To create or setup a workspace with the assets used in these examples, run the [setup script](setup-workspace.py).
> If you do not have an AML Workspace, run `python setup-workspace.py --subscription-id $ID`, where `$ID` is your Azure subscription id. A resource group, AML Workspace, and other necessary resources will be created in the subscription.
>
> If you have an AML Workspace, [install the AML CLI](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli) and run `az ml folder attach -w $WS -g $RG`, where `$WS` and `$RG` are the workspace and resource group names.
>
> Run `python setup-workspace.py -h` to see other arguments.
[an-introduction](tutorials/an-introduction)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-ai)|[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)|learn the basics of Azure Machine Learning
[automl-with-pycaret](tutorials/automl-with-pycaret)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-awp)|[1.classification.ipynb](tutorials/automl-with-pycaret/1.classification.ipynb)|learn how to automate ML with [PyCaret](https://github.com/pycaret/pycaret)
[deploy-edge](tutorials/deploy-edge)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-de)|[ase-gpu.ipynb](tutorials/deploy-edge/ase-gpu.ipynb)|learn how to use Edge device for model deployment and scoring
[deploy-triton](tutorials/deploy-triton)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-dt)|[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 using [triton inference server](https://github.com/triton-inference-server/server)
[using-dask](tutorials/using-dask)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-ud)|[1.intro-to-dask.ipynb](tutorials/using-dask/1.intro-to-dask.ipynb)<br>[2.dask-cloudprovider.ipynb](tutorials/using-dask/2.dask-cloudprovider.ipynb)|learn how to read from cloud data and scale PyData tools (Numpy, Pandas, Scikit-Learn, etc.) with [Dask](https://dask.org)
[using-pytorch-lightning](tutorials/using-pytorch-lightning)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-upl)|[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)
[using-rapids](tutorials/using-rapids)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-ur)|[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)
[using-xgboost](tutorials/using-xgboost)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Arun-tutorial-ux)|[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) on Azure
[workflows/train/fastai/mnist-mlproject/job.py](workflows/train/fastai/mnist-mlproject/job.py)|AML - CPU|mlproject|train fastai resnet18 model on mnist data via mlflow mlproject
[workflows/train/fastai/mnist/job.py](workflows/train/fastai/mnist/job.py)|AML - CPU|conda|train fastai resnet18 model on mnist data
[workflows/train/fastai/pets/job.py](workflows/train/fastai/pets/job.py)|AML - GPU|docker|train fastai resnet34 model on pets data
[workflows/train/lightgbm/iris/job.py](workflows/train/lightgbm/iris/job.py)|AML - CPU|pip|train a lightgbm model on iris data
[workflows/train/pytorch/mnist-mlproject/job.py](workflows/train/pytorch/mnist-mlproject/job.py)|AML - GPU|mlproject|train a pytorch CNN model on mnist data via mlflow mlproject
[workflows/train/pytorch/mnist/job.py](workflows/train/pytorch/mnist/job.py)|AML - GPU|conda|train a pytorch CNN model on mnist data
[workflows/train/scikit-learn/diabetes-mlproject/job.py](workflows/train/scikit-learn/diabetes-mlproject/job.py)|AML - CPU|mlproject|train sklearn ridge model on diabetes data via mlflow mlproject
[workflows/train/scikit-learn/diabetes/job.py](workflows/train/scikit-learn/diabetes/job.py)|AML - CPU|conda|train sklearn ridge model on diabetes data
[workflows/train/tensorflow/mnist-distributed-horovod/job.py](workflows/train/tensorflow/mnist-distributed-horovod/job.py)|AML - GPU|conda|train tensorflow CNN model on mnist data distributed via horovod
[workflows/train/tensorflow/mnist-distributed/job.py](workflows/train/tensorflow/mnist-distributed/job.py)|AML - GPU|conda|train tensorflow CNN model on mnist data distributed via tensorflow
[workflows/train/tensorflow/mnist/job.py](workflows/train/tensorflow/mnist/job.py)|AML - GPU|conda|train tensorflow NN model on mnist data
[workflows/train/transformers/glue/1-aml-finetune-job.py](workflows/train/transformers/glue/1-aml-finetune-job.py)|AML - GPU|pip|Submit GLUE finetuning with Huggingface transformers library on Azure ML
[workflows/train/transformers/glue/2-aml-comparison-of-sku-job.py](workflows/train/transformers/glue/2-aml-comparison-of-sku-job.py)|AML - CPU|pip|Experiment comparing training performance of GLUE finetuning task with differing hardware.
[workflows/train/transformers/glue/3-aml-hyperdrive-job.py](workflows/train/transformers/glue/3-aml-hyperdrive-job.py)|AML - GPU|pip|Automatic hyperparameter optimization with Azure ML HyperDrive library.
[workflows/deploy/pytorch/mnist/job.py](workflows/deploy/pytorch/mnist/job.py)|unknown|deploy pytorch cnn model trained on mnist data to aks
[workflows/deploy/scikit-learn/diabetes/job.py](workflows/deploy/scikit-learn/diabetes/job.py)|unknown|deploy sklearn ridge model trained on diabetes data to AKS
We welcome contributions and suggestions! Please see the [contributing guidelines](CONTRIBUTING.md) for details.
## Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](CODE_OF_CONDUCT.md) for details.
