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Merged notebook changes from release 1.0.45

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Heather Shapiro
2019-06-26 14:39:09 -04:00
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5
README.md
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@@ -4,10 +4,6 @@ This repository contains example notebooks demonstrating the [Azure Machine Lear
![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
## News
* [Try Azure Machine Learning with MLflow](./how-to-use-azureml/using-mlflow)
## Quick installation
```sh
pip install azureml-sdk
@@ -56,7 +52,6 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
Visit following repos to see projects contributed by Azure ML users:
- [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
- [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)

6
configuration.ipynb
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@@ -103,7 +103,7 @@
"source": [
"import azureml.core\n",
"\n",
"print(\"This notebook was created using version 1.0.43 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.0.45 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -258,7 +258,7 @@
"```shell\n",
"az vm list-skus -o tsv\n",
"```\n",
"* min_nodes - this sets the minimum size of the cluster. If you set the minimum to 0 the cluster will shut down all nodes while not in use. Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
"* min_nodes - this sets the minimum size of the cluster. If you set the minimum to 0 the cluster will shut down all nodes while note in use. Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
"* max_nodes - this sets the maximum size of the cluster. Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
"\n",
"\n",
@@ -380,4 +380,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}

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contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
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contrib/datadrift/azure-ml-datadrift.ipynb Normal file
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@@ -0,0 +1,709 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Track Data Drift between Training and Inference Data in Production \n",
"\n",
"With this notebook, you will learn how to enable the DataDrift service to automatically track and determine whether your inference data is drifting from the data your model was initially trained on. The DataDrift service provides metrics and visualizations to help stakeholders identify which specific features cause the concept drift to occur.\n",
"\n",
"Please email driftfeedback@microsoft.com with any issues. A member from the DataDrift team will respond shortly. \n",
"\n",
"The DataDrift Public Preview API can be found [here](https://docs.microsoft.com/en-us/python/api/azureml-contrib-datadrift/?view=azure-ml-py). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prerequisites and Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install the DataDrift package\n",
"\n",
"Install the azureml-contrib-datadrift, azureml-contrib-opendatasets and lightgbm packages before running this notebook.\n",
"```\n",
"pip install azureml-contrib-datadrift\n",
"pip install azureml-contrib-datasets\n",
"pip install lightgbm\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import time\n",
"from datetime import datetime, timedelta\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import requests\n",
"from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
"from azureml.contrib.opendatasets import NoaaIsdWeather\n",
"from azureml.core import Dataset, Workspace, Run\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import Model\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.widgets import RunDetails\n",
"from sklearn.externals import joblib\n",
"from sklearn.model_selection import train_test_split\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up Configuraton and Create Azure ML Workspace\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Please type in your initials/alias. The prefix is prepended to the names of resources created by this notebook. \n",
"prefix = \"dd\"\n",
"\n",
"# NOTE: Please do not change the model_name, as it's required by the score.py file\n",
"model_name = \"driftmodel\"\n",
"image_name = \"{}driftimage\".format(prefix)\n",
"service_name = \"{}driftservice\".format(prefix)\n",
"\n",
"# optionally, set email address to receive an email alert for DataDrift\n",
"email_address = \"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Train/Testing Data\n",
"\n",
"For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You may replace this step with your own dataset. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',\n",
" '725513', '725254', '726430', '720381', '723074', '726682',\n",
" '725486', '727883', '723177', '722075', '723086', '724053',\n",
" '725070', '722073', '726060', '725224', '725260', '724520',\n",
" '720305', '724020', '726510', '725126', '722523', '703333',\n",
" '722249', '722728', '725483', '722972', '724975', '742079',\n",
" '727468', '722193', '725624', '722030', '726380', '720309',\n",
" '722071', '720326', '725415', '724504', '725665', '725424',\n",
" '725066']\n",
"\n",
"columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
"\n",
"\n",
"def enrich_weather_noaa_data(noaa_df):\n",
" hours_in_day = 23\n",
" week_in_year = 52\n",
" \n",
" noaa_df[\"hour\"] = noaa_df[\"datetime\"].dt.hour\n",
" noaa_df[\"weekofyear\"] = noaa_df[\"datetime\"].dt.week\n",
" \n",
" noaa_df[\"sine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year))\n",
" noaa_df[\"cosine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year))\n",
"\n",
" noaa_df[\"sine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day))\n",
" noaa_df[\"cosine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
" \n",
" return noaa_df\n",
"\n",
"def add_window_col(input_df):\n",
" shift_interval = pd.Timedelta('-7 days') # your X days interval\n",
" df_shifted = input_df.copy()\n",
" df_shifted['datetime'] = df_shifted['datetime'] - shift_interval\n",
" df_shifted.drop(list(input_df.columns.difference(['datetime', 'usaf', 'wban', 'sine_hourofday', 'temperature'])), axis=1, inplace=True)\n",
"\n",
" # merge, keeping only observations where -1 lag is present\n",
" df2 = pd.merge(input_df,\n",
" df_shifted,\n",
" on=['datetime', 'usaf', 'wban', 'sine_hourofday'],\n",
" how='inner', # use 'left' to keep observations without lags\n",
" suffixes=['', '-7'])\n",
" return df2\n",
"\n",
"def get_noaa_data(start_time, end_time, cols, station_list):\n",
" isd = NoaaIsdWeather(start_time, end_time, cols=cols)\n",
" # Read into Pandas data frame.\n",
" noaa_df = isd.to_pandas_dataframe()\n",
" noaa_df = noaa_df.rename(columns={\"stationName\": \"station_name\"})\n",
" \n",
" df_filtered = noaa_df[noaa_df[\"usaf\"].isin(station_list)]\n",
" df_filtered.reset_index(drop=True)\n",
" \n",
" # Enrich with time features\n",
" df_enriched = enrich_weather_noaa_data(df_filtered)\n",
" \n",
" return df_enriched\n",
"\n",
"def get_featurized_noaa_df(start_time, end_time, cols, station_list):\n",
" df_1 = get_noaa_data(start_time - timedelta(days=7), start_time - timedelta(seconds=1), cols, station_list)\n",
" df_2 = get_noaa_data(start_time, end_time, cols, station_list)\n",
" noaa_df = pd.concat([df_1, df_2])\n",
" \n",
" print(\"Adding window feature\")\n",
" df_window = add_window_col(noaa_df)\n",
" \n",
" cat_columns = df_window.dtypes == object\n",
" cat_columns = cat_columns[cat_columns == True]\n",
" \n",
" print(\"Encoding categorical columns\")\n",
" df_encoded = pd.get_dummies(df_window, columns=cat_columns.keys().tolist())\n",
" \n",
" print(\"Dropping unnecessary columns\")\n",
" df_featurized = df_encoded.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna().drop_duplicates()\n",
" \n",
" return df_featurized"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Train model on Jan 1 - 14, 2009 data\n",
"df = get_featurized_noaa_df(datetime(2009, 1, 1), datetime(2009, 1, 14, 23, 59, 59), columns, usaf_list)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = \"temperature\"\n",
"x_df = df.drop(label, axis=1)\n",
"y_df = df[[label]]\n",
"x_train, x_test, y_train, y_test = train_test_split(df, y_df, test_size=0.2, random_state=223)\n",
"print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
"\n",
"training_dir = 'outputs/training'\n",
"training_file = \"training.csv\"\n",
"\n",
"# Generate training dataframe to register as Training Dataset\n",
"os.makedirs(training_dir, exist_ok=True)\n",
"training_df = pd.merge(x_train.drop(label, axis=1), y_train, left_index=True, right_index=True)\n",
"training_df.to_csv(training_dir + \"/\" + training_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create/Register Training Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset_name = \"dataset\"\n",
"name_suffix = datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
"snapshot_name = \"snapshot-{}\".format(name_suffix)\n",
"\n",
"dstore = ws.get_default_datastore()\n",
"dstore.upload(training_dir, \"data/training\", show_progress=True)\n",
"dpath = dstore.path(\"data/training/training.csv\")\n",
"trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
"trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
"\n",
"trainingDataSnapshot = trainingDataset.create_snapshot(snapshot_name=snapshot_name, compute_target=None, create_data_snapshot=True)\n",
"datasets = [(Dataset.Scenario.TRAINING, trainingDataSnapshot)]\n",
"print(\"dataset registration done.\\n\")\n",
"datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Save Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import lightgbm as lgb\n",
"\n",
"train = lgb.Dataset(data=x_train, \n",
" label=y_train)\n",
"\n",
"test = lgb.Dataset(data=x_test, \n",
" label=y_test,\n",
" reference=train)\n",
"\n",
"params = {'learning_rate' : 0.1,\n",
" 'boosting' : 'gbdt',\n",
" 'metric' : 'rmse',\n",
" 'feature_fraction' : 1,\n",
" 'bagging_fraction' : 1,\n",
" 'max_depth': 6,\n",
" 'num_leaves' : 31,\n",
" 'objective' : 'regression',\n",
" 'bagging_freq' : 1,\n",
" \"verbose\": -1,\n",
" 'min_data_per_leaf': 100}\n",
"\n",
"model = lgb.train(params, \n",
" num_boost_round=500,\n",
" train_set=train,\n",
" valid_sets=[train, test],\n",
" verbose_eval=50,\n",
" early_stopping_rounds=25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_file = 'outputs/{}.pkl'.format(model_name)\n",
"\n",
"os.makedirs('outputs', exist_ok=True)\n",
"joblib.dump(model, model_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Model.register(model_path=model_file,\n",
" model_name=model_name,\n",
" workspace=ws,\n",
" datasets=datasets)\n",
"\n",
"print(model_name, image_name, service_name, model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy Model To AKS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare Environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn', 'joblib', 'lightgbm', 'pandas'],\n",
" pip_packages=['azureml-monitoring', 'azureml-sdk[automl]'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Image creation may take up to 15 minutes.\n",
"\n",
"image_name = image_name + str(model.version)\n",
"\n",
"if not image_name in ws.images:\n",
" # Use the score.py defined in this directory as the execution script\n",
" # NOTE: The Model Data Collector must be enabled in the execution script for DataDrift to run correctly\n",
" image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
" runtime=\"python\",\n",
" conda_file=\"myenv.yml\",\n",
" description=\"Image with weather dataset model\")\n",
" image = ContainerImage.create(name=image_name,\n",
" models=[model],\n",
" image_config=image_config,\n",
" workspace=ws)\n",
"\n",
" image.wait_for_creation(show_output=True)\n",
"else:\n",
" image = ws.images[image_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Compute Target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_name = 'dd-demo-e2e'\n",
"prov_config = AksCompute.provisioning_configuration()\n",
"\n",
"if not aks_name in ws.compute_targets:\n",
" aks_target = ComputeTarget.create(workspace=ws,\n",
" name=aks_name,\n",
" provisioning_configuration=prov_config)\n",
"\n",
" aks_target.wait_for_completion(show_output=True)\n",
" print(aks_target.provisioning_state)\n",
" print(aks_target.provisioning_errors)\n",
"else:\n",
" aks_target=ws.compute_targets[aks_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service_name = service_name\n",
"\n",
"if not aks_service_name in ws.webservices:\n",
" aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)\n",
" aks_service = Webservice.deploy_from_image(workspace=ws,\n",
" name=aks_service_name,\n",
" image=image,\n",
" deployment_config=aks_config,\n",
" deployment_target=aks_target)\n",
" aks_service.wait_for_deployment(show_output=True)\n",
" print(aks_service.state)\n",
"else:\n",
" aks_service = ws.webservices[aks_service_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run DataDrift Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Send Scoring Data to Service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download Scoring Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Score Model on March 15, 2016 data\n",
"scoring_df = get_noaa_data(datetime(2016, 3, 15) - timedelta(days=7), datetime(2016, 3, 16), columns, usaf_list)\n",
"# Add the window feature column\n",
"scoring_df = add_window_col(scoring_df)\n",
"\n",
"# Drop features not used by the model\n",
"print(\"Dropping unnecessary columns\")\n",
"scoring_df = scoring_df.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna()\n",
"scoring_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# One Hot Encode the scoring dataset to match the training dataset schema\n",
"columns_dict = model.datasets[\"training\"][0].get_profile().columns\n",
"extra_cols = ('Path', 'Column1')\n",
"for k in extra_cols:\n",
" columns_dict.pop(k, None)\n",
"training_columns = list(columns_dict.keys())\n",
"\n",
"categorical_columns = scoring_df.dtypes == object\n",
"categorical_columns = categorical_columns[categorical_columns == True]\n",
"\n",
"test_df = pd.get_dummies(scoring_df[categorical_columns.keys().tolist()])\n",
"encoded_df = scoring_df.join(test_df)\n",
"\n",
"# Populate missing OHE columns with 0 values to match traning dataset schema\n",
"difference = list(set(training_columns) - set(encoded_df.columns.tolist()))\n",
"for col in difference:\n",
" encoded_df[col] = 0\n",
"encoded_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Serialize dataframe to list of row dictionaries\n",
"encoded_dict = encoded_df.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit Scoring Data to Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"# retreive the API keys. AML generates two keys.\n",
"key1, key2 = aks_service.get_keys()\n",
"\n",
"total_count = len(scoring_df)\n",
"i = 0\n",
"load = []\n",
"for row in encoded_dict:\n",
" load.append(row)\n",
" i = i + 1\n",
" if i % 100 == 0:\n",
" payload = json.dumps({\"data\": load})\n",
" \n",
" # construct raw HTTP request and send to the service\n",
" payload_binary = bytes(payload,encoding = 'utf8')\n",
" headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
" resp = requests.post(aks_service.scoring_uri, payload_binary, headers=headers)\n",
" \n",
" print(\"prediction:\", resp.content, \"Progress: {}/{}\".format(i, total_count)) \n",
"\n",
" load = []\n",
" time.sleep(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure DataDrift"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"services = [service_name]\n",
"start = datetime.now() - timedelta(days=2)\n",
"end = datetime(year=2020, month=1, day=22, hour=15, minute=16)\n",
"feature_list = ['usaf', 'wban', 'latitude', 'longitude', 'station_name', 'p_k', 'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
"alert_config = AlertConfiguration([email_address]) if email_address else None\n",
"\n",
"# there will be an exception indicating using get() method if DataDrift object already exist\n",
"try:\n",
" datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", alert_config=alert_config)\n",
"except KeyError:\n",
" datadrift = DataDriftDetector.get(ws, model.name, model.version)\n",
" \n",
"print(\"Details of DataDrift Object:\\n{}\".format(datadrift))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run an Adhoc DataDriftDetector Run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_date = datetime.today()\n",
"run = datadrift.run(target_date, services, feature_list=feature_list, create_compute_target=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"exp = Experiment(ws, datadrift._id)\n",
"dd_run = Run(experiment=exp, run_id=run)\n",
"RunDetails(dd_run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Drift Analysis Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"children = list(dd_run.get_children())\n",
"for child in children:\n",
" child.wait_for_completion()\n",
"\n",
"drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
"drift_metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show all drift figures, one per serivice.\n",
"# If setting with_details is False (by default), only drift will be shown; if it's True, all details will be shown.\n",
"\n",
"drift_figures = datadrift.show(with_details=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enable DataDrift Schedule"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datadrift.enable_schedule()"
]
}
],
"metadata": {
"authors": [
{
"name": "rafarmah"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

58
contrib/datadrift/score.py Normal file
View File

@@ -0,0 +1,58 @@
import pickle
import json
import numpy
import azureml.train.automl
from sklearn.externals import joblib
from sklearn.linear_model import Ridge
from azureml.core.model import Model
from azureml.core.run import Run
from azureml.monitoring import ModelDataCollector
import time
import pandas as pd
def init():
global model, inputs_dc, prediction_dc, feature_names, categorical_features
print("Model is initialized" + time.strftime("%H:%M:%S"))
model_path = Model.get_model_path(model_name="driftmodel")
model = joblib.load(model_path)
feature_names = ["usaf", "wban", "latitude", "longitude", "station_name", "p_k",
"sine_weekofyear", "cosine_weekofyear", "sine_hourofday", "cosine_hourofday",
"temperature-7"]
categorical_features = ["usaf", "wban", "p_k", "station_name"]
inputs_dc = ModelDataCollector(model_name="driftmodel",
identifier="inputs",
feature_names=feature_names)
prediction_dc = ModelDataCollector("driftmodel",
identifier="predictions",
feature_names=["temperature"])
def run(raw_data):
global inputs_dc, prediction_dc
try:
data = json.loads(raw_data)["data"]
data = pd.DataFrame(data)
# Remove the categorical features as the model expects OHE values
input_data = data.drop(categorical_features, axis=1)
result = model.predict(input_data)
# Collect the non-OHE dataframe
collected_df = data[feature_names]
inputs_dc.collect(collected_df.values)
prediction_dc.collect(result)
return result.tolist()
except Exception as e:
error = str(e)
print(error + time.strftime("%H:%M:%S"))
return error

20
how-to-use-azureml/automated-machine-learning/README.md
View File

@@ -179,6 +179,26 @@ jupyter notebook
- Simple example of using automated ML for classification with ONNX models
- Uses local compute for training
- [auto-ml-bank-marketing-subscribers-with-deployment.ipynb](bank-marketing-subscribers-with-deployment/auto-ml-bank-marketing-with-deployment.ipynb)
- Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
- Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
- Uses azure compute for training
- [auto-ml-creditcard-with-deployment.ipynb](credit-card-fraud-detection-with-deployment/auto-ml-creditcard-with-deployment.ipynb)
- Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
- Simple example of using automated ML for classification to fraudulent credit card transactions
- Uses azure compute for training
- [auto-ml-hardware-performance-with-deployment.ipynb](hardware-performance-prediction-with-deployment/auto-ml-hardware-performance-with-deployment.ipynb)
- Dataset: UCI's [computer hardware dataset](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware)
- Simple example of using automated ML for regression to predict the performance of certain combinations of hardware components
- Uses azure compute for training
- [auto-ml-concrete-strength-with-deployment.ipynb](predicting-concrete-strength-with-deployment/auto-ml-concrete-strength-with-deployment.ipynb)
- Dataset: UCI's [concrete compressive strength dataset](https://www.kaggle.com/pavanraj159/concrete-compressive-strength-data-set)
- Simple example of using automated ML for regression to predict the strength predict the compressive strength of concrete based off of different ingredient combinations and quantities of those ingredients
- Uses azure compute for training
<a name="documentation"></a>
See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.

11
how-to-use-azureml/automated-machine-learning/automl_setup.cmd
View File

@@ -9,6 +9,8 @@ IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
IF NOT EXIST %automl_env_file% GOTO YmlMissing
IF "%CONDA_EXE%"=="" GOTO CondaMissing
call conda activate %conda_env_name% 2>nul:
if not errorlevel 1 (
@@ -42,6 +44,15 @@ IF NOT "%options%"=="nolaunch" (
goto End
:CondaMissing
echo Please run this script from an Anaconda Prompt window.
echo You can start an Anaconda Prompt window by
echo typing Anaconda Prompt on the Start menu.
echo If you don't see the Anaconda Prompt app, install Miniconda.
echo If you are running an older version of Miniconda or Anaconda,
echo you can upgrade using the command: conda update conda
goto End
:YmlMissing
echo File %automl_env_file% not found.

742
how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb Normal file
View File

@@ -0,0 +1,742 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Classification with Deployment using a Bank Marketing Dataset**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Deploy](#Deploy)\n",
"1. [Test](#Test)\n",
"1. [Acknowledgements](#Acknowledgements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an experiment using an existing workspace.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using local compute.\n",
"4. Explore the results.\n",
"5. Register the model.\n",
"6. Create a container image.\n",
"7. Create an Azure Container Instance (ACI) service.\n",
"8. Test the ACI service."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import logging\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from sklearn import datasets\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-classification-bmarketing'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-classification-bankmarketing'\n",
"\n",
"experiment=Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create or Attach existing AmlCompute\n",
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"automlcl\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
"cts = ws.compute_targets\n",
"if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
" found = True\n",
" print('Found existing compute target.')\n",
" compute_target = cts[amlcompute_cluster_name]\n",
" \n",
"if not found:\n",
" print('Creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
" #vm_priority = 'lowpriority', # optional\n",
" max_nodes = 6)\n",
"\n",
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data() script to be utilized in azure compute. To do this first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_Run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data\n",
"\n",
"Here we create the script to be run in azure comput for loading the data, we load the bank marketing dataset into X_train and y_train. Next X_train and y_train is returned for training the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def _read_x_y(file_name, label_col):\n",
" df = pd.read_csv(file_name)\n",
" y = None\n",
" if label_col in df.columns:\n",
" y = df.pop(label_col)\n",
" y = y.values[:, None]\n",
" X = df.values\n",
" return X, y\n",
" \n",
"def get_data():\n",
" # Load the bank marketing datasets.\n",
" from sklearn.datasets import load_diabetes\n",
" from sklearn.model_selection import train_test_split\n",
"\n",
" X_train, y_train = _read_x_y('https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv', \"y\")\n",
"\n",
" columns = ['age','job','marital','education','default','housing','loan','contact','month','day_of_week','duration','campaign','pdays','previous','poutcome','emp.var.rate','cons.price.idx','cons.conf.idx','euribor3m','nr.employed','y']\n",
"\n",
" return { \"X\" : X_train, \"y\" : y_train[:,0] }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 5,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 2,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": True,\n",
" \"max_concurrent_iterations\": 5,\n",
" \"verbosity\": logging.INFO,\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )"
]
},
{
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Widget for Monitoring Runs\n",
"\n",
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
"\n",
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy\n",
"\n",
"### Retrieve the Best Model\n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = remote_run.get_output()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Register the Fitted Model for Deployment\n",
"If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"description = 'AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
"tags = None\n",
"model = remote_run.register_model(description = description, tags = tags)\n",
"\n",
"print(remote_run.model_id) # This will be written to the script file later in the notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Scoring Script\n",
"The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"import azureml.train.automl\n",
"from sklearn.externals import joblib\n",
"from azureml.core.model import Model\n",
"\n",
"\n",
"def init():\n",
" global model\n",
" model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"def run(rawdata):\n",
" try:\n",
" data = json.loads(rawdata)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"error\": result})\n",
" return json.dumps({\"result\":result.tolist()})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a YAML File for the Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Substitute the actual version number in the environment file.\n",
"# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
"# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
"\n",
"with open(conda_env_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
"script_file_name = 'score.py'\n",
"\n",
"with open(script_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(script_file_name, 'w') as cefw:\n",
" cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"bmData\", 'type': \"automl_classification\"},\n",
" description = \"Image for automl classification sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
" description = 'sample service for Automl Classification')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'automl-sample-bankmarketing'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete a Web Service\n",
"\n",
"Deletes the specified web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Logs from a Deployed Web Service\n",
"\n",
"Gets logs from a deployed web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test\n",
"\n",
"Now that the model is trained split our data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def _read_x_y(file_name, label_col):\n",
" df = pd.read_csv(file_name)\n",
" y = None\n",
" if label_col in df.columns:\n",
" y = df.pop(label_col)\n",
" y = y.values[:, None]\n",
" X = df.values\n",
" return X, y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the bank marketing datasets.\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.model_selection import train_test_split\n",
"from numpy import array\n",
"\n",
"\n",
"X_test, y_test = _read_x_y('https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv',\"y\")\n",
"\n",
"columns = ['age','job','marital','education','default','housing','loan','contact','month','day_of_week','duration','campaign','pdays','previous','poutcome','emp.var.rate','cons.price.idx','cons.conf.idx','euribor3m','nr.employed','y']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = fitted_model.predict(X_test)\n",
"actual = array(y_test.tolist())\n",
"print(y_pred.shape, \" \", actual[:,0].shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate metrics for the prediction\n",
"\n",
"Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
"from the trained model that was returned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test = y_test[:,0]# Plot outputs\n",
"%matplotlib notebook\n",
"test_pred = plt.scatter(y_test, y_pred, color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acknowledgements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n",
"\n",
"_**Acknowledgements**_\n",
"This data set is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
"\n",
"[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
]
}
],
"metadata": {
"authors": [
{
"name": "v-rasav"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Classification with Deployment using Credit Card Dataset**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Deploy](#Deploy)\n",
"1. [Test](#Test)\n",
"1. [Acknowledgements](#Acknowledgements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if a creditcard transaction is or is not considered a fraudulent charge.\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an experiment using an existing workspace.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using local compute.\n",
"4. Explore the results.\n",
"5. Register the model.\n",
"6. Create a container image.\n",
"7. Create an Azure Container Instance (ACI) service.\n",
"8. Test the ACI service."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-classification-ccard'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-classification-creditcard'\n",
"\n",
"experiment=Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create or Attach existing AmlCompute\n",
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"automlcl\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
"cts = ws.compute_targets\n",
"if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
" found = True\n",
" print('Found existing compute target.')\n",
" compute_target = cts[amlcompute_cluster_name]\n",
" \n",
"if not found:\n",
" print('Creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
" #vm_priority = 'lowpriority', # optional\n",
" max_nodes = 6)\n",
"\n",
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data\n",
"\n",
"Here create the script to be run in azure compute for loading the data, load the credit card dataset into cards and store the Class column (y) in the y variable and store the remaining data in the x variable. Next split the data using train_test_split and return X_train and y_train for training the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
" \n",
"def get_data():\n",
" cards = pd.read_csv(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\")\n",
" y = cards.Class\n",
" x = cards.drop('Class', axis=1)\n",
" X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.2, random_state=1)\n",
" \n",
" return { \"X\" : X_train, \"y\" : y_train.values}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### If you would like to see even better results increase \"iteration_time_out minutes\" to 10+ mins and increase \"iterations\" to a minimum of 30"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 5,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 2,\n",
" \"primary_metric\": 'average_precision_score_weighted',\n",
" \"preprocess\": True,\n",
" \"max_concurrent_iterations\": 5,\n",
" \"verbosity\": logging.INFO,\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors_20190417.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )"
]
},
{
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Widget for Monitoring Runs\n",
"\n",
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
"\n",
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy\n",
"\n",
"### Retrieve the Best Model\n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = remote_run.get_output()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Register the Fitted Model for Deployment\n",
"If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"description = 'AutoML Model'\n",
"tags = None\n",
"model = remote_run.register_model(description = description, tags = tags)\n",
"\n",
"print(remote_run.model_id) # This will be written to the script file later in the notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Scoring Script\n",
"The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"import azureml.train.automl\n",
"from sklearn.externals import joblib\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"def run(rawdata):\n",
" try:\n",
" data = json.loads(rawdata)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"error\": result})\n",
" return json.dumps({\"result\":result.tolist()})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a YAML File for the Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Substitute the actual version number in the environment file.\n",
"# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
"# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
"\n",
"with open(conda_env_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
"script_file_name = 'score.py'\n",
"\n",
"with open(script_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(script_file_name, 'w') as cefw:\n",
" cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"cards\", 'type': \"automl_classification\"},\n",
" description = \"Image for automl classification sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"cards\", 'type': \"automl_classification\"}, \n",
" description = 'sample service for Automl Classification')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'automl-sample-creditcard'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete a Web Service\n",
"\n",
"Deletes the specified web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Logs from a Deployed Web Service\n",
"\n",
"Gets logs from a deployed web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test\n",
"\n",
"Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cards = pd.read_csv(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\")\n",
"print(cards.head())\n",
"y = cards.Class\n",
"x = cards.drop('Class', axis=1)\n",
"X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.2, random_state=1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = fitted_model.predict(X_test)\n",
"y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate metrics for the prediction\n",
"\n",
"Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
"from the trained model that was returned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Randomly select and test\n",
"# Plot outputs\n",
"%matplotlib notebook\n",
"test_pred = plt.scatter(y_test, y_pred, color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acknowledgements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
"\n",
"\n",
"The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
"Please cite the following works: \n",
"\u00e2\u20ac\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
"\u00e2\u20ac\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
"\u00e2\u20ac\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
"o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
"\u00e2\u20ac\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
"\u00e2\u20ac\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing"
]
}
],
"metadata": {
"authors": [
{
"name": "v-rasav"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

27
how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
View File

@@ -129,6 +129,22 @@
" test_size=0.2, \n",
" random_state=0)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ensure the x_train and x_test are pandas DataFrame."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
"# This is needed for initializing the input variable names of ONNX model, \n",
"# and the prediction with the ONNX model using the inference helper.\n",
@@ -158,6 +174,13 @@
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set the preprocess=True, currently the InferenceHelper only supports this mode."
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -299,7 +322,7 @@
" onnxrt_present = False\n",
"\n",
"def get_onnx_res(run):\n",
" res_path = '_debug_y_trans_converter.json'\n",
" res_path = 'onnx_resource.json'\n",
" run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
" with open(res_path) as f:\n",
" onnx_res = json.load(f)\n",
@@ -316,7 +339,7 @@
" print(pred_prob_onnx)\n",
"else:\n",
" if not python_version_compatible:\n",
" print('Please use Python version 3.6 to run the inference helper.') \n",
" print('Please use Python version 3.6 or 3.7 to run the inference helper.') \n",
" if not onnxrt_present:\n",
" print('Please install the onnxruntime package to do the prediction with ONNX model.')"
]

2
how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
View File

@@ -21,7 +21,7 @@
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)**_\n",
"_**Prepare Data using `azureml.dataprep` for Remote Execution (AmlCompute)**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",

1
how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
View File

@@ -72,7 +72,6 @@
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.train.automl import AutoMLConfig\n",

4
how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
View File

@@ -65,10 +65,6 @@
"import pandas as pd\n",
"import numpy as np\n",
"import logging\n",
"import warnings\n",
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",

4
how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
View File

@@ -67,10 +67,6 @@
"import pandas as pd\n",
"import numpy as np\n",
"import logging\n",
"import warnings\n",
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",

812
how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb Normal file
View File

@@ -0,0 +1,812 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Regression with Deployment using Hardware Performance Dataset**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Test](#Test)\n",
"1. [Acknowledgements](#Acknowledgements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"In this example we use the Predicting Compressive Strength of Concrete Dataset to showcase how you can use AutoML for a regression problem. The regression goal is to predict the compressive strength of concrete based off of different ingredient combinations and the quantities of those ingredients.\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using local compute.\n",
"4. Explore the results.\n",
"5. Test the best fitted model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"As part of the setup you have already created an Azure ML Workspace object. For AutoML you will need to create an Experiment object, which is a named object in a Workspace used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
" \n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"experiment_name = 'automl-regression-concrete'\n",
"project_folder = './sample_projects/automl-regression-concrete'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create or Attach existing AmlCompute\n",
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"automlcl\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
"cts = ws.compute_targets\n",
"if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
" found = True\n",
" print('Found existing compute target.')\n",
" compute_target = cts[amlcompute_cluster_name]\n",
" \n",
"if not found:\n",
" print('Creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
" #vm_priority = 'lowpriority', # optional\n",
" max_nodes = 6)\n",
"\n",
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data\n",
"\n",
"Here create the script to be run in azure compute for loading the data, load the concrete strength dataset into the X and y variables. Next, split the data using train_test_split and return X_train and y_train for training the model. Finally, return X_train and y_train for training the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def _read_x_y(file_name, label_col):\n",
" df = pd.read_csv(file_name)\n",
" y = None\n",
" if label_col in df.columns:\n",
" y = df.pop(label_col)\n",
" y = y.values[:, None]\n",
" X = df.values\n",
" return X, y\n",
" \n",
"def get_data():\n",
" X,y = _read_x_y(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/compresive_strength_concrete.csv\",\"CONCRETE\")\n",
" X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
" \n",
" return { \"X\" : X_train, \"y\" : y_train[:,0] }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### If you would like to see even better results increase \"iteration_time_out minutes\" to 10+ mins and increase \"iterations\" to a minimum of 30"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 5,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'spearman_correlation',\n",
" \"preprocess\": True,\n",
" \"max_concurrent_iterations\": 5,\n",
" \"verbosity\": logging.INFO,\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'regression',\n",
" debug_log = 'automl.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results\n",
"Widget for Monitoring Runs\n",
"The widget will first report a \u00e2\u20ac\u0153loading status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
"Note: The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Retrieve All Child Runs\n",
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"children = list(remote_run.get_children())\n",
"metricslist = {}\n",
"for run in children:\n",
" properties = run.get_properties()\n",
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
" metricslist[int(properties['iteration'])] = metrics\n",
"\n",
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
"rundata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieve the Best Model\n",
"Below we select the best pipeline from our iterations. The get_output method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = remote_run.get_output()\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Best Model Based on Any Other Metric\n",
"Show the run and the model that has the smallest root_mean_squared_error value (which turned out to be the same as the one with largest spearman_correlation value):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"root_mean_squared_error\"\n",
"best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 3\n",
"third_run, third_model = remote_run.get_output(iteration = iteration)\n",
"print(third_run)\n",
"print(third_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register the Fitted Model for Deployment\n",
"If neither metric nor iteration are specified in the register_model call, the iteration with the best primary metric is registered."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"description = 'AutoML Model'\n",
"tags = None\n",
"model = remote_run.register_model(description = description, tags = tags)\n",
"\n",
"print(remote_run.model_id) # This will be written to the script file later in the notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Scoring Script\n",
"The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"import azureml.train.automl\n",
"from sklearn.externals import joblib\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"def run(rawdata):\n",
" try:\n",
" data = json.loads(rawdata)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"error\": result})\n",
" return json.dumps({\"result\":result.tolist()})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a YAML File for the Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Substitute the actual version number in the environment file.\n",
"# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
"# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
"\n",
"with open(conda_env_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
"script_file_name = 'score.py'\n",
"\n",
"with open(script_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(script_file_name, 'w') as cefw:\n",
" cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"},\n",
" description = \"Image for automl regression sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"}, \n",
" description = 'sample service for Automl Regression')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'automl-sample-concrete'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete a Web Service\n",
"\n",
"Deletes the specified web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Logs from a Deployed Web Service\n",
"\n",
"Gets logs from a deployed web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test\n",
"\n",
"Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def _read_x_y(file_name, label_col):\n",
" df = pd.read_csv(file_name)\n",
" y = None\n",
" if label_col in df.columns:\n",
" y = df.pop(label_col)\n",
" y = y.values[:, None]\n",
" X = df.values\n",
" return X, y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Predict on training and test set, and calculate residual values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X,y = _read_x_y(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/compresive_strength_concrete.csv\",\"CONCRETE\")\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
"\n",
"y_pred_train = fitted_model.predict(X_train)\n",
"y_residual_train = y_train - y_pred_train\n",
"\n",
"y_pred_test = fitted_model.predict(X_test)\n",
"y_residual_test = y_test - y_pred_test\n",
"\n",
"y_residual_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"# Set up a multi-plot chart.\n",
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
"f.suptitle('Regression Residual Values', fontsize = 18)\n",
"f.set_figheight(6)\n",
"f.set_figwidth(16)\n",
"\n",
"# Plot residual values of training set.\n",
"a0.axis([0, 360, -200, 200])\n",
"a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
"a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
"a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
"a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
"a0.set_xlabel('Training samples', fontsize = 12)\n",
"a0.set_ylabel('Residual Values', fontsize = 12)\n",
"\n",
"# Plot a histogram.\n",
"#a0.hist(y_residual_train, orientation = 'horizontal', color = ['b']*len(y_residual_train), bins = 10, histtype = 'step')\n",
"#a0.hist(y_residual_train, orientation = 'horizontal', color = ['b']*len(y_residual_train), alpha = 0.2, bins = 10)\n",
"\n",
"# Plot residual values of test set.\n",
"a1.axis([0, 90, -200, 200])\n",
"a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
"a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
"a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
"a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
"a1.set_xlabel('Test samples', fontsize = 12)\n",
"a1.set_yticklabels([])\n",
"\n",
"# Plot a histogram.\n",
"#a1.hist(y_residual_test, orientation = 'horizontal', color = ['b']*len(y_residual_test), bins = 10, histtype = 'step')\n",
"#a1.hist(y_residual_test, orientation = 'horizontal', color = ['b']*len(y_residual_test), alpha = 0.2, bins = 10)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate metrics for the prediction\n",
"\n",
"Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
"from the trained model that was returned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot outputs\n",
"%matplotlib notebook\n",
"test_pred = plt.scatter(y_test, y_pred_test, color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acknowledgements\n",
"\n",
"This Predicting Compressive Strength of Concrete Dataset is made available under the CC0 1.0 Universal (CC0 1.0)\n",
"Public Domain Dedication License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the CC0 1.0 Universal (CC0 1.0)\n",
"Public Domain Dedication License: https://creativecommons.org/publicdomain/zero/1.0/ . The dataset itself can be found here: https://www.kaggle.com/pavanraj159/concrete-compressive-strength-data-set and http://archive.ics.uci.edu/ml/datasets/concrete+compressive+strength\n",
"\n",
"I-Cheng Yeh, \"Modeling of strength of high performance concrete using artificial neural networks,\" Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998). \n",
"\n",
"Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science."
]
}
],
"metadata": {
"authors": [
{
"name": "v-rasav"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Regression with Deployment using Hardware Performance Dataset**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Test](#Test)\n",
"1. [Acknowledgements](#Acknowledgements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using local compute.\n",
"4. Explore the results.\n",
"5. Test the best fitted model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"As part of the setup you have already created an Azure ML Workspace object. For AutoML you will need to create an Experiment object, which is a named object in a Workspace used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
" \n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"experiment_name = 'automl-regression-hardware'\n",
"project_folder = './sample_projects/automl-remote-regression'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create or Attach existing AmlCompute\n",
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"automlcl\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
"cts = ws.compute_targets\n",
"if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
" found = True\n",
" print('Found existing compute target.')\n",
" compute_target = cts[amlcompute_cluster_name]\n",
" \n",
"if not found:\n",
" print('Creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
" #vm_priority = 'lowpriority', # optional\n",
" max_nodes = 6)\n",
"\n",
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data\n",
"\n",
"Here create the script to be run in azure compute for loading the data, load the hardware dataset into the X and y variables. Next split the data using train_test_split and return X_train and y_train for training the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def _read_x_y(file_name, label_col):\n",
" df = pd.read_csv(file_name)\n",
" y = None\n",
" if label_col in df.columns:\n",
" y = df.pop(label_col)\n",
" y = y.values[:, None]\n",
" X = df.values\n",
" return X, y\n",
" \n",
"def get_data():\n",
" X,y = _read_x_y(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\",\"ERP\")\n",
" X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
" \n",
" return { \"X\" : X_train, \"y\" : y_train[:,0] }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Train\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### If you would like to see even better results increase \"iteration_time_out minutes\" to 10+ mins and increase \"iterations\" to a minimum of 30"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 5,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'spearman_correlation',\n",
" \"preprocess\": True,\n",
" \"max_concurrent_iterations\": 5,\n",
" \"verbosity\": logging.INFO,\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'regression',\n",
" debug_log = 'automl_errors_20190417.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Widget for Monitoring Runs\n",
"\n",
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
"\n",
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.run import AutoMLRun\n",
"setup_run = AutoMLRun(experiment, remote_run.id + \"_setup\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Wait until the run finishes.\n",
"remote_run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieve All Child Runs\n",
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"children = list(remote_run.get_children())\n",
"metricslist = {}\n",
"for run in children:\n",
" properties = run.get_properties()\n",
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
" metricslist[int(properties['iteration'])] = metrics\n",
"\n",
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
"rundata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieve the Best Model\n",
"Below we select the best pipeline from our iterations. The get_output method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = remote_run.get_output()\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Best Model Based on Any Other Metric\n",
"Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"root_mean_squared_error\"\n",
"best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 3\n",
"third_run, third_model = remote_run.get_output(iteration = iteration)\n",
"print(third_run)\n",
"print(third_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register the Fitted Model for Deployment\n",
"If neither metric nor iteration are specified in the register_model call, the iteration with the best primary metric is registered."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"description = 'AutoML Model'\n",
"tags = None\n",
"model = remote_run.register_model(description = description, tags = tags)\n",
"\n",
"print(remote_run.model_id) # This will be written to the script file later in the notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Scoring Script\n",
"The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"import azureml.train.automl\n",
"from sklearn.externals import joblib\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"def run(rawdata):\n",
" try:\n",
" data = json.loads(rawdata)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"error\": result})\n",
" return json.dumps({\"result\":result.tolist()})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a YAML File for the Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Substitute the actual version number in the environment file.\n",
"# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
"# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
"\n",
"with open(conda_env_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
"script_file_name = 'score.py'\n",
"\n",
"with open(script_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(script_file_name, 'w') as cefw:\n",
" cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"},\n",
" description = \"Image for automl regression sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"}, \n",
" description = 'sample service for Automl Regression')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'automl-sample-hardware'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete a Web Service\n",
"\n",
"Deletes the specified web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Logs from a Deployed Web Service\n",
"\n",
"Gets logs from a deployed web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test\n",
"\n",
"Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def _read_x_y(file_name, label_col):\n",
" df = pd.read_csv(file_name)\n",
" y_split = None\n",
" if label_col in df.columns:\n",
" y_split = df.pop(label_col)\n",
" y_split = y_split.values[:, None]\n",
" X_split = df.values\n",
" return X_split, y_split\n",
" \n",
"\n",
"X,y = _read_x_y(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\",\"ERP\")\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Predict on training and test set, and calculate residual values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred_train = fitted_model.predict(X_train)\n",
"y_residual_train = y_train - y_pred_train\n",
"\n",
"y_pred_test = fitted_model.predict(X_test)\n",
"y_residual_test = y_test - y_pred_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate metrics for the prediction\n",
"\n",
"Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
"from the trained model that was returned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"# Set up a multi-plot chart.\n",
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
"f.suptitle('Regression Residual Values', fontsize = 18)\n",
"f.set_figheight(6)\n",
"f.set_figwidth(16)\n",
"\n",
"# Plot residual values of training set.\n",
"a0.axis([0, 360, -200, 200])\n",
"a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
"a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
"a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
"a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
"a0.set_xlabel('Training samples', fontsize = 12)\n",
"a0.set_ylabel('Residual Values', fontsize = 12)\n",
"\n",
"# Plot residual values of test set.\n",
"a1.axis([0, 90, -200, 200])\n",
"a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
"a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
"a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
"a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
"a1.set_xlabel('Test samples', fontsize = 12)\n",
"a1.set_yticklabels([])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acknowledgements\n",
"This Predicting Hardware Performance Dataset is made available under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication License: https://creativecommons.org/publicdomain/zero/1.0/ . The dataset itself can be found here: https://www.kaggle.com/faizunnabi/comp-hardware-performance and https://archive.ics.uci.edu/ml/datasets/Computer+Hardware\n",
"\n",
"_**Citation Found Here**_\n"
]
}
],
"metadata": {
"authors": [
{
"name": "v-rasav"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Azure ML Hardware Accelerated Object Detection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial will show you how to deploy an object detection service based on the SSD-VGG model in just a few minutes using the Azure Machine Learning Accelerated AI service.\n",
"\n",
"We will use the SSD-VGG model accelerated on an FPGA. Our Accelerated Models Service handles translating deep neural networks (DNN) into an FPGA program.\n",
"\n",
"The steps in this notebook are: \n",
"1. [Setup Environment](#set-up-environment)\n",
"* [Construct Model](#construct-model)\n",
" * Image Preprocessing\n",
" * Featurizer\n",
" * Save Model\n",
" * Save input and output tensor names\n",
"* [Create Image](#create-image)\n",
"* [Deploy Image](#deploy-image)\n",
"* [Test the Service](#test-service)\n",
" * Create Client\n",
" * Serve the model\n",
"* [Cleanup](#cleanup)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"set-up-environment\"></a>\n",
"## 1. Set up Environment\n",
"### 1.a. Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.b. Retrieve Workspace\n",
"If you haven't created a Workspace, please follow [this notebook](\"../../../configuration.ipynb\") to do so. If you have, run the codeblock below to retrieve it. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"construct-model\"></a>\n",
"## 2. Construct model\n",
"### 2.a. Image preprocessing\n",
"We'd like our service to accept JPEG images as input. However the input to SSD-VGG is a float tensor of shape \\[1, 300, 300, 3\\]. The first dimension is batch, then height, width, and channels (i.e. NHWC). To bridge this gap, we need code that decodes JPEG images and resizes them appropriately for input to SSD-VGG. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as TensorFlow strings) and produces a tensor that is ready to be featurized by SSD-VGG.\n",
"\n",
"**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
"import azureml.accel.models.utils as utils\n",
"tf.reset_default_graph()\n",
"\n",
"in_images = tf.placeholder(tf.string)\n",
"image_tensors = utils.preprocess_array(in_images, output_width=300, output_height=300, preserve_aspect_ratio=False)\n",
"print(image_tensors.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.b. Featurizer\n",
"The SSD-VGG model is different from our other models in that it generates 12 tensor outputs. These corresponds to x,y displacements of the anchor boxes and the detection confidence (for 21 classes). Because these outputs are not convenient to work with, we will later use a pre-defined post-processing utility to transform the outputs into a simplified list of bounding boxes with their respective class and confidence.\n",
"\n",
"For more information about the output tensors, take this example: the output tensor 'ssd_300_vgg/block4_box/Reshape_1:0' has a shape of [None, 37, 37, 4, 21]. This gives the pre-softmax confidence for 4 anchor boxes situated at each site of a 37 x 37 grid imposed on the image, one confidence score for each of the 21 classes. The first dimension is the batch dimension. Likewise, 'ssd_300_vgg/block4_box/Reshape:0' has shape [None, 37, 37, 4, 4] and encodes the (cx, cy) center shift and rescaling (sw, sh) relative to each anchor box. Refer to the [SSD-VGG paper](https://arxiv.org/abs/1512.02325) to understand how these are computed. The other 10 tensors are defined similarly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.accel.models import SsdVgg\n",
"\n",
"saved_model_dir = os.path.join(os.path.expanduser('~'), 'models')\n",
"model_graph = SsdVgg(saved_model_dir, is_frozen = True)\n",
"\n",
"print('SSD-VGG Input Tensors:')\n",
"for idx, input_name in enumerate(model_graph.input_tensor_list):\n",
" print('{}, {}'.format(input_name, model_graph.get_input_dims(idx)))\n",
" \n",
"print('SSD-VGG Output Tensors:')\n",
"for idx, output_name in enumerate(model_graph.output_tensor_list):\n",
" print('{}, {}'.format(output_name, model_graph.get_output_dims(idx)))\n",
"\n",
"ssd_outputs = model_graph.import_graph_def(image_tensors, is_training=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.c. Save Model\n",
"Now that we loaded both parts of the tensorflow graph (preprocessor and SSD-VGG featurizer), we can save the graph and associated variables to a directory which we can register as an Azure ML Model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_name = \"ssdvgg\"\n",
"model_save_path = os.path.join(saved_model_dir, model_name, \"saved_model\")\n",
"print(\"Saving model in {}\".format(model_save_path))\n",
"\n",
"output_map = {}\n",
"for i, output in enumerate(ssd_outputs):\n",
" output_map['out_{}'.format(i)] = output\n",
"\n",
"with tf.Session() as sess:\n",
" model_graph.restore_weights(sess)\n",
" tf.saved_model.simple_save(sess, \n",
" model_save_path, \n",
" inputs={'images': in_images}, \n",
" outputs=output_map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.d. Important! Save names of input and output tensors\n",
"\n",
"These input and output tensors that were created during the preprocessing and classifier steps are also going to be used when **converting the model** to an Accelerated Model that can run on FPGA's and for **making an inferencing request**. It is very important to save this information!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from file"
]
},
"outputs": [],
"source": [
"input_tensors = in_images.name\n",
"# We will use the list of output tensors during inferencing\n",
"output_tensors = [output.name for output in ssd_outputs]\n",
"# However, for multiple output tensors, our AccelOnnxConverter will \n",
"# accept comma-delimited strings (lists will cause error)\n",
"output_tensors_str = \",\".join(output_tensors)\n",
"\n",
"print(input_tensors)\n",
"print(output_tensors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"create-image\"></a>\n",
"## 3. Create AccelContainerImage\n",
"Below we will execute all the same steps as in the [Quickstart](./accelerated-models-quickstart.ipynb#create-image) to package the model we have saved locally into an accelerated Docker image saved in our workspace. To complete all the steps, it may take a few minutes. For more details on each step, check out the [Quickstart section on model registration](./accelerated-models-quickstart.ipynb#register-model)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"from azureml.core.model import Model\n",
"from azureml.core.image import Image\n",
"from azureml.accel import AccelOnnxConverter\n",
"from azureml.accel import AccelContainerImage\n",
"\n",
"# Retrieve workspace\n",
"ws = Workspace.from_config()\n",
"print(\"Successfully retrieved workspace:\", ws.name, ws.resource_group, ws.location, ws.subscription_id, '\\n')\n",
"\n",
"# Register model\n",
"registered_model = Model.register(workspace = ws,\n",
" model_path = model_save_path,\n",
" model_name = model_name)\n",
"print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
"\n",
"# Convert model\n",
"convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
"# If it fails, you can run wait_for_completion again with show_output=True.\n",
"convert_request.wait_for_completion(show_output=False)\n",
"converted_model = convert_request.result\n",
"print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"\n",
"# Package into AccelContainerImage\n",
"image_config = AccelContainerImage.image_configuration()\n",
"# Image name must be lowercase\n",
"image_name = \"{}-image\".format(model_name)\n",
"image = Image.create(name = image_name,\n",
" models = [converted_model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"image.wait_for_creation()\n",
"print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"deploy-image\"></a>\n",
"## 4. Deploy image\n",
"Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
"\n",
"### 4.a. Deploy to Databox Edge Machine using IoT Hub\n",
"See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
"\n",
"### 4.b. Deploy to AKS Cluster\n",
"Same as in the [Quickstart section on image deployment](./accelerated-models-quickstart.ipynb#deploy-image), we are going to create an AKS cluster with FPGA-enabled machines, then deploy our service to it.\n",
"#### Create AKS ComputeTarget"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"\n",
"# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
"# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
"prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
" agent_count = 1, \n",
" location = \"eastus\")\n",
"\n",
"aks_name = 'aks-pb6-obj'\n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Deploy AccelContainerImage to AKS ComputeTarget"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice, AksWebservice\n",
"\n",
"# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
"# Authentication is enabled by default, but for testing we specify False\n",
"aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
" num_replicas=1,\n",
" auth_enabled = False)\n",
"\n",
"aks_service_name ='my-aks-service'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws,\n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"test-service\"></a>\n",
"## 5. Test the service\n",
"<a id=\"create-client\"></a>\n",
"### 5.a. Create Client\n",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
"\n",
"**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Using the grpc client in AzureML Accelerated Models SDK\n",
"from azureml.accel.client import PredictionClient\n",
"\n",
"address = aks_service.scoring_uri\n",
"ssl_enabled = address.startswith(\"https\")\n",
"address = address[address.find('/')+2:].strip('/')\n",
"port = 443 if ssl_enabled else 80\n",
"\n",
"# Initialize AzureML Accelerated Models client\n",
"client = PredictionClient(address=address,\n",
" port=port,\n",
" use_ssl=ssl_enabled,\n",
" service_name=aks_service.name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can adapt the client [code](https://github.com/Azure/aml-real-time-ai/blob/master/pythonlib/amlrealtimeai/client.py) to meet your needs. There is also an example C# [client](https://github.com/Azure/aml-real-time-ai/blob/master/sample-clients/csharp).\n",
"\n",
"The service provides an API that is compatible with TensorFlow Serving. There are instructions to download a sample client [here](https://www.tensorflow.org/serving/setup)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"serve-model\"></a>\n",
"### 5.b. Serve the model\n",
"The SSD-VGG model returns the confidence and bounding boxes for all possible anchor boxes. As mentioned earlier, we will use a post-processing routine to transform this into a list of bounding boxes (y1, x1, y2, x2) where x, y are fractional coordinates measured from left and top respectively. A respective list of classes and scores is also returned to tag each bounding box. Below we make use of this information to draw the bounding boxes on top the original image. Note that in the post-processing routine we select a confidence threshold of 0.5."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from matplotlib import pyplot as plt\n",
"\n",
"colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
" (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
" (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
" (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
" (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
"\n",
"\n",
"def draw_boxes_on_img(img, classes, scores, bboxes, thickness=2):\n",
" shape = img.shape\n",
" for i in range(bboxes.shape[0]):\n",
" bbox = bboxes[i]\n",
" color = colors_tableau[classes[i]]\n",
" # Draw bounding box...\n",
" p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))\n",
" p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))\n",
" cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)\n",
" # Draw text...\n",
" s = '%s/%.3f' % (classes[i], scores[i])\n",
" p1 = (p1[0]-5, p1[1])\n",
" cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.accel._external.ssdvgg_utils as ssdvgg_utils\n",
"\n",
"result = client.score_file(path=\"meeting.jpg\", input_name=input_tensors, outputs=output_tensors)\n",
"classes, scores, bboxes = ssdvgg_utils.postprocess(result, select_threshold=0.5)\n",
"\n",
"img = cv2.imread('meeting.jpg', 1)\n",
"img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
"draw_boxes_on_img(img, classes, scores, bboxes)\n",
"plt.imshow(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cleanup\"></a>\n",
"## 6. Cleanup\n",
"It's important to clean up your resources, so that you won't incur unnecessary costs. In the [next notebook](./accelerated-models-training.ipynb) you will learn how to train a classfier on a new dataset using transfer learning."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service.delete()\n",
"aks_target.delete()\n",
"image.delete()\n",
"registered_model.delete()\n",
"converted_model.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "coverste"
},
{
"name": "paledger"
},
{
"name": "sukha"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Azure ML Hardware Accelerated Object Detection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial will show you how to deploy an object detection service based on the SSD-VGG model in just a few minutes using the Azure Machine Learning Accelerated AI service.\n",
"\n",
"We will use the SSD-VGG model accelerated on an FPGA. Our Accelerated Models Service handles translating deep neural networks (DNN) into an FPGA program.\n",
"\n",
"The steps in this notebook are: \n",
"1. [Setup Environment](#set-up-environment)\n",
"* [Construct Model](#construct-model)\n",
" * Image Preprocessing\n",
" * Featurizer\n",
" * Save Model\n",
" * Save input and output tensor names\n",
"* [Create Image](#create-image)\n",
"* [Deploy Image](#deploy-image)\n",
"* [Test the Service](#test-service)\n",
" * Create Client\n",
" * Serve the model\n",
"* [Cleanup](#cleanup)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"set-up-environment\"></a>\n",
"## 1. Set up Environment\n",
"### 1.a. Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.b. Retrieve Workspace\n",
"If you haven't created a Workspace, please follow [this notebook](\"../../../configuration.ipynb\") to do so. If you have, run the codeblock below to retrieve it. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"construct-model\"></a>\n",
"## 2. Construct model\n",
"### 2.a. Image preprocessing\n",
"We'd like our service to accept JPEG images as input. However the input to SSD-VGG is a float tensor of shape \\[1, 300, 300, 3\\]. The first dimension is batch, then height, width, and channels (i.e. NHWC). To bridge this gap, we need code that decodes JPEG images and resizes them appropriately for input to SSD-VGG. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as TensorFlow strings) and produces a tensor that is ready to be featurized by SSD-VGG.\n",
"\n",
"**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
"import azureml.accel.models.utils as utils\n",
"tf.reset_default_graph()\n",
"\n",
"in_images = tf.placeholder(tf.string)\n",
"image_tensors = utils.preprocess_array(in_images, output_width=300, output_height=300, preserve_aspect_ratio=False)\n",
"print(image_tensors.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.b. Featurizer\n",
"The SSD-VGG model is different from our other models in that it generates 12 tensor outputs. These corresponds to x,y displacements of the anchor boxes and the detection confidence (for 21 classes). Because these outputs are not convenient to work with, we will later use a pre-defined post-processing utility to transform the outputs into a simplified list of bounding boxes with their respective class and confidence.\n",
"\n",
"For more information about the output tensors, take this example: the output tensor 'ssd_300_vgg/block4_box/Reshape_1:0' has a shape of [None, 37, 37, 4, 21]. This gives the pre-softmax confidence for 4 anchor boxes situated at each site of a 37 x 37 grid imposed on the image, one confidence score for each of the 21 classes. The first dimension is the batch dimension. Likewise, 'ssd_300_vgg/block4_box/Reshape:0' has shape [None, 37, 37, 4, 4] and encodes the (cx, cy) center shift and rescaling (sw, sh) relative to each anchor box. Refer to the [SSD-VGG paper](https://arxiv.org/abs/1512.02325) to understand how these are computed. The other 10 tensors are defined similarly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.accel.models import SsdVgg\n",
"\n",
"saved_model_dir = os.path.join(os.path.expanduser('~'), 'models')\n",
"model_graph = SsdVgg(saved_model_dir, is_frozen = True)\n",
"\n",
"print('SSD-VGG Input Tensors:')\n",
"for idx, input_name in enumerate(model_graph.input_tensor_list):\n",
" print('{}, {}'.format(input_name, model_graph.get_input_dims(idx)))\n",
" \n",
"print('SSD-VGG Output Tensors:')\n",
"for idx, output_name in enumerate(model_graph.output_tensor_list):\n",
" print('{}, {}'.format(output_name, model_graph.get_output_dims(idx)))\n",
"\n",
"ssd_outputs = model_graph.import_graph_def(image_tensors, is_training=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.c. Save Model\n",
"Now that we loaded both parts of the tensorflow graph (preprocessor and SSD-VGG featurizer), we can save the graph and associated variables to a directory which we can register as an Azure ML Model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_name = \"ssdvgg\"\n",
"model_save_path = os.path.join(saved_model_dir, model_name, \"saved_model\")\n",
"print(\"Saving model in {}\".format(model_save_path))\n",
"\n",
"output_map = {}\n",
"for i, output in enumerate(ssd_outputs):\n",
" output_map['out_{}'.format(i)] = output\n",
"\n",
"with tf.Session() as sess:\n",
" model_graph.restore_weights(sess)\n",
" tf.saved_model.simple_save(sess, \n",
" model_save_path, \n",
" inputs={'images': in_images}, \n",
" outputs=output_map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.d. Important! Save names of input and output tensors\n",
"\n",
"These input and output tensors that were created during the preprocessing and classifier steps are also going to be used when **converting the model** to an Accelerated Model that can run on FPGA's and for **making an inferencing request**. It is very important to save this information!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from file"
]
},
"outputs": [],
"source": [
"input_tensors = in_images.name\n",
"# We will use the list of output tensors during inferencing\n",
"output_tensors = [output.name for output in ssd_outputs]\n",
"# However, for multiple output tensors, our AccelOnnxConverter will \n",
"# accept comma-delimited strings (lists will cause error)\n",
"output_tensors_str = \",\".join(output_tensors)\n",
"\n",
"print(input_tensors)\n",
"print(output_tensors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"create-image\"></a>\n",
"## 3. Create AccelContainerImage\n",
"Below we will execute all the same steps as in the [Quickstart](./accelerated-models-quickstart.ipynb#create-image) to package the model we have saved locally into an accelerated Docker image saved in our workspace. To complete all the steps, it may take a few minutes. For more details on each step, check out the [Quickstart section on model registration](./accelerated-models-quickstart.ipynb#register-model)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"from azureml.core.model import Model\n",
"from azureml.core.image import Image\n",
"from azureml.accel import AccelOnnxConverter\n",
"from azureml.accel import AccelContainerImage\n",
"\n",
"# Retrieve workspace\n",
"ws = Workspace.from_config()\n",
"print(\"Successfully retrieved workspace:\", ws.name, ws.resource_group, ws.location, ws.subscription_id, '\\n')\n",
"\n",
"# Register model\n",
"registered_model = Model.register(workspace = ws,\n",
" model_path = model_save_path,\n",
" model_name = model_name)\n",
"print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
"\n",
"# Convert model\n",
"convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
"# If it fails, you can run wait_for_completion again with show_output=True.\n",
"convert_request.wait_for_completion(show_output=False)\n",
"converted_model = convert_request.result\n",
"print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"\n",
"# Package into AccelContainerImage\n",
"image_config = AccelContainerImage.image_configuration()\n",
"# Image name must be lowercase\n",
"image_name = \"{}-image\".format(model_name)\n",
"image = Image.create(name = image_name,\n",
" models = [converted_model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"image.wait_for_creation()\n",
"print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"deploy-image\"></a>\n",
"## 4. Deploy image\n",
"Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
"\n",
"### 4.a. Deploy to Databox Edge Machine using IoT Hub\n",
"See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
"\n",
"### 4.b. Deploy to AKS Cluster\n",
"Same as in the [Quickstart section on image deployment](./accelerated-models-quickstart.ipynb#deploy-image), we are going to create an AKS cluster with FPGA-enabled machines, then deploy our service to it.\n",
"#### Create AKS ComputeTarget"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"\n",
"# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
"# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
"prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
" agent_count = 1, \n",
" location = \"eastus\")\n",
"\n",
"aks_name = 'aks-pb6-obj'\n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Deploy AccelContainerImage to AKS ComputeTarget"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice, AksWebservice\n",
"\n",
"# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
"# Authentication is enabled by default, but for testing we specify False\n",
"aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
" num_replicas=1,\n",
" auth_enabled = False)\n",
"\n",
"aks_service_name ='my-aks-service'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws,\n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"test-service\"></a>\n",
"## 5. Test the service\n",
"<a id=\"create-client\"></a>\n",
"### 5.a. Create Client\n",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
"\n",
"**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Using the grpc client in AzureML Accelerated Models SDK\n",
"from azureml.accel.client import PredictionClient\n",
"\n",
"address = aks_service.scoring_uri\n",
"ssl_enabled = address.startswith(\"https\")\n",
"address = address[address.find('/')+2:].strip('/')\n",
"port = 443 if ssl_enabled else 80\n",
"\n",
"# Initialize AzureML Accelerated Models client\n",
"client = PredictionClient(address=address,\n",
" port=port,\n",
" use_ssl=ssl_enabled,\n",
" service_name=aks_service.name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can adapt the client [code](https://github.com/Azure/aml-real-time-ai/blob/master/pythonlib/amlrealtimeai/client.py) to meet your needs. There is also an example C# [client](https://github.com/Azure/aml-real-time-ai/blob/master/sample-clients/csharp).\n",
"\n",
"The service provides an API that is compatible with TensorFlow Serving. There are instructions to download a sample client [here](https://www.tensorflow.org/serving/setup)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"serve-model\"></a>\n",
"### 5.b. Serve the model\n",
"The SSD-VGG model returns the confidence and bounding boxes for all possible anchor boxes. As mentioned earlier, we will use a post-processing routine to transform this into a list of bounding boxes (y1, x1, y2, x2) where x, y are fractional coordinates measured from left and top respectively. A respective list of classes and scores is also returned to tag each bounding box. Below we make use of this information to draw the bounding boxes on top the original image. Note that in the post-processing routine we select a confidence threshold of 0.5."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from matplotlib import pyplot as plt\n",
"\n",
"colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
" (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
" (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
" (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
" (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
"\n",
"\n",
"def draw_boxes_on_img(img, classes, scores, bboxes, thickness=2):\n",
" shape = img.shape\n",
" for i in range(bboxes.shape[0]):\n",
" bbox = bboxes[i]\n",
" color = colors_tableau[classes[i]]\n",
" # Draw bounding box...\n",
" p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))\n",
" p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))\n",
" cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)\n",
" # Draw text...\n",
" s = '%s/%.3f' % (classes[i], scores[i])\n",
" p1 = (p1[0]-5, p1[1])\n",
" cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.accel._external.ssdvgg_utils as ssdvgg_utils\n",
"\n",
"result = client.score_file(path=\"meeting.jpg\", input_name=input_tensors, outputs=output_tensors)\n",
"classes, scores, bboxes = ssdvgg_utils.postprocess(result, select_threshold=0.5)\n",
"\n",
"img = cv2.imread('meeting.jpg', 1)\n",
"img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
"draw_boxes_on_img(img, classes, scores, bboxes)\n",
"plt.imshow(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cleanup\"></a>\n",
"## 6. Cleanup\n",
"It's important to clean up your resources, so that you won't incur unnecessary costs. In the [next notebook](./accelerated-models-training.ipynb) you will learn how to train a classfier on a new dataset using transfer learning."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service.delete()\n",
"aks_target.delete()\n",
"image.delete()\n",
"registered_model.delete()\n",
"converted_model.delete()"
]
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
"metadata": {
"authors": [
{
"name": "coverste"
},
{
"name": "paledger"
},
{
"name": "sukha"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat": 4,
"nbformat_minor": 2
}

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@@ -1,407 +1,407 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
"This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
"We then test and delete the service, image and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get workspace\n",
"Load existing workspace from the config file info."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.workspace import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register the model\n",
"Register an existing trained model, add descirption and tags. Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. You can download a [pretrained resnet50](https://github.com/tensorflow/models/tree/master/official/resnet#pre-trained-model) and unpack it to that directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = \"resnet50\", # this points to a local file\n",
" model_name = \"resnet50\", # this is the name the model is registered as\n",
" tags = {'area': \"Image classification\", 'type': \"classification\"},\n",
" description = \"Image classification trained on Imagenet Dataset\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create an image\n",
"Create an image using the registered model the script that will load and run the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import ujson\n",
"from azureml.core.model import Model\n",
"from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
"from azureml.contrib.services.aml_response import AMLResponse\n",
"\n",
"def init():\n",
" global session\n",
" global input_name\n",
" global output_name\n",
" \n",
" session = tf.Session()\n",
"\n",
" model_path = Model.get_model_path('resnet50')\n",
" model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
" if len(model.signature_def['serving_default'].inputs) > 1:\n",
" raise ValueError(\"This score.py only supports one input\")\n",
" if len(model.signature_def['serving_default'].outputs) > 1:\n",
" raise ValueError(\"This score.py only supports one input\")\n",
" input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
" output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()][0]\n",
" \n",
"\n",
"@rawhttp\n",
"def run(request):\n",
" if request.method == 'POST':\n",
" reqBody = request.get_data(False)\n",
" resp = score(reqBody)\n",
" return AMLResponse(resp, 200)\n",
" if request.method == 'GET':\n",
" respBody = str.encode(\"GET is not supported\")\n",
" return AMLResponse(respBody, 405)\n",
" return AMLResponse(\"bad request\", 500)\n",
"\n",
"def score(data):\n",
" result = session.run(output_name, {input_name: [data]})\n",
" return ujson.dumps(result[0])\n",
"\n",
"if __name__ == \"__main__\":\n",
" init()\n",
" with open(\"test_image.jpg\", 'rb') as f:\n",
" content = f.read()\n",
" print(score(content))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy','ujson','azureml-contrib-services'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" gpu_enabled = True\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"GpuImage\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Provision the AKS Cluster\n",
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the default configuration (can also provide parameters to customize)\n",
"prov_config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\")\n",
"\n",
"aks_name = 'my-aks-9' \n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create AKS Cluster in an existing virtual network (optional)\n",
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"from azureml.core.compute import ComputeTarget, AksCompute\n",
"\n",
"# Create the compute configuration and set virtual network information\n",
"config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\", location=\"eastus2\")\n",
"config.vnet_resourcegroup_name = \"mygroup\"\n",
"config.vnet_name = \"mynetwork\"\n",
"config.subnet_name = \"default\"\n",
"config.service_cidr = \"10.0.0.0/16\"\n",
"config.dns_service_ip = \"10.0.0.10\"\n",
"config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
"\n",
"# Create the compute target\n",
"aks_target = ComputeTarget.create(workspace = ws,\n",
" name = \"myaks\",\n",
" provisioning_configuration = config)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enable SSL on the AKS Cluster (optional)\n",
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optional step: Attach existing AKS cluster\n",
"\n",
"If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Use the default configuration (can also provide parameters to customize)\n",
"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
"\n",
"create_name='my-existing-aks' \n",
"# Create the cluster\n",
"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
"aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
"# Wait for the operation to complete\n",
"aks_target.wait_for_completion(True)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy web service to AKS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-service-1'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test the web service\n",
"We test the web sevice by passing the test images content."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import requests\n",
"key1, key2 = aks_service.get_keys()\n",
"\n",
"headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
"test_sampe = open('test_image.jpg', 'rb').read()\n",
"resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clean up\n",
"Delete the service, image, model and compute target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service.delete()\n",
"image.delete()\n",
"model.delete()\n",
"aks_target.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "aashishb"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
"This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
"We then test and delete the service, image and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get workspace\n",
"Load existing workspace from the config file info."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.workspace import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register the model\n",
"Register an existing trained model, add descirption and tags. Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. You can download a [pretrained resnet50](https://github.com/tensorflow/models/tree/master/official/resnet#pre-trained-model) and unpack it to that directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = \"resnet50\", # this points to a local file\n",
" model_name = \"resnet50\", # this is the name the model is registered as\n",
" tags = {'area': \"Image classification\", 'type': \"classification\"},\n",
" description = \"Image classification trained on Imagenet Dataset\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create an image\n",
"Create an image using the registered model the script that will load and run the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import ujson\n",
"from azureml.core.model import Model\n",
"from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
"from azureml.contrib.services.aml_response import AMLResponse\n",
"\n",
"def init():\n",
" global session\n",
" global input_name\n",
" global output_name\n",
" \n",
" session = tf.Session()\n",
"\n",
" model_path = Model.get_model_path('resnet50')\n",
" model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
" if len(model.signature_def['serving_default'].inputs) > 1:\n",
" raise ValueError(\"This score.py only supports one input\")\n",
" if len(model.signature_def['serving_default'].outputs) > 1:\n",
" raise ValueError(\"This score.py only supports one input\")\n",
" input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
" output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()][0]\n",
" \n",
"\n",
"@rawhttp\n",
"def run(request):\n",
" if request.method == 'POST':\n",
" reqBody = request.get_data(False)\n",
" resp = score(reqBody)\n",
" return AMLResponse(resp, 200)\n",
" if request.method == 'GET':\n",
" respBody = str.encode(\"GET is not supported\")\n",
" return AMLResponse(respBody, 405)\n",
" return AMLResponse(\"bad request\", 500)\n",
"\n",
"def score(data):\n",
" result = session.run(output_name, {input_name: [data]})\n",
" return ujson.dumps(result[0])\n",
"\n",
"if __name__ == \"__main__\":\n",
" init()\n",
" with open(\"test_image.jpg\", 'rb') as f:\n",
" content = f.read()\n",
" print(score(content))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy','ujson','azureml-contrib-services'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" gpu_enabled = True\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"GpuImage\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Provision the AKS Cluster\n",
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the default configuration (can also provide parameters to customize)\n",
"prov_config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\")\n",
"\n",
"aks_name = 'my-aks-9' \n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create AKS Cluster in an existing virtual network (optional)\n",
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"from azureml.core.compute import ComputeTarget, AksCompute\n",
"\n",
"# Create the compute configuration and set virtual network information\n",
"config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\", location=\"eastus2\")\n",
"config.vnet_resourcegroup_name = \"mygroup\"\n",
"config.vnet_name = \"mynetwork\"\n",
"config.subnet_name = \"default\"\n",
"config.service_cidr = \"10.0.0.0/16\"\n",
"config.dns_service_ip = \"10.0.0.10\"\n",
"config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
"\n",
"# Create the compute target\n",
"aks_target = ComputeTarget.create(workspace = ws,\n",
" name = \"myaks\",\n",
" provisioning_configuration = config)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enable SSL on the AKS Cluster (optional)\n",
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optional step: Attach existing AKS cluster\n",
"\n",
"If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Use the default configuration (can also provide parameters to customize)\n",
"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
"\n",
"create_name='my-existing-aks' \n",
"# Create the cluster\n",
"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
"aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
"# Wait for the operation to complete\n",
"aks_target.wait_for_completion(True)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy web service to AKS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-service-1'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test the web service\n",
"We test the web sevice by passing the test images content."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import requests\n",
"key1, key2 = aks_service.get_keys()\n",
"\n",
"headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
"test_sampe = open('test_image.jpg', 'rb').read()\n",
"resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clean up\n",
"Delete the service, image, model and compute target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service.delete()\n",
"image.delete()\n",
"model.delete()\n",
"aks_target.delete()"
]
}
],
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
"metadata": {
"authors": [
{
"name": "aashishb"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat": 4,
"nbformat_minor": 2
}

6
how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.ipynb
View File

@@ -265,7 +265,7 @@
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"cpucluster\"\n",
"cpu_cluster_name = \"cpu-cluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
@@ -370,7 +370,7 @@
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"cpucluster\"\n",
"cpu_cluster_name = \"cpu-cluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
@@ -506,7 +506,7 @@
"outputs": [],
"source": [
"# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
"# 'cpucluster' in this case but use a different VM family for instance.\n",
"# 'cpu-cluster' in this case but use a different VM family for instance.\n",
"\n",
"# cpu_cluster.delete()"
]

73
how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb
View File

@@ -36,22 +36,6 @@
"4. Visualize the global and local explanations with the visualization dashboard."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example needs sklearn-pandas. If it is not installed, uncomment and run the following line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!pip install sklearn-pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -63,7 +47,6 @@
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
"from sklearn_pandas import DataFrameMapper\n",
"import pandas as pd\n",
"import numpy as np"
]
@@ -113,6 +96,13 @@
"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"sklearn imports"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -121,7 +111,51 @@
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"\n",
"transformations = ColumnTransformer([\n",
" (\"age_fare\", Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
" ]), [\"age\", \"fare\"]),\n",
" (\"embarked\", Pipeline(steps=[\n",
" (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
" (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
" (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"]) \n",
"])\n",
"\n",
"\n",
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
" ('classifier', LogisticRegression(solver='lbfgs'))])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Uncomment below if sklearn-pandas is not installed\n",
"#!pip install sklearn-pandas\n",
"from sklearn_pandas import DataFrameMapper\n",
"\n",
"# Impute, standardize the numeric features and one-hot encode the categorical features. \n",
@@ -141,7 +175,8 @@
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
" ('classifier', LogisticRegression(solver='lbfgs'))])"
" ('classifier', LogisticRegression(solver='lbfgs'))])\n",
"'''"
]
},
{

24
how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/compare/compare.py Normal file
View File

@@ -0,0 +1,24 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import argparse
import os
print("In compare.py")
print("As a data scientist, this is where I use my compare code.")
parser = argparse.ArgumentParser("compare")
parser.add_argument("--compare_data1", type=str, help="compare_data1 data")
parser.add_argument("--compare_data2", type=str, help="compare_data2 data")
parser.add_argument("--output_compare", type=str, help="output_compare directory")
parser.add_argument("--pipeline_param", type=int, help="pipeline parameter")
args = parser.parse_args()
print("Argument 1: %s" % args.compare_data1)
print("Argument 2: %s" % args.compare_data2)
print("Argument 3: %s" % args.output_compare)
print("Argument 4: %s" % args.pipeline_param)
if not (args.output_compare is None):
os.makedirs(args.output_compare, exist_ok=True)
print("%s created" % args.output_compare)

21
how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/extract/extract.py Normal file
View File

@@ -0,0 +1,21 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import argparse
import os
print("In extract.py")
print("As a data scientist, this is where I use my extract code.")
parser = argparse.ArgumentParser("extract")
parser.add_argument("--input_extract", type=str, help="input_extract data")
parser.add_argument("--output_extract", type=str, help="output_extract directory")
args = parser.parse_args()
print("Argument 1: %s" % args.input_extract)
print("Argument 2: %s" % args.output_extract)
if not (args.output_extract is None):
os.makedirs(args.output_extract, exist_ok=True)
print("%s created" % args.output_extract)

22
how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train/train.py Normal file
View File

@@ -0,0 +1,22 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import argparse
import os
print("In train.py")
print("As a data scientist, this is where I use my training code.")
parser = argparse.ArgumentParser("train")
parser.add_argument("--input_data", type=str, help="input data")
parser.add_argument("--output_train", type=str, help="output_train directory")
args = parser.parse_args()
print("Argument 1: %s" % args.input_data)
print("Argument 2: %s" % args.output_train)
if not (args.output_train is None):
os.makedirs(args.output_train, exist_ok=True)
print("%s created" % args.output_train)

8
how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azure-ml.ipynb
View File

@@ -19,12 +19,12 @@
"The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
]
},
{
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.png)"
]
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.png)"
]
},
{
"cell_type": "code",
@@ -257,4 +257,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}

2
how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb
View File

@@ -299,7 +299,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also specify a custom Docker image for exeution. In this case, you probably want to tell the system not to build a new conda environment for you. Instead, you can specify the path to an existing Python environment in the custom Docker image.\n",
"You can also specify a custom Docker image for execution. In this case, you probably want to tell the system not to build a new conda environment for you. Instead, you can specify the path to an existing Python environment in the custom Docker image. If custom Docker image information is not specified, Azure ML uses the default Docker image to run your training. For more information about Docker containers used in Azure ML training, please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers).\n",
"\n",
"**Note**: since the below example points to the preinstalled Python environment in the miniconda3 image maintained by continuum.io on Docker Hub where Azure ML SDK is not present, the logging metric code is not triggered. But a run history record is still recorded. "
]

1486
how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
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File diff suppressed because it is too large Load Diff

2
how-to-use-azureml/training/logging-api/logging-api.ipynb
View File

@@ -100,7 +100,7 @@
"\n",
"# Check core SDK version number\n",
"\n",
"print(\"This notebook was created using SDK version 1.0.43, you are currently running version\", azureml.core.VERSION)"
"print(\"This notebook was created using SDK version 1.0.45, you are currently running version\", azureml.core.VERSION)"
]
},
{

1
how-to-use-azureml/using-mlflow/README.md
View File

@@ -7,6 +7,5 @@ Try out the sample notebooks:
* [Use MLflow with Azure Machine Learning for Local Training Run](./train-local/train-local.ipynb)
* [Use MLflow with Azure Machine Learning for Remote Training Run](./train-remote/train-remote.ipynb)
* [Deploy Model as Azure Machine Learning Web Service using MLflow](./deploy-model/deploy-model.ipynb)
* [Train and Deploy PyTorch Image Classifier](./train-deploy-pytorch/train-deploy-pytorch.ipynb)
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/README..png)

638
how-to-use-azureml/using-mlflow/deploy-model/deploy-model.ipynb
View File

@@ -1,322 +1,322 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/deploy-model/deploy-model.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy Model as Azure Machine Learning Web Service using MLflow\n",
"\n",
"This example shows you how to use mlflow together with Azure Machine Learning services for deploying a model as a web service. You'll learn how to:\n",
"\n",
" 1. Retrieve a previously trained scikit-learn model\n",
" 2. Create a Docker image from the model\n",
" 3. Deploy the model as a web service on Azure Container Instance\n",
" 4. Make a scoring request against the web service.\n",
"\n",
"## Prerequisites and Set-up\n",
"\n",
"This notebook requires you to first complete the [Use MLflow with Azure Machine Learning for Local Training Run](../train-local/train-local.ipnyb) or [Use MLflow with Azure Machine Learning for Remote Training Run](../train-remote/train-remote.ipnyb) notebook, so as to have an experiment run with uploaded model in your Azure Machine Learning Workspace.\n",
"\n",
"Also install following packages if you haven't already\n",
"\n",
"```\n",
"pip install azureml-mlflow pandas\n",
"```\n",
"\n",
"Then, import necessary packages:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlflow\n",
"import azureml.mlflow\n",
"import azureml.core\n",
"from azureml.core import Workspace\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to workspace and set MLflow tracking URI\n",
"\n",
"Setting the tracking URI is required for retrieving the model and creating an image using the MLflow APIs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve model from previous run\n",
"\n",
"Let's retrieve the experiment from training notebook, and list the runs within that experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = \"experiment-with-mlflow\"\n",
"exp = ws.experiments[experiment_name]\n",
"\n",
"runs = list(exp.get_runs())\n",
"runs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, let's select the most recent training run and find its ID. You also need to specify the path in run history where the model was saved. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"runid = runs[0].id\n",
"model_save_path = \"model\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Docker image\n",
"\n",
"To create a Docker image with Azure Machine Learning for Model Management, use ```mlflow.azureml.build_image``` method. Specify the model path, your workspace, run ID and other parameters.\n",
"\n",
"MLflow automatically recognizes the model framework as scikit-learn, and creates the scoring logic and includes library dependencies for you.\n",
"\n",
"Note that the image creation can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlflow.azureml\n",
"\n",
"azure_image, azure_model = mlflow.azureml.build_image(model_uri=\"runs:/{}/{}\".format(runid, model_save_path),\n",
" workspace=ws,\n",
" model_name='diabetes-sklearn-model',\n",
" image_name='diabetes-sklearn-image',\n",
" synchronous=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy web service\n",
"\n",
"Let's use Azure Machine Learning SDK to deploy the image as a web service. \n",
"\n",
"First, specify the deployment configuration. Azure Container Instance is a suitable choice for a quick dev-test deployment, while Azure Kubernetes Service is suitable for scalable production deployments.\n",
"\n",
"Then, deploy the image using Azure Machine Learning SDK's ```deploy_from_image``` method.\n",
"\n",
"Note that the deployment can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"\n",
"aci_config = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={\"method\" : \"sklearn\"}, \n",
" description='Diabetes model',\n",
" location='eastus2')\n",
"\n",
"\n",
"# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
"webservice = Webservice.deploy_from_image(\n",
" image=azure_image, workspace=ws, name=\"diabetes-model-1\", deployment_config=aci_config)\n",
"\n",
"\n",
"webservice.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make a scoring request\n",
"\n",
"Let's take the first few rows of test data and score them using the web service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_rows = [\n",
" [0.01991321, 0.05068012, 0.10480869, 0.07007254, -0.03596778,\n",
" -0.0266789 , -0.02499266, -0.00259226, 0.00371174, 0.04034337],\n",
" [-0.01277963, -0.04464164, 0.06061839, 0.05285819, 0.04796534,\n",
" 0.02937467, -0.01762938, 0.03430886, 0.0702113 , 0.00720652],\n",
" [ 0.03807591, 0.05068012, 0.00888341, 0.04252958, -0.04284755,\n",
" -0.02104223, -0.03971921, -0.00259226, -0.01811827, 0.00720652]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MLflow-based web service for scikit-learn model requires the data to be converted to Pandas DataFrame, and then serialized as JSON. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"\n",
"test_rows_as_json = pd.DataFrame(test_rows).to_json(orient=\"split\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's pass the conveted and serialized data to web service to get the predictions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predictions = webservice.run(test_rows_as_json)\n",
"\n",
"print(predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use the web service's scoring URI to make a raw HTTP request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"webservice.scoring_uri"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can diagnose the web service using ```get_logs``` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"webservice.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next Steps\n",
"\n",
"Learn about [model management and inferencing in Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "rastala"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/deploy-model/deploy-model.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy Model as Azure Machine Learning Web Service using MLflow\n",
"\n",
"This example shows you how to use mlflow together with Azure Machine Learning services for deploying a model as a web service. You'll learn how to:\n",
"\n",
" 1. Retrieve a previously trained scikit-learn model\n",
" 2. Create a Docker image from the model\n",
" 3. Deploy the model as a web service on Azure Container Instance\n",
" 4. Make a scoring request against the web service.\n",
"\n",
"## Prerequisites and Set-up\n",
"\n",
"This notebook requires you to first complete the [Use MLflow with Azure Machine Learning for Local Training Run](../train-local/train-local.ipnyb) or [Use MLflow with Azure Machine Learning for Remote Training Run](../train-remote/train-remote.ipnyb) notebook, so as to have an experiment run with uploaded model in your Azure Machine Learning Workspace.\n",
"\n",
"Also install following packages if you haven't already\n",
"\n",
"```\n",
"pip install azureml-mlflow pandas\n",
"```\n",
"\n",
"Then, import necessary packages:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlflow\n",
"import azureml.mlflow\n",
"import azureml.core\n",
"from azureml.core import Workspace\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to workspace and set MLflow tracking URI\n",
"\n",
"Setting the tracking URI is required for retrieving the model and creating an image using the MLflow APIs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve model from previous run\n",
"\n",
"Let's retrieve the experiment from training notebook, and list the runs within that experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = \"experiment-with-mlflow\"\n",
"exp = ws.experiments[experiment_name]\n",
"\n",
"runs = list(exp.get_runs())\n",
"runs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, let's select the most recent training run and find its ID. You also need to specify the path in run history where the model was saved. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"runid = runs[0].id\n",
"model_save_path = \"model\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Docker image\n",
"\n",
"To create a Docker image with Azure Machine Learning for Model Management, use ```mlflow.azureml.build_image``` method. Specify the model path, your workspace, run ID and other parameters.\n",
"\n",
"MLflow automatically recognizes the model framework as scikit-learn, and creates the scoring logic and includes library dependencies for you.\n",
"\n",
"Note that the image creation can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlflow.azureml\n",
"\n",
"azure_image, azure_model = mlflow.azureml.build_image(model_uri=\"runs:/{}/{}\".format(runid, model_save_path),\n",
" workspace=ws,\n",
" model_name='diabetes-sklearn-model',\n",
" image_name='diabetes-sklearn-image',\n",
" synchronous=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy web service\n",
"\n",
"Let's use Azure Machine Learning SDK to deploy the image as a web service. \n",
"\n",
"First, specify the deployment configuration. Azure Container Instance is a suitable choice for a quick dev-test deployment, while Azure Kubernetes Service is suitable for scalable production deployments.\n",
"\n",
"Then, deploy the image using Azure Machine Learning SDK's ```deploy_from_image``` method.\n",
"\n",
"Note that the deployment can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"\n",
"aci_config = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={\"method\" : \"sklearn\"}, \n",
" description='Diabetes model',\n",
" location='eastus2')\n",
"\n",
"\n",
"# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
"webservice = Webservice.deploy_from_image(\n",
" image=azure_image, workspace=ws, name=\"diabetes-model-1\", deployment_config=aci_config)\n",
"\n",
"\n",
"webservice.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make a scoring request\n",
"\n",
"Let's take the first few rows of test data and score them using the web service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_rows = [\n",
" [0.01991321, 0.05068012, 0.10480869, 0.07007254, -0.03596778,\n",
" -0.0266789 , -0.02499266, -0.00259226, 0.00371174, 0.04034337],\n",
" [-0.01277963, -0.04464164, 0.06061839, 0.05285819, 0.04796534,\n",
" 0.02937467, -0.01762938, 0.03430886, 0.0702113 , 0.00720652],\n",
" [ 0.03807591, 0.05068012, 0.00888341, 0.04252958, -0.04284755,\n",
" -0.02104223, -0.03971921, -0.00259226, -0.01811827, 0.00720652]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MLflow-based web service for scikit-learn model requires the data to be converted to Pandas DataFrame, and then serialized as JSON. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"\n",
"test_rows_as_json = pd.DataFrame(test_rows).to_json(orient=\"split\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's pass the conveted and serialized data to web service to get the predictions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predictions = webservice.run(test_rows_as_json)\n",
"\n",
"print(predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use the web service's scoring URI to make a raw HTTP request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"webservice.scoring_uri"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can diagnose the web service using ```get_logs``` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"webservice.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next Steps\n",
"\n",
"Learn about [model management and inferencing in Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
"metadata": {
"authors": [
{
"name": "rastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat": 4,
"nbformat_minor": 2
}

956
how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb
View File

@@ -1,481 +1,481 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-deploy-pytorch.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier\n",
"\n",
"This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a PyTorch model to classify MNIST digit images, and then deploy the model as a web service. You'll learn how to:\n",
"\n",
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
" 2. Create experiment\n",
" 3. Instrument your model with MLflow tracking\n",
" 4. Train a PyTorch model locally\n",
" 5. Train a model on GPU compute on Azure\n",
" 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
" 7. Create a Docker image from the trained model\n",
" 8. Deploy the model as a web service on Azure Container Instance\n",
" 9. Call the model to make predictions\n",
" \n",
"### Pre-requisites\n",
" \n",
"Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
"\n",
"Also, install mlflow-azureml package using ```pip install mlflow-azureml```. Note that mlflow-azureml installs mlflow package itself as a dependency, if you haven't done so previously.\n",
"\n",
"### Set-up\n",
"\n",
"Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys, os\n",
"import mlflow\n",
"import mlflow.azureml\n",
"import mlflow.sklearn\n",
"\n",
"import azureml.core\n",
"from azureml.core import Workspace\n",
"\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)\n",
"print(\"MLflow version:\", mlflow.version.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"ws.get_details()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set tracking URI\n",
"\n",
"Set the MLFlow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLFlow APIs will go to Azure ML services and will be tracked under your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Experiment\n",
"\n",
"In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = \"pytorch-with-mlflow\"\n",
"mlflow.set_experiment(experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train model locally while logging metrics and artifacts\n",
"\n",
"The ```scripts/train.py``` program contains the code to load the image dataset, and train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
"\n",
"Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, ```mlflow.log_metric``` functions are used to track the convergence of the neural network training iterations. Finally ```mlflow.pytorch.save_model``` is used to save the trained model in framework-aware manner.\n",
"\n",
"Let's add the program to search path, import it as a module, and then invoke the driver function. Note that the training can take few minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lib_path = os.path.abspath(\"scripts\")\n",
"sys.path.append(lib_path)\n",
"\n",
"import train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = train.driver()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can view the metrics of the run at Azure Portal"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(azureml.mlflow.get_portal_url(run))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train model on GPU compute on Azure\n",
"\n",
"Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
"\n",
"Create a PyTorch estimator to specify the training configuration: script, compute as well as additional packages needed. To enable MLflow tracking, include ```azureml-mlflow``` as pip package. The low-level specifications for the training run are encapsulated in the estimator instance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import PyTorch\n",
"\n",
"pt = PyTorch(source_directory=\"./scripts\", \n",
" entry_script = \"train.py\", \n",
" compute_target = \"gpu-cluster\", \n",
" node_count = 1, \n",
" process_count_per_node = 1, \n",
" use_gpu=True,\n",
" pip_packages = [\"azureml-mlflow\", \"Pillow==6.0.0\"])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a reference to the experiment you created previously, but this time, as Azure Machine Learning experiment object.\n",
"\n",
"Then, use ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as cached image is used."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"exp = Experiment(ws, experiment_name)\n",
"run = exp.submit(pt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can monitor the run and its metrics on Azure Portal."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, you can wait for run to complete."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy model as web service\n",
"\n",
"To deploy a web service, first create a Docker image, and then deploy that Docker image on inferencing compute.\n",
"\n",
"The ```mlflow.azureml.build_image``` function builds a Docker image from saved PyTorch model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specififies package dependencies for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_file_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then build a docker image using *runs:/&lt;run.id&gt;/model* as the model_uri path.\n",
"\n",
"Note that the image building can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"model\"\n",
"\n",
"\n",
"azure_image, azure_model = mlflow.azureml.build_image(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
" workspace=ws,\n",
" model_name='pytorch_mnist',\n",
" image_name='pytorch-mnist-img',\n",
" synchronous=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
"\n",
"[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
"\n",
"Note that the service deployment can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
" memory_gb=5, \n",
" tags={\"data\": \"MNIST\", \"method\" : \"pytorch\"}, \n",
" description=\"Predict using webservice\")\n",
"\n",
"\n",
"# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
"webservice = Webservice.deploy_from_image(\n",
" image=azure_image, workspace=ws, name=\"pytorch-mnist-1\", deployment_config=aci_config)\n",
"\n",
"\n",
"webservice.wait_for_deployment()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the deployment has completed you can check the scoring URI of the web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make predictions using web service\n",
"\n",
"To make the web service, create a test data set as normalized PyTorch tensors. \n",
"\n",
"Then, let's define a utility function that takes a random image and converts it into format and shape suitable for as input to PyTorch inferencing end-point. The conversion is done by: \n",
"\n",
" 1. Select a random (image, label) tuple\n",
" 2. Take the image and converting the tensor to NumPy array \n",
" 3. Reshape array into 1 x 1 x N array\n",
" * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
" * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
" 4. Convert the NumPy array to list to make it into a built-in type.\n",
" 5. Create a dictionary {\"data\", &lt;list&gt;} that can be converted to JSON string for web service requests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from torchvision import datasets, transforms\n",
"import random\n",
"import numpy as np\n",
"\n",
"test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.1307,), (0.3081,))]))\n",
"\n",
"\n",
"def get_random_image():\n",
" image_idx = random.randint(0,len(test_data))\n",
" image_as_tensor = test_data[image_idx][0]\n",
" return {\"data\": elem for elem in image_as_tensor.numpy().reshape(1,1,-1).tolist()}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, invoke the web service using a random test image. Convert the dictionary containing the image to JSON string before passing it to web service.\n",
"\n",
"The response contains the raw scores for each label, with greater value indicating higher probability. Sort the labels and select the one with greatest score to get the prediction. Let's also plot the image sent to web service for comparison purposes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"\n",
"test_image = get_random_image()\n",
"\n",
"response = webservice.run(json.dumps(test_image))\n",
"\n",
"response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
"\n",
"\n",
"print(\"Predicted label:\", response[0][0])\n",
"plt.imshow(np.array(test_image[\"data\"]).reshape(28,28), cmap = \"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also call the web service using a raw POST method against the web service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"response = requests.post(url=webservice.scoring_uri, data=json.dumps(test_image),headers={\"Content-type\": \"application/json\"})\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-deploy-pytorch.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier\n",
"\n",
"This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a PyTorch model to classify MNIST digit images, and then deploy the model as a web service. You'll learn how to:\n",
"\n",
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
" 2. Create experiment\n",
" 3. Instrument your model with MLflow tracking\n",
" 4. Train a PyTorch model locally\n",
" 5. Train a model on GPU compute on Azure\n",
" 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
" 7. Create a Docker image from the trained model\n",
" 8. Deploy the model as a web service on Azure Container Instance\n",
" 9. Call the model to make predictions\n",
" \n",
"### Pre-requisites\n",
" \n",
"Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
"\n",
"Also, install mlflow-azureml package using ```pip install mlflow-azureml```. Note that mlflow-azureml installs mlflow package itself as a dependency, if you haven't done so previously.\n",
"\n",
"### Set-up\n",
"\n",
"Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys, os\n",
"import mlflow\n",
"import mlflow.azureml\n",
"import mlflow.sklearn\n",
"\n",
"import azureml.core\n",
"from azureml.core import Workspace\n",
"\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)\n",
"print(\"MLflow version:\", mlflow.version.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"ws.get_details()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set tracking URI\n",
"\n",
"Set the MLFlow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLFlow APIs will go to Azure ML services and will be tracked under your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Experiment\n",
"\n",
"In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = \"pytorch-with-mlflow\"\n",
"mlflow.set_experiment(experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train model locally while logging metrics and artifacts\n",
"\n",
"The ```scripts/train.py``` program contains the code to load the image dataset, and train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
"\n",
"Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, ```mlflow.log_metric``` functions are used to track the convergence of the neural network training iterations. Finally ```mlflow.pytorch.save_model``` is used to save the trained model in framework-aware manner.\n",
"\n",
"Let's add the program to search path, import it as a module, and then invoke the driver function. Note that the training can take few minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lib_path = os.path.abspath(\"scripts\")\n",
"sys.path.append(lib_path)\n",
"\n",
"import train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = train.driver()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can view the metrics of the run at Azure Portal"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(azureml.mlflow.get_portal_url(run))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train model on GPU compute on Azure\n",
"\n",
"Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
"\n",
"Create a PyTorch estimator to specify the training configuration: script, compute as well as additional packages needed. To enable MLflow tracking, include ```azureml-mlflow``` as pip package. The low-level specifications for the training run are encapsulated in the estimator instance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import PyTorch\n",
"\n",
"pt = PyTorch(source_directory=\"./scripts\", \n",
" entry_script = \"train.py\", \n",
" compute_target = \"gpu-cluster\", \n",
" node_count = 1, \n",
" process_count_per_node = 1, \n",
" use_gpu=True,\n",
" pip_packages = [\"azureml-mlflow\", \"Pillow==6.0.0\"])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a reference to the experiment you created previously, but this time, as Azure Machine Learning experiment object.\n",
"\n",
"Then, use ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as cached image is used."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"exp = Experiment(ws, experiment_name)\n",
"run = exp.submit(pt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can monitor the run and its metrics on Azure Portal."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, you can wait for run to complete."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy model as web service\n",
"\n",
"To deploy a web service, first create a Docker image, and then deploy that Docker image on inferencing compute.\n",
"\n",
"The ```mlflow.azureml.build_image``` function builds a Docker image from saved PyTorch model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specififies package dependencies for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_file_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then build a docker image using *runs:/&lt;run.id&gt;/model* as the model_uri path.\n",
"\n",
"Note that the image building can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"model\"\n",
"\n",
"\n",
"azure_image, azure_model = mlflow.azureml.build_image(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
" workspace=ws,\n",
" model_name='pytorch_mnist',\n",
" image_name='pytorch-mnist-img',\n",
" synchronous=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
"\n",
"[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
"\n",
"Note that the service deployment can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
" memory_gb=5, \n",
" tags={\"data\": \"MNIST\", \"method\" : \"pytorch\"}, \n",
" description=\"Predict using webservice\")\n",
"\n",
"\n",
"# Deploy the image to Azure Container Instances (ACI) for real-time serving\n",
"webservice = Webservice.deploy_from_image(\n",
" image=azure_image, workspace=ws, name=\"pytorch-mnist-1\", deployment_config=aci_config)\n",
"\n",
"\n",
"webservice.wait_for_deployment()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the deployment has completed you can check the scoring URI of the web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make predictions using web service\n",
"\n",
"To make the web service, create a test data set as normalized PyTorch tensors. \n",
"\n",
"Then, let's define a utility function that takes a random image and converts it into format and shape suitable for as input to PyTorch inferencing end-point. The conversion is done by: \n",
"\n",
" 1. Select a random (image, label) tuple\n",
" 2. Take the image and converting the tensor to NumPy array \n",
" 3. Reshape array into 1 x 1 x N array\n",
" * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
" * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
" 4. Convert the NumPy array to list to make it into a built-in type.\n",
" 5. Create a dictionary {\"data\", &lt;list&gt;} that can be converted to JSON string for web service requests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from torchvision import datasets, transforms\n",
"import random\n",
"import numpy as np\n",
"\n",
"test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.1307,), (0.3081,))]))\n",
"\n",
"\n",
"def get_random_image():\n",
" image_idx = random.randint(0,len(test_data))\n",
" image_as_tensor = test_data[image_idx][0]\n",
" return {\"data\": elem for elem in image_as_tensor.numpy().reshape(1,1,-1).tolist()}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, invoke the web service using a random test image. Convert the dictionary containing the image to JSON string before passing it to web service.\n",
"\n",
"The response contains the raw scores for each label, with greater value indicating higher probability. Sort the labels and select the one with greatest score to get the prediction. Let's also plot the image sent to web service for comparison purposes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"\n",
"test_image = get_random_image()\n",
"\n",
"response = webservice.run(json.dumps(test_image))\n",
"\n",
"response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
"\n",
"\n",
"print(\"Predicted label:\", response[0][0])\n",
"plt.imshow(np.array(test_image[\"data\"]).reshape(28,28), cmap = \"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also call the web service using a raw POST method against the web service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"response = requests.post(url=webservice.scoring_uri, data=json.dumps(test_image),headers={\"Content-type\": \"application/json\"})\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"celltoolbar": "Edit Metadata",
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"celltoolbar": "Edit Metadata",
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
},
"name": "mlflow-sparksummit-pytorch",
"notebookId": 2495374963457641
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
},
"name": "mlflow-sparksummit-pytorch",
"notebookId": 2495374963457641
},
"nbformat": 4,
"nbformat_minor": 1
}
"nbformat": 4,
"nbformat_minor": 1
}

490
how-to-use-azureml/using-mlflow/train-local/train-local.ipynb
View File

@@ -1,248 +1,248 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-local/train-local.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use MLflow with Azure Machine Learning for Local Training Run\n",
"\n",
"This example shows you how to use mlflow tracking APIs together with Azure Machine Learning services for storing your metrics and artifacts, from local Notebook run. You'll learn how to:\n",
"\n",
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
" 2. Create experiment\n",
" 3. Train a model on your local computer while logging metrics and artifacts\n",
" 4. View your experiment within your Azure ML Workspace in Azure Portal.\n",
"\n",
"## Prerequisites and Set-up\n",
"\n",
"Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
"\n",
"Install azureml-mlflow package before running this notebook. Note that mlflow itself gets installed as dependency if you haven't installed it yet.\n",
"\n",
"```\n",
"pip install azureml-mlflow\n",
"```\n",
"\n",
"This example also uses scikit-learn and matplotlib packages. Install them:\n",
"```\n",
"pip install scikit-learn matplotlib\n",
"```\n",
"\n",
"Then, import necessary packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlflow\n",
"import mlflow.sklearn\n",
"import azureml.core\n",
"from azureml.core import Workspace\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set tracking URI\n",
"\n",
"Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = \"experiment-with-mlflow\"\n",
"mlflow.set_experiment(experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create training and test data set\n",
"\n",
"This example uses diabetes dataset to build a simple regression model. Let's load the dataset and split it into training and test sets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"data = {\n",
" \"train\":{\"X\": X_train, \"y\": y_train}, \n",
" \"test\":{\"X\": X_test, \"y\": y_test}\n",
"}\n",
"\n",
"print (\"Data contains\", len(data['train']['X']), \"training samples and\",len(data['test']['X']), \"test samples\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train while logging metrics and artifacts\n",
"\n",
"Next, start a mlflow run to train a scikit-learn regression model. Note that the training script has been instrumented using MLflow to:\n",
" * Log model hyperparameter alpha value\n",
" * Log mean squared error against test set\n",
" * Save the scikit-learn based regression model produced by training\n",
" * Save an image that shows actuals vs predictions against test set.\n",
" \n",
"These metrics and artifacts have been recorded to your Azure ML Workspace; in the next step you'll learn how to view them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a run object in the experiment\n",
"model_save_path = \"model\"\n",
"\n",
"with mlflow.start_run() as run:\n",
" # Log the algorithm parameter alpha to the run\n",
" mlflow.log_metric('alpha', 0.03)\n",
" # Create, fit, and test the scikit-learn Ridge regression model\n",
" regression_model = Ridge(alpha=0.03)\n",
" regression_model.fit(data['train']['X'], data['train']['y'])\n",
" preds = regression_model.predict(data['test']['X'])\n",
"\n",
" # Log mean squared error\n",
" print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
" mlflow.log_metric('mse', mean_squared_error(data['test']['y'], preds))\n",
" \n",
" # Save the model to the outputs directory for capture\n",
" mlflow.sklearn.log_model(regression_model,model_save_path)\n",
" \n",
" # Plot actuals vs predictions and save the plot within the run\n",
" fig = plt.figure(1)\n",
" idx = np.argsort(data['test']['y'])\n",
" plt.plot(data['test']['y'][idx],preds[idx])\n",
" fig.savefig(\"actuals_vs_predictions.png\")\n",
" mlflow.log_artifact(\"actuals_vs_predictions.png\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can open the report page for your experiment and runs within it from Azure Portal.\n",
"\n",
"Select one of the runs to view the metrics, and the plot you saved. The saved scikit-learn model appears under **outputs** tab."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws.experiments[experiment_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next steps\n",
"\n",
"Try out these notebooks to learn more about MLflow-Azure Machine Learning integration:\n",
"\n",
" * [Train a model using remote compute on Azure Cloud](../train-on-remote/train-on-remote.ipynb)\n",
" * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "rastala"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-local/train-local.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use MLflow with Azure Machine Learning for Local Training Run\n",
"\n",
"This example shows you how to use mlflow tracking APIs together with Azure Machine Learning services for storing your metrics and artifacts, from local Notebook run. You'll learn how to:\n",
"\n",
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
" 2. Create experiment\n",
" 3. Train a model on your local computer while logging metrics and artifacts\n",
" 4. View your experiment within your Azure ML Workspace in Azure Portal.\n",
"\n",
"## Prerequisites and Set-up\n",
"\n",
"Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
"\n",
"Install azureml-mlflow package before running this notebook. Note that mlflow itself gets installed as dependency if you haven't installed it yet.\n",
"\n",
"```\n",
"pip install azureml-mlflow\n",
"```\n",
"\n",
"This example also uses scikit-learn and matplotlib packages. Install them:\n",
"```\n",
"pip install scikit-learn matplotlib\n",
"```\n",
"\n",
"Then, import necessary packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlflow\n",
"import mlflow.sklearn\n",
"import azureml.core\n",
"from azureml.core import Workspace\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set tracking URI\n",
"\n",
"Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = \"experiment-with-mlflow\"\n",
"mlflow.set_experiment(experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create training and test data set\n",
"\n",
"This example uses diabetes dataset to build a simple regression model. Let's load the dataset and split it into training and test sets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"data = {\n",
" \"train\":{\"X\": X_train, \"y\": y_train}, \n",
" \"test\":{\"X\": X_test, \"y\": y_test}\n",
"}\n",
"\n",
"print (\"Data contains\", len(data['train']['X']), \"training samples and\",len(data['test']['X']), \"test samples\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train while logging metrics and artifacts\n",
"\n",
"Next, start a mlflow run to train a scikit-learn regression model. Note that the training script has been instrumented using MLflow to:\n",
" * Log model hyperparameter alpha value\n",
" * Log mean squared error against test set\n",
" * Save the scikit-learn based regression model produced by training\n",
" * Save an image that shows actuals vs predictions against test set.\n",
" \n",
"These metrics and artifacts have been recorded to your Azure ML Workspace; in the next step you'll learn how to view them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a run object in the experiment\n",
"model_save_path = \"model\"\n",
"\n",
"with mlflow.start_run() as run:\n",
" # Log the algorithm parameter alpha to the run\n",
" mlflow.log_metric('alpha', 0.03)\n",
" # Create, fit, and test the scikit-learn Ridge regression model\n",
" regression_model = Ridge(alpha=0.03)\n",
" regression_model.fit(data['train']['X'], data['train']['y'])\n",
" preds = regression_model.predict(data['test']['X'])\n",
"\n",
" # Log mean squared error\n",
" print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
" mlflow.log_metric('mse', mean_squared_error(data['test']['y'], preds))\n",
" \n",
" # Save the model to the outputs directory for capture\n",
" mlflow.sklearn.log_model(regression_model,model_save_path)\n",
" \n",
" # Plot actuals vs predictions and save the plot within the run\n",
" fig = plt.figure(1)\n",
" idx = np.argsort(data['test']['y'])\n",
" plt.plot(data['test']['y'][idx],preds[idx])\n",
" fig.savefig(\"actuals_vs_predictions.png\")\n",
" mlflow.log_artifact(\"actuals_vs_predictions.png\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can open the report page for your experiment and runs within it from Azure Portal.\n",
"\n",
"Select one of the runs to view the metrics, and the plot you saved. The saved scikit-learn model appears under **outputs** tab."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws.experiments[experiment_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next steps\n",
"\n",
"Try out these notebooks to learn more about MLflow-Azure Machine Learning integration:\n",
"\n",
" * [Train a model using remote compute on Azure Cloud](../train-on-remote/train-on-remote.ipynb)\n",
" * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
"metadata": {
"authors": [
{
"name": "rastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat": 4,
"nbformat_minor": 2
}

630
how-to-use-azureml/using-mlflow/train-remote/train-remote.ipynb
View File

@@ -1,318 +1,318 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-remote/train-remote.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use MLflow with Azure Machine Learning for Remote Training Run\n",
"\n",
"This example shows you how to use MLflow tracking APIs together with Azure Machine Learning services for storing your metrics and artifacts, from local Notebook run. You'll learn how to:\n",
"\n",
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
" 2. Create experiment\n",
" 3. Train a model on Machine Learning Compute while logging metrics and artifacts\n",
" 4. View your experiment within your Azure ML Workspace in Azure Portal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"\n",
"Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set-up\n",
"\n",
"Check Azure ML SDK version installed on your computer, and then connect to your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"from azureml.core import Workspace, Experiment\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)\n",
"\n",
"ws = Workspace.from_config()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also create a Machine Learning Compute cluster for submitting the remote run. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"cpu-cluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
" print(\"Found existing cpu-cluster\")\n",
"except ComputeTargetException:\n",
" print(\"Creating new cpu-cluster\")\n",
" \n",
" # Specify the configuration for the new cluster\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
" min_nodes=0,\n",
" max_nodes=1)\n",
"\n",
" # Create the cluster with the specified name and configuration\n",
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
" \n",
" # Wait for the cluster to complete, show the output log\n",
" cpu_cluster.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Azure ML Experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following steps show how to submit a training Python script to a cluster as an Azure ML run, while logging happens through MLflow APIs to your Azure ML Workspace. Let's first create an experiment to hold the training runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = \"experiment-with-mlflow\"\n",
"exp = Experiment(workspace=ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instrument remote training script using MLflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use [*train_diabetes.py*](train_diabetes.py) to train a regression model against diabetes dataset as the example. Note that the training script uses mlflow.start_run() to start logging, and then logs metrics, saves the trained scikit-learn model, and saves a plot as an artifact.\n",
"\n",
"Run following command to view the script file. Notice the mlflow logging statements, and also notice that the script doesn't have explicit dependencies on azureml library."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"training_script = 'train_diabetes.py'\n",
"with open(training_script, 'r') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit Run to Cluster \n",
"\n",
"Let's submit the run to cluster. When running on the remote cluster as submitted run, Azure ML sets the MLflow tracking URI to point to your Azure ML Workspace, so that the metrics and artifacts are automatically logged there.\n",
"\n",
"Note that you have to specify the packages your script depends on, including *azureml-mlflow* that implicitly enables the MLflow logging to Azure ML. \n",
"\n",
"First, create a environment with Docker enable and required package dependencies specified."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"mlflow"
]
},
"outputs": [],
"source": [
"from azureml.core import Environment\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"env = Environment(name=\"mlflow-env\")\n",
"\n",
"env.docker.enabled = True\n",
"\n",
"# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n",
"cd = CondaDependencies.create(\n",
" conda_packages=[\"scikit-learn\", \"matplotlib\"],\n",
" pip_packages=[\"azureml-mlflow\", \"numpy\"]\n",
" )\n",
"\n",
"env.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, specify a script run configuration that includes the training script, environment and CPU cluster created earlier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=\".\", script=training_script)\n",
"src.run_config.environment = env\n",
"src.run_config.target = cpu_cluster.name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, submit the run. Note that the first instance of the run typically takes longer as the Docker-based environment is created, several minutes. Subsequent runs reuse the image and are faster."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = exp.submit(src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can navigate to your Azure ML Workspace at Azure Portal to view the run metrics and artifacts. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also get the metrics and bring them to your local notebook, and view the details of the run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws.get_details()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next steps\n",
"\n",
" * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)\n",
" * [Learn more about Azure Machine Learning compute options](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "rastala"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-remote/train-remote.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use MLflow with Azure Machine Learning for Remote Training Run\n",
"\n",
"This example shows you how to use MLflow tracking APIs together with Azure Machine Learning services for storing your metrics and artifacts, from local Notebook run. You'll learn how to:\n",
"\n",
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
" 2. Create experiment\n",
" 3. Train a model on Machine Learning Compute while logging metrics and artifacts\n",
" 4. View your experiment within your Azure ML Workspace in Azure Portal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"\n",
"Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set-up\n",
"\n",
"Check Azure ML SDK version installed on your computer, and then connect to your Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"from azureml.core import Workspace, Experiment\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)\n",
"\n",
"ws = Workspace.from_config()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also create a Machine Learning Compute cluster for submitting the remote run. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"cpu-cluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
" print(\"Found existing cpu-cluster\")\n",
"except ComputeTargetException:\n",
" print(\"Creating new cpu-cluster\")\n",
" \n",
" # Specify the configuration for the new cluster\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
" min_nodes=0,\n",
" max_nodes=1)\n",
"\n",
" # Create the cluster with the specified name and configuration\n",
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
" \n",
" # Wait for the cluster to complete, show the output log\n",
" cpu_cluster.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Azure ML Experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following steps show how to submit a training Python script to a cluster as an Azure ML run, while logging happens through MLflow APIs to your Azure ML Workspace. Let's first create an experiment to hold the training runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = \"experiment-with-mlflow\"\n",
"exp = Experiment(workspace=ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instrument remote training script using MLflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use [*train_diabetes.py*](train_diabetes.py) to train a regression model against diabetes dataset as the example. Note that the training script uses mlflow.start_run() to start logging, and then logs metrics, saves the trained scikit-learn model, and saves a plot as an artifact.\n",
"\n",
"Run following command to view the script file. Notice the mlflow logging statements, and also notice that the script doesn't have explicit dependencies on azureml library."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"training_script = 'train_diabetes.py'\n",
"with open(training_script, 'r') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit Run to Cluster \n",
"\n",
"Let's submit the run to cluster. When running on the remote cluster as submitted run, Azure ML sets the MLflow tracking URI to point to your Azure ML Workspace, so that the metrics and artifacts are automatically logged there.\n",
"\n",
"Note that you have to specify the packages your script depends on, including *azureml-mlflow* that implicitly enables the MLflow logging to Azure ML. \n",
"\n",
"First, create a environment with Docker enable and required package dependencies specified."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"mlflow"
]
},
"outputs": [],
"source": [
"from azureml.core import Environment\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"env = Environment(name=\"mlflow-env\")\n",
"\n",
"env.docker.enabled = True\n",
"\n",
"# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n",
"cd = CondaDependencies.create(\n",
" conda_packages=[\"scikit-learn\", \"matplotlib\"],\n",
" pip_packages=[\"azureml-mlflow\", \"numpy\"]\n",
" )\n",
"\n",
"env.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, specify a script run configuration that includes the training script, environment and CPU cluster created earlier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=\".\", script=training_script)\n",
"src.run_config.environment = env\n",
"src.run_config.target = cpu_cluster.name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, submit the run. Note that the first instance of the run typically takes longer as the Docker-based environment is created, several minutes. Subsequent runs reuse the image and are faster."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = exp.submit(src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can navigate to your Azure ML Workspace at Azure Portal to view the run metrics and artifacts. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also get the metrics and bring them to your local notebook, and view the details of the run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws.get_details()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next steps\n",
"\n",
" * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)\n",
" * [Learn more about Azure Machine Learning compute options](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
"metadata": {
"authors": [
{
"name": "rastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat": 4,
"nbformat_minor": 2
}

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"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},

861
how-to-use-azureml/work-with-data/datasets/datasets-tutorial.ipynb
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@@ -1,430 +1,437 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial: Learn how to use Datasets in Azure ML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, you learn how to use Azure ML Datasets to train a regression model with the Azure Machine Learning SDK for Python. You will\n",
"\n",
"* Explore and prepare data for training the model\n",
"* Register the Dataset in your workspace to share it with others\n",
"* Take snapshots of data to ensure models can be trained with the same data every time\n",
"* Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, you:\n",
"\n",
"&#x2611; Setup a Python environment and import packages\n",
"\n",
"&#x2611; Load the Titanic data from your Azure Blob Storage. (The [original data](https://www.kaggle.com/c/titanic/data) can be found on Kaggle)\n",
"\n",
"&#x2611; Explore and cleanse the data to remove anomalies\n",
"\n",
"&#x2611; Register the Dataset in your workspace, allowing you to use it in model training \n",
"\n",
"&#x2611; Take a Dataset snapshot for repeatability and train a model with the snapshot\n",
"\n",
"&#x2611; Make changes to the dataset's definition without breaking the production model or the daily data pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-requisites:\n",
"Skip to Set up your development environment to read through the notebook steps, or use the instructions below to get the notebook and run it on Azure Notebooks or your own notebook server. To run the notebook you will need:\n",
"\n",
"A Python 3.6 notebook server with the following installed:\n",
"* The Azure Machine Learning SDK for Python\n",
"* The Azure Machine Learning Data Prep SDK for Python\n",
"* The tutorial notebook\n",
"\n",
"Data and train.py script to store in your Azure Blob Storage Account.\n",
" * [Titanic data](./train-dataset/Titanic.csv)\n",
" * [train.py](./train-dataset/train.py)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create and register Datasets you need:\n",
"\n",
" * An Azure subscription. If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.\n",
"\n",
" * An Azure Machine Learning service workspace. See the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
"\n",
" * The Azure Machine Learning SDK for Python (version 1.0.21 or later). To install or update to the latest version of the SDK, see [Install or update the SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).\n",
"\n",
"\n",
"For more information on how to set up your workspace, see the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
"\n",
"The first part that needs to be done is setting up your python environment. You will need to import all of your python packages including `azureml.dataprep` and `azureml.core.dataset`. Then access your workspace through your Azure subscription and set up your compute target. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"import azureml.core\n",
"import pandas as pd\n",
"import logging\n",
"import os\n",
"import shutil\n",
"from azureml.core import Workspace, Datastore, Dataset\n",
"\n",
"# Get existing workspace from config.json file in the same folder as the tutorial notebook\n",
"# You can download the config file from your workspace\n",
"workspace = Workspace.from_config()\n",
"print(\"Workspace\")\n",
"print(workspace)\n",
"print(\"Compute targets\")\n",
"print(workspace.compute_targets)\n",
"\n",
"# Get compute target that has already been attached to the workspace\n",
"# Pick the right compute target from the list of computes attached to your workspace\n",
"\n",
"compute_target_name = 'dataset-test'\n",
"remote_compute_target = workspace.compute_targets[compute_target_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To load data to your dataset, you will access the data through your datastore. After you create your dataset, you can use `get_profile()` to see your data's statistics.\n",
"\n",
"We will now upload the [original data](https://www.kaggle.com/c/titanic/data) to the default datastore(blob) within your workspace.."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datastore = workspace.get_default_datastore()\n",
"datastore.upload_files(files=['./train-dataset/Titanic.csv'],\n",
" target_path='train-dataset/',\n",
" overwrite=True,\n",
" show_progress=True)\n",
"\n",
"dataset = Dataset.auto_read_files(path=datastore.path('train-dataset/Titanic.csv'))\n",
"\n",
"#Display Dataset Profile of the Titanic Dataset\n",
"dataset.get_profile()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To predict if a person survived the Titanic's sinking or not, the columns that are relevant to train the model are 'Survived','Pclass', 'Sex','SibSp', and 'Parch'. You can update your dataset's deinition and only keep these columns you will need. You will also need to convert values (\"male\",\"female\") in the \"Sex\" column to 0 or 1, because the algorithm in the train.py file will be using numeric values instead of strings.\n",
"\n",
"For more examples of preparing data with Datasets, see [Explore and prepare data with the Dataset class](aka.ms/azureml/howto/exploreandpreparedata)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds_def = dataset.get_definition()\n",
"ds_def = ds_def.keep_columns(['Survived','Pclass', 'Sex','SibSp', 'Parch', 'Fare'])\n",
"ds_def = ds_def.replace('Sex','male', 0)\n",
"ds_def = ds_def.replace('Sex','female', 1)\n",
"ds_def.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have cleaned your data, you can register your dataset in your workspace. \n",
"\n",
"Registering your dataset allows you to easily have access to your processed data and share it with other people in your organization using the same workspace. It can be accessed in any notebook or script that is connected to your workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = dataset.update_definition(ds_def, 'Cleaned Data')\n",
"\n",
"dataset.generate_profile(compute_target='local').get_result()\n",
"\n",
"dataset_name = 'clean_Titanic_tutorial'\n",
"dataset = dataset.register(workspace=workspace,\n",
" name=dataset_name,\n",
" description='training dataset',\n",
" tags = {'year':'2019', 'month':'Apr'},\n",
" exist_ok=True)\n",
"workspace.datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also take a snapshot of your dataset. This makes for easily reproducing your data as it is in that moment. Even if you changed the definition of your dataset, or have data that refreshes regularly, you can always go back to your snapshot to compare. Since this snapshot is being created on a compute in your workspace, it may take a signficant amount of time to provision the compute before running the action itself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(dataset.get_all_snapshots())\n",
"snapshot_name = 'train_snapshot'\n",
"\n",
"print(\"Compute target status\")\n",
"print(remote_compute_target.get_status().provisioning_state)\n",
"\n",
"snapshot = dataset.create_snapshot(snapshot_name=snapshot_name, \n",
" compute_target=remote_compute_target, \n",
" create_data_snapshot=True)\n",
"snapshot.wait_for_completion()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that you have registered your dataset and created a snapshot, you can call up the dataset and it's snapshot to use it in your train.py script.\n",
"\n",
"The following code snippit will train your model locally using the train.py script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment, RunConfiguration\n",
"\n",
"experiment_name = 'training-datasets'\n",
"experiment = Experiment(workspace = workspace, name = experiment_name)\n",
"project_folder = './train-dataset/'\n",
"\n",
"# create a new RunConfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"run_config.environment.python.user_managed_dependencies = True\n",
"\n",
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=project_folder, \n",
" script='train.py', \n",
" run_config=run_config) \n",
"run = experiment.submit(config=src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also use the same script with your dataset snapshot for your Pipeline's Python Script Step.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.pipeline.core import Pipeline, PipelineData\n",
"from azureml.pipeline.steps import PythonScriptStep\n",
"from azureml.data.data_reference import DataReference\n",
"\n",
"trainStep = PythonScriptStep(script_name=\"train.py\",\n",
" compute_target=remote_compute_target,\n",
" source_directory=project_folder)\n",
"\n",
"pipeline = Pipeline(workspace=workspace,\n",
" steps=trainStep)\n",
"\n",
"pipeline_run = experiment.submit(pipeline)\n",
"pipeline_run.wait_for_completion()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"During any point of your workflow, you can get a previous snapshot of your dataset and use that version in your pipeline to quickly see how different versions of your data can effect your model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"snapshot = dataset.get_snapshot(snapshot_name=snapshot_name)\n",
"snapshot.to_pandas_dataframe().head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can make changes to the dataset's definition without breaking the production model or the daily data pipeline. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can call get_definitions to see that there are several versions. After each change to a dataset's version, another one is added."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.get_definitions()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = Dataset.get(workspace=workspace, name=dataset_name)\n",
"ds_def = dataset.get_definition()\n",
"ds_def = ds_def.drop_columns(['Fare'])\n",
"dataset = dataset.update_definition(ds_def, 'Dropping Fare as PClass and Fare are strongly correlated')\n",
"dataset.generate_profile(compute_target='local').get_result()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dataset definitions can be deprecated when usage is no longer recommended and a replacement is available. When a deprecated dataset definition is used in an AML Experimentation/Pipeline scenario, a warning message gets returned but execution will not be blocked. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Deprecate dataset definition 1 by the 2nd definition\n",
"ds_def = dataset.get_definition('1')\n",
"ds_def.deprecate(deprecate_by_dataset_id=dataset._id, deprecated_by_definition_version='2')\n",
"dataset.get_definitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dataset definitions can be archived when definitions are not supposed to be used for any reasons (such as underlying data no longer available). When an archived dataset definition is used in an AML Experimentation/Pipeline scenario, execution will be blocked with error. No further actions can be performed on archived Dataset definitions, but the references will be kept intact. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Archive the deprecated dataset definition #1\n",
"ds_def = dataset.get_definition('1')\n",
"ds_def.archive()\n",
"dataset.get_definitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also reactivate any defition that you archived for later use."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds_def = dataset.get_definition('1')\n",
"ds_def.reactivate()\n",
"dataset.get_definitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now delete the current snapshot name to clean up your resource's space."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.delete_snapshot(snapshot_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have now finished using a dataset from start to finish of your experiment!"
]
}
],
"metadata": {
"authors": [
{
"name": "cforbe"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial: Learn how to use Datasets in Azure ML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, you learn how to use Azure ML Datasets to train a regression model with the Azure Machine Learning SDK for Python. You will\n",
"\n",
"* Explore and prepare data for training the model\n",
"* Register the Dataset in your workspace to share it with others\n",
"* Take snapshots of data to ensure models can be trained with the same data every time\n",
"* Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, you:\n",
"\n",
"&#x2611; Setup a Python environment and import packages\n",
"\n",
"&#x2611; Load the Titanic data from your Azure Blob Storage. (The [original data](https://www.kaggle.com/c/titanic/data) can be found on Kaggle)\n",
"\n",
"&#x2611; Explore and cleanse the data to remove anomalies\n",
"\n",
"&#x2611; Register the Dataset in your workspace, allowing you to use it in model training \n",
"\n",
"&#x2611; Take a Dataset snapshot for repeatability and train a model with the snapshot\n",
"\n",
"&#x2611; Make changes to the dataset's definition without breaking the production model or the daily data pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-requisites:\n",
"Skip to Set up your development environment to read through the notebook steps, or use the instructions below to get the notebook and run it on Azure Notebooks or your own notebook server. To run the notebook you will need:\n",
"\n",
"A Python 3.6 notebook server with the following installed:\n",
"* The Azure Machine Learning SDK for Python\n",
"* The Azure Machine Learning Data Prep SDK for Python\n",
"* The tutorial notebook\n",
"\n",
"Data and train.py script to store in your Azure Blob Storage Account.\n",
" * [Titanic data](./train-dataset/Titanic.csv)\n",
" * [train.py](./train-dataset/train.py)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create and register Datasets you need:\n",
"\n",
" * An Azure subscription. If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.\n",
"\n",
" * An Azure Machine Learning service workspace. See the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
"\n",
" * The Azure Machine Learning SDK for Python (version 1.0.21 or later). To install or update to the latest version of the SDK, see [Install or update the SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).\n",
"\n",
"\n",
"For more information on how to set up your workspace, see the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
"\n",
"The first part that needs to be done is setting up your python environment. You will need to import all of your python packages including `azureml.dataprep` and `azureml.core.dataset`. Then access your workspace through your Azure subscription and set up your compute target. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"import azureml.core\n",
"import pandas as pd\n",
"import logging\n",
"import os\n",
"import shutil\n",
"from azureml.core import Workspace, Datastore, Dataset\n",
"\n",
"# Get existing workspace from config.json file in the same folder as the tutorial notebook\n",
"# You can download the config file from your workspace\n",
"workspace = Workspace.from_config()\n",
"print(\"Workspace\")\n",
"print(workspace)\n",
"print(\"Compute targets\")\n",
"print(workspace.compute_targets)\n",
"\n",
"# Get compute target that has already been attached to the workspace\n",
"# Pick the right compute target from the list of computes attached to your workspace\n",
"\n",
"compute_target_name = 'dataset-test'\n",
"remote_compute_target = workspace.compute_targets[compute_target_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To load data to your dataset, you will access the data through your datastore. After you create your dataset, you can use `get_profile()` to see your data's statistics.\n",
"\n",
"We will now upload the [original data](https://www.kaggle.com/c/titanic/data) to the default datastore(blob) within your workspace.."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datastore = workspace.get_default_datastore()\n",
"datastore.upload_files(files=['./train-dataset/Titanic.csv'],\n",
" target_path='train-dataset/',\n",
" overwrite=True,\n",
" show_progress=True)\n",
"\n",
"dataset = Dataset.auto_read_files(path=datastore.path('train-dataset/Titanic.csv'))\n",
"\n",
"#Display Dataset Profile of the Titanic Dataset\n",
"dataset.get_profile()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To predict if a person survived the Titanic's sinking or not, the columns that are relevant to train the model are 'Survived','Pclass', 'Sex','SibSp', and 'Parch'. You can update your dataset's deinition and only keep these columns you will need. You will also need to convert values (\"male\",\"female\") in the \"Sex\" column to 0 or 1, because the algorithm in the train.py file will be using numeric values instead of strings.\n",
"\n",
"For more examples of preparing data with Datasets, see [Explore and prepare data with the Dataset class](aka.ms/azureml/howto/exploreandpreparedata)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds_def = dataset.get_definition()\n",
"ds_def = ds_def.keep_columns(['Survived','Pclass', 'Sex','SibSp', 'Parch', 'Fare'])\n",
"ds_def = ds_def.replace('Sex','male', 0)\n",
"ds_def = ds_def.replace('Sex','female', 1)\n",
"ds_def.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have cleaned your data, you can register your dataset in your workspace. \n",
"\n",
"Registering your dataset allows you to easily have access to your processed data and share it with other people in your organization using the same workspace. It can be accessed in any notebook or script that is connected to your workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = dataset.update_definition(ds_def, 'Cleaned Data')\n",
"\n",
"dataset.generate_profile(compute_target='local').get_result()\n",
"\n",
"dataset_name = 'clean_Titanic_tutorial'\n",
"dataset = dataset.register(workspace=workspace,\n",
" name=dataset_name,\n",
" description='training dataset',\n",
" tags = {'year':'2019', 'month':'Apr'},\n",
" exist_ok=True)\n",
"workspace.datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also take a snapshot of your dataset. This makes for easily reproducing your data as it is in that moment. Even if you changed the definition of your dataset, or have data that refreshes regularly, you can always go back to your snapshot to compare. Since this snapshot is being created on a compute in your workspace, it may take a signficant amount of time to provision the compute before running the action itself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(dataset.get_all_snapshots())\n",
"snapshot_name = 'train_snapshot'\n",
"\n",
"print(\"Compute target status\")\n",
"print(remote_compute_target.get_status().provisioning_state)\n",
"\n",
"snapshot = dataset.create_snapshot(snapshot_name=snapshot_name, \n",
" compute_target=remote_compute_target, \n",
" create_data_snapshot=True)\n",
"snapshot.wait_for_completion()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that you have registered your dataset and created a snapshot, you can call up the dataset and it's snapshot to use it in your train.py script.\n",
"\n",
"The following code snippit will train your model locally using the train.py script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment, RunConfiguration\n",
"\n",
"experiment_name = 'training-datasets'\n",
"experiment = Experiment(workspace = workspace, name = experiment_name)\n",
"project_folder = './train-dataset/'\n",
"\n",
"# create a new RunConfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"run_config.environment.python.user_managed_dependencies = True\n",
"\n",
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=project_folder, \n",
" script='train.py', \n",
" run_config=run_config) \n",
"run = experiment.submit(config=src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also use the same script with your dataset snapshot for your Pipeline's Python Script Step.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.pipeline.core import Pipeline, PipelineData\n",
"from azureml.pipeline.steps import PythonScriptStep\n",
"from azureml.data.data_reference import DataReference\n",
"\n",
"trainStep = PythonScriptStep(script_name=\"train.py\",\n",
" compute_target=remote_compute_target,\n",
" source_directory=project_folder)\n",
"\n",
"pipeline = Pipeline(workspace=workspace,\n",
" steps=trainStep)\n",
"\n",
"pipeline_run = experiment.submit(pipeline)\n",
"pipeline_run.wait_for_completion()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"During any point of your workflow, you can get a previous snapshot of your dataset and use that version in your pipeline to quickly see how different versions of your data can effect your model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"snapshot = dataset.get_snapshot(snapshot_name=snapshot_name)\n",
"snapshot.to_pandas_dataframe().head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can make changes to the dataset's definition without breaking the production model or the daily data pipeline. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can call get_definitions to see that there are several versions. After each change to a dataset's version, another one is added."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.get_definitions()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = Dataset.get(workspace=workspace, name=dataset_name)\n",
"ds_def = dataset.get_definition()\n",
"ds_def = ds_def.drop_columns(['Fare'])\n",
"dataset = dataset.update_definition(ds_def, 'Dropping Fare as PClass and Fare are strongly correlated')\n",
"dataset.generate_profile(compute_target='local').get_result()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dataset definitions can be deprecated when usage is no longer recommended and a replacement is available. When a deprecated dataset definition is used in an AML Experimentation/Pipeline scenario, a warning message gets returned but execution will not be blocked. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Deprecate dataset definition 1 by the 2nd definition\n",
"ds_def = dataset.get_definition('1')\n",
"ds_def.deprecate(deprecate_by_dataset_id=dataset._id, deprecated_by_definition_version='2')\n",
"dataset.get_definitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dataset definitions can be archived when definitions are not supposed to be used for any reasons (such as underlying data no longer available). When an archived dataset definition is used in an AML Experimentation/Pipeline scenario, execution will be blocked with error. No further actions can be performed on archived Dataset definitions, but the references will be kept intact. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Archive the deprecated dataset definition #1\n",
"ds_def = dataset.get_definition('1')\n",
"ds_def.archive()\n",
"dataset.get_definitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also reactivate any defition that you archived for later use."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds_def = dataset.get_definition('1')\n",
"ds_def.reactivate()\n",
"dataset.get_definitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now delete the current snapshot name to clean up your resource's space."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.delete_snapshot(snapshot_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have now finished using a dataset from start to finish of your experiment!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets/datasets-tutorial.png)"
]
}
],
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# Set up your notebook environment for Azure Machine Learning
To run the notebooks in this repository use one of following options.
## **Option 1: Use Azure Notebooks**
Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
1. Open one of the sample notebooks
**Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus.
## **Option 2: Use your own notebook server**
### Quick installation
We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
```sh
# install just the base SDK
pip install azureml-sdk
# clone the sample repoistory
git clone https://github.com/Azure/MachineLearningNotebooks.git
# below steps are optional
# install the base SDK, Jupyter notebook server and tensorboard
pip install azureml-sdk[notebooks,tensorboard]
# install model explainability component
pip install azureml-sdk[explain]
# install automated ml components
pip install azureml-sdk[automl]
# install experimental features (not ready for production use)
pip install azureml-sdk[contrib]
```
Note the _extras_ (the keywords inside the square brackets) can be combined. For example:
```sh
# install base SDK, Jupyter notebook and automated ml components
pip install azureml-sdk[notebooks,automl]
```
### Full instructions
[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
### Video walkthrough:
[!VIDEO https://youtu.be/VIsXeTuW3FU]
## **Option 3: Use Docker**
You need to have Docker engine installed locally and running. Open a command line window and type the following command.
__Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like.
```sh
# clone the sample repoistory
git clone https://github.com/Azure/MachineLearningNotebooks.git
# change current directory to the folder
# where Dockerfile of the specific SDK version is located.
cd MachineLearningNotebooks/Dockerfiles/1.0.10
# build a Docker image with the a name (azuremlsdk for example)
# and a version number tag (1.0.10 for example).
# this can take several minutes depending on your computer speed and network bandwidth.
docker build . -t azuremlsdk:1.0.10
# launch the built Docker container which also automatically starts
# a Jupyter server instance listening on port 8887 of the host machine
docker run -it -p 8887:8887 azuremlsdk:1.0.10
```
Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory.
If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example:
```sh
# install the core SDK and automated ml components
pip install azureml-sdk[automl]
# install the core SDK and model explainability component
pip install azureml-sdk[explain]
# install the core SDK and experimental components
pip install azureml-sdk[contrib]
```
Drag and Drop
The image will be downloaded by Fatkun

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Configuration\n",
"\n",
"_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
"\n",
"---\n",
"---\n",
"\n",
"## Table of Contents\n",
"\n",
"1. [Introduction](#Introduction)\n",
" 1. What is an Azure Machine Learning workspace\n",
"1. [Setup](#Setup)\n",
" 1. Azure subscription\n",
" 1. Azure ML SDK and other library installation\n",
" 1. Azure Container Instance registration\n",
"1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
" 1. Workspace parameters\n",
" 1. Access your workspace\n",
" 1. Create a new workspace\n",
"1. [Next steps](#Next%20steps)\n",
"\n",
"---\n",
"\n",
"## Introduction\n",
"\n",
"This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace. In this case, a library contains all of the notebooks in the current folder and any nested folders. You can configure this notebook library to use an existing workspace or create a new workspace.\n",
"\n",
"Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here. If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
"\n",
"In this notebook you will\n",
"* Learn about getting an Azure subscription\n",
"* Specify your workspace parameters\n",
"* Access or create your workspace\n",
"* Add a default compute cluster for your workspace\n",
"\n",
"### What is an Azure Machine Learning workspace\n",
"\n",
"An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inferencing, and the monitoring of deployed models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"This section describes activities required before you can access any Azure ML services functionality."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Azure Subscription\n",
"\n",
"In order to create an Azure ML Workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com). Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
"\n",
"### 2. Azure ML SDK and other library installation\n",
"\n",
"If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment). If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
"\n",
"Also install following libraries to your environment. Many of the example notebooks depend on them\n",
"\n",
"```\n",
"(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
"```\n",
"\n",
"Once installation is complete, the following cell checks the Azure ML SDK version:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"install"
]
},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"print(\"This notebook was created using version 1.0.45 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
"\n",
"### 3. Azure Container Instance registration\n",
"Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI. If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands. Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
"\n",
"```shell\n",
"# check to see if ACI is already registered\n",
"(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
"\n",
"# if ACI is not registered, run this command.\n",
"# note you need to be the subscription owner in order to execute this command successfully.\n",
"(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure your Azure ML workspace\n",
"\n",
"### Workspace parameters\n",
"\n",
"To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
"* Your subscription id\n",
"* A resource group name\n",
"* (optional) The region that will host your workspace\n",
"* A name for your workspace\n",
"\n",
"You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
"\n",
"You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com). If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
"\n",
"The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service). You should pick a region that is close to your location or that contains your data.\n",
"\n",
"The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
"\n",
"The following cell allows you to specify your workspace parameters. This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values. \n",
"\n",
"If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace! You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
"\n",
"Replace the default values in the cell below with your workspace parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
"resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
"workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
"workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Access your workspace\n",
"\n",
"The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters. If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method. The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"try:\n",
" ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
" # write the details of the workspace to a configuration file to the notebook library\n",
" ws.write_config()\n",
" print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
"except:\n",
" print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a new workspace\n",
"\n",
"If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
"\n",
"**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
"\n",
"This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
"\n",
"This will fail if:\n",
"* You do not have permission to create a workspace in the resource group\n",
"* You do not have permission to create a resource group if it's non-existing.\n",
"* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
"\n",
"If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"# Create the workspace using the specified parameters\n",
"ws = Workspace.create(name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group, \n",
" location = workspace_region,\n",
" create_resource_group = True,\n",
" exist_ok = True)\n",
"ws.get_details()\n",
"\n",
"# write the details of the workspace to a configuration file to the notebook library\n",
"ws.write_config()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"## Next steps\n",
"\n",
"In this notebook you configured this notebook library to connect easily to an Azure ML workspace. You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
"\n",
"If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models. A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
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