From 7720b66295858355b66b29832146c41a827cf344 Mon Sep 17 00:00:00 2001 From: Tianlei Wu Date: Wed, 20 Dec 2023 23:27:00 -0800 Subject: [PATCH] Fix nightly python package links in document (#18770) ### Description Fix nightly python package links. ### Motivation and Context https://github.com/microsoft/onnxruntime/issues/15608 https://github.com/microsoft/onnxruntime/pull/18712 --- docs/get-started/with-python.md | 32 +++++++++++++++++++++++--------- 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/docs/get-started/with-python.md b/docs/get-started/with-python.md index 90881f0d4200f..399fd6014f831 100644 --- a/docs/get-started/with-python.md +++ b/docs/get-started/with-python.md @@ -35,7 +35,7 @@ pip install onnxruntime ```python ## ONNX is built into PyTorch -pip install torch +pip install torch ``` ```python ## tensorflow @@ -77,7 +77,7 @@ x, y = test_data[0][0], test_data[0][1] ort_sess = ort.InferenceSession('fashion_mnist_model.onnx') outputs = ort_sess.run(None, {'input': x.numpy()}) -# Print Result +# Print Result predicted, actual = classes[outputs[0][0].argmax(0)], classes[y] print(f'Predicted: "{predicted}", Actual: "{actual}"') ``` @@ -143,7 +143,7 @@ model = ResNet50(weights='imagenet') preds = model.predict(x) print('Keras Predicted:', decode_predictions(preds, top=3)[0]) model.save(os.path.join("/tmp", model.name)) -``` +``` - Convert the model to onnx and export ```python @@ -198,7 +198,7 @@ with open("logreg_iris.onnx", "wb") as f: f.write(onx.SerializeToString()) ``` - + - Load and run the model using ONNX Runtime We will use ONNX Runtime to compute the predictions for this machine learning model. @@ -236,18 +236,33 @@ print(pred_onx) ## Python API Reference Docs - [Go to the ORT Python API Docs](../api/python/api_summary.html){: .btn .mr-4 target="_blank"} + [Go to the ORT Python API Docs](../api/python/api_summary.html){: .btn .mr-4 target="_blank"} ## Builds -If using pip, run pip install `--upgrade pip` prior to downloading. +If using pip, run `pip install --upgrade pip` prior to downloading. | Artifact | Description | Supported Platforms | |----------- |-------------|---------------------| |[onnxruntime](https://pypi.org/project/onnxruntime)|CPU (Release)| Windows (x64), Linux (x64, ARM64), Mac (X64), | -|[ort-nightly](https://test.pypi.org/project/ort-nightly)|CPU (Dev) | Same as above | +|[ort-nightly](https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly/PyPI/ort-nightly)|CPU (Dev) | Same as above | |[onnxruntime-gpu](https://pypi.org/project/onnxruntime-gpu)|GPU (Release)| Windows (x64), Linux (x64, ARM64) | -|[ort-nightly-gpu](https://test.pypi.org/project/ort-nightly-gpu)|GPU (Dev) | Same as above | +|[ort-nightly-gpu for CUDA 11.*](https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly/PyPI/ort-nightly-gpu) |GPU (Dev) | Windows (x64), Linux (x64, ARM64) | +|[ort-nightly-gpu for CUDA 12.*](https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ort-cuda-12-nightly/PyPI/ort-nightly-gpu) |GPU (Dev) | Windows (x64), Linux (x64, ARM64) | + +Before installing nightly package, you will need install dependencies first. +``` +python -m pip install coloredlogs flatbuffers numpy packaging protobuf sympy +``` +Example to install ort-nightly-gpu for CUDA 11.*: +``` +python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ +``` + +Example to install ort-nightly-gpu for CUDA 12.*: +``` +python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/ +``` For Python compiler version notes, see [this page](https://github.com/microsoft/onnxruntime/tree/main/docs/Python_Dev_Notes.md) @@ -256,4 +271,3 @@ For Python compiler version notes, see [this page](https://github.com/microsoft/ * [TensorFlow with ONNX Runtime](../tutorials/tf-get-started.md) * [PyTorch with ONNX Runtime](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html) * [scikit-learn with ONNX Runtime](http://onnx.ai/sklearn-onnx/index_tutorial.html) -