From 6c629f1eda513fd6df4f65dc437e28d89cf5fa41 Mon Sep 17 00:00:00 2001 From: amlrelsa-ms Date: Mon, 6 Jul 2020 22:05:24 +0000 Subject: [PATCH] update samples from Release-57 as a part of SDK release --- configuration.ipynb | 2 +- ...fication-bank-marketing-all-features.ipynb | 9 +- ...sification-bank-marketing-all-features.yml | 4 - ...-ml-classification-credit-card-fraud.ipynb | 2 +- ...to-ml-classification-credit-card-fraud.yml | 3 - .../auto-ml-classification-text-dnn.ipynb | 2 +- .../auto-ml-classification-text-dnn.yml | 8 - .../auto-ml-continuous-retraining.ipynb | 4 +- .../auto-ml-continuous-retraining.yml | 4 - .../auto-ml-forecasting-beer-remote.ipynb | 2 +- .../auto-ml-forecasting-beer-remote.yml | 7 - .../auto-ml-forecasting-bike-share.ipynb | 2 +- .../auto-ml-forecasting-bike-share.yml | 6 - .../auto-ml-forecasting-energy-demand.ipynb | 2 +- .../auto-ml-forecasting-energy-demand.yml | 5 - .../auto-ml-forecasting-function.ipynb | 2 +- .../auto-ml-forecasting-function.yml | 6 - ...to-ml-forecasting-orange-juice-sales.ipynb | 2 +- ...auto-ml-forecasting-orange-juice-sales.yml | 6 - ...assification-credit-card-fraud-local.ipynb | 2 +- ...classification-credit-card-fraud-local.yml | 3 - ...regression-explanation-featurization.ipynb | 2 +- ...l-regression-explanation-featurization.yml | 3 - .../regression/auto-ml-regression.ipynb | 2 +- .../regression/auto-ml-regression.yml | 4 - .../deployment/accelerated-models/README.md | 10 +- .../deploy-multi-model/first_model.pkl | Bin 658 -> 0 bytes .../multi-model-register-and-deploy.ipynb | 93 ++- .../multi-model-register-and-deploy.yml | 2 + .../deployment/deploy-multi-model/myenv.yml | 8 - .../deploy-multi-model/second_model.pkl | Bin 645 -> 0 bytes .../deployment/deploy-to-cloud/features.csv | 442 ----------- .../deployment/deploy-to-cloud/labels.csv | 442 ----------- .../model-register-and-deploy.ipynb | 78 +- .../model-register-and-deploy.yml | 2 + .../sklearn_regression_model.pkl | Bin 658 -> 0 bytes .../deployment/deploy-to-local/myenv.yml | 8 - ...register-model-deploy-local-advanced.ipynb | 108 +-- .../register-model-deploy-local-advanced.yml | 5 + .../register-model-deploy-local.ipynb | 219 +++--- .../register-model-deploy-local.yml | 5 + .../deployment/deploy-to-local/score.py | 35 - .../sklearn_regression_model.pkl | Bin 658 -> 0 bytes .../parallel-run/README.md | 2 +- .../tabular-dataset-inference-iris.ipynb | 2 +- .../mpi_scripts/neural_style.py | 185 +++++ .../mpi_scripts/neural_style_mpi.py | 207 +++++ .../pipeline-style-transfer-mpi.ipynb | 728 ++++++++++++++++++ ...er.yml => pipeline-style-transfer-mpi.yml} | 2 +- ...ipeline-style-transfer-parallel-run.ipynb} | 2 +- .../pipeline-style-transfer-parallel-run.yml | 7 + .../cartpole_ci.ipynb | 27 +- .../cartpole_ci.yml | 1 - .../cartpole_sc.ipynb | 44 +- .../cartpole_sc.yml | 1 - .../files/utils/callbacks.py | 6 + .../logging-api/logging-api.ipynb | 2 +- .../train-remote/train-remote.ipynb | 2 +- .../training/train-on-amlcompute/train.py | 7 +- .../train-on-computeinstance/train.py | 6 +- .../training/train-on-local/train.py | 6 +- .../training/train-on-remote-vm/train.py | 7 +- .../training/train-within-notebook/score.py | 6 +- index.md | 1 + setup-environment/configuration.ipynb | 2 +- ...ipeline-batch-scoring-classification.ipynb | 2 +- .../regression-automated-ml.yml | 2 - 67 files changed, 1470 insertions(+), 1338 deletions(-) delete mode 100644 how-to-use-azureml/deployment/deploy-multi-model/first_model.pkl delete mode 100644 how-to-use-azureml/deployment/deploy-multi-model/myenv.yml delete mode 100644 how-to-use-azureml/deployment/deploy-multi-model/second_model.pkl delete mode 100644 how-to-use-azureml/deployment/deploy-to-cloud/features.csv delete mode 100644 how-to-use-azureml/deployment/deploy-to-cloud/labels.csv delete mode 100644 how-to-use-azureml/deployment/deploy-to-cloud/sklearn_regression_model.pkl delete mode 100644 how-to-use-azureml/deployment/deploy-to-local/myenv.yml create mode 100644 how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.yml create mode 100644 how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.yml delete mode 100644 how-to-use-azureml/deployment/deploy-to-local/score.py delete mode 100644 how-to-use-azureml/deployment/deploy-to-local/sklearn_regression_model.pkl create mode 100644 how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/mpi_scripts/neural_style.py create mode 100644 how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/mpi_scripts/neural_style_mpi.py create mode 100644 how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-mpi.ipynb rename how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/{pipeline-style-transfer.yml => pipeline-style-transfer-mpi.yml} (74%) rename how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/{pipeline-style-transfer.ipynb => pipeline-style-transfer-parallel-run.ipynb} (99%) create mode 100644 how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.yml diff --git a/configuration.ipynb b/configuration.ipynb index 3a92e33f..fcef9920 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -103,7 +103,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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 d085be60..5b609cf8 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 @@ -105,7 +105,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -675,10 +675,8 @@ "model_name = best_run.properties['model_name']\n", "\n", "script_file_name = 'inference/score.py'\n", - "conda_env_file_name = 'inference/env.yml'\n", "\n", - "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n", - "best_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/env.yml')" + "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')" ] }, { @@ -721,8 +719,7 @@ "from azureml.core.model import Model\n", "from azureml.core.environment import Environment\n", "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n", - "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n", + "inference_config = InferenceConfig(entry_script=script_file_name)\n", "\n", "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", " memory_gb = 1, \n", diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml index a0d7ad03..0f30214b 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml +++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml @@ -2,7 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features dependencies: - pip: - azureml-sdk - - azureml-train-automl - - azureml-widgets - - matplotlib - - onnxruntime==1.0.0 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 2c42ea18..80e51c79 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 @@ -93,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml index 5edf8b84..148f33d5 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml +++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml @@ -2,6 +2,3 @@ name: auto-ml-classification-credit-card-fraud dependencies: - pip: - azureml-sdk - - azureml-train-automl - - azureml-widgets - - matplotlib 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 256d12a8..e5ce6556 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 @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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-text-dnn/auto-ml-classification-text-dnn.yml b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml index beb81945..4c952264 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml +++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml @@ -2,11 +2,3 @@ name: auto-ml-classification-text-dnn dependencies: - pip: - azureml-sdk - - azureml-train-automl - - azureml-widgets - - matplotlib - - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl - - sentencepiece==0.1.82 - - pytorch-transformers==1.0 - - spacy==2.1.8 - - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb index dda283d4..92e893a0 100644 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb +++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb @@ -88,7 +88,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -201,7 +201,7 @@ "conda_run_config.environment.docker.enabled = True\n", "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n", "\n", - "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets'], \n", + "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n", " conda_packages=['numpy==1.16.2'], \n", " pin_sdk_version=False)\n", "#cd.add_pip_package('azureml-explain-model')\n", diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml index 229de3be..9b05ea1f 100644 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml +++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml @@ -2,7 +2,3 @@ name: auto-ml-continuous-retraining dependencies: - pip: - azureml-sdk - - azureml-train-automl - - azureml-widgets - - matplotlib - - azureml-pipeline 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 58e5e373..8d24e995 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 @@ -114,7 +114,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml index a70c7033..103560d8 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml @@ -1,11 +1,4 @@ name: auto-ml-forecasting-beer-remote dependencies: -- py-xgboost<=0.90 - pip: - azureml-sdk - - numpy==1.16.2 - - pandas==0.23.4 - - azureml-train-automl - - azureml-widgets - - matplotlib - - azureml-train diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb index 80212f26..497f495b 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb @@ -87,7 +87,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/forecasting-bike-share/auto-ml-forecasting-bike-share.yml b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml index c488ffc3..70a3271c 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml @@ -1,10 +1,4 @@ name: auto-ml-forecasting-bike-share dependencies: -- py-xgboost<=0.90 - pip: - azureml-sdk - - numpy==1.16.2 - - pandas==0.23.4 - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb index dead6f3c..f4cd5e78 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml index 30672301..13bd78f8 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml @@ -2,8 +2,3 @@ name: auto-ml-forecasting-energy-demand dependencies: - pip: - azureml-sdk - - numpy==1.16.2 - - pandas==0.23.4 - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb index d04818ad..030bee04 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/forecasting-forecast-function/auto-ml-forecasting-function.yml b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml index b91ef178..144797d6 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml @@ -1,10 +1,4 @@ name: auto-ml-forecasting-function dependencies: -- py-xgboost<=0.90 - pip: - azureml-sdk - - numpy==1.16.2 - - pandas==0.23.4 - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb index 90f67f78..19fa29cb 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb @@ -82,7 +82,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml index 7d20e174..a6cc3e71 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml @@ -1,10 +1,4 @@ name: auto-ml-forecasting-orange-juice-sales dependencies: -- py-xgboost<=0.90 - pip: - azureml-sdk - - numpy==1.16.2 - - pandas==0.23.4 - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb index 47759fc4..1fee1fce 100644 --- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb +++ b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb @@ -96,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml index ed7aa7e0..6c817042 100644 --- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml +++ b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml @@ -2,6 +2,3 @@ name: auto-ml-classification-credit-card-fraud-local dependencies: - pip: - azureml-sdk - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb index de34c7f7..c0b70f1e 100644 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb +++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb @@ -98,7 +98,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml index 75fe1e52..9db24f2b 100644 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml +++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml @@ -2,6 +2,3 @@ name: auto-ml-regression-explanation-featurization dependencies: - pip: - azureml-sdk - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb index 25e908fc..37b3075f 100644 --- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb +++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb @@ -92,7 +92,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.8.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.9.0 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/regression/auto-ml-regression.yml b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml index 01892fb3..4e84e13a 100644 --- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml +++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml @@ -2,7 +2,3 @@ name: auto-ml-regression dependencies: - pip: - azureml-sdk - - pandas==0.23.4 - - azureml-train-automl - - azureml-widgets - - matplotlib diff --git a/how-to-use-azureml/deployment/accelerated-models/README.md b/how-to-use-azureml/deployment/accelerated-models/README.md index f49b6b30..9ebf26b1 100644 --- a/how-to-use-azureml/deployment/accelerated-models/README.md +++ b/how-to-use-azureml/deployment/accelerated-models/README.md @@ -50,10 +50,12 @@ pip install azureml-accel-models[gpu] ### Step 4: Follow our notebooks -The notebooks in this repo walk through the following scenarios: -* [Quickstart](accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet -* [Object Detection](accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection -* [Training models](accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets +We provide notebooks to walk through the following scenarios, linked below: +* [Quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet +* [Object Detection](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection +* [Training models](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets + +**Note**: the above notebooks work only for tensorflow >= 1.6,<2.0. ## Model Classes diff --git a/how-to-use-azureml/deployment/deploy-multi-model/first_model.pkl b/how-to-use-azureml/deployment/deploy-multi-model/first_model.pkl deleted file mode 100644 index 095bbeab7f9fb407aea372945cb4c5235ebef092..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 658 zcmX|<&rj4q6vw;l0;RGFqWB9$6m`8+G;xE`xHWq)($JX39}_Q=X*eZ78cdzx}L66?J8WTOZCMF*7$Lv6Ohc`3dH*emX&&;#3n?7QcB$3X7NQ^k- z9%XtGcnegsr3dB0b11DqPYv}dib7cI6DYe;9>y)*} z?1->bU*rx899@#Zao`HFn$^X0v&-f|LgJVTHb(@{qbqQ%Cc8^>$7Li<;rK4H$lE~) zCuF(QCozRFQ(J{`De%M-t)7(ZnlK?Xi5X7$D$=?aV2nS(VMt<_Z0F9@Y~$g_`&Hx1 z=G>JVlceyAr=!)cEn{(WrZ)DC8Pk82?b|P}6&LH{_UgU6#g9*R=grnaVVc7~M=&<( z6IT8A_uN=sf8W`tBLnq|8j(bb;j|@7?!5_yGk(Qao9c_spLZY1t^rePaMp%%Xh>w? 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b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb @@ -80,9 +80,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Register input and output datasets\n", + "## Create trained model\n", "\n", - "For this example, we have provided a small model (`sklearn_regression_model.pkl` in the notebook's directory) that was trained on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). Here, you will register the data used to create this model in your workspace." + "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). " ] }, { @@ -91,9 +91,42 @@ "metadata": {}, "outputs": [], "source": [ + "import joblib\n", + "\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "\n", + "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", + "\n", + "model = Ridge().fit(dataset_x, dataset_y)\n", + "\n", + "joblib.dump(model, 'sklearn_regression_model.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register input and output datasets\n", + "\n", + "Here, you will register the data used to create the model in your workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", "from azureml.core import Dataset\n", "\n", "\n", + "np.savetxt('features.csv', dataset_x, delimiter=',')\n", + "np.savetxt('labels.csv', dataset_y, delimiter=',')\n", + "\n", "datastore = ws.get_default_datastore()\n", "datastore.upload_files(files=['./features.csv', './labels.csv'],\n", " target_path='sklearn_regression/',\n", @@ -125,6 +158,8 @@ }, "outputs": [], "source": [ + "import sklearn\n", + "\n", "from azureml.core import Model\n", "from azureml.core.resource_configuration import ResourceConfiguration\n", "\n", @@ -133,7 +168,7 @@ " model_name='my-sklearn-model', # Name of the registered model in your workspace.\n", " model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n", " model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n", - " model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n", + " model_framework_version=sklearn.__version__, # Version of scikit-learn used to create the model.\n", " sample_input_dataset=input_dataset,\n", " sample_output_dataset=output_dataset,\n", " resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n", @@ -174,19 +209,9 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core import Webservice\n", - "from azureml.exceptions import WebserviceException\n", - "\n", - "\n", "service_name = 'my-sklearn-service'\n", "\n", - "# Remove any existing service under the same name.\n", - "try:\n", - " Webservice(ws, service_name).delete()\n", - "except WebserviceException:\n", - " pass\n", - "\n", - "service = Model.deploy(ws, service_name, [model])\n", + "service = Model.deploy(ws, service_name, [model], overwrite=True)\n", "service.wait_for_deployment(show_output=True)" ] }, @@ -207,10 +232,7 @@ "\n", "\n", "input_payload = json.dumps({\n", - " 'data': [\n", - " [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n", - " -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n", - " ],\n", + " 'data': dataset_x[0:2].tolist(),\n", " 'method': 'predict' # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n", "})\n", "\n", @@ -262,7 +284,7 @@ " 'inference-schema[numpy-support]',\n", " 'joblib',\n", " 'numpy',\n", - " 'scikit-learn'\n", + " 'scikit-learn=={}'.format(sklearn.__version__)\n", "])" ] }, @@ -303,20 +325,12 @@ }, "outputs": [], "source": [ - "from azureml.core import Webservice\n", "from azureml.core.model import InferenceConfig\n", "from azureml.core.webservice import AciWebservice\n", - "from azureml.exceptions import WebserviceException\n", "\n", "\n", "service_name = 'my-custom-env-service'\n", "\n", - "# Remove any existing service under the same name.\n", - "try:\n", - " Webservice(ws, service_name).delete()\n", - "except WebserviceException:\n", - " pass\n", - "\n", "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", "\n", @@ -324,7 +338,8 @@ " name=service_name,\n", " models=[model],\n", " inference_config=inference_config,\n", - " deployment_config=aci_config)\n", + " deployment_config=aci_config,\n", + " overwrite=True)\n", "service.wait_for_deployment(show_output=True)" ] }, @@ -342,10 +357,7 @@ "outputs": [], "source": [ "input_payload = json.dumps({\n", - " 'data': [\n", - " [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n", - " -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n", - " ]\n", + " 'data': dataset_x[0:2].tolist()\n", "})\n", "\n", "output = service.run(input_payload)\n", @@ -471,7 +483,7 @@ " 'inference-schema[numpy-support]',\n", " 'joblib',\n", " 'numpy',\n", - " 'scikit-learn'\n", + " 'scikit-learn=={}'.format(sklearn.__version__)\n", "])\n", "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", "# if cpu and memory_in_gb parameters are not provided\n", diff --git a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml index ca6bae19..99efaf15 100644 --- a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml +++ b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml @@ -2,3 +2,5 @@ name: model-register-and-deploy dependencies: - pip: - azureml-sdk + - numpy + - scikit-learn diff --git a/how-to-use-azureml/deployment/deploy-to-cloud/sklearn_regression_model.pkl b/how-to-use-azureml/deployment/deploy-to-cloud/sklearn_regression_model.pkl deleted file mode 100644 index d10309b6cf4c8e87846850edfe8b8d54a1aa64f2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 658 zcmX|<&rcLF6vt=T1%{DT5XE00;?FfP6EJauKjMzrgOLdnGx0~`rRmHRcC<6?n;*&= zN%R0L{srpQlL>dP>%oH_y>T~@%bJ)Nz2J{-A-uzD`+a?Vub=jL(N7;SN|M-QVJt@+ z@qjWj34;Y{xXOce{sk14pr?X*HBBQ-Gzb*^IFCfr^m#(fC}&wnl7uvk)F+H229&nr zMvyfHHJ}&u$kh26=(9DuunPSy=oPz&3R1lW1CHa&{*$Jhtz}?%b^Xoju5Hv{&k78> zP)6nM5n+bIIHQSAMFx9YXh4cFPa?v?rxf!-0Au_Kjv^vpvXy(kXKN2W-78z0 z>vNZ`Pm(rkKN~H7ZCY%7rZV=8Sr`9mTen_aZBJFl-Q~M?+P^-#owpkc?F;ts_YsV( zD-%xj=Voq+)eoJuDzZ?&tPv}u7*0B>= 1.0.45 as a pip dependency for your environemnt. This package contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile source_directory/env/myenv.yml\n", - "name: project_environment\n", - "dependencies:\n", - " - python=3.6.2\n", - " - pip:\n", - " - azureml-defaults\n", - " - scikit-learn\n", - " - numpy\n", - " - inference-schema[numpy-support]" - ] - }, { "cell_type": "code", "execution_count": null, @@ -249,11 +250,16 @@ "metadata": {}, "outputs": [], "source": [ + "import sklearn\n", + "\n", "from azureml.core.environment import Environment\n", "from azureml.core.model import InferenceConfig\n", "\n", "\n", - "myenv = Environment.from_conda_specification(name='myenv', file_path='myenv.yml')\n", + "myenv = Environment('myenv')\n", + "myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n", + "myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n", + "myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n", "\n", "# explicitly set base_image to None when setting base_dockerfile\n", "myenv.docker.base_image = None\n", @@ -262,7 +268,7 @@ "\n", "inference_config = InferenceConfig(source_directory=source_directory,\n", " entry_script=\"x/y/score.py\",\n", - " environment=myenv)\n" + " environment=myenv)" ] }, { @@ -352,15 +358,10 @@ "import json\n", "\n", "sample_input = json.dumps({\n", - " 'data': [\n", - " [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", - " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n", - " ]\n", + " 'data': dataset_x[0:2].tolist()\n", "})\n", "\n", - "sample_input = bytes(sample_input, encoding='utf-8')\n", - "\n", - "print(local_service.run(input_data=sample_input))" + "print(local_service.run(sample_input))" ] }, { @@ -379,12 +380,10 @@ "outputs": [], "source": [ "%%writefile source_directory/x/y/score.py\n", - "import os\n", - "import pickle\n", + "import joblib\n", "import json\n", "import numpy as np\n", - "from sklearn.externals import joblib\n", - "from sklearn.linear_model import Ridge\n", + "import os\n", "\n", "from inference_schema.schema_decorators import input_schema, output_schema\n", "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", @@ -395,17 +394,18 @@ " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # deserialize the model file back into a sklearn model\n", + " # Deserialize the model file back into a sklearn model.\n", " model = joblib.load(model_path)\n", + "\n", " global name, from_location\n", - " # note here, entire source directory on inference config gets added into image\n", - " # bellow is the example how you can use any extra files in image\n", + " # Note here, the entire source directory from inference config gets added into image.\n", + " # Below is an example of how you can use any extra files in image.\n", " with open('source_directory/extradata.json') as json_file: \n", " data = json.load(json_file)\n", " name = data[\"people\"][0][\"name\"]\n", " from_location = data[\"people\"][0][\"from\"]\n", "\n", - "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", "output_sample = np.array([3726.995])\n", "\n", "@input_schema('data', NumpyParameterType(input_sample))\n", @@ -413,8 +413,8 @@ "def run(data):\n", " try:\n", " result = model.predict(data)\n", - " # you can return any datatype as long as it is JSON-serializable\n", - " return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n", + " # You can return any JSON-serializable object.\n", + " return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n", " except Exception as e:\n", " error = str(e)\n", " return error" @@ -430,7 +430,7 @@ "print(\"--------------------------------------------------------------\")\n", "\n", "# After calling reload(), run() will return the updated message.\n", - "local_service.run(input_data=sample_input)" + "local_service.run(sample_input)" ] }, { diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.yml b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.yml new file mode 100644 index 00000000..48faca60 --- /dev/null +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.yml @@ -0,0 +1,5 @@ +name: register-model-deploy-local-advanced +dependencies: +- pip: + - azureml-sdk + - scikit-learn 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 69a72c4b..0c216969 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 @@ -71,6 +71,33 @@ "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create trained model\n", + "\n", + "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", + "\n", + "sk_model = Ridge().fit(dataset_x, dataset_y)\n", + "\n", + "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -82,9 +109,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n", + "Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n", "\n", - "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric." + "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric." ] }, { @@ -119,11 +146,62 @@ "metadata": {}, "outputs": [], "source": [ - "from azureml.core.conda_dependencies import CondaDependencies\n", + "import sklearn\n", + "\n", "from azureml.core.environment import Environment\n", "\n", "environment = Environment(\"LocalDeploy\")\n", - "environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")" + "environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n", + "environment.python.conda_dependencies.add_pip_package(\"joblib\")\n", + "environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Provide the Scoring Script\n", + "\n", + "This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import joblib\n", + "import json\n", + "import numpy as np\n", + "import os\n", + "\n", + "from inference_schema.schema_decorators import input_schema, output_schema\n", + "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # Deserialize the model file back into a sklearn model.\n", + " model = joblib.load(model_path)\n", + "\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", + "output_sample = np.array([3726.995])\n", + "\n", + "@input_schema('data', NumpyParameterType(input_sample))\n", + "@output_schema(NumpyParameterType(output_sample))\n", + "def run(data):\n", + " try:\n", + " result = model.predict(data)\n", + " # You can return any JSON-serializable object.\n", + " return result.tolist()\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" ] }, { @@ -145,114 +223,6 @@ " environment=environment)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Profiling\n", - "\n", - "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", - "\n", - "In order to profile your model you will need:\n", - "- a registered model\n", - "- an entry script\n", - "- an inference configuration\n", - "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", - "\n", - "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n", - "\n", - "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", - "\n", - "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azureml.core import Datastore\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.data import dataset_type_definitions\n", - "\n", - "\n", - "# create a string that can be put in the body of the request\n", - "serialized_input_json = json.dumps({\n", - " 'data': [\n", - " [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", - " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n", - " ]\n", - "})\n", - "dataset_content = []\n", - "for i in range(100):\n", - " dataset_content.append(serialized_input_json)\n", - "dataset_content = '\\n'.join(dataset_content)\n", - "file_name = 'sample_request_data_diabetes.txt'\n", - "f = open(file_name, 'w')\n", - "f.write(dataset_content)\n", - "f.close()\n", - "\n", - "# upload the txt file created above to the Datastore and create a dataset from it\n", - "data_store = Datastore.get_default(ws)\n", - "data_store.upload_files(['./' + file_name], target_path='sample_request_data_diabetes')\n", - "datastore_path = [(data_store, 'sample_request_data_diabetes' +'/' + file_name)]\n", - "sample_request_data_diabetes = Dataset.Tabular.from_delimited_files(\n", - " datastore_path,\n", - " separator='\\n',\n", - " infer_column_types=True,\n", - " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", - "sample_request_data_diabetes = sample_request_data_diabetes.register(workspace=ws,\n", - " name='sample_request_data_diabetes',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.model import Model, InferenceConfig\n", - "\n", - "\n", - "environment = Environment('my-sklearn-environment')\n", - "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", - " 'azureml-defaults',\n", - " 'inference-schema[numpy-support]',\n", - " 'joblib',\n", - " 'numpy',\n", - " 'scikit-learn==0.19.1',\n", - " 'scipy'\n", - "])\n", - "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", - "# if cpu and memory_in_gb parameters are not provided\n", - "# the model will be profiled on default configuration of\n", - "# 3.5CPU and 15GB memory\n", - "profile = Model.profile(ws,\n", - " 'profile-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", - " [model],\n", - " inference_config,\n", - " input_dataset=sample_request_data_diabetes,\n", - " cpu=1.0,\n", - " memory_in_gb=0.5)\n", - "\n", - "# profiling is a long running operation and may take up to 25 min\n", - "profile.wait_for_completion(True)\n", - "details = profile.get_details()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -339,15 +309,10 @@ "import json\n", "\n", "sample_input = json.dumps({\n", - " 'data': [\n", - " [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", - " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n", - " ]\n", + " 'data': dataset_x[0:2].tolist()\n", "})\n", "\n", - "sample_input = bytes(sample_input, encoding='utf-8')\n", - "\n", - "local_service.run(input_data=sample_input)" + "local_service.run(sample_input)" ] }, { @@ -366,12 +331,10 @@ "outputs": [], "source": [ "%%writefile score.py\n", - "import os\n", - "import pickle\n", + "import joblib\n", "import json\n", "import numpy as np\n", - "from sklearn.externals import joblib\n", - "from sklearn.linear_model import Ridge\n", + "import os\n", "\n", "from inference_schema.schema_decorators import input_schema, output_schema\n", "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", @@ -382,10 +345,10 @@ " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # deserialize the model file back into a sklearn model\n", + " # Deserialize the model file back into a sklearn model.\n", " model = joblib.load(model_path)\n", "\n", - "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", "output_sample = np.array([3726.995])\n", "\n", "@input_schema('data', NumpyParameterType(input_sample))\n", @@ -393,8 +356,8 @@ "def run(data):\n", " try:\n", " result = model.predict(data)\n", - " # you can return any datatype as long as it is JSON-serializable\n", - " return 'hello from updated score.py'\n", + " # You can return any JSON-serializable object.\n", + " return 'Hello from the updated score.py: ' + str(result.tolist())\n", " except Exception as e:\n", " error = str(e)\n", " return error" @@ -410,7 +373,7 @@ "print(\"--------------------------------------------------------------\")\n", "\n", "# After calling reload(), run() will return the updated message.\n", - "local_service.run(input_data=sample_input)" + "local_service.run(sample_input)" ] }, { diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.yml b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.yml new file mode 100644 index 00000000..d6220b47 --- /dev/null +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.yml @@ -0,0 +1,5 @@ +name: register-model-deploy-local +dependencies: +- pip: + - azureml-sdk + - scikit-learn diff --git a/how-to-use-azureml/deployment/deploy-to-local/score.py b/how-to-use-azureml/deployment/deploy-to-local/score.py deleted file mode 100644 index 26bda6ef..00000000 --- a/how-to-use-azureml/deployment/deploy-to-local/score.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import pickle -import json -import numpy as np -from sklearn.externals import joblib -from sklearn.linear_model import Ridge - -from inference_schema.schema_decorators import input_schema, output_schema -from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType - - -def init(): - global model - # AZUREML_MODEL_DIR is an environment variable created during deployment. - # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION) - # For multiple models, it points to the folder containing all deployed models (./azureml-models) - model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl') - # deserialize the model file back into a sklearn model - model = joblib.load(model_path) - - -input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]) -output_sample = np.array([3726.995]) - - -@input_schema('data', NumpyParameterType(input_sample)) -@output_schema(NumpyParameterType(output_sample)) -def run(data): - try: - result = model.predict(data) - # you can return any datatype as long as it is JSON-serializable - return result.tolist() - except Exception as e: - error = str(e) - return error diff --git a/how-to-use-azureml/deployment/deploy-to-local/sklearn_regression_model.pkl b/how-to-use-azureml/deployment/deploy-to-local/sklearn_regression_model.pkl deleted file mode 100644 index d10309b6cf4c8e87846850edfe8b8d54a1aa64f2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 658 zcmX|<&rcLF6vt=T1%{DT5XE00;?FfP6EJauKjMzrgOLdnGx0~`rRmHRcC<6?n;*&= zN%R0L{srpQlL>dP>%oH_y>T~@%bJ)Nz2J{-A-uzD`+a?Vub=jL(N7;SN|M-QVJt@+ z@qjWj34;Y{xXOce{sk14pr?X*HBBQ-Gzb*^IFCfr^m#(fC}&wnl7uvk)F+H229&nr zMvyfHHJ}&u$kh26=(9DuunPSy=oPz&3R1lW1CHa&{*$Jhtz}?%b^Xoju5Hv{&k78> zP)6nM5n+bIIHQSAMFx9YXh4cFPa?v?rxf!-0Au_Kjv^vpvXy(kXKN2W-78z0 z>vNZ`Pm(rkKN~H7ZCY%7rZV=8Sr`9mTen_aZBJFl-Q~M?+P^-#owpkc?F;ts_YsV( zD-%xj=Voq+)eoJuDzZ?&tPv}u7*0B>