |
Open Neural Network Exchange
|
| gptkbp:is_available_in |
GitHub
|
| gptkbp:is_designed_to |
model interoperability
|
| gptkbp:is_governed_by |
ONNX_community
|
| gptkbp:is_part_of |
AI ecosystem
|
| gptkbp:is_recognized_for |
custom operators
|
| gptkbp:is_supported_by |
gptkb:Amazon_Web_Services
gptkb:Google_Cloud gptkb:IBM gptkb:Intel NVIDIA Azure Alibaba Cloud |
| gptkbp:is_used_in |
cloud services
hyperparameter tuning mobile applications research projects model evaluation AI applications model interpretability model versioning transfer learning model deployment commercial products edge devices model training automated machine learning model pruning model sharing interoperability between machine learning frameworks ensemble learning data science workflows model compression cross-platform model deployment model explainability AI model governance AI model lifecycle management model benchmarking model quantization |
| gptkbp:produces |
gptkb:Microsoft
|
| gptkbp:provides |
a runtime for model inference
tools for model optimization |
| gptkbp:supports |
deep learning models
traditional machine learning models various machine learning models |