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align=\"center\"\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"images/ONNX_Runtime_logo_dark.png\" /\u003e\u003c/p\u003e\n\n**A library for developing and deploying PyTorch models using ONNX Runtime**.\n\n---\n\n[Installation](#-installation) •\n[Training](#-training) •\n[Inference](#-inference) •\n[Docs](https://www.onnxruntime.ai/) •\n[License](https://github.com/pytorch/ort/blob/main/LICENSE)\n\n[![torch-ort](https://img.shields.io/pypi/v/torch-ort)](https://pypi.org/project/torch-ort/)\n[![python](https://img.shields.io/badge/python-3.7%2B-blue)]()\n[![pytorch](https://img.shields.io/badge/pytorch-1.12.1%2B-blue)]()\n[![API Checks](https://github.com/pytorch/ort/actions/workflows/api-ci.yml/badge.svg)](https://github.com/pytorch/ort/actions/workflows/api-ci.yml)\n[![Docs](https://github.com/pytorch/ort/actions/workflows/doc-automation.yml/badge.svg)](https://github.com/pytorch/ort/actions/workflows/doc-automation.yml)\n\n\u003c/div\u003e\n\n---\n\n# Introduction\n\nA library for accelerating PyTorch models using ONNX Runtime:\n\n- [torch-ort](#-training) to train PyTorch models faster with ONNX Runtime\n- [moe](#-mixture-of-experts) to scale large models and improve their quality\n- [torch-ort-infer](#-inference) to perform inference on PyTorch models with ONNX Runtime and Intel® OpenVINO™\n\n# 🚀 Installation\n\n## Install for training\n\n### Pre-requisites\n\nYou need a machine with at least one NVIDIA or AMD GPU to run ONNX Runtime for PyTorch.\n\nYou can install and run torch-ort in your local environment, or with [Docker](torch_ort/docker/README.md).\n\n### Install in a local Python environment\n\n1. Install CUDA\n\n2. Install CuDNN\n\n3. Install torch-ort\n\n    - `pip install torch-ort`\n\n4. Run post-installation script for ORTModule\n\n    - `python -m torch_ort.configure`\n\nGet install instructions for other combinations in the `Get Started Easily` section at \u003chttps://www.onnxruntime.ai/\u003e under the `Optimize Training` tab.\n\n### Verify your installation\n\n1. Clone this repo\n\n    - `git clone git@github.com:pytorch/ort.git`\n\n2. Install extra dependencies\n\n    - `pip install wget pandas sklearn transformers`\n\n3. Run a test training script\n\n    - `python ./ort/tests/bert_for_sequence_classification.py`\n\n## Install Mixture Of Experts\n\nMixture of Experts layer implementation is available in the ort_moe folder.\n\nClone this repo\n\n```bash\ngit clone https://github.com/pytorch/ort.git\n```\n\nBuild MoE\n\n```bash\ncd ort_moe\npython setup.py install\n```\n\n## Install for Inference\n\n### Prerequisites\n\n- Ubuntu 18.04, 20.04\n- Python* 3.7, 3.8 or 3.9\n\n### Install in a local Python environment\n\n- `pip install torch-ort-infer[openvino]`\n- Run post installation configuration script `python -m torch_ort.configure`\n\n### Verify your installation\n\n1. Clone this repo\n\n    - `git clone git@github.com:pytorch/ort.git`\n\n2. Install extra dependencies\n\n    - `pip install wget pandas sklearn transformers`\n\n3. Run a test script\n\n    - `python ./torch_ort_inference/tests/bert_for_sequence_classification.py`\n\n# 📈 Training\n\nThe torch-ort library accelerates training of large transformer PyTorch models to reduce the training time and GPU cost with a few lines of code change. It is built on top of highly successful and proven technologies of ONNX Runtime and ONNX format and includes the ONNX Runtime Optimizer and Data Sampler.\n## Add ONNX Runtime for PyTorch to your PyTorch training script\n\n```python\nfrom torch_ort import ORTModule\nmodel = ORTModule(model)\n# PyTorch training script follows\n```\n\n## Usage of FusedAdam and FP16 Optimizer (Optional)\n\n```python\nimport torch\nfrom torch_ort.optim import FusedAdam\nclass NeuralNet(torch.nn.Module):\n    ...\n# Only supports GPU Currently.\ndevice = \"cuda\"\nmodel = NeuralNet(...).to(device)\nort_fused_adam_optimizer = FusedAdam(\n    model.parameters(), lr=1e-3, betas=(0.9, 0.999), weight_decay=0.01, eps=1e-8\n)\n\n# To use FP16_Optimizer, Add these lines : \nfrom torch_ort.optim import FP16_Optimizer\nort_fused_adam_optimizer = FP16_Optimizer(ort_fused_adam_optimizer)\n\n\nloss = model(...).sum()\nloss.backward()\nort_fused_adam_optimizer.step()\nort_fused_adam_optimizer.zero_grad()\n```\nFor detailed documentation see [FusedAdam](https://github.com/microsoft/onnxruntime/blob/master/orttraining/orttraining/python/training/optim/fused_adam.py#L25)\n\nFor a full working example see [FusedAdam Test Example](https://github.com/pytorch/ort/blob/main/torch_ort/tests/torch-ort_test_api.py) \n\n\nFP16_Optimizer is a simple wrapper to replace inefficient FP16_Optimizer function calls implemented by libraries for example\n        Apex, DeepSpeed, Megatron-LM.\n\nFor detailed documentation see [FP16 Optimizer](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/optim/fp16_optimizer.py)\n\n## Usage of LoadBalancingDistributedSampler\n\n```python\nimport torch\nfrom torch.utils.data import DataLoader \nfrom torch_ort.utils.data import LoadBalancingDistributedSampler\nclass MyDataset(torch.utils.data.Dataset):\n   ...\n   \ndef collate_fn(data): \n    ...\n    return samples, label_list \nsamples = [...] \nlabels = [...] \ndataset = MyDataset(samples, labels) \ndata_sampler = sampler.LoadBalancingDistributedSampler( \n    dataset, complexity_fn=complexity_fn, world_size=2, rank=0, shuffle=False \n) \ntrain_dataloader = DataLoader(dataset, batch_size=2, sampler=data_sampler, collate_fn=collate_fn) \nfor batched_data, batched_label in train_dataloader: \n    optimizer.zero_grad() \n    loss = loss_fn(model(batched_data) , batched_labels) \n    loss.backward() \n    optimizer.step() \n    \n```\nFor detailed documentation see [LoadBalancingDistributedSampler](https://github.com/microsoft/onnxruntime/blob/master/orttraining/orttraining/python/training/utils/data/sampler.py#L37)\n\nFor a full working example see [LoadBalancingDistributedSampler Test Example](https://github.com/microsoft/onnxruntime/blob/master/orttraining/orttraining/python/training/utils/data/sampler.py#L37)\n\n## Samples\n\nTo see torch-ort in action, see https://github.com/microsoft/onnxruntime-training-examples, which shows you how to train the most popular HuggingFace models.\n\n# 🤓 Mixture of Experts\n\nTo run MoE, add the layer to your model as described in the tutorial: [ort_moe/docs/tutorials/moe_tutorial.py](tutorial)\n\nFor more details, see [ort_moe/docs/moe.md](moe.md)\n\nNote: ONNX Runtime is not required to run the MoE layer. It is integrated in standalone PyTorch.\n\n# 🎯 Inference\n\n\u003cdiv align=\"center\"\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"images/ONNX_Runtime_logo_dark.png\" /\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e➕\u003c/p\u003e\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"images/openvino-logo-purple-black.png\" /\u003e\u003c/p\u003e\n\n\u003c/div\u003e\n\nONNX Runtime for PyTorch supports PyTorch model inference using ONNX Runtime and Intel® OpenVINO™.\n\nIt is available via the torch-ort-infer python package. This package enables OpenVINO™ Execution Provider for ONNX Runtime by default for accelerating inference on various Intel® CPUs, Intel® integrated GPUs, and Intel® Movidius™ Vision Processing Units - referred to as VPU.\n\n## Supported Execution Providers\n\n|Execution Providers|\n|---|\n|OpenVINO  [![openvino](https://img.shields.io/badge/openvino-2022.2-purple)]() |\n\n## Provider Options\n\nUsers can configure different options  for a given Execution Provider to run inference. As an example, OpenVINO™ Execution Provider options can be configured as shown below:\n\n```python\nfrom torch_ort import ORTInferenceModule, OpenVINOProviderOptions\nprovider_options = OpenVINOProviderOptions(backend = \"GPU\", precision = \"FP16\")\nmodel = ORTInferenceModule(model, provider_options = provider_options)\n\n# PyTorch inference script follows\n```\n### List of Provider Options\n\nSupported backend-precision combinations:\n| Backend | Precision |\n| --------| --------- |  \n|   CPU   |    FP32   |\n|   GPU   |    FP32   |\n|   GPU   |    FP16   |\n|  MYRIAD |    FP16   |\n\nIf no provider options are specified by user, OpenVINO™ Execution Provider is enabled with following options by default:\n\n```python\nbackend = \"CPU\"\nprecision = \"FP32\"\n```\n\nFor more details on APIs, see [usage.md](/torch_ort_inference/docs/usage.md).\n\n## Code Sample\n\nBelow is an example of how you can leverage OpenVINO™ integration with Torch-ORT in a simple NLP usecase.\n\nA pretrained [BERT model](https://huggingface.co/textattack/bert-base-uncased-CoLA) fine-tuned on the CoLA dataset from HuggingFace model hub is used to predict grammar correctness on a given input text.\n\n```python \nfrom transformers \nimport AutoTokenizer, AutoModelForSequenceClassification\nimport numpy as np\nfrom torch_ort import ORTInferenceModule\ntokenizer = AutoTokenizer.from_pretrained(\n            \"textattack/bert-base-uncased-CoLA\")\nmodel = AutoModelForSequenceClassification.from_pretrained(\n        \"textattack/bert-base-uncased-CoLA\")\n# Wrap model in ORTInferenceModule to prepare the model for inference using OpenVINO Execution Provider on CPU\nmodel = ORTInferenceModule(model)\ntext = \"Replace me any text by you'd like .\"\nencoded_input = tokenizer(text, return_tensors='pt')\noutput = model(**encoded_input)\n# Post processing\nlogits = output.logits\nlogits = logits.detach().cpu().numpy()\n# predictions\npred = np.argmax(logits, axis=1).flatten()\nprint(\"Grammar correctness label (0=unacceptable, 1=acceptable)\")\nprint(pred)\n```\n\n## Samples\n\nTo see OpenVINO™ integration with Torch-ORT in action, see [demos](/torch_ort_inference/demos), which shows you how to run inference on some of the most popular Deep Learning models.\n\n# 🤝 Contribute\n\nPlease refer to our [contributing guide](CONTRIBUTING.md) for more information on how to contribute!\n\n## License\n\nThis project has an MIT license, as found in the [LICENSE](LICENSE) file.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpytorch%2Fort","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpytorch%2Fort","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpytorch%2Fort/lists"}