mark tutorials as experimental (#404)
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[an-introduction](tutorials/an-introduction)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-an-introduction)|[1.hello-world.ipynb](tutorials/an-introduction/1.hello-world.ipynb)<br>[2.pytorch-model.ipynb](tutorials/an-introduction/2.pytorch-model.ipynb)<br>[3.pytorch-model-cloud-data.ipynb](tutorials/an-introduction/3.pytorch-model-cloud-data.ipynb)|Run 'hello world' and train a simple model on Azure Machine Learning.
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[automl-with-pycaret](tutorials/automl-with-pycaret)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-automl-with-pycaret)|[1.classification.ipynb](tutorials/automl-with-pycaret/1.classification.ipynb)|Learn how to use [PyCaret](https://github.com/pycaret/pycaret) for automated machine learning, with tracking and scaling in Azure ML.
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[deploy-edge](tutorials/deploy-edge)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-deploy-edge)|[ase-gpu.ipynb](tutorials/deploy-edge/ase-gpu.ipynb)|Learn how to deploy models to Edge devices using Azure ML.
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[deploy-triton](tutorials/deploy-triton)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-deploy-triton)|[1.densenet-local.ipynb](tutorials/deploy-triton/1.densenet-local.ipynb)<br>[2.bidaf-aks-v100.ipynb](tutorials/deploy-triton/2.bidaf-aks-v100.ipynb)|Learn how to efficiently deploy to GPUs with the [Triton inference server](https://github.com/triton-inference-server/server) and Azure ML.
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[deploy-triton](tutorials/deploy-triton)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-deploy-triton)|[1.densenet-local.ipynb](tutorials/deploy-triton/1.densenet-local.ipynb)<br>[2.bidaf-aks-v100.ipynb](tutorials/deploy-triton/2.bidaf-aks-v100.ipynb)|[Experimental] Learn how to efficiently deploy to GPUs with the [Triton inference server](https://github.com/triton-inference-server/server) and Azure ML.
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[using-dask](tutorials/using-dask)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-dask)|[1.intro-to-dask.ipynb](tutorials/using-dask/1.intro-to-dask.ipynb)|Learn how to read from cloud data and scale PyData tools (Numpy, Pandas, Scikit-Learn, etc.) with [Dask](https://dask.org) and Azure ML.
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[using-pytorch-lightning](tutorials/using-pytorch-lightning)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-pytorch-lightning)|[1.train-single-node.ipynb](tutorials/using-pytorch-lightning/1.train-single-node.ipynb)<br>[2.log-with-tensorboard.ipynb](tutorials/using-pytorch-lightning/2.log-with-tensorboard.ipynb)<br>[3.log-with-mlflow.ipynb](tutorials/using-pytorch-lightning/3.log-with-mlflow.ipynb)<br>[4.train-multi-node-ddp.ipynb](tutorials/using-pytorch-lightning/4.train-multi-node-ddp.ipynb)|Learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) and Azure ML.
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[using-pytorch-lightning](tutorials/using-pytorch-lightning)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-pytorch-lightning)|[1.train-single-node.ipynb](tutorials/using-pytorch-lightning/1.train-single-node.ipynb)<br>[2.log-with-tensorboard.ipynb](tutorials/using-pytorch-lightning/2.log-with-tensorboard.ipynb)<br>[3.log-with-mlflow.ipynb](tutorials/using-pytorch-lightning/3.log-with-mlflow.ipynb)<br>[4.train-multi-node-ddp.ipynb](tutorials/using-pytorch-lightning/4.train-multi-node-ddp.ipynb)|[Experimental] Learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) and Azure ML.
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[using-rapids](tutorials/using-rapids)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-rapids)|[1.train-and-hpo.ipynb](tutorials/using-rapids/1.train-and-hpo.ipynb)<br>[2.train-multi-gpu.ipynb](tutorials/using-rapids/2.train-multi-gpu.ipynb)|Learn how to accelerate PyData tools (Numpy, Pandas, Scikit-Learn, etc.) on NVIDIA GPUs with [RAPIDS](https://github.com/rapidsai) and Azure ML.
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[using-xgboost](tutorials/using-xgboost)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-xgboost)|[1.local-eda.ipynb](tutorials/using-xgboost/1.local-eda.ipynb)<br>[2.distributed-cpu.ipynb](tutorials/using-xgboost/2.distributed-cpu.ipynb)|Learn how to use [XGBoost](https://github.com/dmlc/xgboost) with Azure ML.
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[using-xgboost](tutorials/using-xgboost)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-xgboost)|[1.local-eda.ipynb](tutorials/using-xgboost/1.local-eda.ipynb)<br>[2.distributed-cpu.ipynb](tutorials/using-xgboost/2.distributed-cpu.ipynb)|[Experimental] Learn how to use [XGBoost](https://github.com/dmlc/xgboost) with Azure ML.
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**Notebooks** ([notebooks](notebooks))
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- azurecli
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products:
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- azure-machine-learning
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description: Learn how to efficiently deploy to GPUs with the [Triton inference server](https://github.com/triton-inference-server/server) and Azure ML.
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description: [Experimental] Learn how to efficiently deploy to GPUs with the [Triton inference server](https://github.com/triton-inference-server/server) and Azure ML.
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---
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# Real-time inference on GPUs in Azure Machine Learning
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**Note**: this tutorial is experimental and prone to failure
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The notebooks in this directory show how to take advantage of the interoperability between Azure Machine Learning and [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) for cost-effective real time inference on GPUs.
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## Python instructions
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- azurecli
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products:
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- azure-machine-learning
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description: Learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) and Azure ML.
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description: [Experimental] Learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) and Azure ML.
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---
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# Train with PyTorch Lightning
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**Note**: this tutorial is experimental and prone to failure
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[PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) is a lightweight open-source library that provides a high-level interface for PyTorch.
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The model training code for this tutorial can be found in [`src`](src). This tutorial goes over the steps to run PyTorch Lightning on Azure ML, and it includes the following parts:
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- azurecli
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products:
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- azure-machine-learning
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description: Learn how to use [XGBoost](https://github.com/dmlc/xgboost) with Azure ML.
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description: [Experimental] Learn how to use [XGBoost](https://github.com/dmlc/xgboost) with Azure ML.
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---
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# XGBoost
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**Note**: this tutorial is experimental and prone to failure
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This tutorial demonstrates how to run XGBoost on Azure through a series of Python notebooks to demonstrate how a project might develop. This tutorial leverages the [Microsoft Kaggle Malware](https://kaggle.com/c/microsoft-malware-prediction), repartitioned and hosted in Azure Blob.
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This tutorial consists of two notebooks:
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