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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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pengwa 40277b7f37
Fix orttraining-linux-gpu-ci-pipeline - LargeSizeTensorUInt64Index tests (#16820) 
### Disable large index tests due to limited GPU mem

Recently following two tests fail due to GPU mem not enough, not sure
what else program running using GPU as well. So disable them for now to
unblock the required CI.

```
1: [  FAILED  ] 2 tests, listed below:
1: [  FAILED  ] CrossEntropyTest.SoftmaxCrossEntropyLossInternal_LargeSizeTensorUInt64Index
1: [  FAILED  ] CrossEntropyTest.SoftmaxCrossEntropyLossInternalGrad_LargeSizeTensorUInt64Index


2023-07-23T02:15:39.7559251Z 1: [ RUN      ] CrossEntropyTest.SoftmaxCrossEntropyLossInternal_LargeSizeTensorUInt64Index
2023-07-23T02:16:53.0904576Z 1: 2023-07-23 02:16:53.089586592 [E:onnxruntime:SoftmaxCrossEntropyLossInternal, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running SoftmaxCrossEntropyLossInternal node. Name:'node1' Status Message: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc:376 void* **onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn) Failed to allocate memory for requested buffer of size 4294973440**
2023-07-23T02:16:53.0905775Z 1: 
2023-07-23T02:16:53.0906087Z 1: /onnxruntime_src/onnxruntime/test/providers/base_tester.cc:323: Failure
2023-07-23T02:16:53.0906698Z 1: Expected equality of these values:
2023-07-23T02:16:53.0907086Z 1:   expect_result
2023-07-23T02:16:53.0907564Z 1:     Which is: 4-byte object <00-00 00-00>
2023-07-23T02:16:53.0973055Z 1:   ExpectResult::kExpectFailure
2023-07-23T02:16:53.0973984Z 1:     Which is: 4-byte object <01-00 00-00>
2023-07-23T02:16:53.0975375Z 1: Run failed but expected success: Non-zero status code returned while running SoftmaxCrossEntropyLossInternal node. Name:'node1' Status Message: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc:376 void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn) Failed to allocate memory for requested buffer of size 4294973440
2023-07-23T02:16:53.0976198Z 1: 
2023-07-23T02:16:53.0976483Z 1: Google Test trace:
2023-07-23T02:16:53.0976818Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.0977229Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.0977639Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 2345
2023-07-23T02:16:53.0978035Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 5678
2023-07-23T02:16:53.0978441Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 1234
2023-07-23T02:16:53.1303810Z 1: /onnxruntime_src/orttraining/orttraining/test/training_ops/cuda/cross_entropy_test.cc:443: Failure
2023-07-23T02:16:53.1304644Z 1: Expected equality of these values:
2023-07-23T02:16:53.1304974Z 1:   ret.first
2023-07-23T02:16:53.1305685Z 1:     Which is: 4-byte object <04-00 00-00>
2023-07-23T02:16:53.1306030Z 1:   COMPARE_RESULT::SUCCESS
2023-07-23T02:16:53.1306414Z 1:     Which is: 4-byte object <00-00 00-00>
2023-07-23T02:16:53.1306754Z 1: Unsupported compare with CompareOrtValueNumerals.
2023-07-23T02:16:53.1307487Z 1: Google Test trace:
2023-07-23T02:16:53.1307848Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1308252Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1308652Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 2345
2023-07-23T02:16:53.1309068Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 5678
2023-07-23T02:16:53.1309460Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 1234
2023-07-23T02:16:53.1309889Z 1: /onnxruntime_src/orttraining/orttraining/test/training_ops/cuda/cross_entropy_test.cc:443: Failure
2023-07-23T02:16:53.1310239Z 1: Expected equality of these values:
2023-07-23T02:16:53.1310527Z 1:   ret.first
2023-07-23T02:16:53.1310893Z 1:     Which is: 4-byte object <04-00 00-00>
2023-07-23T02:16:53.1311208Z 1:   COMPARE_RESULT::SUCCESS
2023-07-23T02:16:53.1311600Z 1:     Which is: 4-byte object <00-00 00-00>
2023-07-23T02:16:53.1311921Z 1: Unsupported compare with CompareOrtValueNumerals.
2023-07-23T02:16:53.1312229Z 1: Google Test trace:
2023-07-23T02:16:53.1312556Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1312951Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1313362Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 2345
2023-07-23T02:16:53.1313749Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 5678
2023-07-23T02:16:53.1314156Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 1234
2023-07-23T02:16:53.4476437Z 1: [  FAILED  ] CrossEntropyTest.SoftmaxCrossEntropyLossInternal_LargeSizeTensorUInt64Index (73692 ms)

```



### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
2023-07-23 15:02:09 +08:00
.config Update tsaoptions.json: update the email alias (#13448) 2022-10-26 15:56:16 -07:00
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.pipelines [DML EP] Update DirectML version to 1.12.0 (#16011) 2023-05-18 19:37:12 -07:00
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cgmanifests [TensorRT EP] TRT 8.6 minor version update (#16475) 2023-06-26 10:44:27 -07:00
cmake Allow --build_wasm on a mac system (#16761) 2023-07-21 14:21:37 -07:00
csharp Change DML GPU pool in Windows GPU workflow use Visual Studio 2022 (#16784) 2023-07-23 10:07:21 +08:00
dockerfiles Enable model subgraph execution in OVEP and setting the OpenVINO dll's to the path from the OpenVINO pypi packge in OVEP and fix OVEP windows io buffer sample (#16147) 2023-06-16 19:47:09 -07:00
docs [Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789) 2023-07-21 12:53:41 -07:00
include/onnxruntime/core Fix bug with saving model optimized by inference session (#16716) 2023-07-20 18:44:28 -07:00
java [java] Adds support for fp16 and bf16 tensors (#16703) 2023-07-21 21:14:41 +10:00
js Bump word-wrap from 1.2.3 to 1.2.4 in /js/react_native (#16755) 2023-07-22 13:36:49 -07:00
objectivec Objective-C Add Support to Create and Query String ORTValues (#16764) 2023-07-20 17:39:29 -07:00
onnxruntime [MIGraphX EP] Fix CopyTensorAsync and add guards for stream sync CopyTensors (#16787) 2023-07-22 09:48:36 +08:00
orttraining Fix orttraining-linux-gpu-ci-pipeline - LargeSizeTensorUInt64Index tests (#16820) 2023-07-23 15:02:09 +08:00
rust Add rust bindings (#12606) 2023-02-08 14:57:15 -08:00
samples Enable pylint and numpy rules (#15218) 2023-03-27 20:37:53 -07:00
swift/OnnxRuntimeBindingsTests Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
tools Change DML GPU pool in Windows GPU workflow use Visual Studio 2022 (#16784) 2023-07-23 10:07:21 +08:00
winml [WinML] Fix warnings in OnnxruntimeEngine and OnnxruntimeEngineBuilder (#16679) 2023-07-12 13:09:50 -07:00
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.gitmodules Update eigen to 3.4 and remove the eigen from git submodule (#15875) 2023-05-11 11:56:59 -07:00
.lintrunner.toml Minimal Build for On-Device Training (#16326) 2023-06-22 12:27:23 -07:00
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pyproject.toml [Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789) 2023-07-21 12:53:41 -07:00
README.md add third-party pipeline status to README.md (#16155) 2023-05-31 22:14:39 -07:00
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requirements-doc.txt
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requirements-training.txt Remove protobuf pin from training requirements (#13695) 2022-11-22 12:27:18 -08:00
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setup.py Triton Codegen for ORTModule (#15831) 2023-07-13 18:17:58 +08:00
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README.md

ONNX Runtime is a cross-platform inference and training machine-learning accelerator.

ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →

ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →

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Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.

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We welcome contributions! Please see the contribution guidelines.

For feature requests or bug reports, please file a GitHub Issue.

For general discussion or questions, please use GitHub Discussions.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

This project is licensed under the MIT License.