From 33d6def8c30d3dd3a5bfbea50b9c727788185faf Mon Sep 17 00:00:00 2001 From: vizhur Date: Mon, 11 Nov 2019 16:57:02 +0000 Subject: [PATCH] update samples from Release-158 as a part of 1.0.74 SDK release --- configuration.ipynb | 2 +- ...fication-bank-marketing-all-features.ipynb | 3 +- ...-ml-classification-credit-card-fraud.ipynb | 3 +- .../auto-ml-classification-text-dnn.ipynb | 13 +- .../auto-ml-forecasting-beer-remote.ipynb | 2 +- .../accelerated-models-quickstart.ipynb | 6 +- .../accelerated-models-training.ipynb | 6 +- .../register-model-deploy-local.ipynb | 6 +- .../onnx-convert-aml-deploy-tinyyolo.ipynb | 28 +++- .../onnx/onnx-convert-aml-deploy-tinyyolo.yml | 1 + ...facial-expression-recognition-deploy.ipynb | 25 +++- .../onnx/onnx-inference-mnist-deploy.ipynb | 25 +++- .../onnx-modelzoo-aml-deploy-resnet50.ipynb | 24 +++- .../onnx-train-pytorch-aml-deploy-mnist.ipynb | 22 ++- ...er-model-create-image-deploy-service.ipynb | 9 +- ...urebatch-to-run-a-windows-executable.ipynb | 6 +- ...pipelines-use-adla-as-compute-target.ipynb | 6 +- ...nes-use-databricks-as-compute-target.ipynb | 6 +- .../data-drift/drift-on-aks.ipynb | 2 +- .../logging-api/logging-api.ipynb | 2 +- .../tensorboard/tensorboard.yml | 2 +- ...yperparameter-tune-deploy-with-keras.ipynb | 6 +- ...-hyperparameter-tune-deploy-with-keras.yml | 4 +- .../train-in-spark/train-in-spark.ipynb | 42 +++--- .../train-on-amlcompute.ipynb | 66 ++++++--- .../train-on-remote-vm.ipynb | 6 +- .../datadrift-tutorial.ipynb | 2 +- index.md | 131 ++++++++++++++++-- setup-environment/configuration.ipynb | 2 +- 29 files changed, 356 insertions(+), 102 deletions(-) diff --git a/configuration.ipynb b/configuration.ipynb index 4fadc4f7..2a050788 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -103,7 +103,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.0.72.1 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb index caad10b5..49c70655 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb +++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb @@ -334,8 +334,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n", - "In this example, we specify `show_output = True` to print currently running iterations to the console." + "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while." ] }, { diff --git a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb index 1c7dfd3a..58f18d55 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb +++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb @@ -230,8 +230,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while.\n", - "In this example, we specify `show_output = True` to print currently running iterations to the console." + "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while." ] }, { diff --git a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb index c8d0de1c..5457da69 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb +++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb @@ -308,7 +308,7 @@ "metadata": {}, "outputs": [], "source": [ - "automl_run = experiment.submit(automl_config, show_output=False)" + "automl_run = experiment.submit(automl_config, show_output=True)" ] }, { @@ -320,15 +320,6 @@ "automl_run" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_run.wait_for_completion()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -357,7 +348,7 @@ "metadata": {}, "outputs": [], "source": [ - "#best_run, fitted_model = local_run.get_output()" + "#best_run, fitted_model = automl_run.get_output()" ] }, { diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb index c4b4bc9f..f9dc35b9 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb @@ -376,7 +376,7 @@ "hidePrompt": false }, "source": [ - "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. You will see the currently running iterations printing to the console." + "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required." ] }, { diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb index fdac2adf..cbb94f6b 100644 --- a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb +++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb @@ -345,7 +345,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-akscompute-provision" + ] + }, "outputs": [], "source": [ "from azureml.core.compute import AksCompute, ComputeTarget\n", diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb index 88918c22..7c827398 100644 --- a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb +++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb @@ -682,7 +682,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-akswebservice-deploy-from-image" + ] + }, "outputs": [], "source": [ "%%time\n", diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb index e4f72e44..34a958fd 100644 --- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb @@ -166,7 +166,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-localwebservice-deploy" + ] + }, "outputs": [], "source": [ "from azureml.core.webservice import LocalWebservice\n", diff --git a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb index b7f35149..ee50aaa3 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb @@ -316,9 +316,6 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core.webservice import Webservice\n", - "from random import randint\n", - "\n", "aci_service_name = 'my-aci-service-15ad'\n", "print(\"Service\", aci_service_name)\n", "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", @@ -386,6 +383,22 @@ "name": "viswamy" } ], + "category": "deployment", + "compute": [ + "local" + ], + "datasets": [ + "PASCAL VOC" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Convert and deploy TinyYolo with ONNX Runtime", + "index_order": 5, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -402,7 +415,14 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" - } + }, + "star_tag": [ + "featured" + ], + "tags": [ + "ONNX Converter" + ], + "task": "Object Detection" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml index 3c34a84a..06c455f0 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml +++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml @@ -2,5 +2,6 @@ name: onnx-convert-aml-deploy-tinyyolo dependencies: - pip: - azureml-sdk + - numpy - git+https://github.com/apple/coremltools@v2.1 - onnxmltools==1.3.1 diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb index 6d09bed0..779cb6f4 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb @@ -391,8 +391,6 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core.webservice import Webservice\n", - "\n", "aci_service_name = 'onnx-demo-emotion'\n", "print(\"Service\", aci_service_name)\n", "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", @@ -755,6 +753,22 @@ "name": "viswamy" } ], + "category": "deployment", + "compute": [ + "local" + ], + "datasets": [ + "Emotion FER" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime", + "index_order": 2, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -772,7 +786,12 @@ "pygments_lexer": "ipython3", "version": "3.6.5" }, - "msauthor": "vinitra.swamy" + "msauthor": "vinitra.swamy", + "star_tag": [], + "tags": [ + "ONNX Model Zoo" + ], + "task": "Facial Expression Recognition" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb index e9289ebe..e0ef68cd 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb @@ -378,8 +378,6 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core.webservice import Webservice\n", - "\n", "aci_service_name = 'onnx-demo-mnist'\n", "print(\"Service\", aci_service_name)\n", "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", @@ -763,6 +761,22 @@ "name": "viswamy" } ], + "category": "deployment", + "compute": [ + "local" + ], + "datasets": [ + "MNIST" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Deploy MNIST digit recognition with ONNX Runtime", + "index_order": 1, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -780,7 +794,12 @@ "pygments_lexer": "ipython3", "version": "3.6.5" }, - "msauthor": "vinitra.swamy" + "msauthor": "vinitra.swamy", + "star_tag": [], + "tags": [ + "ONNX Model Zoo" + ], + "task": "Image Classification" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb index 9788b0c2..144b94e6 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb @@ -302,7 +302,6 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core.webservice import Webservice\n", "from random import randint\n", "\n", "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n", @@ -372,6 +371,22 @@ "name": "viswamy" } ], + "category": "deployment", + "compute": [ + "local" + ], + "datasets": [ + "ImageNet" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Deploy ResNet50 with ONNX Runtime", + "index_order": 4, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -388,7 +403,12 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" - } + }, + "star_tag": [], + "tags": [ + "ONNX Model Zoo" + ], + "task": "Image Classification" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb index 42a87b9c..e69ba0d8 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb @@ -477,7 +477,6 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core.webservice import Webservice\n", "from azureml.core.model import Model\n", "from random import randint\n", "\n", @@ -548,6 +547,22 @@ "name": "viswamy" } ], + "category": "deployment", + "compute": [ + "AML Compute" + ], + "datasets": [ + "MNIST" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime", + "index_order": 3, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -565,6 +580,11 @@ "pygments_lexer": "ipython3", "version": "3.6.6" }, + "star_tag": [], + "tags": [ + "ONNX Converter" + ], + "task": "Image Classification", "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { diff --git a/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb b/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb index ef28c4f3..67815252 100644 --- a/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb +++ b/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb @@ -235,7 +235,8 @@ "execution_count": null, "metadata": { "tags": [ - "create image" + "create image", + "sample-image-create" ] }, "outputs": [], @@ -330,7 +331,8 @@ "metadata": { "tags": [ "deploy service", - "aci" + "aci", + "sample-aciwebservice-deploy-config" ] }, "outputs": [], @@ -349,7 +351,8 @@ "metadata": { "tags": [ "deploy service", - "aci" + "aci", + "sample-aciwebservice-deploy-from-image" ] }, "outputs": [], diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb index 19d493df..338d6822 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb @@ -110,7 +110,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-batchcompute-attach" + ] + }, "outputs": [], "source": [ "batch_compute_name = 'mybatchcompute' # Name to associate with new compute in workspace\n", diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb index 075104f4..8c51bc3d 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb @@ -88,7 +88,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-adlacompute-attach" + ] + }, "outputs": [], "source": [ "adla_compute_name = 'testadl' # Name to associate with new compute in workspace\n", diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb index 6509db0b..684ab67d 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb @@ -142,7 +142,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-databrickscompute-attach" + ] + }, "outputs": [], "source": [ "# Replace with your account info before running.\n", diff --git a/how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb b/how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb index 6a20b9c1..94f8161d 100644 --- a/how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb +++ b/how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb @@ -475,7 +475,7 @@ "metadata": { "authors": [ { - "name": "copeters" + "name": "jamgan" } ], "category": "tutorial", diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb index ea5557d9..c18afdb4 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb @@ -100,7 +100,7 @@ "\n", "# Check core SDK version number\n", "\n", - "print(\"This notebook was created using SDK version 1.0.72.1, you are currently running version\", azureml.core.VERSION)" + "print(\"This notebook was created using SDK version 1.0.74, you are currently running version\", azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml index 3709016d..cd0618eb 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml +++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml @@ -3,4 +3,4 @@ dependencies: - pip: - azureml-sdk - azureml-tensorboard - - tensorflow<2.0.0 + - tensorflow<1.15 diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb index 8c4ab19f..0ec5afe8 100644 --- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb +++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb @@ -920,6 +920,8 @@ "metadata": {}, "outputs": [], "source": [ + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", "cd = CondaDependencies.create()\n", "cd.add_tensorflow_conda_package()\n", "cd.add_conda_package('keras==2.2.5')\n", @@ -1041,7 +1043,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can retreive the API keys used for accessing the HTTP endpoint." + "We can retrieve the API keys used for accessing the HTTP endpoint." ] }, { @@ -1050,7 +1052,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retreive the API keys. two keys were generated.\n", + "# Retrieve the API keys. Two keys were generated.\n", "key1, Key2 = service.get_keys()\n", "print(key1)" ] diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml index 9c8b11bd..49b37af7 100644 --- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml +++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml @@ -1,10 +1,10 @@ name: train-hyperparameter-tune-deploy-with-keras dependencies: -- matplotlib - pip: - azureml-sdk - azureml-widgets - tensorflow==1.13.1 - keras==2.2.5 - - pandas + - matplotlib==3.0.3 - numpy==1.16.2 + - pandas diff --git a/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb b/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb index e426751b..baff52c9 100644 --- a/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb +++ b/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb @@ -133,7 +133,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-hdinsightcompute-attach" + ] + }, "outputs": [], "source": [ "from azureml.core.compute import ComputeTarget, HDInsightCompute\n", @@ -262,6 +266,22 @@ "name": "aashishb" } ], + "category": "training", + "compute": [ + "HDI cluster" + ], + "datasets": [ + "None" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "PySpark" + ], + "friendly_name": "Training in Spark", + "index_order": 1, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -279,26 +299,10 @@ "pygments_lexer": "ipython3", "version": "3.6.7" }, - "friendly_name": "Training in Spark", - "exclude_from_index": false, - "index_order": 1, - "category": "training", - "task": "Submiting a run on a spark cluster", - "datasets": [ - "None" - ], - "compute": [ - "HDI cluster" - ], - "deployment": [ - "None" - ], - "framework": [ - "PySpark" - ], "tags": [ "None" - ] + ], + "task": "Submiting a run on a spark cluster" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb b/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb index 38b7d320..38d1cbe6 100644 --- a/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb +++ b/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb @@ -203,7 +203,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-amlcompute-provision" + ] + }, "outputs": [], "source": [ "from azureml.core.compute import ComputeTarget, AmlCompute\n", @@ -293,7 +297,11 @@ "* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n", "* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n", "* `vnet_name`: Name of VNet\n", - "* `subnet_name`: Name of SubNet within the VNet" + "* `subnet_name`: Name of SubNet within the VNet\n", + "* `admin_username`: Name of Admin user account which will be created on all the nodes of the cluster\n", + "* `admin_user_password`: Password that you want to set for the user account above\n", + "* `admin_user_ssh_key`: SSH Key for the user account above. You can specify either a password or an SSH key or both\n", + "* `remote_login_port_public_access`: Flag to enable or disable the public SSH port. If you dont specify, AmlCompute will smartly close the port when deploying inside a VNet" ] }, { @@ -320,7 +328,11 @@ " idle_seconds_before_scaledown='300',\n", " vnet_resourcegroup_name='',\n", " vnet_name='',\n", - " subnet_name='')\n", + " subnet_name='',\n", + " admin_username='',\n", + " admin_user_password='',\n", + " admin_user_ssh_key='',\n", + " remote_login_port_public_access='enabled')\n", " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", "\n", "cpu_cluster.wait_for_completion(show_output=True)" @@ -381,10 +393,20 @@ "metadata": {}, "outputs": [], "source": [ - "#Get_status () gets the latest status of the AmlCompute target\n", + "#get_status () gets the latest status of the AmlCompute target\n", "cpu_cluster.get_status().serialize()\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#list_nodes () gets the list of nodes on the cluster with status, IP and associated run\n", + "cpu_cluster.list_nodes()\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -425,6 +447,22 @@ "name": "nigup" } ], + "category": "training", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Diabetes" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Train on Azure Machine Learning Compute", + "index_order": 1, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -442,26 +480,10 @@ "pygments_lexer": "ipython3", "version": "3.6.6" }, - "friendly_name": "Train on Azure Machine Learning Compute", - "exclude_from_index": false, - "index_order": 1, - "category": "training", - "task": "Submit an Azure Machine Leaarning Compute run", - "datasets": [ - "Diabetes" - ], - "compute": [ - "AML Compute" - ], - "deployment": [ - "None" - ], - "framework": [ - "None" - ], "tags": [ "None" - ] + ], + "task": "Submit a run on Azure Machine Learning Compute." }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb index 37736cc3..415fb061 100644 --- a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb +++ b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb @@ -243,7 +243,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "sample-remotecompute-attach" + ] + }, "outputs": [], "source": [ "from azureml.core.compute import ComputeTarget, RemoteCompute\n", diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb b/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb index 13992fbe..bfc3a3b7 100644 --- a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb +++ b/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb @@ -409,7 +409,7 @@ "metadata": { "authors": [ { - "name": "sihhu" + "name": "jamgan" } ], "category": "tutorial", diff --git a/index.md b/index.md index caa4488a..4395c291 100644 --- a/index.md +++ b/index.md @@ -10,6 +10,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| | [Using Azure ML environments](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/using-environments/using-environments.ipynb) | Creating and registering environments | None | Local | None | None | None | + | [Estimators in AML with hyperparameter tuning](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb) | Use the Estimator pattern in Azure Machine Learning SDK | None | AML Compute | None | None | None | @@ -18,34 +19,63 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| | [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | forecasting | BikeShare | remote | None | Azure ML AutoML | Forecasting | -| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | remote | Azure Container Instance | Azure ML AutoML | None | -| [Forecasting with automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.ipynb) | Forecasting | NYC Energy | Local | None | Azure ML AutoML | None | + +| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | remote | Azure Container Instance | Azure ML AutoML | | + +| [Forecasting with automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.ipynb) | Forecasting | NYC Energy | Local | None | Azure ML AutoML | | + | [Setup automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb) | None | None | None | None | Azure ML AutoML | | -| [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | None | local | Local | None | None | + +| [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | | local | Local | None | None | + | :star:[Data drift on aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb) | Filtering | NOAA | remote | AKS | Azure ML | Dataset, Timeseries, Drift | -| [Training and deploying a model from a notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None | + +| [](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None | + | :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | remote | None | Azure ML | Dataset, Timeseries, Drift | + | :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | local | None | Azure ML | Dataset, Tabular Timeseries | + | :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets.ipynb) | Filtering | Iris, Daibetes | remote | None | Azure ML | Dataset | + | [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb) | forecasting | None | remote | None | Azure ML AutoML | Forecasting, Confidence Intervals | + | [Automated ML run with basic edition features.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) | Classification | Bankmarketing | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | + | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | creditcard | AML Compute | None | None | remote_run, AutomatedML | + | [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | + | [Use MLflow with Azure Machine Learning for training and deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb) | Use MLflow with Azure Machine Learning to train and deploy Pa yTorch image classifier model | MNIST | AML Compute | Azure Container Instance | PyTorch | None | + | :star:[Azure Machine Learning Pipeline with DataTranferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) | Demonstrates the use of DataTranferStep | Custom | ADF | None | Azure ML | None | + | [Getting Started with Azure Machine Learning Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) | Getting Started notebook for ANML Pipelines | Custom | AML Compute | None | Azure ML | None | + | [Azure Machine Learning Pipeline with AzureBatchStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb) | Demonstrates the use of AzureBatchStep | Custom | Azure Batch | None | Azure ML | None | + | [Azure Machine Learning Pipeline with EstimatorStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb) | Demonstrates the use of EstimatorStep | Custom | AML Compute | None | Azure ML | None | + | :star:[How to use ModuleStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb) | Demonstrates the use of ModuleStep | Custom | AML Compute | None | Azure ML | None | + | :star:[How to use Pipeline Drafts to create a Published Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb) | Demonstrates the use of Pipeline Drafts | Custom | AML Compute | None | Azure ML | None | + | :star:[Azure Machine Learning Pipeline with HyperDriveStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb) | Demonstrates the use of HyperDriveStep | Custom | AML Compute | None | Azure ML | None | + | :star:[How to Publish a Pipeline and Invoke the REST endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) | Demonstrates the use of Published Pipelines | Custom | AML Compute | None | Azure ML | None | + | :star:[How to Setup a Schedule for a Published Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb) | Demonstrates the use of Schedules for Published Pipelines | Custom | AML Compute | None | Azure ML | None | + | [How to setup a versioned Pipeline Endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb) | Demonstrates the use of PipelineEndpoint to run a specific version of the Published Pipeline | Custom | AML Compute | None | Azure ML | None | + | :star:[How to use DataPath as a PipelineParameter](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb) | Demonstrates the use of DataPath as a PipelineParameter | Custom | AML Compute | None | Azure ML | None | + | [How to use AdlaStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb) | Demonstrates the use of AdlaStep | Custom | Azure Data Lake Analytics | None | Azure ML | None | + | :star:[How to use DatabricksStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb) | Demonstrates the use of DatabricksStep | Custom | Azure Databricks | None | Azure ML, Azure Databricks | None | + | :star:[How to use AutoMLStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb) | Demonstrates the use of AutoMLStep | Custom | AML Compute | None | Automated Machine Learning | None | + | :star:[Azure Machine Learning Pipelines with Data Dependency](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb) | Demonstrates how to construct a Pipeline with data dependency between steps | Custom | AML Compute | None | Azure ML | None | @@ -54,25 +84,45 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| | [Train a model with hyperparameter tuning](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb) | Train a Convolutional Neural Network (CNN) | MNIST | AML Compute | Azure Container Instance | Chainer | None | + | [Distributed Training with Chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.ipynb) | Use the Chainer estimator to perform distributed training | MNIST | AML Compute | None | Chainer | None | + | [Training with hyperparameter tuning using PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) | Train an image classification model using transfer learning with the PyTorch estimator | ImageNet | AML Compute | Azure Container Instance | PyTorch | None | + | [Distributed PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb) | Train a model using the distributed training via Horovod | MNIST | AML Compute | None | PyTorch | None | + | [Distributed training with PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.ipynb) | Train a model using distributed training via Nccl/Gloo | MNIST | AML Compute | None | PyTorch | None | + | [Training and hyperparameter tuning with Scikit-learn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) | Train a support vector machine (SVM) to perform classification | Iris | AML Compute | None | Scikit-learn | None | + | [Training and hyperparameter tuning using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | + | [Distributed training using TensorFlow with Horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb) | Use the TensorFlow estimator to train a word2vec model | None | AML Compute | None | TensorFlow | None | + | [Distributed TensorFlow with parameter server](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb) | Use the TensorFlow estimator to train a model using distributed training | MNIST | AML Compute | None | TensorFlow | None | + | [Hyperparameter tuning and warm start using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | + | [Resuming a model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) | Resume a model in TensorFlow from a previously submitted run | MNIST | AML Compute | None | TensorFlow | None | + | [Training in Spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) | Submiting a run on a spark cluster | None | HDI cluster | None | PySpark | None | -| [Train on Azure Machine Learning Compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) | Submit an Azure Machine Leaarning Compute run | Diabetes | AML Compute | None | None | None | + +| [Train on Azure Machine Learning Compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) | Submit a run on Azure Machine Learning Compute. | Diabetes | AML Compute | None | None | None | + | [Train on local compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-local/train-on-local.ipynb) | Train a model locally | Diabetes | Local | None | None | None | + | [Train in a remote Linux virtual machine](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) | Configure and execute a run | Diabetes | Data Science Virtual Machine | None | None | None | + | [Using Tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) | Export the run history as Tensorboard logs | None | None | None | TensorFlow | None | + | [Train a DNN using hyperparameter tuning and deploying with Keras](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb) | Create a multi-class classifier | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | + | [Managing your training runs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb) | Monitor and complete runs | None | Local | None | None | None | + | [Tensorboard integration with run history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb) | Run a TensorFlow job and view its Tensorboard output live | None | Local, DSVM, AML Compute | None | TensorFlow | None | + | [Use MLflow with AML for a local training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb) | Use MLflow tracking APIs together with Azure Machine Learning for storing your metrics and artifacts | Diabetes | Local | None | None | None | + | [Use MLflow with AML for a remote training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb) | Use MLflow tracking APIs together with AML for storing your metrics and artifacts | Diabetes | AML Compute | None | None | None | @@ -82,67 +132,124 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| +| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | local | Azure Container Instance | ONNX | ONNX Model Zoo | + +| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | local | Azure Container Instance | ONNX | ONNX Model Zoo | + | :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None | + +| [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter | + +| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | local | Azure Container Instance | ONNX | ONNX Model Zoo | + | [Deploy a model as a web service using MLflow](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb) | Use MLflow with AML | Diabetes | None | Azure Container Instance | Scikit-learn | None | +| :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter | + ## Other Notebooks |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| -| [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None | None | None | None | None | +| [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None | | None | None | None | + | [Automated ML Grouping with Pipeline.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb) | Use AzureML Pipeline to trigger multiple Automated ML runs. | Orange Juice Sales | AML Compute | Azure Container Instance | Scikit-learn, Pytorch | AutomatedML | + | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) | | | | | | | + | [file-dataset-image-inference-mnist](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/batch_inferencing/file-dataset-image-inference-mnist.ipynb) | | | | | | | + | [tabular-dataset-inference-iris](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/batch_inferencing/tabular-dataset-inference-iris.ipynb) | | | | | | | + | [lightgbm-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/gbdt/lightgbm/lightgbm-example.ipynb) | | | | | | | + | [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) | | | | | | | + | [auto-ml-continuous-retraining](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) | | | | | | | + | [auto-ml-forecasting-beer-remote](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb) | | | | | | | -| [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) | | | | | | | + +| :star:[auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) | Forecasting | | | | | | + | [auto-ml-regression](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb) | | | | | | | + | [build-model-run-history-03](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb) | | | | | | | + | [deploy-to-aci-04](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb) | | | | | | | + | [deploy-to-aks-05](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-05.ipynb) | | | | | | | + | [ingest-data-02](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb) | | | | | | | + | [installation-and-configuration-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb) | | | | | | | + | [automl-databricks-local-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb) | | | | | | | + | [automl-databricks-local-with-deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb) | | | | | | | + | [aml-pipelines-use-databricks-as-compute-target](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb) | | | | | | | + | [accelerated-models-object-detection](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb) | | | | | | | + | [accelerated-models-quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb) | | | | | | | + | [accelerated-models-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb) | | | | | | | + | [multi-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb) | | | | | | | + | [register-model-deploy-local-advanced](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb) | | | | | | | + | [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) | | | | | | | -| [onnx-convert-aml-deploy-tinyyolo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | | | | | | | -| [onnx-inference-facial-expression-recognition-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | | | | | | | -| [onnx-inference-mnist-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | | | | | | | + | [onnx-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb) | | | | | | | -| [onnx-modelzoo-aml-deploy-resnet50](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | | | | | | | -| [onnx-train-pytorch-aml-deploy-mnist](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | | | | | | | + | [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) | | | | | | | + | [register-model-create-image-deploy-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb) | | | | | | | + | [tensorflow-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/tensorflow/tensorflow-model-register-and-deploy.ipynb) | | | | | | | + | [explain-model-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb) | | | | | | | + | [save-retrieve-explanations-run-history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb) | | | | | | | + | [train-explain-model-locally-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb) | | | | | | | + | [train-explain-model-on-amlcompute-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb) | | | | | | | + | [advanced-feature-transformations-explain-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb) | | | | | | | + | [explain-binary-classification-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb) | | | | | | | + | [explain-multiclass-classification-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb) | | | | | | | + | [explain-regression-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb) | | | | | | | + | [simple-feature-transformations-explain-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb) | | | | | | | + | [nyc-taxi-data-regression-model-building](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) | | | | | | | + | [pipeline-batch-scoring](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb) | | | | | | | + | [pipeline-style-transfer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb) | | | | | | | + | [authentication-in-azureml](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) | | | | | | | + +| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | | None | None | None | None | + | [distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb) | | | | | | | + | [notebook_example](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/notebook_example.ipynb) | | | | | | | + | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master//setup-environment/configuration.ipynb) | | | | | | | + | [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part1-training.ipynb) | | | | | | | + | [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part2-deploy.ipynb) | | | | | | | + | [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automated-ml.ipynb) | | | | | | | + | [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-1st-experiment-sdk-train.ipynb) | | | | | | | + | [tutorial-pipeline-batch-scoring-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-pipeline-batch-scoring-classification.ipynb) | | | | | | | diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb index 3cf89af4..0db25103 100644 --- a/setup-environment/configuration.ipynb +++ b/setup-environment/configuration.ipynb @@ -102,7 +102,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.0.72.1 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] },