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@@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.57.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -1,621 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved. \n",
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"Licensed under the MIT License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Unfairness Mitigation with Fairlearn and Azure Machine Learning\n",
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"**This notebook shows how to upload results from Fairlearn's GridSearch mitigation algorithm into a dashboard in Azure Machine Learning Studio**\n",
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"\n",
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"## Table of Contents\n",
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"\n",
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"1. [Introduction](#Introduction)\n",
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"1. [Loading the Data](#LoadingData)\n",
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||||
"1. [Training an Unmitigated Model](#UnmitigatedModel)\n",
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"1. [Mitigation with GridSearch](#Mitigation)\n",
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"1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
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" 1. Registering models\n",
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" 1. Computing Fairness Metrics\n",
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" 1. Uploading to Azure\n",
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"1. [Conclusion](#Conclusion)\n",
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"\n",
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"<a id=\"Introduction\"></a>\n",
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"## Introduction\n",
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"This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.org) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.org/).\n",
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"\n",
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"We will apply the [grid search algorithm](https://fairlearn.org/v0.4.6/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
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"\n",
|
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"### Setup\n",
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"\n",
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||||
"To use this notebook, an Azure Machine Learning workspace is required.\n",
|
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"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
|
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"This notebook also requires the following packages:\n",
|
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"* `azureml-contrib-fairness`\n",
|
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"* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
|
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"* `joblib`\n",
|
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"* `liac-arff`\n",
|
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"* `raiwidgets`\n",
|
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"\n",
|
||||
"Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
|
||||
]
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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"metadata": {},
|
||||
"outputs": [],
|
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"source": [
|
||||
"# !pip install --upgrade scikit-learn>=0.22.1"
|
||||
]
|
||||
},
|
||||
{
|
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"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
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"metadata": {},
|
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"source": [
|
||||
"<a id=\"LoadingData\"></a>\n",
|
||||
"## Loading the Data\n",
|
||||
"We use the well-known `adult` census dataset, which we will fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
|
||||
"from raiwidgets import FairnessDashboard\n",
|
||||
"\n",
|
||||
"from sklearn.compose import ColumnTransformer\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
|
||||
"from sklearn.compose import make_column_selector as selector\n",
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"\n",
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now load and inspect the data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairness_nb_utils import fetch_census_dataset\n",
|
||||
"\n",
|
||||
"data = fetch_census_dataset()\n",
|
||||
" \n",
|
||||
"# Extract the items we want\n",
|
||||
"X_raw = data.data\n",
|
||||
"y = (data.target == '>50K') * 1\n",
|
||||
"\n",
|
||||
"X_raw[\"race\"].value_counts().to_dict()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We are going to treat the sex and race of each individual as protected attributes, and in this particular case we are going to remove these attributes from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). Protected attributes are often denoted by 'A' in the literature, and we follow that convention here:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = X_raw[['sex','race']]\n",
|
||||
"X_raw = X_raw.drop(labels=['sex', 'race'], axis = 1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
|
||||
" X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Ensure indices are aligned between X, y and A,\n",
|
||||
"# after all the slicing and splitting of DataFrames\n",
|
||||
"# and Series\n",
|
||||
"\n",
|
||||
"X_train = X_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"y_train = y_train.reset_index(drop=True)\n",
|
||||
"y_test = y_test.reset_index(drop=True)\n",
|
||||
"A_train = A_train.reset_index(drop=True)\n",
|
||||
"A_test = A_test.reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
|
||||
"\n",
|
||||
"For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"numeric_transformer = Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\"impute\", SimpleImputer()),\n",
|
||||
" (\"scaler\", StandardScaler()),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"categorical_transformer = Pipeline(\n",
|
||||
" [\n",
|
||||
" (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
|
||||
" (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"preprocessor = ColumnTransformer(\n",
|
||||
" transformers=[\n",
|
||||
" (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
|
||||
" (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
|
||||
" ]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = preprocessor.fit_transform(X_train)\n",
|
||||
"X_test = preprocessor.transform(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"UnmitigatedModel\"></a>\n",
|
||||
"## Training an Unmitigated Model\n",
|
||||
"\n",
|
||||
"So we have a point of comparison, we first train a model (specifically, logistic regression from scikit-learn) on the raw data, without applying any mitigation algorithm:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
|
||||
"\n",
|
||||
"unmitigated_predictor.fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can view this model in the fairness dashboard, and see the disparities which appear:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"FairnessDashboard(sensitive_features=A_test,\n",
|
||||
" y_true=y_test,\n",
|
||||
" y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Looking at the disparity in accuracy when we select 'Sex' as the sensitive feature, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n",
|
||||
"\n",
|
||||
"Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"Mitigation\"></a>\n",
|
||||
"## Mitigation with GridSearch\n",
|
||||
"\n",
|
||||
"The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox - for this simple example, we shall use the logistic regression estimator from scikit-learn. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n",
|
||||
"\n",
|
||||
"For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). *We are using this metric for the sake of simplicity* in this example; the appropriate fairness metric can only be selected after *careful examination of the broader context* in which the model is to be used."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),\n",
|
||||
" constraints=DemographicParity(),\n",
|
||||
" grid_size=71)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"With our estimator created, we can fit it to the data. After `fit()` completes, we extract the full set of predictors from the `GridSearch` object.\n",
|
||||
"\n",
|
||||
"The following cell trains a many copies of the underlying estimator, and may take a minute or two to run:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sweep.fit(X_train, y_train,\n",
|
||||
" sensitive_features=A_train.sex)\n",
|
||||
"\n",
|
||||
"# For Fairlearn pre-v0.5.0, need sweep._predictors\n",
|
||||
"predictors = sweep.predictors_"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the protected attribute; other potentially protected attributes will *not* be mitigated). In general, one might not want to do this, since there may be other considerations beyond the strict optimisation of error and disparity (of the given protected attribute)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"errors, disparities = [], []\n",
|
||||
"for predictor in predictors:\n",
|
||||
" error = ErrorRate()\n",
|
||||
" error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
|
||||
" disparity = DemographicParity()\n",
|
||||
" disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
|
||||
" \n",
|
||||
" errors.append(error.gamma(predictor.predict)[0])\n",
|
||||
" disparities.append(disparity.gamma(predictor.predict).max())\n",
|
||||
" \n",
|
||||
"all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
|
||||
"\n",
|
||||
"dominant_models_dict = dict()\n",
|
||||
"base_name_format = \"census_gs_model_{0}\"\n",
|
||||
"row_id = 0\n",
|
||||
"for row in all_results.itertuples():\n",
|
||||
" model_name = base_name_format.format(row_id)\n",
|
||||
" errors_for_lower_or_eq_disparity = all_results[\"error\"][all_results[\"disparity\"]<=row.disparity]\n",
|
||||
" if row.error <= errors_for_lower_or_eq_disparity.min():\n",
|
||||
" dominant_models_dict[model_name] = row.predictor\n",
|
||||
" row_id = row_id + 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can construct predictions for the dominant models (we include the unmitigated predictor as well, for comparison):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions_dominant = {\"census_unmitigated\": unmitigated_predictor.predict(X_test)}\n",
|
||||
"models_dominant = {\"census_unmitigated\": unmitigated_predictor}\n",
|
||||
"for name, predictor in dominant_models_dict.items():\n",
|
||||
" value = predictor.predict(X_test)\n",
|
||||
" predictions_dominant[name] = value\n",
|
||||
" models_dominant[name] = predictor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"These predictions may then be viewed in the fairness dashboard. We include the race column from the dataset, as an alternative basis for assessing the models. However, since we have not based our mitigation on it, the variation in the models with respect to race can be large."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"FairnessDashboard(sensitive_features=A_test, \n",
|
||||
" y_true=y_test.tolist(),\n",
|
||||
" y_pred=predictions_dominant)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When using sex as the sensitive feature and accuracy as the metric, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
|
||||
"\n",
|
||||
"By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"AzureUpload\"></a>\n",
|
||||
"## Uploading a Fairness Dashboard to Azure\n",
|
||||
"\n",
|
||||
"Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
|
||||
"1. Register the dominant models\n",
|
||||
"1. Precompute all the required metrics\n",
|
||||
"1. Upload to Azure\n",
|
||||
"\n",
|
||||
"Before that, we need to connect to Azure Machine Learning Studio:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Experiment, Model\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"RegisterModels\"></a>\n",
|
||||
"### Registering Models\n",
|
||||
"\n",
|
||||
"The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `<name>:<version>` pairs) of registered models in the workspace. We register each of the models in the `models_dominant` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.makedirs('models', exist_ok=True)\n",
|
||||
"def register_model(name, model):\n",
|
||||
" print(\"Registering \", name)\n",
|
||||
" model_path = \"models/{0}.pkl\".format(name)\n",
|
||||
" joblib.dump(value=model, filename=model_path)\n",
|
||||
" registered_model = Model.register(model_path=model_path,\n",
|
||||
" model_name=name,\n",
|
||||
" workspace=ws)\n",
|
||||
" print(\"Registered \", registered_model.id)\n",
|
||||
" return registered_model.id\n",
|
||||
"\n",
|
||||
"model_name_id_mapping = dict()\n",
|
||||
"for name, model in models_dominant.items():\n",
|
||||
" m_id = register_model(name, model)\n",
|
||||
" model_name_id_mapping[name] = m_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, produce new predictions dictionaries, with the updated names:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions_dominant_ids = dict()\n",
|
||||
"for name, y_pred in predictions_dominant.items():\n",
|
||||
" predictions_dominant_ids[model_name_id_mapping[name]] = y_pred"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"PrecomputeMetrics\"></a>\n",
|
||||
"### Precomputing Metrics\n",
|
||||
"\n",
|
||||
"We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `predictions_dominant_ids` dictionary we just created:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sf = { 'sex': A_test.sex, 'race': A_test.race }\n",
|
||||
"\n",
|
||||
"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"dash_dict = _create_group_metric_set(y_true=y_test,\n",
|
||||
" predictions=predictions_dominant_ids,\n",
|
||||
" sensitive_features=sf,\n",
|
||||
" prediction_type='binary_classification')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"DashboardUpload\"></a>\n",
|
||||
"### Uploading the Dashboard\n",
|
||||
"\n",
|
||||
"Now, we import our `contrib` package which contains the routine to perform the upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we can create an Experiment, then a Run, and upload our dashboard to it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp = Experiment(ws, \"Test_Fairlearn_GridSearch_Census_Demo\")\n",
|
||||
"print(exp)\n",
|
||||
"\n",
|
||||
"run = exp.start_logging()\n",
|
||||
"try:\n",
|
||||
" dashboard_title = \"Dominant Models from GridSearch\"\n",
|
||||
" upload_id = upload_dashboard_dictionary(run,\n",
|
||||
" dash_dict,\n",
|
||||
" dashboard_name=dashboard_title)\n",
|
||||
" print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
|
||||
"\n",
|
||||
" downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
|
||||
"finally:\n",
|
||||
" run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The dashboard can be viewed in the Run Details page.\n",
|
||||
"\n",
|
||||
"Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(dash_dict == downloaded_dict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"Conclusion\"></a>\n",
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.org/) provides that discussion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "riedgar"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,13 +0,0 @@
|
||||
name: fairlearn-azureml-mitigation
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-contrib-fairness
|
||||
- fairlearn>=0.6.2,<=0.7.0
|
||||
- joblib
|
||||
- liac-arff
|
||||
- raiwidgets~=0.36.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- protobuf==3.20.0
|
||||
- numpy<1.24.0
|
||||
@@ -1,111 +0,0 @@
|
||||
# ---------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# ---------------------------------------------------------
|
||||
|
||||
"""Utilities for azureml-contrib-fairness notebooks."""
|
||||
|
||||
import arff
|
||||
from collections import OrderedDict
|
||||
from contextlib import closing
|
||||
import gzip
|
||||
import pandas as pd
|
||||
from sklearn.datasets import fetch_openml
|
||||
from sklearn.utils import Bunch
|
||||
import time
|
||||
|
||||
|
||||
def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
|
||||
"""Fetch a given dataset from OpenML with retries as specified."""
|
||||
for i in range(max_retries):
|
||||
try:
|
||||
print("Download attempt {0} of {1}".format(i + 1, max_retries))
|
||||
data = fetch_openml(data_id=data_id, as_frame=True)
|
||||
break
|
||||
except Exception as e: # noqa: B902
|
||||
print("Download attempt failed with exception:")
|
||||
print(e)
|
||||
if i + 1 != max_retries:
|
||||
print("Will retry after {0} seconds".format(retry_delay))
|
||||
time.sleep(retry_delay)
|
||||
retry_delay = retry_delay * 2
|
||||
else:
|
||||
raise RuntimeError("Unable to download dataset from OpenML")
|
||||
|
||||
return data
|
||||
|
||||
|
||||
_categorical_columns = [
|
||||
'workclass',
|
||||
'education',
|
||||
'marital-status',
|
||||
'occupation',
|
||||
'relationship',
|
||||
'race',
|
||||
'sex',
|
||||
'native-country'
|
||||
]
|
||||
|
||||
|
||||
def fetch_census_dataset():
|
||||
"""Fetch the Adult Census Dataset.
|
||||
|
||||
This uses a particular URL for the Adult Census dataset. The code
|
||||
is a simplified version of fetch_openml() in sklearn.
|
||||
|
||||
The data are copied from:
|
||||
https://openml.org/data/v1/download/1595261.gz
|
||||
(as of 2021-03-31)
|
||||
"""
|
||||
try:
|
||||
from urllib import urlretrieve
|
||||
except ImportError:
|
||||
from urllib.request import urlretrieve
|
||||
|
||||
filename = "1595261.gz"
|
||||
data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/"
|
||||
|
||||
remaining_attempts = 5
|
||||
sleep_duration = 10
|
||||
while remaining_attempts > 0:
|
||||
try:
|
||||
urlretrieve(data_url + filename, filename)
|
||||
|
||||
http_stream = gzip.GzipFile(filename=filename, mode='rb')
|
||||
|
||||
with closing(http_stream):
|
||||
def _stream_generator(response):
|
||||
for line in response:
|
||||
yield line.decode('utf-8')
|
||||
|
||||
stream = _stream_generator(http_stream)
|
||||
data = arff.load(stream)
|
||||
except Exception as exc: # noqa: B902
|
||||
remaining_attempts -= 1
|
||||
print("Error downloading dataset from {} ({} attempt(s) remaining)"
|
||||
.format(data_url, remaining_attempts))
|
||||
print(exc)
|
||||
time.sleep(sleep_duration)
|
||||
sleep_duration *= 2
|
||||
continue
|
||||
else:
|
||||
# dataset successfully downloaded
|
||||
break
|
||||
else:
|
||||
raise Exception("Could not retrieve dataset from {}.".format(data_url))
|
||||
|
||||
attributes = OrderedDict(data['attributes'])
|
||||
arff_columns = list(attributes)
|
||||
|
||||
raw_df = pd.DataFrame(data=data['data'], columns=arff_columns)
|
||||
|
||||
target_column_name = 'class'
|
||||
target = raw_df.pop(target_column_name)
|
||||
for col_name in _categorical_columns:
|
||||
dtype = pd.api.types.CategoricalDtype(attributes[col_name])
|
||||
raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False)
|
||||
|
||||
result = Bunch()
|
||||
result.data = raw_df
|
||||
result.target = target
|
||||
|
||||
return result
|
||||
@@ -1,545 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Upload a Fairness Dashboard to Azure Machine Learning Studio\n",
|
||||
"**This notebook shows how to generate and upload a fairness assessment dashboard from Fairlearn to AzureML Studio**\n",
|
||||
"\n",
|
||||
"## Table of Contents\n",
|
||||
"\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Loading the Data](#LoadingData)\n",
|
||||
"1. [Processing the Data](#ProcessingData)\n",
|
||||
"1. [Training Models](#TrainingModels)\n",
|
||||
"1. [Logging in to AzureML](#LoginAzureML)\n",
|
||||
"1. [Registering the Models](#RegisterModels)\n",
|
||||
"1. [Using the Fairness Dashboard](#LocalDashboard)\n",
|
||||
"1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
|
||||
" 1. Computing Fairness Metrics\n",
|
||||
" 1. Uploading to Azure\n",
|
||||
"1. [Conclusion](#Conclusion)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"<a id=\"Introduction\"></a>\n",
|
||||
"## Introduction\n",
|
||||
"\n",
|
||||
"In this notebook, we walk through a simple example of using the `azureml-contrib-fairness` package to upload a collection of fairness statistics for a fairness dashboard. It is an example of integrating the [open source Fairlearn package](https://www.github.com/fairlearn/fairlearn) with Azure Machine Learning. This is not an example of fairness analysis or mitigation - this notebook simply shows how to get a fairness dashboard into the Azure Machine Learning portal. We will load the data and train a couple of simple models. We will then use Fairlearn to generate data for a Fairness dashboard, which we can upload to Azure Machine Learning portal and view there.\n",
|
||||
"\n",
|
||||
"### Setup\n",
|
||||
"\n",
|
||||
"To use this notebook, an Azure Machine Learning workspace is required.\n",
|
||||
"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
|
||||
"This notebook also requires the following packages:\n",
|
||||
"* `azureml-contrib-fairness`\n",
|
||||
"* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
|
||||
"* `joblib`\n",
|
||||
"* `liac-arff`\n",
|
||||
"* `raiwidgets`\n",
|
||||
"\n",
|
||||
"Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# !pip install --upgrade scikit-learn>=0.22.1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LoadingData\"></a>\n",
|
||||
"## Loading the Data\n",
|
||||
"We use the well-known `adult` census dataset, which we fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import svm\n",
|
||||
"from sklearn.compose import ColumnTransformer\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
|
||||
"from sklearn.compose import make_column_selector as selector\n",
|
||||
"from sklearn.pipeline import Pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we can load the data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairness_nb_utils import fetch_census_dataset\n",
|
||||
"\n",
|
||||
"data = fetch_census_dataset()\n",
|
||||
" \n",
|
||||
"# Extract the items we want\n",
|
||||
"X_raw = data.data\n",
|
||||
"y = (data.target == '>50K') * 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can take a look at some of the data. For example, the next cells shows the counts of the different races identified in the dataset:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(X_raw[\"race\"].value_counts().to_dict())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"ProcessingData\"></a>\n",
|
||||
"## Processing the Data\n",
|
||||
"\n",
|
||||
"With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and leave the rest of the feature data in `X_raw`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = X_raw[['sex','race']]\n",
|
||||
"X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
|
||||
" X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Ensure indices are aligned between X, y and A,\n",
|
||||
"# after all the slicing and splitting of DataFrames\n",
|
||||
"# and Series\n",
|
||||
"\n",
|
||||
"X_train = X_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"y_train = y_train.reset_index(drop=True)\n",
|
||||
"y_test = y_test.reset_index(drop=True)\n",
|
||||
"A_train = A_train.reset_index(drop=True)\n",
|
||||
"A_test = A_test.reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
|
||||
"\n",
|
||||
"For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"numeric_transformer = Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\"impute\", SimpleImputer()),\n",
|
||||
" (\"scaler\", StandardScaler()),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"categorical_transformer = Pipeline(\n",
|
||||
" [\n",
|
||||
" (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
|
||||
" (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"preprocessor = ColumnTransformer(\n",
|
||||
" transformers=[\n",
|
||||
" (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
|
||||
" (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
|
||||
" ]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = preprocessor.fit_transform(X_train)\n",
|
||||
"X_test = preprocessor.transform(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"TrainingModels\"></a>\n",
|
||||
"## Training Models\n",
|
||||
"\n",
|
||||
"We now train a couple of different models on our data. The `adult` census dataset is a classification problem - the goal is to predict whether a particular individual exceeds an income threshold. For the purpose of generating a dashboard to upload, it is sufficient to train two basic classifiers. First, a logistic regression classifier:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
|
||||
"\n",
|
||||
"lr_predictor.fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And for comparison, a support vector classifier:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"svm_predictor = svm.SVC()\n",
|
||||
"\n",
|
||||
"svm_predictor.fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LoginAzureML\"></a>\n",
|
||||
"## Logging in to AzureML\n",
|
||||
"\n",
|
||||
"With our two classifiers trained, we can log into our AzureML workspace:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Experiment, Model\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"RegisterModels\"></a>\n",
|
||||
"## Registering the Models\n",
|
||||
"\n",
|
||||
"Next, we register our models. By default, the subroutine which uploads the models checks that the names provided correspond to registered models in the workspace. We define a utility routine to do the registering:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.makedirs('models', exist_ok=True)\n",
|
||||
"def register_model(name, model):\n",
|
||||
" print(\"Registering \", name)\n",
|
||||
" model_path = \"models/{0}.pkl\".format(name)\n",
|
||||
" joblib.dump(value=model, filename=model_path)\n",
|
||||
" registered_model = Model.register(model_path=model_path,\n",
|
||||
" model_name=name,\n",
|
||||
" workspace=ws)\n",
|
||||
" print(\"Registered \", registered_model.id)\n",
|
||||
" return registered_model.id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, we register the models. For convenience in subsequent method calls, we store the results in a dictionary, which maps the `id` of the registered model (a string in `name:version` format) to the predictor itself:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model_dict = {}\n",
|
||||
"\n",
|
||||
"lr_reg_id = register_model(\"fairness_linear_regression\", lr_predictor)\n",
|
||||
"model_dict[lr_reg_id] = lr_predictor\n",
|
||||
"svm_reg_id = register_model(\"fairness_svm\", svm_predictor)\n",
|
||||
"model_dict[svm_reg_id] = svm_predictor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LocalDashboard\"></a>\n",
|
||||
"## Using the Fairlearn Dashboard\n",
|
||||
"\n",
|
||||
"We can now examine the fairness of the two models we have training, both as a function of race and (binary) sex. Before uploading the dashboard to the AzureML portal, we will first instantiate a local instance of the Fairlearn dashboard.\n",
|
||||
"\n",
|
||||
"Regardless of the viewing location, the dashboard is based on three things - the true values, the model predictions and the sensitive feature values. The dashboard can use predictions from multiple models and multiple sensitive features if desired (as we are doing here).\n",
|
||||
"\n",
|
||||
"Our first step is to generate a dictionary mapping the `id` of the registered model to the corresponding array of predictions:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ys_pred = {}\n",
|
||||
"for n, p in model_dict.items():\n",
|
||||
" ys_pred[n] = p.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can examine these predictions in a locally invoked Fairlearn dashboard. This can be compared to the dashboard uploaded to the portal (in the next section):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from raiwidgets import FairnessDashboard\n",
|
||||
"\n",
|
||||
"FairnessDashboard(sensitive_features=A_test, \n",
|
||||
" y_true=y_test.tolist(),\n",
|
||||
" y_pred=ys_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"AzureUpload\"></a>\n",
|
||||
"## Uploading a Fairness Dashboard to Azure\n",
|
||||
"\n",
|
||||
"Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
|
||||
"1. Precompute all the required metrics\n",
|
||||
"1. Upload to Azure\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"### Computing Fairness Metrics\n",
|
||||
"We use Fairlearn to create a dictionary which contains all the data required to display a dashboard. This includes both the raw data (true values, predicted values and sensitive features), and also the fairness metrics. The API is similar to that used to invoke the Dashboard locally. However, there are a few minor changes to the API, and the type of problem being examined (binary classification, regression etc.) needs to be specified explicitly:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sf = { 'Race': A_test.race, 'Sex': A_test.sex }\n",
|
||||
"\n",
|
||||
"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
|
||||
"\n",
|
||||
"dash_dict = _create_group_metric_set(y_true=y_test,\n",
|
||||
" predictions=ys_pred,\n",
|
||||
" sensitive_features=sf,\n",
|
||||
" prediction_type='binary_classification')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The `_create_group_metric_set()` method is currently underscored since its exact design is not yet final in Fairlearn."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Uploading to Azure\n",
|
||||
"\n",
|
||||
"We can now import the `azureml.contrib.fairness` package itself. We will round-trip the data, so there are two required subroutines:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we can upload the generated dictionary to AzureML. The upload method requires a run, so we first create an experiment and a run. The uploaded dashboard can be seen on the corresponding Run Details page in AzureML Studio. For completeness, we also download the dashboard dictionary which we uploaded."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp = Experiment(ws, \"notebook-01\")\n",
|
||||
"print(exp)\n",
|
||||
"\n",
|
||||
"run = exp.start_logging()\n",
|
||||
"try:\n",
|
||||
" dashboard_title = \"Sample notebook upload\"\n",
|
||||
" upload_id = upload_dashboard_dictionary(run,\n",
|
||||
" dash_dict,\n",
|
||||
" dashboard_name=dashboard_title)\n",
|
||||
" print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
|
||||
"\n",
|
||||
" downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
|
||||
"finally:\n",
|
||||
" run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(dash_dict == downloaded_dict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"Conclusion\"></a>\n",
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"In this notebook we have demonstrated how to generate and upload a fairness dashboard to AzureML Studio. We have not discussed how to analyse the results and apply mitigations. Those topics will be covered elsewhere."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "riedgar"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,13 +0,0 @@
|
||||
name: upload-fairness-dashboard
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-contrib-fairness
|
||||
- fairlearn>=0.6.2,<=0.7.0
|
||||
- joblib
|
||||
- liac-arff
|
||||
- raiwidgets~=0.36.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- protobuf==3.20.0
|
||||
- numpy<1.24.0
|
||||
@@ -14,14 +14,13 @@ dependencies:
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.57.0
|
||||
- azureml-defaults~=1.57.0
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.57.0/validated_win32_requirements.txt [--no-deps]
|
||||
- azureml-widgets~=1.59.0
|
||||
- azureml-defaults~=1.59.0
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_win32_requirements.txt [--no-deps]
|
||||
- matplotlib==3.7.1
|
||||
- xgboost==1.5.2
|
||||
- prophet==1.1.4
|
||||
- pandas==1.3.5
|
||||
- cmdstanpy==1.1.0
|
||||
- onnx==1.16.1
|
||||
- setuptools-git==1.2
|
||||
- spacy==3.7.4
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz
|
||||
|
||||
@@ -20,11 +20,11 @@ dependencies:
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.57.0
|
||||
- azureml-defaults~=1.57.0
|
||||
- azureml-widgets~=1.59.0
|
||||
- azureml-defaults~=1.59.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==3.7.4
|
||||
- xgboost==1.5.2
|
||||
- prophet==1.1.4
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.57.0/validated_linux_requirements.txt [--no-deps]
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_linux_requirements.txt [--no-deps]
|
||||
|
||||
@@ -15,12 +15,12 @@ dependencies:
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.57.0
|
||||
- azureml-defaults~=1.57.0
|
||||
- azureml-widgets~=1.59.0
|
||||
- azureml-defaults~=1.59.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- prophet==1.1.4
|
||||
- xgboost==1.5.2
|
||||
- spacy==3.7.4
|
||||
- matplotlib==3.7.1
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.57.0/validated_darwin_requirements.txt [--no-deps]
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_darwin_requirements.txt [--no-deps]
|
||||
|
||||
@@ -93,7 +93,8 @@
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.train.automl import AutoMLConfig\n",
|
||||
"from azureml.interpret import ExplanationClient"
|
||||
"from azureml.interpret import ExplanationClient\n",
|
||||
"from azureml.data.datapath import DataPath"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -266,10 +267,12 @@
|
||||
"pd.DataFrame(data).to_csv(\"data/train_data.csv\", index=False)\n",
|
||||
"\n",
|
||||
"ds = ws.get_default_datastore()\n",
|
||||
"ds.upload(\n",
|
||||
" src_dir=\"./data\", target_path=\"bankmarketing\", overwrite=True, show_progress=True\n",
|
||||
"target = DataPath(\n",
|
||||
" datastore=ds, path_on_datastore=\"bankmarketing/train_data.csv\", name=\"bankmarketing\"\n",
|
||||
")\n",
|
||||
"Dataset.File.upload_directory(\n",
|
||||
" src_dir=\"./data\", target=target, overwrite=True, show_progress=True\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Upload the training data as a tabular dataset for access during training on remote compute\n",
|
||||
"train_data = Dataset.Tabular.from_delimited_files(\n",
|
||||
@@ -1090,7 +1093,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.12"
|
||||
"version": "3.10.14"
|
||||
},
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
@@ -1104,5 +1107,5 @@
|
||||
"task": "Classification"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.57.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.57.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -91,7 +91,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.57.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -292,7 +292,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tf_env = Environment.get(ws, name='AzureML-tensorflow-2.16-cuda11')"
|
||||
"tf_env = Environment.get(ws, name='AzureML-tensorflow-2.16-cuda12')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,753 +0,0 @@
|
||||
{
|
||||
"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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Neural style transfer on video\n",
|
||||
"Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n",
|
||||
"1. Split a video into images\n",
|
||||
"2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n",
|
||||
"3. Stitch the image back into a video.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize Workspace\n",
|
||||
"\n",
|
||||
"Initialize a workspace object from persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Experiment\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import AmlCompute, ComputeTarget\n",
|
||||
"from azureml.core import Datastore, Dataset\n",
|
||||
"from azureml.pipeline.core import Pipeline\n",
|
||||
"from azureml.pipeline.steps import PythonScriptStep\n",
|
||||
"from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"from azureml.data import OutputFileDatasetConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Download models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# create directory for model\n",
|
||||
"model_dir = 'models'\n",
|
||||
"if not os.path.isdir(model_dir):\n",
|
||||
" os.mkdir(model_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import urllib.request\n",
|
||||
"\n",
|
||||
"def download_model(model_name):\n",
|
||||
" # downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
|
||||
" url = \"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n",
|
||||
" local_path = os.path.join(model_dir, model_name)\n",
|
||||
" urllib.request.urlretrieve(url, local_path)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register all Models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"mosaic_model = None\n",
|
||||
"candy_model = None\n",
|
||||
"\n",
|
||||
"models = Model.list(workspace=ws, tags=['scenario'])\n",
|
||||
"for m in models:\n",
|
||||
" print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)\n",
|
||||
" if m.name == 'mosaic' and mosaic_model is None:\n",
|
||||
" mosaic_model = m\n",
|
||||
" elif m.name == 'candy' and candy_model is None:\n",
|
||||
" candy_model = m\n",
|
||||
"\n",
|
||||
"if mosaic_model is None:\n",
|
||||
" print('Mosaic model does not exist, registering it')\n",
|
||||
" download_model('mosaic.pth')\n",
|
||||
" mosaic_model = Model.register(model_path = os.path.join(model_dir, \"mosaic.pth\"),\n",
|
||||
" model_name = \"mosaic\",\n",
|
||||
" tags = {'type': \"mosaic\", 'scenario': \"Style transfer using batch inference\"},\n",
|
||||
" description = \"Style transfer - Mosaic\",\n",
|
||||
" workspace = ws)\n",
|
||||
"else:\n",
|
||||
" print('Reusing existing mosaic model')\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"if candy_model is None:\n",
|
||||
" print('Candy model does not exist, registering it')\n",
|
||||
" download_model('candy.pth')\n",
|
||||
" candy_model = Model.register(model_path = os.path.join(model_dir, \"candy.pth\"),\n",
|
||||
" model_name = \"candy\",\n",
|
||||
" tags = {'type': \"candy\", 'scenario': \"Style transfer using batch inference\"},\n",
|
||||
" description = \"Style transfer - Candy\",\n",
|
||||
" workspace = ws)\n",
|
||||
"else:\n",
|
||||
" print('Reusing existing candy model')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create or use existing compute\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# AmlCompute\n",
|
||||
"cpu_cluster_name = \"cpu-cluster\"\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
|
||||
" print(\"found existing cluster.\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"creating new cluster\")\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n",
|
||||
" max_nodes = 1)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
|
||||
" cpu_cluster.wait_for_completion(show_output=True)\n",
|
||||
" \n",
|
||||
"# AmlCompute\n",
|
||||
"gpu_cluster_name = \"gpu-cluster\"\n",
|
||||
"try:\n",
|
||||
" gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n",
|
||||
" print(\"found existing cluster.\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"creating new cluster\")\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v3\",\n",
|
||||
" max_nodes = 3)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
|
||||
" gpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Python Scripts\n",
|
||||
"We use an edited version of `neural_style_mpi.py` (original is [here](https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py)). Scripts to split and stitch the video are thin wrappers to calls to `ffmpeg`. \n",
|
||||
"\n",
|
||||
"We install `ffmpeg` through conda dependencies."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scripts_folder = \"scripts\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"process_video_script_file = \"process_video.py\"\n",
|
||||
"\n",
|
||||
"# peek at contents\n",
|
||||
"with open(os.path.join(scripts_folder, process_video_script_file)) as process_video_file:\n",
|
||||
" print(process_video_file.read())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stitch_video_script_file = \"stitch_video.py\"\n",
|
||||
"\n",
|
||||
"# peek at contents\n",
|
||||
"with open(os.path.join(scripts_folder, stitch_video_script_file)) as stitch_video_file:\n",
|
||||
" print(stitch_video_file.read())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The sample video **organutan.mp4** is stored at a publicly shared datastore. We are registering the datastore below. If you want to take a look at the original video, click here. (https://pipelinedata.blob.core.windows.net/sample-videos/orangutan.mp4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# datastore for input video\n",
|
||||
"account_name = \"pipelinedata\"\n",
|
||||
"video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"sample-videos\",\n",
|
||||
" account_name=account_name, overwrite=True)\n",
|
||||
"\n",
|
||||
"# the default blob store attached to a workspace\n",
|
||||
"default_datastore = ws.get_default_datastore()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Sample video"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"video_name=os.getenv(\"STYLE_TRANSFER_VIDEO_NAME\", \"orangutan.mp4\") \n",
|
||||
"orangutan_video = Dataset.File.from_files((video_ds,video_name))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cd = CondaDependencies.create(python_version=\"3.8\", conda_packages=['pip==20.2.4'])\n",
|
||||
"\n",
|
||||
"cd.add_channel(\"conda-forge\")\n",
|
||||
"cd.add_conda_package(\"ffmpeg==4.0.2\")\n",
|
||||
"\n",
|
||||
"# Runconfig\n",
|
||||
"amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
|
||||
"amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n",
|
||||
"amlcompute_run_config.environment.spark.precache_packages = False"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ffmpeg_audio = OutputFileDatasetConfig(name=\"ffmpeg_audio\")\n",
|
||||
"processed_images = OutputFileDatasetConfig(name=\"processed_images\")\n",
|
||||
"output_video = OutputFileDatasetConfig(name=\"output_video\")\n",
|
||||
"\n",
|
||||
"ffmpeg_images = OutputFileDatasetConfig(name=\"ffmpeg_images\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Define tweakable parameters to pipeline\n",
|
||||
"These parameters can be changed when the pipeline is published and rerun from a REST call.\n",
|
||||
"As part of ParallelRunStep following 2 pipeline parameters will be created which can be used to override values.\n",
|
||||
" node_count\n",
|
||||
" process_count_per_node"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core.graph import PipelineParameter\n",
|
||||
"# create a parameter for style (one of \"candy\", \"mosaic\") to transfer the images to\n",
|
||||
"style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n",
|
||||
"# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n",
|
||||
"nodecount_param = PipelineParameter(name=\"nodecount\", default_value=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"split_video_step = PythonScriptStep(\n",
|
||||
" name=\"split video\",\n",
|
||||
" script_name=\"process_video.py\",\n",
|
||||
" arguments=[\"--input_video\", orangutan_video.as_mount(),\n",
|
||||
" \"--output_audio\", ffmpeg_audio,\n",
|
||||
" \"--output_images\", ffmpeg_images],\n",
|
||||
" compute_target=cpu_cluster,\n",
|
||||
" runconfig=amlcompute_run_config,\n",
|
||||
" source_directory=scripts_folder\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"stitch_video_step = PythonScriptStep(\n",
|
||||
" name=\"stitch\",\n",
|
||||
" script_name=\"stitch_video.py\",\n",
|
||||
" arguments=[\"--images_dir\", processed_images.as_input(), \n",
|
||||
" \"--input_audio\", ffmpeg_audio.as_input(), \n",
|
||||
" \"--output_dir\", output_video],\n",
|
||||
" compute_target=cpu_cluster,\n",
|
||||
" runconfig=amlcompute_run_config,\n",
|
||||
" source_directory=scripts_folder\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create environment, parallel step run config and parallel run step"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
|
||||
"\n",
|
||||
"parallel_cd = CondaDependencies.create(python_version=\"3.8\", conda_packages=['pip==20.2.4', 'numpy==1.19'])\n",
|
||||
"\n",
|
||||
"parallel_cd.add_channel(\"pytorch\")\n",
|
||||
"parallel_cd.add_conda_package(\"pytorch\")\n",
|
||||
"parallel_cd.add_conda_package(\"torchvision\")\n",
|
||||
"parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n",
|
||||
"\n",
|
||||
"styleenvironment = Environment(name=\"styleenvironment\")\n",
|
||||
"styleenvironment.python.conda_dependencies=parallel_cd\n",
|
||||
"styleenvironment.docker.base_image = DEFAULT_GPU_IMAGE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PipelineParameter\n",
|
||||
"from azureml.pipeline.steps import ParallelRunConfig\n",
|
||||
"\n",
|
||||
"parallel_run_config = ParallelRunConfig(\n",
|
||||
" environment=styleenvironment,\n",
|
||||
" entry_script='transform.py',\n",
|
||||
" output_action='summary_only',\n",
|
||||
" mini_batch_size=\"1\",\n",
|
||||
" error_threshold=1,\n",
|
||||
" source_directory=scripts_folder,\n",
|
||||
" compute_target=gpu_cluster, \n",
|
||||
" node_count=nodecount_param,\n",
|
||||
" process_count_per_node=2\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.steps import ParallelRunStep\n",
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"parallel_step_name = 'styletransfer-' + datetime.now().strftime('%Y%m%d%H%M')\n",
|
||||
"\n",
|
||||
"distributed_style_transfer_step = ParallelRunStep(\n",
|
||||
" name=parallel_step_name,\n",
|
||||
" inputs=[ffmpeg_images], # Input file share/blob container/file dataset\n",
|
||||
" output=processed_images, # Output file share/blob container\n",
|
||||
" arguments=[\"--style\", style_param],\n",
|
||||
" parallel_run_config=parallel_run_config,\n",
|
||||
" allow_reuse=False #[optional - default value True]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Run the pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n",
|
||||
"\n",
|
||||
"pipeline.validate()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n",
|
||||
"pipeline_run = Experiment(ws, 'styletransfer_parallel_mosaic').submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Monitor pipeline run\n",
|
||||
"\n",
|
||||
"The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This will output information of the pipeline run, including the link to the details page of portal.\n",
|
||||
"pipeline_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Optional: View detailed logs (streaming) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait the run for completion and show output log to console\n",
|
||||
"pipeline_run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Download output video"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Downloads the video in `output_video` folder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def download_video(run, target_dir=None):\n",
|
||||
" stitch_run = run.find_step_run(stitch_video_step.name)[0]\n",
|
||||
" port_data = stitch_run.get_details()['outputDatasets'][0]['dataset']\n",
|
||||
" port_data.download(target_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_run.wait_for_completion()\n",
|
||||
"download_video(pipeline_run, \"output_video_mosaic\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Publish pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_name = \"style-transfer-batch-inference\"\n",
|
||||
"print(pipeline_name)\n",
|
||||
"\n",
|
||||
"published_pipeline = pipeline.publish(\n",
|
||||
" name=pipeline_name, \n",
|
||||
" description=pipeline_name)\n",
|
||||
"print(\"Newly published pipeline id: {}\".format(published_pipeline.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get published pipeline\n",
|
||||
"This is another way to get the published pipeline."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PublishedPipeline\n",
|
||||
"\n",
|
||||
"# You could retrieve all pipelines that are published, or \n",
|
||||
"# just get the published pipeline object that you have the ID for.\n",
|
||||
"\n",
|
||||
"# Get all published pipeline objects in the workspace\n",
|
||||
"all_pub_pipelines = PublishedPipeline.list(ws)\n",
|
||||
"\n",
|
||||
"# We will iterate through the list of published pipelines and \n",
|
||||
"# use the last ID in the list for Schelue operations: \n",
|
||||
"print(\"Published pipelines found in the workspace:\")\n",
|
||||
"for pub_pipeline in all_pub_pipelines:\n",
|
||||
" print(\"Name:\", pub_pipeline.name,\"\\tDescription:\", pub_pipeline.description, \"\\tId:\", pub_pipeline.id, \"\\tStatus:\", pub_pipeline.status)\n",
|
||||
" if(pub_pipeline.name == pipeline_name):\n",
|
||||
" published_pipeline = pub_pipeline\n",
|
||||
"\n",
|
||||
"print(\"Published pipeline id: {}\".format(published_pipeline.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Run pipeline through REST calls for other styles\n",
|
||||
"\n",
|
||||
"# Get AAD token"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.authentication import InteractiveLoginAuthentication\n",
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"auth = InteractiveLoginAuthentication()\n",
|
||||
"aad_token = auth.get_authentication_header()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get endpoint URL"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rest_endpoint = published_pipeline.endpoint\n",
|
||||
"print(\"Pipeline REST endpoing: {}\".format(rest_endpoint))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Send request and monitor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"experiment_name = 'styletransfer_parallel_candy'\n",
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": experiment_name,\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"candy\", \"NodeCount\": 3}})\n",
|
||||
"\n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
"\n",
|
||||
"from azureml.pipeline.core.run import PipelineRun\n",
|
||||
"published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n",
|
||||
"\n",
|
||||
"# Show detail information of run\n",
|
||||
"published_pipeline_run_candy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Download output from re-run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline_run_candy.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sanpil joringer asraniwa pansav tracych"
|
||||
}
|
||||
],
|
||||
"category": "Other notebooks",
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": true,
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"friendly_name": "Style transfer using ParallelRunStep",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
},
|
||||
"tags": [
|
||||
"Batch Inferencing",
|
||||
"Pipeline"
|
||||
],
|
||||
"task": "Style transfer"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,22 +0,0 @@
|
||||
import argparse
|
||||
import glob
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
parser = argparse.ArgumentParser(description="Process input video")
|
||||
parser.add_argument('--input_video', required=True)
|
||||
parser.add_argument('--output_audio', required=True)
|
||||
parser.add_argument('--output_images', required=True)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
os.makedirs(args.output_audio, exist_ok=True)
|
||||
os.makedirs(args.output_images, exist_ok=True)
|
||||
|
||||
subprocess.run("ffmpeg -i {} {}/video.aac".format(args.input_video, args.output_audio),
|
||||
shell=True,
|
||||
check=True)
|
||||
|
||||
subprocess.run("ffmpeg -i {} {}/%05d_video.jpg -hide_banner".format(args.input_video, args.output_images),
|
||||
shell=True,
|
||||
check=True)
|
||||
@@ -1,22 +0,0 @@
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
parser = argparse.ArgumentParser(description="Process input video")
|
||||
parser.add_argument('--images_dir', required=True)
|
||||
parser.add_argument('--input_audio', required=True)
|
||||
parser.add_argument('--output_dir', required=True)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
subprocess.run("ffmpeg -framerate 30 -i {}/%05d_video.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p "
|
||||
"-y {}/video_without_audio.mp4"
|
||||
.format(args.images_dir, args.output_dir),
|
||||
shell=True, check=True)
|
||||
|
||||
subprocess.run("ffmpeg -i {}/video_without_audio.mp4 -i {}/video.aac -map 0:0 -map 1:0 -vcodec "
|
||||
"copy -acodec copy -y {}/video_with_audio.mp4"
|
||||
.format(args.output_dir, args.input_audio, args.output_dir),
|
||||
shell=True, check=True)
|
||||
@@ -1,172 +0,0 @@
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import re
|
||||
import json
|
||||
import traceback
|
||||
from PIL import Image
|
||||
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
|
||||
from azureml.core.model import Model
|
||||
|
||||
style_model = None
|
||||
|
||||
|
||||
class TransformerNet(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(TransformerNet, self).__init__()
|
||||
# Initial convolution layers
|
||||
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
|
||||
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
|
||||
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
|
||||
self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
|
||||
self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
|
||||
self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
|
||||
# Residual layers
|
||||
self.res1 = ResidualBlock(128)
|
||||
self.res2 = ResidualBlock(128)
|
||||
self.res3 = ResidualBlock(128)
|
||||
self.res4 = ResidualBlock(128)
|
||||
self.res5 = ResidualBlock(128)
|
||||
# Upsampling Layers
|
||||
self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
|
||||
self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
|
||||
self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
|
||||
self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
|
||||
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
|
||||
# Non-linearities
|
||||
self.relu = torch.nn.ReLU()
|
||||
|
||||
def forward(self, X):
|
||||
y = self.relu(self.in1(self.conv1(X)))
|
||||
y = self.relu(self.in2(self.conv2(y)))
|
||||
y = self.relu(self.in3(self.conv3(y)))
|
||||
y = self.res1(y)
|
||||
y = self.res2(y)
|
||||
y = self.res3(y)
|
||||
y = self.res4(y)
|
||||
y = self.res5(y)
|
||||
y = self.relu(self.in4(self.deconv1(y)))
|
||||
y = self.relu(self.in5(self.deconv2(y)))
|
||||
y = self.deconv3(y)
|
||||
return y
|
||||
|
||||
|
||||
class ConvLayer(torch.nn.Module):
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride):
|
||||
super(ConvLayer, self).__init__()
|
||||
reflection_padding = kernel_size // 2
|
||||
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
|
||||
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
|
||||
|
||||
def forward(self, x):
|
||||
out = self.reflection_pad(x)
|
||||
out = self.conv2d(out)
|
||||
return out
|
||||
|
||||
|
||||
class ResidualBlock(torch.nn.Module):
|
||||
"""ResidualBlock
|
||||
introduced in: https://arxiv.org/abs/1512.03385
|
||||
recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
|
||||
"""
|
||||
|
||||
def __init__(self, channels):
|
||||
super(ResidualBlock, self).__init__()
|
||||
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
|
||||
self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
|
||||
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
|
||||
self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
|
||||
self.relu = torch.nn.ReLU()
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
out = self.relu(self.in1(self.conv1(x)))
|
||||
out = self.in2(self.conv2(out))
|
||||
out = out + residual
|
||||
return out
|
||||
|
||||
|
||||
class UpsampleConvLayer(torch.nn.Module):
|
||||
"""UpsampleConvLayer
|
||||
Upsamples the input and then does a convolution. This method gives better results
|
||||
compared to ConvTranspose2d.
|
||||
ref: http://distill.pub/2016/deconv-checkerboard/
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
|
||||
super(UpsampleConvLayer, self).__init__()
|
||||
self.upsample = upsample
|
||||
if upsample:
|
||||
self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
|
||||
reflection_padding = kernel_size // 2
|
||||
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
|
||||
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
|
||||
|
||||
def forward(self, x):
|
||||
x_in = x
|
||||
if self.upsample:
|
||||
x_in = self.upsample_layer(x_in)
|
||||
out = self.reflection_pad(x_in)
|
||||
out = self.conv2d(out)
|
||||
return out
|
||||
|
||||
|
||||
def load_image(filename):
|
||||
img = Image.open(filename)
|
||||
return img
|
||||
|
||||
|
||||
def save_image(filename, data):
|
||||
img = data.clone().clamp(0, 255).numpy()
|
||||
img = img.transpose(1, 2, 0).astype("uint8")
|
||||
img = Image.fromarray(img)
|
||||
img.save(filename)
|
||||
|
||||
|
||||
def init():
|
||||
global output_path, args
|
||||
global style_model, device
|
||||
output_path = os.environ['AZUREML_BI_OUTPUT_PATH']
|
||||
print(f'output path: {output_path}')
|
||||
print(f'Cuda available? {torch.cuda.is_available()}')
|
||||
|
||||
arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
|
||||
arg_parser.add_argument("--style", type=str, help="style name")
|
||||
args, unknown_args = arg_parser.parse_known_args()
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
with torch.no_grad():
|
||||
style_model = TransformerNet()
|
||||
model_path = Model.get_model_path(args.style)
|
||||
state_dict = torch.load(os.path.join(model_path))
|
||||
# remove saved deprecated running_* keys in InstanceNorm from the checkpoint
|
||||
for k in list(state_dict.keys()):
|
||||
if re.search(r'in\d+\.running_(mean|var)$', k):
|
||||
del state_dict[k]
|
||||
style_model.load_state_dict(state_dict)
|
||||
style_model.to(device)
|
||||
print(f'Model loaded successfully. Path: {model_path}')
|
||||
|
||||
|
||||
def run(mini_batch):
|
||||
|
||||
result = []
|
||||
for image_file_path in mini_batch:
|
||||
img = load_image(image_file_path)
|
||||
|
||||
with torch.no_grad():
|
||||
content_transform = transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Lambda(lambda x: x.mul(255))
|
||||
])
|
||||
content_image = content_transform(img)
|
||||
content_image = content_image.unsqueeze(0).to(device)
|
||||
|
||||
output = style_model(content_image).cpu()
|
||||
output_file_path = os.path.join(output_path, os.path.basename(image_file_path))
|
||||
save_image(output_file_path, output[0])
|
||||
result.append(output_file_path)
|
||||
|
||||
return result
|
||||
@@ -293,7 +293,7 @@
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.13-cuda11.7')"
|
||||
"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-2.2-cuda12.1')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,378 +0,0 @@
|
||||
{
|
||||
"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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Distributed PyTorch with Horovod\n",
|
||||
"In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod) across a GPU cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
|
||||
"* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Diagnostics\n",
|
||||
"Opt-in diagnostics for better experience, quality, and security of future releases."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"Diagnostics"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.telemetry import set_diagnostics_collection\n",
|
||||
"\n",
|
||||
"set_diagnostics_collection(send_diagnostics=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize workspace\n",
|
||||
"\n",
|
||||
"Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `Standard_NC6s_v3` GPU cluster that autoscales from `0` to `4` nodes.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
|
||||
"\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](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 cluster\n",
|
||||
"cluster_name = \"gpu-cluster\"\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
|
||||
" print('Found existing compute target.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('Creating a new compute target...')\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC6s_v3',\n",
|
||||
" max_nodes=4)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
" compute_target.wait_for_completion(show_output=True)\n",
|
||||
"\n",
|
||||
"# use get_status() to get a detailed status for the current AmlCompute. \n",
|
||||
"print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train model on the remote compute\n",
|
||||
"Now that we have the AmlCompute ready to go, let's run our distributed training job."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a project directory\n",
|
||||
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"project_folder = './pytorch-distr-hvd'\n",
|
||||
"os.makedirs(project_folder, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Prepare training script\n",
|
||||
"Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_horovod_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n",
|
||||
"\n",
|
||||
"However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n",
|
||||
"\n",
|
||||
"To do so, in `pytorch_horovod_mnist.py`, we will first access the Azure ML `Run` object within the script:\n",
|
||||
"```Python\n",
|
||||
"from azureml.core.run import Run\n",
|
||||
"run = Run.get_context()\n",
|
||||
"```\n",
|
||||
"Later within the script, we log the loss metric to our run:\n",
|
||||
"```Python\n",
|
||||
"run.log('loss', loss.item())\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once your script is ready, copy the training script `pytorch_horovod_mnist.py` into the project directory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import shutil\n",
|
||||
"\n",
|
||||
"shutil.copy('pytorch_horovod_mnist.py', project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create an experiment\n",
|
||||
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"experiment_name = 'pytorch-distr-hvd'\n",
|
||||
"experiment = Experiment(ws, name=experiment_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create an environment\n",
|
||||
"\n",
|
||||
"In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment. The curated environment includes the `torch`, `torchvision` and `horovod` packages required by the training script."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"pytorch_env = Environment.get(ws, name='AzureML-acpt-pytorch-1.13-cuda11.7')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure the training job\n",
|
||||
"\n",
|
||||
"Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n",
|
||||
"\n",
|
||||
"In order to execute a distributed run using MPI/Horovod, you must create an `MpiConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code will configure a 2-node distributed job running one process per node. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in `MpiConfiguration` (the default is `1`)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"from azureml.core.runconfig import MpiConfiguration\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory=project_folder,\n",
|
||||
" script='pytorch_horovod_mnist.py',\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" environment=pytorch_env,\n",
|
||||
" distributed_job_config=MpiConfiguration(node_count=2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Submit job\n",
|
||||
"Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run = experiment.submit(src)\n",
|
||||
"print(run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Monitor your run\n",
|
||||
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Alternatively, you can block until the script has completed training before running more code."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True) # this provides a verbose log"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "ninhu"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"MNIST"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"PyTorch"
|
||||
],
|
||||
"friendly_name": "Distributed PyTorch",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.18"
|
||||
},
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Train a model using the distributed training via Horovod"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,181 +0,0 @@
|
||||
# Copyright (c) 2017, PyTorch contributors
|
||||
# Modifications copyright (C) Microsoft Corporation
|
||||
# Licensed under the BSD license
|
||||
# Adapted from https://github.com/uber/horovod/blob/master/examples/pytorch_mnist.py
|
||||
|
||||
from __future__ import print_function
|
||||
import argparse
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
from torchvision import datasets, transforms
|
||||
import torch.utils.data.distributed
|
||||
import horovod.torch as hvd
|
||||
|
||||
from azureml.core.run import Run
|
||||
# get the Azure ML run object
|
||||
run = Run.get_context()
|
||||
|
||||
print("Torch version:", torch.__version__)
|
||||
|
||||
# Training settings
|
||||
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
|
||||
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
|
||||
help='input batch size for training (default: 64)')
|
||||
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
|
||||
help='input batch size for testing (default: 1000)')
|
||||
parser.add_argument('--epochs', type=int, default=10, metavar='N',
|
||||
help='number of epochs to train (default: 10)')
|
||||
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
|
||||
help='learning rate (default: 0.01)')
|
||||
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
|
||||
help='SGD momentum (default: 0.5)')
|
||||
parser.add_argument('--no-cuda', action='store_true', default=False,
|
||||
help='disables CUDA training')
|
||||
parser.add_argument('--seed', type=int, default=42, metavar='S',
|
||||
help='random seed (default: 42)')
|
||||
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
|
||||
help='how many batches to wait before logging training status')
|
||||
parser.add_argument('--fp16-allreduce', action='store_true', default=False,
|
||||
help='use fp16 compression during allreduce')
|
||||
args = parser.parse_args()
|
||||
args.cuda = not args.no_cuda and torch.cuda.is_available()
|
||||
|
||||
hvd.init()
|
||||
torch.manual_seed(args.seed)
|
||||
|
||||
if args.cuda:
|
||||
# Horovod: pin GPU to local rank.
|
||||
torch.cuda.set_device(hvd.local_rank())
|
||||
torch.cuda.manual_seed(args.seed)
|
||||
|
||||
|
||||
kwargs = {}
|
||||
# MNIST dataset
|
||||
datasets.MNIST.resources = [
|
||||
("train-images-idx3-ubyte.gz",
|
||||
"f68b3c2dcbeaaa9fbdd348bbdeb94873"),
|
||||
("train-labels-idx1-ubyte.gz",
|
||||
"d53e105ee54ea40749a09fcbcd1e9432"),
|
||||
("t10k-images-idx3-ubyte.gz",
|
||||
"9fb629c4189551a2d022fa330f9573f3"),
|
||||
("t10k-labels-idx1-ubyte.gz",
|
||||
"ec29112dd5afa0611ce80d1b7f02629c")
|
||||
]
|
||||
train_dataset = \
|
||||
datasets.MNIST('data-%d' % hvd.rank(), train=True, download=True,
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))
|
||||
]))
|
||||
train_sampler = torch.utils.data.distributed.DistributedSampler(
|
||||
train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_dataset, batch_size=args.batch_size, sampler=train_sampler, **kwargs)
|
||||
|
||||
test_dataset = \
|
||||
datasets.MNIST('data-%d' % hvd.rank(), train=False, transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))
|
||||
]))
|
||||
test_sampler = torch.utils.data.distributed.DistributedSampler(
|
||||
test_dataset, num_replicas=hvd.size(), rank=hvd.rank())
|
||||
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size,
|
||||
sampler=test_sampler, **kwargs)
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
|
||||
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
|
||||
self.conv2_drop = nn.Dropout2d()
|
||||
self.fc1 = nn.Linear(320, 50)
|
||||
self.fc2 = nn.Linear(50, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(F.max_pool2d(self.conv1(x), 2))
|
||||
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
|
||||
x = x.view(-1, 320)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.dropout(x, training=self.training)
|
||||
x = self.fc2(x)
|
||||
return F.log_softmax(x)
|
||||
|
||||
|
||||
model = Net()
|
||||
|
||||
if args.cuda:
|
||||
# Move model to GPU.
|
||||
model.cuda()
|
||||
|
||||
# Horovod: broadcast parameters.
|
||||
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
|
||||
|
||||
# Horovod: scale learning rate by the number of GPUs.
|
||||
optimizer = optim.SGD(model.parameters(), lr=args.lr * hvd.size(),
|
||||
momentum=args.momentum)
|
||||
|
||||
# Horovod: (optional) compression algorithm.
|
||||
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
|
||||
|
||||
# Horovod: wrap optimizer with DistributedOptimizer.
|
||||
optimizer = hvd.DistributedOptimizer(optimizer,
|
||||
named_parameters=model.named_parameters(),
|
||||
compression=compression)
|
||||
|
||||
|
||||
def train(epoch):
|
||||
model.train()
|
||||
train_sampler.set_epoch(epoch)
|
||||
for batch_idx, (data, target) in enumerate(train_loader):
|
||||
if args.cuda:
|
||||
data, target = data.cuda(), target.cuda()
|
||||
optimizer.zero_grad()
|
||||
output = model(data)
|
||||
loss = F.nll_loss(output, target)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if batch_idx % args.log_interval == 0:
|
||||
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
|
||||
epoch, batch_idx * len(data), len(train_sampler),
|
||||
100. * batch_idx / len(train_loader), loss.item()))
|
||||
|
||||
# log the loss to the Azure ML run
|
||||
run.log('loss', loss.item())
|
||||
|
||||
|
||||
def metric_average(val, name):
|
||||
tensor = torch.tensor(val)
|
||||
avg_tensor = hvd.allreduce(tensor, name=name)
|
||||
return avg_tensor.item()
|
||||
|
||||
|
||||
def test():
|
||||
model.eval()
|
||||
test_loss = 0.
|
||||
test_accuracy = 0.
|
||||
for data, target in test_loader:
|
||||
if args.cuda:
|
||||
data, target = data.cuda(), target.cuda()
|
||||
output = model(data)
|
||||
# sum up batch loss
|
||||
test_loss += F.nll_loss(output, target, size_average=False).item()
|
||||
# get the index of the max log-probability
|
||||
pred = output.data.max(1, keepdim=True)[1]
|
||||
test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float().sum()
|
||||
|
||||
test_loss /= len(test_sampler)
|
||||
test_accuracy /= len(test_sampler)
|
||||
|
||||
test_loss = metric_average(test_loss, 'avg_loss')
|
||||
test_accuracy = metric_average(test_accuracy, 'avg_accuracy')
|
||||
|
||||
if hvd.rank() == 0:
|
||||
print('\nTest set: Average loss: {:.4f}, Accuracy: {:.2f}%\n'.format(
|
||||
test_loss, 100. * test_accuracy))
|
||||
|
||||
|
||||
for epoch in range(1, args.epochs + 1):
|
||||
train(epoch)
|
||||
test()
|
||||
@@ -273,7 +273,7 @@
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.13-cuda11.7')"
|
||||
"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-2.2-cuda12.1')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,344 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Distributed TensorFlow with Horovod\n",
|
||||
"In this tutorial, you will train a model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
|
||||
"* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../../configuration.ipynb) to:\n",
|
||||
" * install the AML SDK\n",
|
||||
" * create a workspace and its configuration file (`config.json`)\n",
|
||||
"* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Diagnostics\n",
|
||||
"Opt-in diagnostics for better experience, quality, and security of future releases."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"Diagnostics"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.telemetry import set_diagnostics_collection\n",
|
||||
"\n",
|
||||
"set_diagnostics_collection(send_diagnostics=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize workspace\n",
|
||||
"Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\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](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 cluster\n",
|
||||
"cluster_name = \"gpu-cluster\"\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
|
||||
" print('Found existing compute target')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('Creating a new compute target...')\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC6s_v3', \n",
|
||||
" max_nodes=4)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
" compute_target.wait_for_completion(show_output=True)\n",
|
||||
"\n",
|
||||
"# use get_status() to get a detailed status for the current cluster. \n",
|
||||
"print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You may want to register datasets using the register() method to your workspace so that the dataset can be shared with others, reused across various experiments, and referred to by name in your training script."
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train model on the remote compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create an experiment\n",
|
||||
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"experiment_name = 'tf-distr-hvd'\n",
|
||||
"experiment = Experiment(ws, name=experiment_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create an environment\n",
|
||||
"\n",
|
||||
"In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the TensorFlow 1.13 GPU curated environment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"tf_env = Environment.get(ws, name='azureml-tensorflow-2.11-cuda11')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure the training job\n",
|
||||
"\n",
|
||||
"Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n",
|
||||
"\n",
|
||||
"In order to execute a distributed run using MPI/Horovod, you must create an `MpiConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code will configure a 2-node distributed job running one process per node. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in `MpiConfiguration` (the default is `1`)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"from azureml.core.runconfig import MpiConfiguration\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory=\"src\",\n",
|
||||
" script='train.py',\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" environment=tf_env,\n",
|
||||
" distributed_job_config=MpiConfiguration(node_count=2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Submit job\n",
|
||||
"Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run = experiment.submit(src)\n",
|
||||
"print(run)\n",
|
||||
"run.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Monitor your run\n",
|
||||
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Alternatively, you can block until the script has completed training before running more code."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "minxia"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"None"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"TensorFlow"
|
||||
],
|
||||
"friendly_name": "Distributed training using TensorFlow with Horovod",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
},
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Use the TensorFlow estimator to train a word2vec model"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,120 +0,0 @@
|
||||
# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
# Script adapted from: https://github.com/horovod/horovod/blob/master/examples/tensorflow2_keras_mnist.py
|
||||
# ==============================================================================
|
||||
|
||||
import tensorflow as tf
|
||||
import horovod.tensorflow.keras as hvd
|
||||
|
||||
import os
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--learning-rate", "-lr", type=float, default=0.001)
|
||||
parser.add_argument("--epochs", type=int, default=24)
|
||||
parser.add_argument("--steps-per-epoch", type=int, default=500)
|
||||
args = parser.parse_args()
|
||||
|
||||
# Horovod: initialize Horovod.
|
||||
hvd.init()
|
||||
|
||||
# Horovod: pin GPU to be used to process local rank (one GPU per process)
|
||||
gpus = tf.config.experimental.list_physical_devices("GPU")
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
if gpus:
|
||||
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU")
|
||||
|
||||
(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
|
||||
path="mnist-%d.npz" % hvd.rank()
|
||||
)
|
||||
|
||||
dataset = tf.data.Dataset.from_tensor_slices(
|
||||
(
|
||||
tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32),
|
||||
tf.cast(mnist_labels, tf.int64),
|
||||
)
|
||||
)
|
||||
dataset = dataset.repeat().shuffle(10000).batch(128)
|
||||
|
||||
mnist_model = tf.keras.Sequential(
|
||||
[
|
||||
tf.keras.layers.Conv2D(32, [3, 3], activation="relu"),
|
||||
tf.keras.layers.Conv2D(64, [3, 3], activation="relu"),
|
||||
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
|
||||
tf.keras.layers.Dropout(0.25),
|
||||
tf.keras.layers.Flatten(),
|
||||
tf.keras.layers.Dense(128, activation="relu"),
|
||||
tf.keras.layers.Dropout(0.5),
|
||||
tf.keras.layers.Dense(10, activation="softmax"),
|
||||
]
|
||||
)
|
||||
|
||||
# Horovod: adjust learning rate based on number of GPUs.
|
||||
scaled_lr = args.learning_rate * hvd.size()
|
||||
opt = tf.optimizers.Adam(scaled_lr)
|
||||
|
||||
# Horovod: add Horovod DistributedOptimizer.
|
||||
opt = hvd.DistributedOptimizer(opt)
|
||||
|
||||
# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
|
||||
# uses hvd.DistributedOptimizer() to compute gradients.
|
||||
mnist_model.compile(
|
||||
loss=tf.losses.SparseCategoricalCrossentropy(),
|
||||
optimizer=opt,
|
||||
metrics=["accuracy"],
|
||||
experimental_run_tf_function=False,
|
||||
)
|
||||
|
||||
callbacks = [
|
||||
# Horovod: broadcast initial variable states from rank 0 to all other processes.
|
||||
# This is necessary to ensure consistent initialization of all workers when
|
||||
# training is started with random weights or restored from a checkpoint.
|
||||
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
|
||||
# Horovod: average metrics among workers at the end of every epoch.
|
||||
#
|
||||
# Note: This callback must be in the list before the ReduceLROnPlateau,
|
||||
# TensorBoard or other metrics-based callbacks.
|
||||
hvd.callbacks.MetricAverageCallback(),
|
||||
# Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
|
||||
# accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
|
||||
# the first three epochs. See https://arxiv.org/abs/1706.02677 for details.
|
||||
hvd.callbacks.LearningRateWarmupCallback(
|
||||
warmup_epochs=3, initial_lr=scaled_lr, verbose=1
|
||||
),
|
||||
]
|
||||
|
||||
# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
|
||||
if hvd.rank() == 0:
|
||||
output_dir = "./outputs"
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
callbacks.append(
|
||||
tf.keras.callbacks.ModelCheckpoint(
|
||||
os.path.join(output_dir, "checkpoint-{epoch}.h5")
|
||||
)
|
||||
)
|
||||
|
||||
# Horovod: write logs on worker 0.
|
||||
verbose = 1 if hvd.rank() == 0 else 0
|
||||
|
||||
# Train the model.
|
||||
# Horovod: adjust number of steps based on number of GPUs.
|
||||
mnist_model.fit(
|
||||
dataset,
|
||||
steps_per_epoch=args.steps_per_epoch // hvd.size(),
|
||||
callbacks=callbacks,
|
||||
epochs=args.epochs,
|
||||
verbose=verbose,
|
||||
)
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 119 KiB |
@@ -1,190 +0,0 @@
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
import numpy as np
|
||||
import argparse
|
||||
import os
|
||||
import re
|
||||
import tensorflow as tf
|
||||
import time
|
||||
import glob
|
||||
|
||||
from azureml.core import Run
|
||||
from utils import load_data
|
||||
from tensorflow.keras import Model, layers
|
||||
|
||||
|
||||
# Create TF Model.
|
||||
class NeuralNet(Model):
|
||||
# Set layers.
|
||||
def __init__(self):
|
||||
super(NeuralNet, self).__init__()
|
||||
# First hidden layer.
|
||||
self.h1 = layers.Dense(n_h1, activation=tf.nn.relu)
|
||||
# Second hidden layer.
|
||||
self.h2 = layers.Dense(n_h2, activation=tf.nn.relu)
|
||||
self.out = layers.Dense(n_outputs)
|
||||
|
||||
# Set forward pass.
|
||||
def call(self, x, is_training=False):
|
||||
x = self.h1(x)
|
||||
x = self.h2(x)
|
||||
x = self.out(x)
|
||||
if not is_training:
|
||||
# Apply softmax when not training.
|
||||
x = tf.nn.softmax(x)
|
||||
return x
|
||||
|
||||
|
||||
def cross_entropy_loss(y, logits):
|
||||
# Convert labels to int 64 for tf cross-entropy function.
|
||||
y = tf.cast(y, tf.int64)
|
||||
# Apply softmax to logits and compute cross-entropy.
|
||||
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
|
||||
# Average loss across the batch.
|
||||
return tf.reduce_mean(loss)
|
||||
|
||||
|
||||
# Accuracy metric.
|
||||
def accuracy(y_pred, y_true):
|
||||
# Predicted class is the index of highest score in prediction vector (i.e. argmax).
|
||||
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))
|
||||
return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)
|
||||
|
||||
|
||||
# Optimization process.
|
||||
def run_optimization(x, y):
|
||||
# Wrap computation inside a GradientTape for automatic differentiation.
|
||||
with tf.GradientTape() as g:
|
||||
# Forward pass.
|
||||
logits = neural_net(x, is_training=True)
|
||||
# Compute loss.
|
||||
loss = cross_entropy_loss(y, logits)
|
||||
|
||||
# Variables to update, i.e. trainable variables.
|
||||
trainable_variables = neural_net.trainable_variables
|
||||
|
||||
# Compute gradients.
|
||||
gradients = g.gradient(loss, trainable_variables)
|
||||
|
||||
# Update W and b following gradients.
|
||||
optimizer.apply_gradients(zip(gradients, trainable_variables))
|
||||
|
||||
|
||||
print("TensorFlow version:", tf.__version__)
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data-folder', type=str, dest='data_folder', default='data', help='data folder mounting point')
|
||||
parser.add_argument('--batch-size', type=int, dest='batch_size', default=128, help='mini batch size for training')
|
||||
parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=128,
|
||||
help='# of neurons in the first layer')
|
||||
parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=128,
|
||||
help='# of neurons in the second layer')
|
||||
parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate')
|
||||
parser.add_argument('--resume-from', type=str, default=None,
|
||||
help='location of the model or checkpoint files from where to resume the training')
|
||||
args = parser.parse_args()
|
||||
|
||||
previous_model_location = args.resume_from
|
||||
# You can also use environment variable to get the model/checkpoint files location
|
||||
# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None))
|
||||
|
||||
data_folder = args.data_folder
|
||||
print('Data folder:', data_folder)
|
||||
|
||||
# load train and test set into numpy arrays
|
||||
# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster.
|
||||
X_train = load_data(glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'),
|
||||
recursive=True)[0], False) / np.float32(255.0)
|
||||
X_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'),
|
||||
recursive=True)[0], False) / np.float32(255.0)
|
||||
y_train = load_data(glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'),
|
||||
recursive=True)[0], True).reshape(-1)
|
||||
y_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'),
|
||||
recursive=True)[0], True).reshape(-1)
|
||||
|
||||
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
|
||||
|
||||
training_set_size = X_train.shape[0]
|
||||
|
||||
n_inputs = 28 * 28
|
||||
n_h1 = args.n_hidden_1
|
||||
n_h2 = args.n_hidden_2
|
||||
n_outputs = 10
|
||||
learning_rate = args.learning_rate
|
||||
n_epochs = 20
|
||||
batch_size = args.batch_size
|
||||
|
||||
# Build neural network model.
|
||||
neural_net = NeuralNet()
|
||||
|
||||
# Stochastic gradient descent optimizer.
|
||||
optimizer = tf.optimizers.SGD(learning_rate)
|
||||
|
||||
# start an Azure ML run
|
||||
run = Run.get_context()
|
||||
|
||||
if previous_model_location:
|
||||
# Restore variables from latest checkpoint.
|
||||
checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer)
|
||||
checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location)
|
||||
checkpoint.restore(checkpoint_file_path)
|
||||
checkpoint_filename = os.path.basename(checkpoint_file_path)
|
||||
num_found = re.search(r'\d+', checkpoint_filename)
|
||||
if num_found:
|
||||
start_epoch = int(num_found.group(0))
|
||||
print("Resuming from epoch {}".format(str(start_epoch)))
|
||||
|
||||
start_time = time.perf_counter()
|
||||
for epoch in range(0, n_epochs):
|
||||
|
||||
# randomly shuffle training set
|
||||
indices = np.random.permutation(training_set_size)
|
||||
X_train = X_train[indices]
|
||||
y_train = y_train[indices]
|
||||
|
||||
# batch index
|
||||
b_start = 0
|
||||
b_end = b_start + batch_size
|
||||
for _ in range(training_set_size // batch_size):
|
||||
# get a batch
|
||||
X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end]
|
||||
|
||||
# update batch index for the next batch
|
||||
b_start = b_start + batch_size
|
||||
b_end = min(b_start + batch_size, training_set_size)
|
||||
|
||||
# train
|
||||
run_optimization(X_batch, y_batch)
|
||||
|
||||
# evaluate training set
|
||||
pred = neural_net(X_batch, is_training=False)
|
||||
acc_train = accuracy(pred, y_batch)
|
||||
|
||||
# evaluate validation set
|
||||
pred = neural_net(X_test, is_training=False)
|
||||
acc_val = accuracy(pred, y_test)
|
||||
|
||||
# log accuracies
|
||||
run.log('training_acc', np.float(acc_train))
|
||||
run.log('validation_acc', np.float(acc_val))
|
||||
print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val)
|
||||
|
||||
# Save checkpoints in the "./outputs" folder so that they are automatically uploaded into run history.
|
||||
checkpoint_dir = './outputs/'
|
||||
checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer)
|
||||
|
||||
if epoch % 2 == 0:
|
||||
checkpoint.save(checkpoint_dir)
|
||||
|
||||
run.log('final_acc', np.float(acc_val))
|
||||
os.makedirs('./outputs/model', exist_ok=True)
|
||||
|
||||
# files saved in the "./outputs" folder are automatically uploaded into run history
|
||||
# this is workaround for https://github.com/tensorflow/tensorflow/issues/33913 and will be fixed once we move to >tf2.1
|
||||
neural_net._set_inputs(X_train)
|
||||
tf.saved_model.save(neural_net, './outputs/model/')
|
||||
|
||||
stop_time = time.perf_counter()
|
||||
training_time = (stop_time - start_time) * 1000
|
||||
print("Total time in milliseconds for training: {}".format(str(training_time)))
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,27 +0,0 @@
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
import gzip
|
||||
import numpy as np
|
||||
import struct
|
||||
|
||||
|
||||
# load compressed MNIST gz files and return numpy arrays
|
||||
def load_data(filename, label=False):
|
||||
with gzip.open(filename) as gz:
|
||||
struct.unpack('I', gz.read(4))
|
||||
n_items = struct.unpack('>I', gz.read(4))
|
||||
if not label:
|
||||
n_rows = struct.unpack('>I', gz.read(4))[0]
|
||||
n_cols = struct.unpack('>I', gz.read(4))[0]
|
||||
res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
|
||||
res = res.reshape(n_items[0], n_rows * n_cols)
|
||||
else:
|
||||
res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
|
||||
res = res.reshape(n_items[0], 1)
|
||||
return res
|
||||
|
||||
|
||||
# one-hot encode a 1-D array
|
||||
def one_hot_encode(array, num_of_classes):
|
||||
return np.eye(num_of_classes)[array.reshape(-1)]
|
||||
@@ -1,17 +0,0 @@
|
||||
# AzureML Responsible AI
|
||||
|
||||
AzureML Responsible AI empowers data scientists and developers to innovate responsibly with a growing set of tools including model interpretability and fairness.
|
||||
|
||||
Follow these sample notebooks to learn about the model interpretability and fairness integration in Azure:
|
||||
|
||||
<a name="samples"></a>
|
||||
|
||||
# Responsible AI Sample Notebooks
|
||||
|
||||
- **Visualize fairness metrics and model explanations**
|
||||
- Dataset: [UCI Adult](https://archive.ics.uci.edu/ml/datasets/Adult)
|
||||
- **[Jupyter Notebook](visualize-upload-loan-decision/rai-loan-decision.ipynb)**
|
||||
- Train a model to predict annual income
|
||||
- Generate fairness and interpretability explanations for the trained model
|
||||
- Visualize the explanations in the notebook widget dashboard
|
||||
- Upload the explanations to Azure to be viewed in AzureML studio
|
||||
Binary file not shown.
@@ -1,718 +0,0 @@
|
||||
{
|
||||
"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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Assess Fairness, Explore Interpretability, and Mitigate Fairness Issues \n",
|
||||
"\n",
|
||||
"This notebook demonstrates how to use [InterpretML](interpret.ml), [Fairlearn](fairlearn.org), and the [Responsible AI Widget's](https://github.com/microsoft/responsible-ai-widgets/) Fairness and Interpretability dashboards to understand a model trained on the Census dataset. This dataset is a classification problem - given a range of data about 32,000 individuals, predict whether their annual income is above or below fifty thousand dollars per year.\n",
|
||||
"\n",
|
||||
"For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan.\n",
|
||||
"\n",
|
||||
"We will first train a fairness-unaware predictor, load its global and local explanations, and use the interpretability and fairness dashboards to demonstrate how this model leads to unfair decisions (under a specific notion of fairness called *demographic parity*). We then mitigate unfairness by applying the `GridSearch` algorithm from `Fairlearn` package.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Install required packages\n",
|
||||
"\n",
|
||||
"This notebook works with Fairlearn v0.7.0, but not with versions pre-v0.5.0. If needed, please uncomment and run the following cell:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install --upgrade fairlearn>=0.6.2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"After installing packages, you must close and reopen the notebook as well as restarting the kernel."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and preprocess the dataset\n",
|
||||
"\n",
|
||||
"For simplicity, we import the dataset from the `shap` package, which contains the data in a cleaned format. We start by importing the various modules we're going to use:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairlearn.reductions import GridSearch\n",
|
||||
"from fairlearn.reductions import DemographicParity\n",
|
||||
"\n",
|
||||
"from sklearn.compose import ColumnTransformer, make_column_selector\n",
|
||||
"from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# SHAP Tabular Explainer\n",
|
||||
"from interpret.ext.blackbox import MimicExplainer\n",
|
||||
"from interpret.ext.glassbox import LGBMExplainableModel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now load and inspect the data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from utilities import fetch_census_dataset\n",
|
||||
"\n",
|
||||
"dataset = fetch_census_dataset()\n",
|
||||
"X_raw, y = dataset['data'], dataset['target']\n",
|
||||
"X_raw[\"race\"].value_counts().to_dict()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data. We then perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sensitive_features = X_raw[['sex','race']]\n",
|
||||
"\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"y = le.fit_transform(y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we split the data into training and test sets:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"X_train, X_test, y_train, y_test, sensitive_features_train, sensitive_features_test = \\\n",
|
||||
" train_test_split(X_raw, y, sensitive_features,\n",
|
||||
" test_size = 0.2, random_state=0, stratify=y)\n",
|
||||
"\n",
|
||||
"# Work around indexing bug\n",
|
||||
"X_train = X_train.reset_index(drop=True)\n",
|
||||
"sensitive_features_train = sensitive_features_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"sensitive_features_test = sensitive_features_test.reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Training a fairness-unaware predictor\n",
|
||||
"\n",
|
||||
"To show the effect of `Fairlearn` we will first train a standard ML predictor that does not incorporate fairness. For speed of demonstration, we use a simple logistic regression estimator from `sklearn`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"numeric_transformer = Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\"impute\", SimpleImputer()),\n",
|
||||
" (\"scaler\", StandardScaler()),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"categorical_transformer = Pipeline(\n",
|
||||
" [\n",
|
||||
" (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
|
||||
" (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\")),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"preprocessor = ColumnTransformer(\n",
|
||||
" transformers=[\n",
|
||||
" (\"num\", numeric_transformer, make_column_selector(dtype_exclude=\"category\")),\n",
|
||||
" (\"cat\", categorical_transformer, make_column_selector(dtype_include=\"category\")),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"model = Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\"preprocessor\", preprocessor),\n",
|
||||
" (\n",
|
||||
" \"classifier\",\n",
|
||||
" LogisticRegression(solver=\"liblinear\", fit_intercept=True),\n",
|
||||
" ),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"model.fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Generate model explanations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Using SHAP KernelExplainer\n",
|
||||
"# clf.steps[-1][1] returns the trained classification model\n",
|
||||
"explainer = MimicExplainer(model.steps[-1][1], \n",
|
||||
" X_train,\n",
|
||||
" LGBMExplainableModel,\n",
|
||||
" features=X_raw.columns, \n",
|
||||
" classes=['Rejected', 'Approved'],\n",
|
||||
" transformations=preprocessor)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Generate global explanations\n",
|
||||
"Explain overall model predictions (global explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Explain the model based on a subset of 1000 rows\n",
|
||||
"global_explanation = explainer.explain_global(X_test[:1000])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"global_explanation.get_feature_importance_dict()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Generate local explanations\n",
|
||||
"Explain local data points (individual instances)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# You can pass a specific data point or a group of data points to the explain_local function\n",
|
||||
"# E.g., Explain the first data point in the test set\n",
|
||||
"instance_num = 1\n",
|
||||
"local_explanation = explainer.explain_local(X_test[:instance_num])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the prediction for the first member of the test set and explain why model made that prediction\n",
|
||||
"prediction_value = model.predict(X_test)[instance_num]\n",
|
||||
"\n",
|
||||
"sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
|
||||
"sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('local importance values: {}'.format(sorted_local_importance_values))\n",
|
||||
"print('local importance names: {}'.format(sorted_local_importance_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Visualize model explanations\n",
|
||||
"Load the interpretability visualization dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from raiwidgets import ExplanationDashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ExplanationDashboard(global_explanation, model, dataset=X_test[:1000], true_y=y_test[:1000])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can load this predictor into the Fairness dashboard, and examine how it is unfair:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Assess model fairness \n",
|
||||
"Load the fairness visualization dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from raiwidgets import FairnessDashboard\n",
|
||||
"\n",
|
||||
"y_pred = model.predict(X_test)\n",
|
||||
"\n",
|
||||
"FairnessDashboard(sensitive_features=sensitive_features_test,\n",
|
||||
" y_true=y_test,\n",
|
||||
" y_pred=y_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Looking at the disparity in accuracy, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n",
|
||||
"\n",
|
||||
"Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Mitigation with Fairlearn (GridSearch)\n",
|
||||
"\n",
|
||||
"The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n",
|
||||
"\n",
|
||||
"For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (are approved for a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). We are using this metric for the sake of simplicity; in general, the appropriate fairness metric will not be obvious."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Fairlearn is not yet fully compatible with Pipelines, so we have to pass the estimator only\n",
|
||||
"X_train_prep = preprocessor.transform(X_train).toarray()\n",
|
||||
"X_test_prep = preprocessor.transform(X_test).toarray()\n",
|
||||
"\n",
|
||||
"sweep = GridSearch(LogisticRegression(solver=\"liblinear\", fit_intercept=True),\n",
|
||||
" constraints=DemographicParity(),\n",
|
||||
" grid_size=70)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Our algorithms provide `fit()` and `predict()` methods, so they behave in a similar manner to other ML packages in Python. We do however have to specify two extra arguments to `fit()` - the column of protected attribute labels, and also the number of predictors to generate in our sweep.\n",
|
||||
"\n",
|
||||
"After `fit()` completes, we extract the full set of predictors from the `GridSearch` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sweep.fit(X_train_prep, y_train,\n",
|
||||
" sensitive_features=sensitive_features_train.sex)\n",
|
||||
"\n",
|
||||
"predictors = sweep.predictors_"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the sensitive feature). In general, one might not want to do this, since there may be other considerations beyond the strict optimization of error and disparity (of the given protected attribute)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairlearn.metrics import demographic_parity_difference\n",
|
||||
"\n",
|
||||
"accuracies, disparities = [], []\n",
|
||||
"\n",
|
||||
"for predictor in predictors:\n",
|
||||
" y_pred = predictor.predict(X_train_prep)\n",
|
||||
" # accuracy_metric_frame = MetricFrame(accuracy_score, y_train, predictor.predict(X_train_prep), sensitive_features=sensitive_features_train.sex)\n",
|
||||
" # selection_rate_metric_frame = MetricFrame(selection_rate, y_train, predictor.predict(X_train_prep), sensitive_features=sensitive_features_train.sex)\n",
|
||||
" accuracies.append(accuracy_score(y_train, y_pred))\n",
|
||||
" disparities.append(demographic_parity_difference(y_train,\n",
|
||||
" y_pred,\n",
|
||||
" sensitive_features=sensitive_features_train.sex))\n",
|
||||
" \n",
|
||||
"all_results = pd.DataFrame({\"predictor\": predictors, \"accuracy\": accuracies, \"disparity\": disparities})\n",
|
||||
"\n",
|
||||
"all_models_dict = {\"unmitigated\": model.steps[-1][1]}\n",
|
||||
"dominant_models_dict = {\"unmitigated\": model.steps[-1][1]}\n",
|
||||
"base_name_format = \"grid_{0}\"\n",
|
||||
"row_id = 0\n",
|
||||
"for row in all_results.itertuples():\n",
|
||||
" model_name = base_name_format.format(row_id)\n",
|
||||
" all_models_dict[model_name] = row.predictor\n",
|
||||
" accuracy_for_lower_or_eq_disparity = all_results[\"accuracy\"][all_results[\"disparity\"] <= row.disparity]\n",
|
||||
" if row.accuracy >= accuracy_for_lower_or_eq_disparity.max():\n",
|
||||
" dominant_models_dict[model_name] = row.predictor\n",
|
||||
" row_id = row_id + 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can construct predictions for all the models, and also for the dominant models:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dashboard_all = {}\n",
|
||||
"for name, predictor in all_models_dict.items():\n",
|
||||
" value = predictor.predict(X_test_prep)\n",
|
||||
" dashboard_all[name] = value\n",
|
||||
" \n",
|
||||
"dominant_all = {}\n",
|
||||
"for name, predictor in dominant_models_dict.items():\n",
|
||||
" dominant_all[name] = predictor.predict(X_test_prep)\n",
|
||||
"\n",
|
||||
"FairnessDashboard(sensitive_features=sensitive_features_test, \n",
|
||||
" y_true=y_test,\n",
|
||||
" y_pred=dominant_all)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can look at just the dominant models in the dashboard:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy.\n",
|
||||
"\n",
|
||||
"By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# AzureML integration\n",
|
||||
"\n",
|
||||
"We will now go through a brief example of the AzureML integration.\n",
|
||||
"\n",
|
||||
"The required package can be installed via:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"pip install azureml-contrib-fairness\n",
|
||||
"pip install azureml-interpret\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Connect to workspace\n",
|
||||
"\n",
|
||||
"Just like in the previous tutorials, we will need to connect to a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class)?view=azure-ml-py).\n",
|
||||
"\n",
|
||||
"The following code will allow you to create a workspace if you don't already have one created. You must have an Azure subscription to create a workspace:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from azureml.core import Workspace\n",
|
||||
"ws = Workspace.create(name='myworkspace',\n",
|
||||
" subscription_id='<azure-subscription-id>',\n",
|
||||
" resource_group='myresourcegroup',\n",
|
||||
" create_resource_group=True,\n",
|
||||
" location='eastus2')\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"**If you are running this on a Notebook VM, you can import the existing workspace.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"## Registering models\n",
|
||||
"\n",
|
||||
"The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `<name>:<version>` pairs) of registered models in the workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Next, we register each of the models in the `dominant_all` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import os\n",
|
||||
"from azureml.core import Model, Experiment\n",
|
||||
"\n",
|
||||
"os.makedirs('models', exist_ok=True)\n",
|
||||
"def register_model(name, model):\n",
|
||||
" print(\"Registering \", name)\n",
|
||||
" model_path = \"models/{0}.pkl\".format(name)\n",
|
||||
" joblib.dump(value=model, filename=model_path)\n",
|
||||
" registered_model = Model.register(model_path=model_path,\n",
|
||||
" model_name=name,\n",
|
||||
" workspace=ws)\n",
|
||||
" print(\"Registered \", registered_model.id)\n",
|
||||
" return registered_model.id\n",
|
||||
"\n",
|
||||
"model_name_id_mapping = dict()\n",
|
||||
"for name, model in dominant_all.items():\n",
|
||||
" m_id = register_model(name, model)\n",
|
||||
" model_name_id_mapping[name] = m_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, produce new predictions dictionaries, with the updated names:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dominant_all_ids = dict()\n",
|
||||
"for name, y_pred in dominant_all.items():\n",
|
||||
" dominant_all_ids[model_name_id_mapping[name]] = y_pred"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Uploading a dashboard\n",
|
||||
"\n",
|
||||
"We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `dashboard_registered` dictionary we just created:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sf = { 'sex': sensitive_features_test.sex, 'race': sensitive_features_test.race }\n",
|
||||
"\n",
|
||||
"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
|
||||
"\n",
|
||||
"dash_dict_all = _create_group_metric_set(y_true=y_test,\n",
|
||||
" predictions=dominant_all_ids,\n",
|
||||
" sensitive_features=sf,\n",
|
||||
" prediction_type='binary_classification')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, we import our `contrib` package which contains the routine to perform the upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we can create an Experiment, then a Run, and upload our dashboard to it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp = Experiment(ws, 'responsible-ai-loan-decision')\n",
|
||||
"print(exp)\n",
|
||||
"\n",
|
||||
"run = exp.start_logging()\n",
|
||||
"try:\n",
|
||||
" dashboard_title = \"Upload MultiAsset from Grid Search with Census Data Notebook\"\n",
|
||||
" upload_id = upload_dashboard_dictionary(run,\n",
|
||||
" dash_dict_all,\n",
|
||||
" dashboard_name=dashboard_title)\n",
|
||||
" print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
|
||||
"\n",
|
||||
" downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
|
||||
"finally:\n",
|
||||
" run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Uploading explanations\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.interpret import ExplanationClient\n",
|
||||
"\n",
|
||||
"client = ExplanationClient.from_run(run)\n",
|
||||
"client.upload_model_explanation(global_explanation, comment = \"census data global explanation\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "chgrego"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,73 +0,0 @@
|
||||
# ---------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# ---------------------------------------------------------
|
||||
|
||||
"""Utilities for azureml-contrib-fairness notebooks."""
|
||||
|
||||
import arff
|
||||
from collections import OrderedDict
|
||||
from contextlib import closing
|
||||
import gzip
|
||||
import pandas as pd
|
||||
from sklearn.utils import Bunch
|
||||
from time import sleep
|
||||
|
||||
|
||||
def _is_gzip_encoded(_fsrc):
|
||||
return _fsrc.info().get('Content-Encoding', '') == 'gzip'
|
||||
|
||||
|
||||
_categorical_columns = [
|
||||
'workclass',
|
||||
'education',
|
||||
'marital-status',
|
||||
'occupation',
|
||||
'relationship',
|
||||
'race',
|
||||
'sex',
|
||||
'native-country'
|
||||
]
|
||||
|
||||
|
||||
def fetch_census_dataset():
|
||||
"""Fetch the Adult Census Dataset
|
||||
|
||||
This uses a particular URL for the Adult Census dataset. The code
|
||||
is a simplified version of fetch_openml() in sklearn.
|
||||
|
||||
The data are copied from:
|
||||
https://openml.org/data/v1/download/1595261.gz
|
||||
(as of 2021-03-31)
|
||||
"""
|
||||
|
||||
dataset_path = "1595261.gz"
|
||||
|
||||
try:
|
||||
file_stream = gzip.GzipFile(filename=dataset_path, mode='rb')
|
||||
|
||||
with closing(file_stream):
|
||||
def _stream_generator(response):
|
||||
for line in response:
|
||||
yield line.decode('utf-8')
|
||||
|
||||
stream = _stream_generator(file_stream)
|
||||
data = arff.load(stream)
|
||||
except Exception as exc:
|
||||
raise Exception("Could not load dataset from {} with exception {}".format(dataset_path, exc))
|
||||
|
||||
attributes = OrderedDict(data['attributes'])
|
||||
arff_columns = list(attributes)
|
||||
|
||||
raw_df = pd.DataFrame(data=data['data'], columns=arff_columns)
|
||||
|
||||
target_column_name = 'class'
|
||||
target = raw_df.pop(target_column_name)
|
||||
for col_name in _categorical_columns:
|
||||
dtype = pd.api.types.CategoricalDtype(attributes[col_name])
|
||||
raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False)
|
||||
|
||||
result = Bunch()
|
||||
result.data = raw_df
|
||||
result.target = target
|
||||
|
||||
return result
|
||||
@@ -101,7 +101,7 @@
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.57.0, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.59.0, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -186,7 +186,7 @@
|
||||
"\n",
|
||||
"# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n",
|
||||
"cd = CondaDependencies.create(\n",
|
||||
" pip_packages=[\"azureml-mlflow\", \"scikit-learn\", \"matplotlib\", \"pandas\", \"numpy\"]\n",
|
||||
" pip_packages=[\"azureml-mlflow\", \"scikit-learn\", \"matplotlib\", \"pandas\", \"numpy\", \"protobuf==5.28.3\"]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies = cd"
|
||||
|
||||
@@ -1,466 +0,0 @@
|
||||
{
|
||||
"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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Analyze data drift in Azure Machine Learning datasets \n",
|
||||
"\n",
|
||||
"In this tutorial, you will setup a data drift monitor on a weather dataset to:\n",
|
||||
"\n",
|
||||
"☑ Analyze historical data for drift\n",
|
||||
"\n",
|
||||
"☑ Setup a monitor to recieve email alerts if data drift is detected going forward\n",
|
||||
"\n",
|
||||
"If your workspace is Enterprise level, view and exlpore the results in the Azure Machine Learning studio. The video below shows the results from this tutorial. \n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"If you are using an Azure Machine Learning Compute instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) if you haven't already established your connection to the AzureML Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print('SDK version:', azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize Workspace\n",
|
||||
"\n",
|
||||
"Initialize a workspace object from persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup target and baseline datasets\n",
|
||||
"\n",
|
||||
"Setup the baseline and target datasets. The baseline will be used to compare each time slice of the target dataset, which is sampled by a given frequency. For further details, see [our documentation](http://aka.ms/datadrift). \n",
|
||||
"\n",
|
||||
"The next few cells will:\n",
|
||||
" * get the default datastore\n",
|
||||
" * upload the `weather-data` to the datastore\n",
|
||||
" * create the Tabular dataset from the data\n",
|
||||
" * add the timeseries trait by specifying the timestamp column `datetime`\n",
|
||||
" * register the dataset\n",
|
||||
" * create the baseline as a time slice of the target dataset\n",
|
||||
" * optionally, register the baseline dataset\n",
|
||||
" \n",
|
||||
"The folder `weather-data` contains weather data from the [NOAA Integrated Surface Data](https://azure.microsoft.com/services/open-datasets/catalog/noaa-integrated-surface-data/) filtered down to to station names containing the string 'FLORIDA' to reduce the size of data. See `get_data.py` to see how this data is curated and modify as desired. This script may take a long time to run, hence the data is provided in the `weather-data` folder for this demo."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# use default datastore\n",
|
||||
"dstore = ws.get_default_datastore()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# upload weather data\n",
|
||||
"dstore.upload('weather-data', 'datadrift-data', overwrite=True, show_progress=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import Dataset class\n",
|
||||
"from azureml.core import Dataset\n",
|
||||
"\n",
|
||||
"# create target dataset \n",
|
||||
"target = Dataset.Tabular.from_parquet_files(dstore.path('datadrift-data/**/data.parquet'))\n",
|
||||
"# set the timestamp column\n",
|
||||
"target = target.with_timestamp_columns('datetime')\n",
|
||||
"# register the target dataset\n",
|
||||
"target = target.register(ws, 'target')\n",
|
||||
"# retrieve the dataset from the workspace by name\n",
|
||||
"target = Dataset.get_by_name(ws, 'target')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import datetime \n",
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"# set baseline dataset as January 2019 weather data\n",
|
||||
"baseline = Dataset.Tabular.from_parquet_files(dstore.path('datadrift-data/2019/01/data.parquet'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# optionally, register the baseline dataset. if skipped, an unregistered dataset will be used\n",
|
||||
"#baseline = baseline.register(ws, 'baseline')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create compute target\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"Create an Azure Machine Learning compute cluster to run the data drift monitor and associated runs. The below cell will create a compute cluster named `'cpu-cluster'`. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import AmlCompute, ComputeTarget\n",
|
||||
"\n",
|
||||
"compute_name = 'cpu-cluster'\n",
|
||||
"\n",
|
||||
"if compute_name in ws.compute_targets:\n",
|
||||
" compute_target = ws.compute_targets[compute_name]\n",
|
||||
" if compute_target and type(compute_target) is AmlCompute:\n",
|
||||
" print('found compute target. just use it. ' + compute_name)\n",
|
||||
"else:\n",
|
||||
" print('creating a new compute target...')\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D3_V2', min_nodes=0, max_nodes=2)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" compute_target = ComputeTarget.create(ws, compute_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()\n",
|
||||
" print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create data drift monitor\n",
|
||||
"\n",
|
||||
"See [our documentation](http://aka.ms/datadrift) for a complete description for all of the parameters. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"datadrift-remarks-sample"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.datadrift import DataDriftDetector, AlertConfiguration\n",
|
||||
"\n",
|
||||
"alert_config = AlertConfiguration(['user@contoso.com']) # replace with your email to recieve alerts from the scheduled pipeline after enabling\n",
|
||||
"\n",
|
||||
"monitor = DataDriftDetector.create_from_datasets(ws, 'weather-monitor', baseline, target, \n",
|
||||
" compute_target='cpu-cluster', # compute target for scheduled pipeline and backfills \n",
|
||||
" frequency='Week', # how often to analyze target data\n",
|
||||
" feature_list=None, # list of features to detect drift on\n",
|
||||
" drift_threshold=None, # threshold from 0 to 1 for email alerting\n",
|
||||
" latency=0, # SLA in hours for target data to arrive in the dataset\n",
|
||||
" alert_config=alert_config) # email addresses to send alert"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Update data drift monitor\n",
|
||||
"\n",
|
||||
"Many settings of the data drift monitor can be updated after creation. In this demo, we will update the `drift_threshold` and `feature_list`. See [our documentation](http://aka.ms/datadrift) for details on which settings can be changed."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get monitor by name\n",
|
||||
"monitor = DataDriftDetector.get_by_name(ws, 'weather-monitor')\n",
|
||||
"\n",
|
||||
"# create feature list - need to exclude columns that naturally drift or increment over time, such as year, day, index\n",
|
||||
"columns = list(baseline.take(1).to_pandas_dataframe())\n",
|
||||
"exclude = ['year', 'day', 'version', '__index_level_0__']\n",
|
||||
"features = [col for col in columns if col not in exclude]\n",
|
||||
"\n",
|
||||
"# update the feature list\n",
|
||||
"monitor = monitor.update(feature_list=features)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Analyze historical data and backfill\n",
|
||||
"\n",
|
||||
"You can use the `backfill` method to:\n",
|
||||
" * analyze historical data\n",
|
||||
" * backfill metrics after updating the settings (mainly the feature list)\n",
|
||||
" * backfill metrics for failed runs\n",
|
||||
" \n",
|
||||
"The below cells will run two backfills that will produce data drift results for 2019 weather data, with January used as the baseline in the monitor. The output can be seen from the `show` method after the runs have completed, or viewed from the Azure Machine Learning studio for Enterprise workspaces.\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"jupyter": {
|
||||
"source_hidden": true
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
">**Tip!** When starting with the data drift capability, start by backfilling on a small section of data to get initial results. Update the feature list as needed by removing columns that are causing drift, but can be ignored, and backfill this section of data until satisfied with the results. Then, backfill on a larger slice of data and/or set the alert configuration, threshold, and enable the schedule to recieve alerts to drift on your dataset. All of this can be done through the UI (Enterprise) or Python SDK."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Although it depends on many factors, the below backfill should typically take less than 20 minutes to run. Results will show as soon as they become available, not when the backfill is completed, so you may begin to see some metrics in a few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# backfill for one month\n",
|
||||
"backfill_start_date = datetime(2019, 9, 1)\n",
|
||||
"backfill_end_date = datetime(2019, 10, 1)\n",
|
||||
"backfill = monitor.backfill(backfill_start_date, backfill_end_date)\n",
|
||||
"backfill"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Query metrics and show results in Python\n",
|
||||
"\n",
|
||||
"The below cell will plot some key data drift metrics, and can be used to query the results. Run `help(monitor.get_output)` for specifics on the object returned."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# make sure the backfill has completed\n",
|
||||
"backfill.wait_for_completion(wait_post_processing=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get results from Python SDK (wait for backfills or monitor runs to finish)\n",
|
||||
"results, metrics = monitor.get_output(start_time=datetime(year=2019, month=9, day=1))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot the results from Python SDK \n",
|
||||
"monitor.show(backfill_start_date, backfill_end_date)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Enable the monitor's pipeline schedule\n",
|
||||
"\n",
|
||||
"Turn on a scheduled pipeline which will anlayze the target dataset for drift every `frequency`. Use the latency parameter to adjust the start time of the pipeline. For instance, if it takes 24 hours for my data processing pipelines for data to arrive in the target dataset, set latency to 24. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# enable the pipeline schedule and recieve email alerts\n",
|
||||
"monitor.enable_schedule()\n",
|
||||
"\n",
|
||||
"# disable the pipeline schedule \n",
|
||||
"#monitor.disable_schedule()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Delete compute target\n",
|
||||
"\n",
|
||||
"Do not delete the compute target if you intend to keep using it for the data drift monitor scheduled runs or otherwise. If the minimum nodes are set to 0, it will scale down soon after jobs are completed, and scale up the next time the cluster is needed."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# optionally delete the compute target\n",
|
||||
"#compute_target.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Delete the DataDriftDetector\n",
|
||||
"\n",
|
||||
"Invoking the `delete()` method on the object deletes the the drift monitor permanently and cannot be undone. You will no longer be able to find it in the UI and the `list()` or `get()` methods. The object on which delete() was called will have its state set to deleted and name suffixed with deleted. The baseline and target datasets and model data that was collected, if any, are not deleted. The compute is not deleted. The DataDrift schedule pipeline is disabled and archived."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"monitor.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
" * See [our documentation](https://aka.ms/datadrift) or [Python SDK reference](https://docs.microsoft.com/python/api/overview/azure/ml/intro)\n",
|
||||
" * [Send requests or feedback](mailto:driftfeedback@microsoft.com) on data drift directly to the team\n",
|
||||
" * Please open issues with data drift here on GitHub or on StackOverflow if others are likely to run into the same issue"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "jamgan"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"compute": [
|
||||
"Remote"
|
||||
],
|
||||
"datasets": [
|
||||
"NOAA"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Data drift quickdemo",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.4"
|
||||
},
|
||||
"star_tag": [
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"Dataset",
|
||||
"Timeseries",
|
||||
"Drift"
|
||||
],
|
||||
"task": "Filtering"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,30 +0,0 @@
|
||||
# import packages
|
||||
import os
|
||||
import pandas as pd
|
||||
from calendar import monthrange
|
||||
from datetime import datetime, timedelta
|
||||
from azureml.core import Dataset, Datastore, Workspace
|
||||
from azureml.opendatasets import NoaaIsdWeather
|
||||
|
||||
# get workspace and datastore
|
||||
ws = Workspace.from_config()
|
||||
dstore = ws.get_default_datastore()
|
||||
|
||||
# adjust parameters as needed
|
||||
target_years = list(range(2010, 2020))
|
||||
start_month = 1
|
||||
|
||||
# get data
|
||||
for year in target_years:
|
||||
for month in range(start_month, 12 + 1):
|
||||
path = 'weather-data/{}/{:02d}/'.format(year, month)
|
||||
try:
|
||||
start = datetime(year, month, 1)
|
||||
end = datetime(year, month, monthrange(year, month)[1]) + timedelta(days=1)
|
||||
isd = NoaaIsdWeather(start, end).to_pandas_dataframe()
|
||||
isd = isd[isd['stationName'].str.contains('FLORIDA', regex=True, na=False)]
|
||||
os.makedirs(path, exist_ok=True)
|
||||
isd.to_parquet(path + 'data.parquet')
|
||||
except Exception as e:
|
||||
print('Month {} in year {} likely has no data.\n'.format(month, year))
|
||||
print('Exception: {}'.format(e))
|
||||
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Reference in New Issue
Block a user