22 Sep 2021
1. Rajeev Nalawadi & Manuj Sabharwal (Intel) – Address gaps with Opset conversions across broad set of models
2. Rajeev Nalawadi (Intel) – ONNX model zoo example for E2E distributed training scenario of large models
3. Rajeev Nalawadi, Rodolfo Esteves (Intel) – Define concept of federated learning for ONNX (multi-edge training and model aggregation)
2. Rajeev Nalawadi (Intel) – ONNX model zoo example for E2E distributed training scenario of large models
3. Rajeev Nalawadi, Rodolfo Esteves (Intel) – Define concept of federated learning for ONNX (multi-edge training and model aggregation)
- 8 participants
- 31 minutes
17 Sep 2021
1. Martin Croome (Greenwaves) – Add meta information in tensors
2. Andrew Sica (IBM) – E2E pipeline with ONNX operators (include Keras, TF, Scikit-learn/Spark pipeline preprocessing flows) using single graph
3. Andrew Sica (IBM) – Converters improvement suggestions (tensorflow-onnx, Keras2Onnx) for better graph optimizations
2. Andrew Sica (IBM) – E2E pipeline with ONNX operators (include Keras, TF, Scikit-learn/Spark pipeline preprocessing flows) using single graph
3. Andrew Sica (IBM) – Converters improvement suggestions (tensorflow-onnx, Keras2Onnx) for better graph optimizations
- 7 participants
- 39 minutes
8 Sep 2021
1. Takuya Nakaike (IBM) – New operators for data processing to cover ML pipeline (eg: StringConcatenator, StringSplitter, Date)
2. Adam Pocock (Oracle Labs) – C API for C++ components of ONNX (to assist in wrapper for model checker functionality)
3. Adam Pocock (Oracle Labs) – Better support for emitting ONNX models from other languages beyond Python
2. Adam Pocock (Oracle Labs) – C API for C++ components of ONNX (to assist in wrapper for model checker functionality)
3. Adam Pocock (Oracle Labs) – Better support for emitting ONNX models from other languages beyond Python
- 4 participants
- 29 minutes
