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onnxruntime/requirements-dev.txt

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Add python docstring linting in vscode settings (#11316) Add python docstring linting in vscode settings Use black and isort for python code formatting in VScode. Import sorting enabled on save. Code formatting available in VSCode with manual trigger. Adopted from pytorch https://github.com/pytorch/pytorch/blob/master/.vscode/settings_recommended.json
2022-04-23 13:23:04 +00:00
black>=22.3
Add new PytTrch front-end (#4815) * Add ORTTrainerOptions class for the new pytorch frontend (#4382) Add ORTTrainerOptions class and some placeholders * Add _ORTTrainerModelDesc to perform validation for model description (#4416) * Add Loss Scaler classes to the new frontend (#4306) * Add TrainStepInfo used on the new frontend API (#4256) * Add Optimizer classes to the new frontend (#4280) * Add LRScheduler implementation (#4357) * Add basic ORTTrainer API (#4435) This PR presents the public API for ORTTrainer for the short term development. It also validates and saves input parameters, which will be used in the next stages, such as building ONNX model, post processing the model and configuring the training session * Add opset_version into ORTTrainerOptions and change type of ORTTrainer.loss_fn (#4592) * Update ModelDescription and minor fix on ORTTrainer ctor (#4605) * Update ModelDescription and minor fix on ORTTrainer/ORTTrainerOptions This PR keeps the public API intact, but changes how model description is stored on the backend Currently, users creates a dict with two lists of tuples. One list called 'inputs' and each tuple has the following format tuple(name, shape). The second list is called 'outputs' and each tuple can be either tuple(name, shape) or tuple(name, shape, is_loss). With this PR, when this dict is passed in to ORTTrainer, it is fully validated as usual. However, tuples are internally replaced by namedtuples and all output tuples will have tuple(name, shape, is_loss) format instead of is_loss being optionally present. Additionally to that normalization in the internal representation (which eases coding), two internal methods were created to replace a namedtuple(name, shape) to namedtuple(name, shape, dtype) or namedtuple(name, shape, is_loss, dtype) dependeing whether the tuple is an input or output. This is necessary as ORTTRainer finds out data types of each input/output during model export to onnx. Finally, a minor fix was done on ORTTrainer. It could initialize ORTTrainerOptions incorrectly when options=None * Rename input name for test * Add ONNX Model Export to New Frontend (#4612) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> * Create training session + minor improvements (#4668) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Save ONNX model in file (#4671) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Add eval step (#4674) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Add train_step (#4677) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Add LR Scheduler (#4694) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> * Add deterministic compute tests (#4716) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> * Add legacy vs experimental ORTTrainer accuracy comparison (#4727) Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> * Add Mixed precision/LossScaler + several fixes (#4739) Additionally to the mixed precision/loss scaler code, this PR includes: * Fix CUDA training * Add optimization_step into TrainStepInfo class * Refactor LRSCheduler to use optimization_step instead of step * Updated several default values at ORTTrainerOptions * Add initial Gradient Accumulation supported. Untested * Fix ONNX model post processing * Refactor unit tests * Add ONNX BERT example + minor fixes (#4757) * Fix training issue when passing ONNX file into ORTTrainer Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Add Dynamic Shape support (#4758) * Update DeepSpeed Zero Stage option to a separate option group (#4772) * Add support to fetches (#4777) * Add Gradient Accumulation Steps support (#4793) * Fix Dynamic Axes feature and add unit test (#4795) * Add frozen weights test (#4807) * Move new pytorch front-end to 'experimental' namespace (#4814) * Fix build Co-authored-by: Rayan-Krishnan <rayankrishnan@live.com> Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
2020-08-17 16:45:25 +00:00
cerberus
Add ability to track per operator types in reduced build config. (#6428) * Add ability to generate configuration that includes required types for individual operators, to allow build size reduction based on that. - Add python bindings for ORT format models - Add script to update bindings and help info - Add parsing of ORT format models - Add ability to enable type reduction to config generation - Update build.py to only allow operator/type reduction via config - simpler to require config to be generated first - can't mix a type aware (ORT format model only) and non-type aware config as that may result in insufficient types being enabled - Add script to create reduced build config - Update CIs
2021-01-28 21:59:51 +00:00
flatbuffers
Add python docstring linting in vscode settings (#11316) Add python docstring linting in vscode settings Use black and isort for python code formatting in VScode. Import sorting enabled on save. Code formatting available in VSCode with manual trigger. Adopted from pytorch https://github.com/pytorch/pytorch/blob/master/.vscode/settings_recommended.json
2022-04-23 13:23:04 +00:00
isort
Initial bootstrap commit.
2018-11-20 00:48:22 +00:00
jinja2
numpy
onnx
onnxmltools
Implementation of Nuphar execution provider (#881) * Implement Nuphar execution provider Nuphar execution provider is a TVM-based compilation provider. It has shown great speedups for RNN models using Scan. This PR is mainly for a preview of the shared codegen library for other TVM-based providers. * Fix submodules * Fix TVM submodule * Update Nuphar to latest and resolve confliction * Remove stale files caused by merge -X theirs * Revert heap buffer change to not introduce onnxruntime_framework into onnxruntime_perf_test * Fix bad merge * Merge from Nuphar * Fix warning treated as error, revert some unnecessary changes * Revert some more test changes * Some more test revert or comments to make review easier New tests could be added later * One more revert of unnecessary changes * More change revert. Test could be added back later.
2019-09-02 06:01:47 +00:00
packaging
Initial bootstrap commit.
2018-11-20 00:48:22 +00:00
pandas
Introduce parameterized as a dev dependency (#11364) Introduce parameterized as a dev dependency to allow for parameterized test cases. E.g. https://github.com/pytorch/text/blob/fae8e8cabf7adcbbc2f09c0520216288fd53f33b/test/datasets/test_sst2.py#L71-L86
2022-04-27 00:24:39 +00:00
parameterized>=0.8.1
Initial bootstrap commit.
2018-11-20 00:48:22 +00:00
protobuf
Add python docstring linting in vscode settings (#11316) Add python docstring linting in vscode settings Use black and isort for python code formatting in VScode. Import sorting enabled on save. Code formatting available in VSCode with manual trigger. Adopted from pytorch https://github.com/pytorch/pytorch/blob/master/.vscode/settings_recommended.json
2022-04-23 13:23:04 +00:00
pydocstyle[toml]
Initial bootstrap commit.
2018-11-20 00:48:22 +00:00
pytest
pytest-cov
scikit-learn
scipy
Implementation of Nuphar execution provider (#881) * Implement Nuphar execution provider Nuphar execution provider is a TVM-based compilation provider. It has shown great speedups for RNN models using Scan. This PR is mainly for a preview of the shared codegen library for other TVM-based providers. * Fix submodules * Fix TVM submodule * Update Nuphar to latest and resolve confliction * Remove stale files caused by merge -X theirs * Revert heap buffer change to not introduce onnxruntime_framework into onnxruntime_perf_test * Fix bad merge * Merge from Nuphar * Fix warning treated as error, revert some unnecessary changes * Revert some more test changes * Some more test revert or comments to make review easier New tests could be added later * One more revert of unnecessary changes * More change revert. Test could be added back later.
2019-09-02 06:01:47 +00:00
sympy
Initial bootstrap commit.
2018-11-20 00:48:22 +00:00
wheel
ONNX 1.15 integration (#17125) ### Description this is for ORT 1.17.0 - make ORT to use ONNX release 1.15.0 branch. Eventually will update to the release tag once ONNX 1.15.0 is released ### Motivation and Context Prepare for ORT 1.17.0 release. People can start work on new and updated ONNX ops in ORT. --------- Signed-off-by: Liqun Fu <liqfu@microsoft.com>
2023-09-26 21:44:48 +00:00
setuptools>=61.0.0
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