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azureml-sd
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azureml-sd
| Author | SHA1 | Date | |
|---|---|---|---|
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0040644e7a | ||
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8aa04307fb | ||
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a525da4488 | ||
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e149565a8a | ||
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75610ec31c | ||
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0c2c450b6b | ||
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0d548eabff |
@@ -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 1.0.76.1 of the Azure ML SDK\")\n",
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||||
"print(\"This notebook was created using version 1.0.79 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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@@ -14,7 +14,7 @@ IF "%CONDA_EXE%"=="" GOTO CondaMissing
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call conda activate %conda_env_name% 2>nul:
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if not errorlevel 1 (
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echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
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echo Upgrading existing conda environment %conda_env_name%
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call pip uninstall azureml-train-automl -y -q
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call conda env update --name %conda_env_name% --file %automl_env_file%
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if errorlevel 1 goto ErrorExit
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@@ -22,7 +22,7 @@ fi
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||||
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if source activate $CONDA_ENV_NAME 2> /dev/null
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||||
then
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||||
echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
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||||
echo "Upgrading existing conda environment" $CONDA_ENV_NAME
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||||
pip uninstall azureml-train-automl -y -q
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conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
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jupyter nbextension uninstall --user --py azureml.widgets
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@@ -22,7 +22,7 @@ fi
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if source activate $CONDA_ENV_NAME 2> /dev/null
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then
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echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
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echo "Upgrading existing conda environment" $CONDA_ENV_NAME
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pip uninstall azureml-train-automl -y -q
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conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
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jupyter nbextension uninstall --user --py azureml.widgets
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@@ -285,7 +285,8 @@
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"|**task**|classification or regression or forecasting|\n",
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"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
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"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
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"|**blacklist_models** or **whitelist_models** |*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
|
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"|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
|
||||
"| **whitelist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n",
|
||||
"|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
|
||||
"|**experiment_timeout_minutes**| Maximum amount of time in minutes that all iterations combined can take before the experiment terminates.|\n",
|
||||
"|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
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@@ -293,6 +294,7 @@
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"|**n_cross_validations**|Number of cross validation splits.|\n",
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"|**training_data**|Input dataset, containing both features and label column.|\n",
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||||
"|**label_column_name**|The name of the label column.|\n",
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"|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
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"\n",
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||||
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
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||||
]
|
||||
@@ -324,6 +326,7 @@
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||||
" training_data = train_data,\n",
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||||
" label_column_name = label,\n",
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" validation_data = validation_dataset,\n",
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||||
" model_explainability=True,\n",
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||||
" **automl_settings\n",
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||||
" )"
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||||
]
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||||
@@ -456,6 +459,72 @@
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||||
"RunDetails(remote_run).show() "
|
||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model's explanation\n",
|
||||
"Retrieve the explanation from the best_run which includes explanations for engineered features and raw features. Make sure that the run for generating explanations for the best model is completed."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait for the best model explanation run to complete\n",
|
||||
"from azureml.train.automl.run import AutoMLRun\n",
|
||||
"model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
|
||||
"print(model_explainability_run_id)\n",
|
||||
"if model_explainability_run_id is not None:\n",
|
||||
" model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
|
||||
" model_explainability_run.wait_for_completion()\n",
|
||||
"\n",
|
||||
"# Get the best run object\n",
|
||||
"best_run, fitted_model = remote_run.get_output()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Download engineered feature importance from artifact store\n",
|
||||
"You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"client = ExplanationClient.from_run(best_run)\n",
|
||||
"engineered_explanations = client.download_model_explanation(raw=False)\n",
|
||||
"exp_data = engineered_explanations.get_feature_importance_dict()\n",
|
||||
"exp_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Download raw feature importance from artifact store\n",
|
||||
"You can use ExplanationClient to download the raw feature explanations from the artifact store of the best_run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"client = ExplanationClient.from_run(best_run)\n",
|
||||
"engineered_explanations = client.download_model_explanation(raw=True)\n",
|
||||
"exp_data = engineered_explanations.get_feature_importance_dict()\n",
|
||||
"exp_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -572,20 +641,6 @@
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||||
"best_run, fitted_model = remote_run.get_output()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"import os\n",
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||||
"import shutil\n",
|
||||
"\n",
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||||
"sript_folder = os.path.join(os.getcwd(), 'inference')\n",
|
||||
"project_folder = '/inference'\n",
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||||
"os.makedirs(project_folder, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
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||||
@@ -42,7 +42,7 @@
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||||
"\n",
|
||||
"AutoML highlights here include built-in holiday featurization, accessing engineered feature names, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
|
||||
"Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"Notebook synopsis:\n",
|
||||
"1. Creating an Experiment in an existing Workspace\n",
|
||||
@@ -202,7 +202,7 @@
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||||
"outputs": [],
|
||||
"source": [
|
||||
"dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'dataset/bike-no.csv')]).with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
|
||||
"dataset.take(5).to_pandas_dataframe()"
|
||||
"dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -221,8 +221,8 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# select data that occurs before a specified date\n",
|
||||
"train = dataset.time_before(datetime(2012, 9, 1))\n",
|
||||
"train.to_pandas_dataframe().tail(5)"
|
||||
"train = dataset.time_before(datetime(2012, 8, 31), include_boundary=True)\n",
|
||||
"train.to_pandas_dataframe().tail(5).reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -231,8 +231,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test = dataset.time_after(datetime(2012, 8, 31))\n",
|
||||
"test.to_pandas_dataframe().head(5)"
|
||||
"test = dataset.time_after(datetime(2012, 9, 1), include_boundary=True)\n",
|
||||
"test.to_pandas_dataframe().head(5).reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -247,7 +247,7 @@
|
||||
"|-|-|\n",
|
||||
"|**task**|forecasting|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
|
||||
"|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels.regression?view=azure-ml-py).|\n",
|
||||
"|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
|
||||
"|**experiment_timeout_minutes**|Experimentation timeout in minutes.|\n",
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
|
||||
@@ -32,18 +32,17 @@ test_dataset = run.input_datasets['test_data']
|
||||
|
||||
grain_column_names = []
|
||||
|
||||
df = test_dataset.to_pandas_dataframe()
|
||||
df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
|
||||
|
||||
X_test_df = test_dataset.drop_columns(columns=[target_column_name])
|
||||
y_test_df = test_dataset.with_timestamp_columns(
|
||||
None).keep_columns(columns=[target_column_name])
|
||||
X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
|
||||
y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
|
||||
|
||||
fitted_model = joblib.load('model.pkl')
|
||||
|
||||
df_all = forecasting_helper.do_rolling_forecast(
|
||||
fitted_model,
|
||||
X_test_df.to_pandas_dataframe(),
|
||||
y_test_df.to_pandas_dataframe().values.T[0],
|
||||
X_test_df,
|
||||
y_test_df.values.T[0],
|
||||
target_column_name,
|
||||
time_column_name,
|
||||
max_horizon,
|
||||
|
||||
@@ -31,8 +31,8 @@
|
||||
"1. [Results](#Results)\n",
|
||||
"\n",
|
||||
"Advanced Forecasting\n",
|
||||
"1. [Advanced Training](#Advanced Training)\n",
|
||||
"1. [Advanced Results](#Advanced Results)"
|
||||
"1. [Advanced Training](#advanced_training)\n",
|
||||
"1. [Advanced Results](#advanced_results)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -211,7 +211,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dataset = Dataset.Tabular.from_delimited_files(path = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/nyc_energy.csv\").with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
|
||||
"dataset.take(5).to_pandas_dataframe()"
|
||||
"dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -253,7 +253,7 @@
|
||||
"source": [
|
||||
"# split into train based on time\n",
|
||||
"train = dataset.time_before(datetime(2017, 8, 8, 5), include_boundary=True)\n",
|
||||
"train.to_pandas_dataframe().sort_values(time_column_name).tail(5)"
|
||||
"train.to_pandas_dataframe().sort_values(time_column_name).tail(5).reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -263,8 +263,8 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# split into test based on time\n",
|
||||
"test = dataset.time_between(datetime(2017, 8, 8, 5), datetime(2017, 8, 10, 5))\n",
|
||||
"test.to_pandas_dataframe().head(5)"
|
||||
"test = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n",
|
||||
"test.to_pandas_dataframe().head(5).reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -301,7 +301,7 @@
|
||||
"|-|-|\n",
|
||||
"|**task**|forecasting|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels.regression?view=azure-ml-py).|\n",
|
||||
"|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
|
||||
"|**experiment_timeout_minutes**|Maximum amount of time in minutes that the experiment take before it terminates.|\n",
|
||||
"|**training_data**|The training data to be used within the experiment.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
@@ -454,7 +454,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_test = test.to_pandas_dataframe()\n",
|
||||
"X_test = test.to_pandas_dataframe().reset_index(drop=True)\n",
|
||||
"y_test = X_test.pop(target_column_name).values"
|
||||
]
|
||||
},
|
||||
@@ -463,11 +463,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Forecast Function\n",
|
||||
"For forecasting, we will use the forecast function instead of the predict function. There are two reasons for this.\n",
|
||||
"\n",
|
||||
"We need to pass the recent values of the target variable y, whereas the scikit-compatible predict function only takes the non-target variables 'test'. In our case, the test data immediately follows the training data, and we fill the target variable with NaN. The NaN serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the forecast origin - the last time when the value of the target is known.\n",
|
||||
"\n",
|
||||
"Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use."
|
||||
"For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb)."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -476,15 +472,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace ALL values in y by NaN.\n",
|
||||
"# The forecast origin will be at the beginning of the first forecast period.\n",
|
||||
"# (Which is the same time as the end of the last training period.)\n",
|
||||
"y_query = y_test.copy().astype(np.float)\n",
|
||||
"y_query.fill(np.nan)\n",
|
||||
"# The featurized data, aligned to y, will also be returned.\n",
|
||||
"# This contains the assumptions that were made in the forecast\n",
|
||||
"# and helps align the forecast to the original data\n",
|
||||
"y_predictions, X_trans = fitted_model.forecast(X_test, y_query)"
|
||||
"y_predictions, X_trans = fitted_model.forecast(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -557,7 +548,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Advanced Training\n",
|
||||
"## Advanced Training <a id=\"advanced_training\"></a>\n",
|
||||
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
|
||||
]
|
||||
},
|
||||
@@ -642,7 +633,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Advanced Results\n",
|
||||
"## Advanced Results<a id=\"advanced_results\"></a>\n",
|
||||
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
|
||||
]
|
||||
},
|
||||
@@ -652,15 +643,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace ALL values in y by NaN.\n",
|
||||
"# The forecast origin will be at the beginning of the first forecast period.\n",
|
||||
"# (Which is the same time as the end of the last training period.)\n",
|
||||
"y_query = y_test.copy().astype(np.float)\n",
|
||||
"y_query.fill(np.nan)\n",
|
||||
"# The featurized data, aligned to y, will also be returned.\n",
|
||||
"# This contains the assumptions that were made in the forecast\n",
|
||||
"# and helps align the forecast to the original data\n",
|
||||
"y_predictions, X_trans = fitted_model_lags.forecast(X_test, y_query)"
|
||||
"y_predictions, X_trans = fitted_model_lags.forecast(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -8,6 +8,7 @@ from azureml.core import RunConfiguration
|
||||
from azureml.core.compute import ComputeTarget
|
||||
from azureml.core.conda_dependencies import CondaDependencies
|
||||
from azureml.core.dataset import Dataset
|
||||
from azureml.data import TabularDataset
|
||||
from azureml.pipeline.core import PipelineData, PipelineParameter, TrainingOutput, StepSequence
|
||||
from azureml.pipeline.steps import PythonScriptStep
|
||||
from azureml.train.automl import AutoMLConfig
|
||||
@@ -34,8 +35,9 @@ def _get_configs(automlconfig: AutoMLConfig,
|
||||
group_name = valid_chars.sub('', group_name)
|
||||
for key in group.index:
|
||||
single = single._dataflow.filter(data._dataflow[key] == group[key])
|
||||
t_dataset = TabularDataset._create(single)
|
||||
group_conf = copy.deepcopy(automlconfig)
|
||||
group_conf.user_settings['training_data'] = single
|
||||
group_conf.user_settings['training_data'] = t_dataset
|
||||
group_conf.user_settings['label_column_name'] = target_column
|
||||
group_conf.user_settings['compute_target'] = compute_target
|
||||
configs[group_name] = group_conf
|
||||
|
||||
@@ -44,7 +44,7 @@ def run(raw_data):
|
||||
model_path = Model.get_model_path(cur_group)
|
||||
model = joblib.load(model_path)
|
||||
models[cur_group] = model
|
||||
_, xtrans = models[cur_group].forecast(df_one, np.repeat(np.nan, len(df_one)))
|
||||
_, xtrans = models[cur_group].forecast(df_one)
|
||||
dfs.append(xtrans)
|
||||
df_ret = pd.concat(dfs)
|
||||
df_ret.reset_index(drop=False, inplace=True)
|
||||
|
||||
@@ -377,9 +377,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"The `X_test` and `y_query` below, taken together, form the **forecast request**. The two are interpreted as aligned - `y_query` could actally be a column in `X_test`. `NaN`s in `y_query` are the question marks. These will be filled with the forecasts.\n",
|
||||
"\n",
|
||||
"When the forecast period immediately follows the training period, the models retain the last few points of data. You can simply fill `y_query` filled with question marks - the model has the data for the lookback already.\n"
|
||||
"We use `X_test` as a **forecast request** to generate the predictions."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -408,8 +406,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_query = np.repeat(np.NaN, X_test.shape[0])\n",
|
||||
"y_pred_no_gap, xy_nogap = fitted_model.forecast(X_test, y_query)\n",
|
||||
"y_pred_no_gap, xy_nogap = fitted_model.forecast(X_test)\n",
|
||||
"\n",
|
||||
"# xy_nogap contains the predictions in the _automl_target_col column.\n",
|
||||
"# Those same numbers are output in y_pred_no_gap\n",
|
||||
@@ -437,7 +434,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"quantiles = fitted_model.forecast_quantiles(X_test, y_query)\n",
|
||||
"quantiles = fitted_model.forecast_quantiles(X_test)\n",
|
||||
"quantiles"
|
||||
]
|
||||
},
|
||||
@@ -460,10 +457,10 @@
|
||||
"# specify which quantiles you would like \n",
|
||||
"fitted_model.quantiles = [0.01, 0.5, 0.95]\n",
|
||||
"# use forecast_quantiles function, not the forecast() one\n",
|
||||
"y_pred_quantiles = fitted_model.forecast_quantiles(X_test, y_query)\n",
|
||||
"y_pred_quantiles = fitted_model.forecast_quantiles(X_test)\n",
|
||||
"\n",
|
||||
"# it all nicely aligns column-wise\n",
|
||||
"pd.concat([X_test.reset_index(), pd.DataFrame({'query' : y_query}), y_pred_quantiles], axis=1)"
|
||||
"pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -539,9 +536,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try: \n",
|
||||
" y_query = y_away.copy()\n",
|
||||
" y_query.fill(np.NaN)\n",
|
||||
" y_pred_away, xy_away = fitted_model.forecast(X_away, y_query)\n",
|
||||
" y_pred_away, xy_away = fitted_model.forecast(X_away)\n",
|
||||
" xy_away\n",
|
||||
"except Exception as e:\n",
|
||||
" print(e)"
|
||||
@@ -551,7 +546,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! Because the model received all `NaN` (and not an actual target value), it is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
|
||||
"How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
|
||||
"\n",
|
||||
"We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
|
||||
]
|
||||
@@ -740,7 +735,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"tags": [
|
||||
"Forecasting",
|
||||
|
||||
@@ -40,7 +40,7 @@
|
||||
"## Introduction\n",
|
||||
"In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration notebook](../configuration.ipynb) before running this notebook.\n",
|
||||
"Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
|
||||
]
|
||||
@@ -454,9 +454,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
|
||||
"\n",
|
||||
"We will first create a query `y_query`, which is aligned index-for-index to `X_test`. This is a vector of target values where each `NaN` serves the function of the question mark to be replaced by forecast. Passing definite values in the `y` argument allows the `forecast` function to make predictions on data that does not immediately follow the train data which contains `y`. In each grain, the last time point where the model sees a definite value of `y` is that grain's _forecast origin_."
|
||||
"To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -465,15 +463,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace ALL values in y by NaN.\n",
|
||||
"# The forecast origin will be at the beginning of the first forecast period.\n",
|
||||
"# (Which is the same time as the end of the last training period.)\n",
|
||||
"y_query = y_test.copy().astype(np.float)\n",
|
||||
"y_query.fill(np.nan)\n",
|
||||
"# The featurized data, aligned to y, will also be returned.\n",
|
||||
"# This contains the assumptions that were made in the forecast\n",
|
||||
"# and helps align the forecast to the original data\n",
|
||||
"y_predictions, X_trans = fitted_model.forecast(X_test, y_query)"
|
||||
"y_predictions, X_trans = fitted_model.forecast(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -640,7 +633,7 @@
|
||||
"import json\n",
|
||||
"# The request data frame needs to have y_query column which corresponds to query.\n",
|
||||
"X_query = X_test.copy()\n",
|
||||
"X_query['y_query'] = y_query\n",
|
||||
"X_query['y_query'] = np.NaN\n",
|
||||
"# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
|
||||
"X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
|
||||
"# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",
|
||||
@@ -705,9 +698,6 @@
|
||||
"framework": [
|
||||
"Azure ML AutoML"
|
||||
],
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"friendly_name": "Forecasting orange juice sales with deployment",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
@@ -725,8 +715,11 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Forecasting"
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -7,7 +7,7 @@ from azureml.core.experiment import Experiment
|
||||
from sklearn.externals import joblib
|
||||
from azureml.core.dataset import Dataset
|
||||
from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
|
||||
automl_setup_model_explanations, automl_check_model_if_explainable
|
||||
automl_setup_model_explanations
|
||||
from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
|
||||
from azureml.explain.model.mimic_wrapper import MimicWrapper
|
||||
from automl.client.core.common.constants import MODEL_PATH
|
||||
@@ -25,11 +25,6 @@ ws = run.experiment.workspace
|
||||
experiment = Experiment(ws, '<<experimnet_name>>')
|
||||
automl_run = Run(experiment=experiment, run_id='<<run_id>>')
|
||||
|
||||
# Check if this AutoML model is explainable
|
||||
if not automl_check_model_if_explainable(automl_run):
|
||||
raise Exception("Model explanations is currently not supported for " + automl_run.get_properties().get(
|
||||
'run_algorithm'))
|
||||
|
||||
# Download the best model from the artifact store
|
||||
automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
|
||||
|
||||
|
||||
@@ -405,7 +405,7 @@
|
||||
"\n",
|
||||
" - To run a production-ready web service, see the [notebook on deployment to Azure Kubernetes Service](../production-deploy-to-aks/production-deploy-to-aks.ipynb).\n",
|
||||
" - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n",
|
||||
" - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets.ipynb).\n",
|
||||
" - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n",
|
||||
" - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n",
|
||||
" - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)."
|
||||
]
|
||||
|
||||
@@ -0,0 +1,369 @@
|
||||
{
|
||||
"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": [
|
||||
"# Deploy models to Azure Kubernetes Service (AKS) using controlled roll out\n",
|
||||
"This notebook will show you how to deploy mulitple AKS webservices with the same scoring endpoint and how to roll out your models in a controlled manner by configuring % of scoring traffic going to each webservice. If you are using a Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create an Azure ML Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check for latest version\n",
|
||||
"import azureml.core\n",
|
||||
"print(azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize workspace\n",
|
||||
"Create a [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register the model\n",
|
||||
"Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n",
|
||||
"In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(workspace=ws,\n",
|
||||
" model_name='sklearn_regression_model.pkl', # Name of the registered model in your workspace.\n",
|
||||
" model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n",
|
||||
" model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n",
|
||||
" model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n",
|
||||
" description='Ridge regression model to predict diabetes progression.',\n",
|
||||
" tags={'area': 'diabetes', 'type': 'regression'})\n",
|
||||
"\n",
|
||||
"print('Name:', model.name)\n",
|
||||
"print('Version:', model.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register an environment (for all models)\n",
|
||||
"\n",
|
||||
"If you control over how your model is run, or if it has special runtime requirements, you can specify your own environment and scoring method.\n",
|
||||
"\n",
|
||||
"Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"environment=Environment('my-sklearn-environment')\n",
|
||||
"environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
|
||||
" 'azureml-defaults',\n",
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn'\n",
|
||||
"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('score.py') as f:\n",
|
||||
" print(f.read())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the InferenceConfig\n",
|
||||
"Create the inference configuration to reference your environment and entry script during deployment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(entry_script='score.py', \n",
|
||||
" source_directory='.',\n",
|
||||
" environment=environment)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Provision the AKS Cluster\n",
|
||||
"If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import AksCompute\n",
|
||||
"from azureml.core.compute import ComputeTarget\n",
|
||||
"# Use the default configuration (can also provide parameters to customize)\n",
|
||||
"prov_config = AksCompute.provisioning_configuration()\n",
|
||||
"\n",
|
||||
"aks_name = 'my-aks' \n",
|
||||
"# Create the cluster\n",
|
||||
"aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = prov_config) \n",
|
||||
"aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create an Endpoint and add a version (AKS service)\n",
|
||||
"This creates a new endpoint and adds a version behind it. By default the first version added is the default version. You can specify the traffic percentile a version takes behind an endpoint. \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# deploying the model and create a new endpoint\n",
|
||||
"from azureml.core.webservice import AksEndpoint\n",
|
||||
"# from azureml.core.compute import ComputeTarget\n",
|
||||
"\n",
|
||||
"#select a created compute\n",
|
||||
"compute = ComputeTarget(ws, 'my-aks')\n",
|
||||
"namespace_name=\"endpointnamespace\"\n",
|
||||
"# define the endpoint name\n",
|
||||
"endpoint_name = \"myendpoint1\"\n",
|
||||
"# define the service name\n",
|
||||
"version_name= \"versiona\"\n",
|
||||
"\n",
|
||||
"endpoint_deployment_config = AksEndpoint.deploy_configuration(tags = {'modelVersion':'firstversion', 'department':'finance'}, \n",
|
||||
" description = \"my first version\", namespace = namespace_name, \n",
|
||||
" version_name = version_name, traffic_percentile = 40)\n",
|
||||
"\n",
|
||||
"endpoint = Model.deploy(ws, endpoint_name, [model], inference_config, endpoint_deployment_config, compute)\n",
|
||||
"endpoint.wait_for_deployment(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint.get_logs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Add another version of the service to an existing endpoint\n",
|
||||
"This adds another version behind an existing endpoint. You can specify the traffic percentile the new version takes. If no traffic_percentile is specified then it defaults to 0. All the unspecified traffic percentile (in this example 50) across all versions goes to default version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Adding a new version to an existing Endpoint.\n",
|
||||
"version_name_add=\"versionb\" \n",
|
||||
"\n",
|
||||
"endpoint.create_version(version_name = version_name_add, inference_config=inference_config, models=[model], tags = {'modelVersion':'secondversion', 'department':'finance'}, \n",
|
||||
" description = \"my second version\", traffic_percentile = 10)\n",
|
||||
"endpoint.wait_for_deployment(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Update an existing version in an endpoint\n",
|
||||
"There are two types of versions: control and treatment. An endpoint contains one or more treatment versions but only one control version. This categorization helps compare the different versions against the defined control version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint.update_version(version_name=endpoint.versions[version_name_add].name, description=\"my second version update\", traffic_percentile=40, is_default=True, is_control_version_type=True)\n",
|
||||
"endpoint.wait_for_deployment(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test the web service using run method\n",
|
||||
"Test the web sevice by passing in data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Scoring on endpoint\n",
|
||||
"import json\n",
|
||||
"test_sample = json.dumps({'data': [\n",
|
||||
" [1,2,3,4,5,6,7,8,9,10], \n",
|
||||
" [10,9,8,7,6,5,4,3,2,1]\n",
|
||||
"]})\n",
|
||||
"\n",
|
||||
"test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
|
||||
"prediction = endpoint.run(input_data=test_sample_encoded)\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Delete Resources"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# deleting a version in an endpoint\n",
|
||||
"endpoint.delete_version(version_name=version_name)\n",
|
||||
"endpoint.wait_for_deployment(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# deleting an endpoint, this will delete all versions in the endpoint and the endpoint itself\n",
|
||||
"endpoint.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "shipatel"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"None"
|
||||
],
|
||||
"datasets": [
|
||||
"Diabetes"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Kubernetes Service"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"Scikit-learn"
|
||||
],
|
||||
"friendly_name": "Deploy models to AKS using controlled roll out",
|
||||
"index_order": 3,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.0"
|
||||
},
|
||||
"star_tag": [
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Deploy a model with Azure Machine Learning"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,4 @@
|
||||
name: deploy-aks-with-controlled-rollout
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -0,0 +1,28 @@
|
||||
import pickle
|
||||
import json
|
||||
import numpy
|
||||
from sklearn.externals import joblib
|
||||
from sklearn.linear_model import Ridge
|
||||
from azureml.core.model import Model
|
||||
|
||||
|
||||
def init():
|
||||
global model
|
||||
# note here "sklearn_regression_model.pkl" is the name of the model registered under
|
||||
# this is a different behavior than before when the code is run locally, even though the code is the same.
|
||||
model_path = Model.get_model_path('sklearn_regression_model.pkl')
|
||||
# deserialize the model file back into a sklearn model
|
||||
model = joblib.load(model_path)
|
||||
|
||||
|
||||
# note you can pass in multiple rows for scoring
|
||||
def run(raw_data):
|
||||
try:
|
||||
data = json.loads(raw_data)['data']
|
||||
data = numpy.array(data)
|
||||
result = model.predict(data)
|
||||
# you can return any data type as long as it is JSON-serializable
|
||||
return result.tolist()
|
||||
except Exception as e:
|
||||
error = str(e)
|
||||
return error
|
||||
Binary file not shown.
@@ -382,10 +382,25 @@
|
||||
" headers=aad_token, \n",
|
||||
" json={\"ExperimentName\": \"My_Pipeline1\",\n",
|
||||
" \"RunSource\": \"SDK\",\n",
|
||||
" \"ParameterAssignments\": {\"pipeline_arg\": 45}})\n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
" \"ParameterAssignments\": {\"pipeline_arg\": 45}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"print(run_id)"
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -494,10 +494,25 @@
|
||||
" headers=aad_token, \n",
|
||||
" json={\"ExperimentName\": \"default_pipeline\",\n",
|
||||
" \"RunSource\": \"SDK\",\n",
|
||||
" \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})\n",
|
||||
" \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
"print(run_id)"
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -578,7 +593,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.2"
|
||||
"version": "3.6.7"
|
||||
},
|
||||
"order_index": 12,
|
||||
"tags": [
|
||||
|
||||
@@ -366,8 +366,15 @@
|
||||
"\n",
|
||||
"rest_endpoint = published_pipeline.endpoint\n",
|
||||
"\n",
|
||||
"print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))\n",
|
||||
"\n",
|
||||
"print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# specify the param when running the pipeline\n",
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token, \n",
|
||||
@@ -381,9 +388,24 @@
|
||||
" },\n",
|
||||
" \"ParameterAssignments\": {\"input_string\": \"sample_string3\"}\n",
|
||||
" }\n",
|
||||
" )\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -15,6 +15,13 @@
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -553,8 +560,25 @@
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token, \n",
|
||||
" json={\"ExperimentName\": \"batch_scoring\",\n",
|
||||
" \"ParameterAssignments\": {\"param_batch_size\": 50}})\n",
|
||||
"run_id = response.json()[\"Id\"]"
|
||||
" \"ParameterAssignments\": {\"param_batch_size\": 50}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -16,6 +16,13 @@
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -276,7 +283,6 @@
|
||||
"# Runconfig\n",
|
||||
"amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
|
||||
"amlcompute_run_config.environment.docker.enabled = True\n",
|
||||
"amlcompute_run_config.environment.docker.gpu_support = True\n",
|
||||
"amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n",
|
||||
"amlcompute_run_config.environment.spark.precache_packages = False"
|
||||
]
|
||||
@@ -538,41 +544,59 @@
|
||||
"## Send request and monitor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the pipeline using PipelineParameter values style='candy' and nodecount=2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# run the pipeline using PipelineParameter values style='candy' and nodecount=2\n",
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}}) \n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core.run import PipelineRun\n",
|
||||
"published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"\n",
|
||||
"RunDetails(published_pipeline_run_candy).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3\n",
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}}) \n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
"\n",
|
||||
"published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"\n",
|
||||
"RunDetails(published_pipeline_run_rain).show()"
|
||||
"Run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -581,15 +605,84 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# run the pipeline using PipelineParameter values style='udnie' and nodecount=4\n",
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 3}}) \n",
|
||||
"run_id = response.json()[\"Id\"]\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"RunDetails(published_pipeline_run_rain).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the pipeline using PipelineParameter values style='udnie' and nodecount=4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 3}})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"\n",
|
||||
"RunDetails(published_pipeline_run_udnie).show()"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -707,7 +707,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "dipeck"
|
||||
"name": "swatig"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
|
||||
@@ -166,7 +166,7 @@ def download_data():
|
||||
from zipfile import ZipFile
|
||||
# download data
|
||||
data_file = './fowl_data.zip'
|
||||
download_url = 'https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip'
|
||||
download_url = 'https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip'
|
||||
urllib.request.urlretrieve(download_url, filename=data_file)
|
||||
|
||||
# extract files
|
||||
|
||||
@@ -174,7 +174,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Download training data\n",
|
||||
"The dataset we will use (located on a public blob [here](https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
|
||||
"The dataset we will use (located on a public blob [here](https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -698,7 +698,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "ninhu"
|
||||
"name": "swatig"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
|
||||
@@ -550,7 +550,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "dipeck"
|
||||
"name": "swatig"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
|
||||
@@ -1140,7 +1140,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "ninhu"
|
||||
"name": "swatig"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
|
||||
@@ -517,7 +517,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.hyperdrive import *\n",
|
||||
"from azureml.train.hyperdrive import RandomParameterSampling, choice, loguniform\n",
|
||||
"\n",
|
||||
"ps = RandomParameterSampling(\n",
|
||||
" {\n",
|
||||
@@ -562,6 +562,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.hyperdrive import TruncationSelectionPolicy\n",
|
||||
"policy = TruncationSelectionPolicy(evaluation_interval=2, truncation_percentage=25)"
|
||||
]
|
||||
},
|
||||
@@ -578,12 +579,13 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.hyperdrive import HyperDriveConfig, PrimaryMetricGoal\n",
|
||||
"htc = HyperDriveConfig(estimator=est, \n",
|
||||
" hyperparameter_sampling=ps, \n",
|
||||
" policy=policy, \n",
|
||||
" primary_metric_name='validation_acc', \n",
|
||||
" primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n",
|
||||
" max_total_runs=20,\n",
|
||||
" max_total_runs=15,\n",
|
||||
" max_concurrent_runs=4)"
|
||||
]
|
||||
},
|
||||
@@ -616,7 +618,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(htr).show()"
|
||||
]
|
||||
},
|
||||
@@ -721,7 +722,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(warm_start_htr).show()"
|
||||
]
|
||||
},
|
||||
@@ -820,7 +820,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(resume_child_runs_htr).show()"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -100,7 +100,7 @@
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.0.76.1, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.0.79, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1131,7 +1131,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "ninhu"
|
||||
"name": "swatig"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
|
||||
@@ -149,7 +149,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"condadependencies-remarks-sample"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.environment import CondaDependencies\n",
|
||||
@@ -169,7 +173,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"condadependencies-remarks-sample2"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"conda_dep.add_pip_package(\"pillow==5.4.1\")\n",
|
||||
|
||||
@@ -10,7 +10,7 @@ With Azure Machine Learning datasets, you can:
|
||||
|
||||
## Learn how to use Azure Machine Learning datasets
|
||||
* [Create and register datasets](https://aka.ms/azureml/howto/createdatasets)
|
||||
* Use [Datasets in training](datasets-tutorial/train-with-datasets.ipynb)
|
||||
* Use [Datasets in training](datasets-tutorial/train-with-datasets/train-with-datasets.ipynb)
|
||||
* Use TabularDatasets in [automated machine learning training](https://aka.ms/automl-dataset)
|
||||
* Use FileDatasets in [image classification](https://aka.ms/filedataset-samplenotebook)
|
||||
* Use FileDatasets in [deep learning with hyperparameter tuning](https://aka.ms/filedataset-hyperdrive)
|
||||
|
||||
@@ -0,0 +1,403 @@
|
||||
{
|
||||
"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": [
|
||||
"# Introduction to labeled datasets\n",
|
||||
"\n",
|
||||
"Labeled datasets are output from Azure Machine Learning [labeling projects](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-labeling-projects). It captures the reference to the data (e.g. image files) and its labels. \n",
|
||||
"\n",
|
||||
"This tutorial introduces the capabilities of labeled datasets and how to use it in training.\n",
|
||||
"\n",
|
||||
"Learn how-to:\n",
|
||||
"\n",
|
||||
"> * Set up your development environment\n",
|
||||
"> * Explore labeled datasets\n",
|
||||
"> * Train a simple deep learning neural network on a remote cluster\n",
|
||||
"\n",
|
||||
"## Prerequisite:\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
|
||||
"* Go through Azure Machine Learning [labeling projects](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-labeling-projects) and export the labels as an Azure Machine Learning dataset\n",
|
||||
"* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
|
||||
" * install the latest version of azureml-sdk\n",
|
||||
" * install the latest version of azureml-contrib-dataset\n",
|
||||
" * install [PyTorch](https://pytorch.org/)\n",
|
||||
" * create a workspace and its configuration file (`config.json`)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Set up your development environment\n",
|
||||
"\n",
|
||||
"All the setup for your development work can be accomplished in a Python notebook. Setup includes:\n",
|
||||
"\n",
|
||||
"* Importing Python packages\n",
|
||||
"* Connecting to a workspace to enable communication between your local computer and remote resources\n",
|
||||
"* Creating an experiment to track all your runs\n",
|
||||
"* Creating a remote compute target to use for training\n",
|
||||
"\n",
|
||||
"### Import packages\n",
|
||||
"\n",
|
||||
"Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import azureml.core\n",
|
||||
"import azureml.contrib.dataset\n",
|
||||
"from azureml.core import Dataset, Workspace, Experiment\n",
|
||||
"from azureml.contrib.dataset import FileHandlingOption\n",
|
||||
"\n",
|
||||
"# check core SDK version number\n",
|
||||
"print(\"Azure ML SDK Version: \", azureml.core.VERSION)\n",
|
||||
"print(\"Azure ML Contrib Version\", azureml.contrib.dataset.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Connect to workspace\n",
|
||||
"\n",
|
||||
"Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# load workspace\n",
|
||||
"workspace = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + workspace.name, \n",
|
||||
" 'Azure region: ' + workspace.location, \n",
|
||||
" 'Subscription id: ' + workspace.subscription_id, \n",
|
||||
" 'Resource group: ' + workspace.resource_group, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create experiment and a directory\n",
|
||||
"\n",
|
||||
"Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# create an ML experiment\n",
|
||||
"exp = Experiment(workspace=workspace, name='labeled-datasets')\n",
|
||||
"\n",
|
||||
"# create a directory\n",
|
||||
"script_folder = './labeled-datasets'\n",
|
||||
"os.makedirs(script_folder, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create or Attach existing compute resource\n",
|
||||
"By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you will create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
|
||||
"\n",
|
||||
"**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 = \"openhack\"\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" compute_target = ComputeTarget(workspace=workspace, 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_NC6', \n",
|
||||
" max_nodes=4)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" compute_target = ComputeTarget.create(workspace, cluster_name, compute_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 uses 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",
|
||||
"# use get_status() to get a detailed status for the current cluster. \n",
|
||||
"print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explore labeled datasets\n",
|
||||
"\n",
|
||||
"**Note**: How to create labeled datasets is not covered in this tutorial. To create labeled datasets, you can go through [labeling projects](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-labeling-projects) and export the output labels as Azure Machine Lerning datasets. \n",
|
||||
"\n",
|
||||
"`animal_labels` used in this tutorial section is the output from a labeling project, with the task type of \"Object Identification\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get animal_labels dataset from the workspace\n",
|
||||
"animal_labels = Dataset.get_by_name(workspace, 'animal_labels')\n",
|
||||
"animal_labels"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can load labeled datasets into pandas DataFrame. There are 3 file handling option that you can choose to load the data files referenced by the labeled datasets:\n",
|
||||
"* Streaming: The default option to load data files.\n",
|
||||
"* Download: Download your data files to a local path.\n",
|
||||
"* Mount: Mount your data files to a mount point. Mount only works for Linux-based compute, including Azure Machine Learning notebook VM and Azure Machine Learning Compute."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"animal_pd = animal_labels.to_pandas_dataframe(file_handling_option=FileHandlingOption.DOWNLOAD, target_path='./download/', overwrite_download=True)\n",
|
||||
"animal_pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import matplotlib.image as mpimg\n",
|
||||
"\n",
|
||||
"# read images from downloaded path\n",
|
||||
"img = mpimg.imread(animal_pd.loc[0,'image_url'])\n",
|
||||
"imgplot = plt.imshow(img)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also load labeled datasets into [torchvision datasets](https://pytorch.org/docs/stable/torchvision/datasets.html), so that you can leverage on the open source libraries provided by PyTorch for image transformation and training."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torchvision.transforms import functional as F\n",
|
||||
"\n",
|
||||
"# load animal_labels dataset into torchvision dataset\n",
|
||||
"pytorch_dataset = animal_labels.to_torchvision()\n",
|
||||
"img = pytorch_dataset[0][0]\n",
|
||||
"print(type(img))\n",
|
||||
"\n",
|
||||
"# use methods from torchvision to transform the img into grayscale\n",
|
||||
"pil_image = F.to_pil_image(img)\n",
|
||||
"gray_image = F.to_grayscale(pil_image, num_output_channels=3)\n",
|
||||
"\n",
|
||||
"imgplot = plt.imshow(gray_image)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train an image classification model\n",
|
||||
"\n",
|
||||
" `crack_labels` dataset used in this tutorial section is the output from a labeling project, with the task type of \"Image Classification Multi-class\". We will use this dataset to train an image classification model that classify whether an image has cracks or not."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get crack_labels dataset from the workspace\n",
|
||||
"crack_labels = Dataset.get_by_name(workspace, 'crack_labels')\n",
|
||||
"crack_labels"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure Estimator for training\n",
|
||||
"\n",
|
||||
"You can ask the system to build a conda environment based on your dependency specification. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"conda_env = Environment('conda-env')\n",
|
||||
"conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk',\n",
|
||||
" 'azureml-contrib-dataset',\n",
|
||||
" 'torch','torchvision',\n",
|
||||
" 'azureml-dataprep[pandas]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create a generic estimator for by specifying\n",
|
||||
"\n",
|
||||
"* The name of the estimator object, `est`\n",
|
||||
"* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
|
||||
"* The training script name, train.py\n",
|
||||
"* The input dataset for training\n",
|
||||
"* The compute target. In this case you will use the AmlCompute you created\n",
|
||||
"* The environment definition for the experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.estimator import Estimator\n",
|
||||
"\n",
|
||||
"est = Estimator(source_directory=script_folder, \n",
|
||||
" entry_script='train.py',\n",
|
||||
" inputs=[crack_labels.as_named_input('crack_labels')],\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" environment_definition= conda_env)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Submit job to run\n",
|
||||
"\n",
|
||||
"Submit the estimator to the Azure ML experiment to kick off the execution."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run = exp.submit(est)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sihhu"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"compute": [
|
||||
"Remote"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Introduction to labeled datasets",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
},
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
},
|
||||
"star_tag": [
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"Dataset",
|
||||
"label",
|
||||
"Estimator"
|
||||
],
|
||||
"task": "Train"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,106 @@
|
||||
import os
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
|
||||
from azureml.core import Dataset, Run
|
||||
import azureml.contrib.dataset
|
||||
from azureml.contrib.dataset import FileHandlingOption, LabeledDatasetTask
|
||||
|
||||
run = Run.get_context()
|
||||
|
||||
# get input dataset by name
|
||||
labeled_dataset = run.input_datasets['crack_labels']
|
||||
pytorch_dataset = labeled_dataset.to_torchvision()
|
||||
|
||||
|
||||
indices = torch.randperm(len(pytorch_dataset)).tolist()
|
||||
dataset_train = torch.utils.data.Subset(pytorch_dataset, indices[:40])
|
||||
dataset_test = torch.utils.data.Subset(pytorch_dataset, indices[-10:])
|
||||
|
||||
trainloader = torch.utils.data.DataLoader(dataset_train, batch_size=4,
|
||||
shuffle=True, num_workers=0)
|
||||
|
||||
testloader = torch.utils.data.DataLoader(dataset_test, batch_size=4,
|
||||
shuffle=True, num_workers=0)
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 6, 5)
|
||||
self.pool = nn.MaxPool2d(2, 2)
|
||||
self.conv2 = nn.Conv2d(6, 16, 5)
|
||||
self.fc1 = nn.Linear(16 * 71 * 71, 120)
|
||||
self.fc2 = nn.Linear(120, 84)
|
||||
self.fc3 = nn.Linear(84, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pool(F.relu(self.conv1(x)))
|
||||
x = self.pool(F.relu(self.conv2(x)))
|
||||
x = x.view(x.size(0), 16 * 71 * 71)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.relu(self.fc2(x))
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
|
||||
|
||||
net = Net()
|
||||
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
|
||||
|
||||
|
||||
for epoch in range(2): # loop over the dataset multiple times
|
||||
|
||||
running_loss = 0.0
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
# get the inputs; data is a list of [inputs, labels]
|
||||
inputs, labels = data
|
||||
|
||||
# zero the parameter gradients
|
||||
optimizer.zero_grad()
|
||||
|
||||
# forward + backward + optimize
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# print statistics
|
||||
running_loss += loss.item()
|
||||
if i % 5 == 4: # print every 5 mini-batches
|
||||
print('[%d, %5d] loss: %.3f' %
|
||||
(epoch + 1, i + 1, running_loss / 5))
|
||||
running_loss = 0.0
|
||||
|
||||
print('Finished Training')
|
||||
classes = trainloader.dataset.dataset.labels
|
||||
PATH = './cifar_net.pth'
|
||||
torch.save(net.state_dict(), PATH)
|
||||
|
||||
dataiter = iter(testloader)
|
||||
images, labels = dataiter.next()
|
||||
|
||||
net = Net()
|
||||
net.load_state_dict(torch.load(PATH))
|
||||
|
||||
outputs = net(images)
|
||||
|
||||
_, predicted = torch.max(outputs, 1)
|
||||
|
||||
correct = 0
|
||||
total = 0
|
||||
with torch.no_grad():
|
||||
for data in testloader:
|
||||
images, labels = data
|
||||
outputs = net(images)
|
||||
_, predicted = torch.max(outputs.data, 1)
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
|
||||
print('Accuracy of the network on the 10 test images: %d %%' % (100 * correct / total))
|
||||
pass
|
||||
@@ -0,0 +1,35 @@
|
||||
import os
|
||||
|
||||
|
||||
def convert(imgf, labelf, outf, n):
|
||||
f = open(imgf, "rb")
|
||||
l = open(labelf, "rb")
|
||||
o = open(outf, "w")
|
||||
|
||||
f.read(16)
|
||||
l.read(8)
|
||||
images = []
|
||||
|
||||
for i in range(n):
|
||||
image = [ord(l.read(1))]
|
||||
for j in range(28 * 28):
|
||||
image.append(ord(f.read(1)))
|
||||
images.append(image)
|
||||
|
||||
for image in images:
|
||||
o.write(",".join(str(pix) for pix in image) + "\n")
|
||||
f.close()
|
||||
o.close()
|
||||
l.close()
|
||||
|
||||
|
||||
mounted_input_path = os.environ['fashion_ds']
|
||||
mounted_output_path = os.environ['AZUREML_DATAREFERENCE_prepared_fashion_ds']
|
||||
os.makedirs(mounted_output_path, exist_ok=True)
|
||||
|
||||
convert(os.path.join(mounted_input_path, 'train-images-idx3-ubyte'),
|
||||
os.path.join(mounted_input_path, 'train-labels-idx1-ubyte'),
|
||||
os.path.join(mounted_output_path, 'mnist_train.csv'), 60000)
|
||||
convert(os.path.join(mounted_input_path, 't10k-images-idx3-ubyte'),
|
||||
os.path.join(mounted_input_path, 't10k-labels-idx1-ubyte'),
|
||||
os.path.join(mounted_output_path, 'mnist_test.csv'), 10000)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,120 @@
|
||||
import keras
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Dense, Dropout, Flatten
|
||||
from keras.layers import Conv2D, MaxPooling2D
|
||||
from keras.layers.normalization import BatchNormalization
|
||||
from keras.utils import to_categorical
|
||||
from keras.callbacks import Callback
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.model_selection import train_test_split
|
||||
from azureml.core import Run
|
||||
|
||||
# dataset object from the run
|
||||
run = Run.get_context()
|
||||
dataset = run.input_datasets['prepared_fashion_ds']
|
||||
|
||||
# split dataset into train and test set
|
||||
(train_dataset, test_dataset) = dataset.random_split(percentage=0.8, seed=111)
|
||||
|
||||
# load dataset into pandas dataframe
|
||||
data_train = train_dataset.to_pandas_dataframe()
|
||||
data_test = test_dataset.to_pandas_dataframe()
|
||||
|
||||
img_rows, img_cols = 28, 28
|
||||
input_shape = (img_rows, img_cols, 1)
|
||||
|
||||
X = np.array(data_train.iloc[:, 1:])
|
||||
y = to_categorical(np.array(data_train.iloc[:, 0]))
|
||||
|
||||
# here we split validation data to optimiza classifier during training
|
||||
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=13)
|
||||
|
||||
# test data
|
||||
X_test = np.array(data_test.iloc[:, 1:])
|
||||
y_test = to_categorical(np.array(data_test.iloc[:, 0]))
|
||||
|
||||
|
||||
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255
|
||||
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255
|
||||
X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1).astype('float32') / 255
|
||||
|
||||
batch_size = 256
|
||||
num_classes = 10
|
||||
epochs = 10
|
||||
|
||||
# construct neuron network
|
||||
model = Sequential()
|
||||
model.add(Conv2D(32, kernel_size=(3, 3),
|
||||
activation='relu',
|
||||
kernel_initializer='he_normal',
|
||||
input_shape=input_shape))
|
||||
model.add(MaxPooling2D((2, 2)))
|
||||
model.add(Dropout(0.25))
|
||||
model.add(Conv2D(64, (3, 3), activation='relu'))
|
||||
model.add(MaxPooling2D(pool_size=(2, 2)))
|
||||
model.add(Dropout(0.25))
|
||||
model.add(Conv2D(128, (3, 3), activation='relu'))
|
||||
model.add(Dropout(0.4))
|
||||
model.add(Flatten())
|
||||
model.add(Dense(128, activation='relu'))
|
||||
model.add(Dropout(0.3))
|
||||
model.add(Dense(num_classes, activation='softmax'))
|
||||
|
||||
model.compile(loss=keras.losses.categorical_crossentropy,
|
||||
optimizer=keras.optimizers.Adam(),
|
||||
metrics=['accuracy'])
|
||||
|
||||
# start an Azure ML run
|
||||
run = Run.get_context()
|
||||
|
||||
|
||||
class LogRunMetrics(Callback):
|
||||
# callback at the end of every epoch
|
||||
def on_epoch_end(self, epoch, log):
|
||||
# log a value repeated which creates a list
|
||||
run.log('Loss', log['loss'])
|
||||
run.log('Accuracy', log['accuracy'])
|
||||
|
||||
|
||||
history = model.fit(X_train, y_train,
|
||||
batch_size=batch_size,
|
||||
epochs=epochs,
|
||||
verbose=1,
|
||||
validation_data=(X_val, y_val),
|
||||
callbacks=[LogRunMetrics()])
|
||||
|
||||
score = model.evaluate(X_test, y_test, verbose=0)
|
||||
|
||||
# log a single value
|
||||
run.log("Final test loss", score[0])
|
||||
print('Test loss:', score[0])
|
||||
|
||||
run.log('Final test accuracy', score[1])
|
||||
print('Test accuracy:', score[1])
|
||||
|
||||
plt.figure(figsize=(6, 3))
|
||||
plt.title('Fashion MNIST with Keras ({} epochs)'.format(epochs), fontsize=14)
|
||||
plt.plot(history.history['accuracy'], 'b-', label='Accuracy', lw=4, alpha=0.5)
|
||||
plt.plot(history.history['loss'], 'r--', label='Loss', lw=4, alpha=0.5)
|
||||
plt.legend(fontsize=12)
|
||||
plt.grid(True)
|
||||
|
||||
# log an image
|
||||
run.log_image('Loss v.s. Accuracy', plot=plt)
|
||||
|
||||
# create a ./outputs/model folder in the compute target
|
||||
# files saved in the "./outputs" folder are automatically uploaded into run history
|
||||
os.makedirs('./outputs/model', exist_ok=True)
|
||||
|
||||
# serialize NN architecture to JSON
|
||||
model_json = model.to_json()
|
||||
# save model JSON
|
||||
with open('./outputs/model/model.json', 'w') as f:
|
||||
f.write(model_json)
|
||||
# save model weights
|
||||
model.save_weights('./outputs/model/model.h5')
|
||||
print("model saved in ./outputs/model folder")
|
||||
@@ -0,0 +1,488 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License [2017] Zalando SE, https://tech.zalando.com"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Build a simple ML pipeline for image classification\n",
|
||||
"\n",
|
||||
"## Introduction\n",
|
||||
"This tutorial shows how to train a simple deep neural network using the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and Keras on Azure Machine Learning. Fashion-MNIST is a dataset of Zalando's article images\u00e2\u20ac\u201dconsisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.\n",
|
||||
"\n",
|
||||
"Learn how to:\n",
|
||||
"\n",
|
||||
"> * Set up your development environment\n",
|
||||
"> * Create the Fashion MNIST dataset\n",
|
||||
"> * Create a machine learning pipeline to train a simple deep learning neural network on a remote cluster\n",
|
||||
"> * Retrieve input datasets from the experiment and register the output model with datasets\n",
|
||||
"\n",
|
||||
"## Prerequisite:\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\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 latest version of AzureML SDK\n",
|
||||
" * create a workspace and its configuration file (`config.json`)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Set up your development environment\n",
|
||||
"\n",
|
||||
"All the setup for your development work can be accomplished in a Python notebook. Setup includes:\n",
|
||||
"\n",
|
||||
"* Importing Python packages\n",
|
||||
"* Connecting to a workspace to enable communication between your local computer and remote resources\n",
|
||||
"* Creating an experiment to track all your runs\n",
|
||||
"* Creating a remote compute target to use for training\n",
|
||||
"\n",
|
||||
"### Import packages\n",
|
||||
"\n",
|
||||
"Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Dataset, Datastore, ComputeTarget, RunConfiguration, Experiment\n",
|
||||
"from azureml.core.runconfig import CondaDependencies\n",
|
||||
"from azureml.pipeline.steps import PythonScriptStep, EstimatorStep\n",
|
||||
"from azureml.pipeline.core import Pipeline, PipelineData\n",
|
||||
"from azureml.train.dnn import TensorFlow\n",
|
||||
"\n",
|
||||
"# check core SDK version number\n",
|
||||
"print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Connect to workspace\n",
|
||||
"\n",
|
||||
"Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# load workspace\n",
|
||||
"workspace = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + workspace.name, \n",
|
||||
" 'Azure region: ' + workspace.location, \n",
|
||||
" 'Subscription id: ' + workspace.subscription_id, \n",
|
||||
" 'Resource group: ' + workspace.resource_group, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create experiment and a directory\n",
|
||||
"\n",
|
||||
"Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# create an ML experiment\n",
|
||||
"exp = Experiment(workspace=workspace, name='keras-mnist-fashion')\n",
|
||||
"\n",
|
||||
"# create a directory\n",
|
||||
"script_folder = './keras-mnist-fashion'\n",
|
||||
"os.makedirs(script_folder, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create or Attach existing compute resource\n",
|
||||
"By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
|
||||
"\n",
|
||||
"**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 = \"your-cluster-name\"\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" compute_target = ComputeTarget(workspace=workspace, 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_NC6', \n",
|
||||
" max_nodes=4)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" compute_target = ComputeTarget.create(workspace, cluster_name, compute_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 uses 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",
|
||||
"# use get_status() to get a detailed status for the current cluster. \n",
|
||||
"print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the Fashion MNIST dataset\n",
|
||||
"\n",
|
||||
"By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. \n",
|
||||
"\n",
|
||||
"Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create a dataset from it. We will now upload the [Fashion MNIST](./keras-mnist-fashion) to the default datastore (blob) within your workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"datastore = workspace.get_default_datastore()\n",
|
||||
"datastore.upload_files(files = ['keras-mnist-fashion/t10k-images-idx3-ubyte', 'keras-mnist-fashion/t10k-labels-idx1-ubyte',\n",
|
||||
" 'keras-mnist-fashion/train-images-idx3-ubyte','keras-mnist-fashion/train-labels-idx1-ubyte'],\n",
|
||||
" target_path = 'mnist-fashion',\n",
|
||||
" overwrite = True,\n",
|
||||
" show_progress = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then we will create an unregistered FileDataset pointing to the path in the datastore. You can also create a dataset from multiple paths. [Learn More](https://aka.ms/azureml/howto/createdatasets) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fashion_ds = Dataset.File.from_files([(datastore, 'mnist-fashion')])\n",
|
||||
"\n",
|
||||
"# list the files referenced by fashion_ds\n",
|
||||
"fashion_ds.to_path()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Build 2-step ML pipeline\n",
|
||||
"\n",
|
||||
"The [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages. [Learn More](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"### Step 1: data preparation\n",
|
||||
"\n",
|
||||
"In step one, we will load the image and labels from Fashion MNIST dataset into mnist_train.csv and mnist_test.csv\n",
|
||||
"\n",
|
||||
"Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. Both mnist_train.csv and mnist_test.csv contain 785 columns. The first column consists of the class labels, which represent the article of clothing. The rest of the columns contain the pixel-values of the associated image."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# set up the compute environment to install required packages\n",
|
||||
"conda = CondaDependencies.create(\n",
|
||||
" pip_packages=['azureml-sdk','azureml-dataprep[fuse,pandas]'],\n",
|
||||
" pin_sdk_version=False)\n",
|
||||
"\n",
|
||||
"conda.set_pip_option('--pre')\n",
|
||||
"\n",
|
||||
"run_config = RunConfiguration()\n",
|
||||
"run_config.environment.python.conda_dependencies = conda"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Intermediate data (or output of a step) is represented by a `PipelineData` object. preprared_fashion_ds is produced as the output of step 1, and used as the input of step 2. PipelineData introduces a data dependency between steps, and creates an implicit execution order in the pipeline. You can register a `PipelineData` as a dataset and version the output data automatically. [Learn More](https://docs.microsoft.com/azure/machine-learning/service/how-to-version-track-datasets#version-a-pipeline-output-dataset) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# define output data\n",
|
||||
"prepared_fashion_ds = PipelineData('prepared_fashion_ds', datastore=datastore).as_dataset()\n",
|
||||
"\n",
|
||||
"# register output data as dataset\n",
|
||||
"prepared_fashion_ds = prepared_fashion_ds.register(name='prepared_fashion_ds', create_new_version=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prep_step = PythonScriptStep(name='prepare step',\n",
|
||||
" script_name=\"prepare.py\",\n",
|
||||
" # mount fashion_ds dataset to the compute_target\n",
|
||||
" inputs=[fashion_ds.as_named_input('fashion_ds').as_mount()],\n",
|
||||
" outputs=[prepared_fashion_ds],\n",
|
||||
" source_directory=script_folder,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" runconfig=run_config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Step 2: train CNN with Keras\n",
|
||||
"\n",
|
||||
"Next, we construct an `azureml.train.dnn.TensorFlow` estimator object. The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed.\n",
|
||||
"\n",
|
||||
"[EstimatorStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py) adds a step to run Tensorflow Estimator in a Pipeline. It takes a dataset as the input."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# set up training step with Tensorflow estimator\n",
|
||||
"est = TensorFlow(entry_script='train.py',\n",
|
||||
" source_directory=script_folder, \n",
|
||||
" pip_packages = ['azureml-sdk','keras','numpy','scikit-learn', 'matplotlib'],\n",
|
||||
" compute_target=compute_target)\n",
|
||||
"\n",
|
||||
"est_step = EstimatorStep(name='train step',\n",
|
||||
" estimator=est,\n",
|
||||
" estimator_entry_script_arguments=[],\n",
|
||||
" # parse prepared_fashion_ds into TabularDataset and use it as the input\n",
|
||||
" inputs=[prepared_fashion_ds.parse_delimited_files()],\n",
|
||||
" compute_target=compute_target)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Build the pipeline\n",
|
||||
"Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py).\n",
|
||||
"\n",
|
||||
"A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/python/api/azureml-core/azureml.core.experiment(class)?view=azure-ml-py#submit-config--tags-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# build pipeline & run experiment\n",
|
||||
"pipeline = Pipeline(workspace, steps=[prep_step, est_step])\n",
|
||||
"run = exp.submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Monitor the PipelineRun"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.find_step_run('train step')[0].get_metrics()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register the input dataset and the output model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Azure Machine Learning dataset makes it easy to trace how your data is used in ML. [Learn More](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-version-track-datasets#track-datasets-in-experiments)<br>\n",
|
||||
"For each Machine Learning experiment, you can easily trace the datasets used as the input through `Run` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get input datasets\n",
|
||||
"prep_step = run.find_step_run('prepare step')[0]\n",
|
||||
"inputs = prep_step.get_details()['inputDatasets']\n",
|
||||
"input_dataset = inputs[0]['dataset']\n",
|
||||
"\n",
|
||||
"# list the files referenced by input_dataset\n",
|
||||
"input_dataset.to_path()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Register the input Fashion MNIST dataset with the workspace so that you can reuse it in other experiments or share it with your colleagues who have access to your workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fashion_ds = input_dataset.register(workspace = workspace,\n",
|
||||
" name = 'fashion_ds',\n",
|
||||
" description = 'image and label files from fashion mnist',\n",
|
||||
" create_new_version = True)\n",
|
||||
"fashion_ds"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Register the output model with dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.find_step_run('train step')[0].register_model(model_name = 'keras-model', model_path = 'outputs/model/', \n",
|
||||
" datasets =[('train test data',fashion_ds)])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sihhu"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"compute": [
|
||||
"Remote"
|
||||
],
|
||||
"datasets": [
|
||||
"Fashion MNIST"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Datasets with ML Pipeline",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
},
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
},
|
||||
"star_tag": [
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"Dataset",
|
||||
"Pipeline",
|
||||
"Estimator",
|
||||
"ScriptRun"
|
||||
],
|
||||
"task": "Train"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -13,23 +13,23 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Train with Azure Machine Learning Datasets\n",
|
||||
"# Train with Azure Machine Learning datasets\n",
|
||||
"Datasets are categorized into TabularDataset and FileDataset based on how users consume them in training. \n",
|
||||
"* A TabularDataset represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame.\n",
|
||||
"* A FileDataset references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.\n",
|
||||
"\n",
|
||||
"In this tutorial, you will learn how to train with Azure Machine Learning Datasets:\n",
|
||||
"In this tutorial, you will learn how to train with Azure Machine Learning datasets:\n",
|
||||
"\n",
|
||||
"☑ Use Datasets directly in your training script\n",
|
||||
"☑ Use datasets directly in your training script\n",
|
||||
"\n",
|
||||
"☑ Use Datasets to mount files to a remote compute"
|
||||
"☑ Use datasets to mount files to a remote compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -149,12 +149,12 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You now have the necessary packages and compute resources to train a model in the cloud.\n",
|
||||
"## Use Datasets directly in training\n",
|
||||
"## Use datasets directly in training\n",
|
||||
"\n",
|
||||
"### Create a TabularDataset\n",
|
||||
"By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. \n",
|
||||
"\n",
|
||||
"Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create Dataset from it. We will now upload the [Iris data](./train-dataset/Iris.csv) to the default datastore (blob) within your workspace."
|
||||
"Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create dataset from it. We will now upload the [Iris data](./train-dataset/Iris.csv) to the default datastore (blob) within your workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -174,7 +174,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then we will create an unregistered TabularDataset pointing to the path in the datastore. You can also create a Dataset from multiple paths. [learn more](https://aka.ms/azureml/howto/createdatasets) "
|
||||
"Then we will create an unregistered TabularDataset pointing to the path in the datastore. You can also create a dataset from multiple paths. [learn more](https://aka.ms/azureml/howto/createdatasets) \n",
|
||||
"\n",
|
||||
"[TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a Pandas or Spark DataFrame. You can create a TabularDataset object from .csv, .tsv, and parquet files, and from SQL query results. For a complete list, see [TabularDatasetFactory](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py) class."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -260,7 +262,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure and use Datasets as the input to Estimator"
|
||||
"### Configure and use datasets as the input to Estimator"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -294,7 +296,7 @@
|
||||
"* The name of the estimator object, `est`\n",
|
||||
"* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
|
||||
"* The training script name, train_titanic.py\n",
|
||||
"* The input Dataset for training\n",
|
||||
"* The input dataset for training\n",
|
||||
"* The compute target. In this case you will use the AmlCompute you created\n",
|
||||
"* The environment definition for the experiment"
|
||||
]
|
||||
@@ -348,9 +350,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use Datasets to mount files to a remote compute\n",
|
||||
"## Use datasets to mount files to a remote compute\n",
|
||||
"\n",
|
||||
"You can use the Dataset object to mount or download files referred by it. When you mount a file system, you attach that file system to a directory (mount point) and make it available to the system. Because mounting load files at the time of processing, it is usually faster than download.<br> \n",
|
||||
"You can use the `Dataset` object to mount or download files referred by it. When you mount a file system, you attach that file system to a directory (mount point) and make it available to the system. Because mounting load files at the time of processing, it is usually faster than download.<br> \n",
|
||||
"Note: mounting is only available for Linux-based compute (DSVM/VM, AMLCompute, HDInsights)."
|
||||
]
|
||||
},
|
||||
@@ -365,7 +367,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
@@ -396,7 +397,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a FileDataset"
|
||||
"### Create a FileDataset\n",
|
||||
"\n",
|
||||
"[FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.file_dataset.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public URLs. Using this method, you can download or mount the files to your compute as a FileDataset object. The files can be in any format, which enables a wider range of machine learning scenarios, including deep learning."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -492,7 +495,7 @@
|
||||
"src = ScriptRunConfig(source_directory=script_folder, \n",
|
||||
" script='train_diabetes.py', \n",
|
||||
" # to mount the dataset on the remote compute and pass the mounted path as an argument to the training script\n",
|
||||
" arguments =[dataset.as_named_input('diabetes').as_mount('tmp/dataset')])\n",
|
||||
" arguments =[dataset.as_named_input('diabetes').as_mount()])\n",
|
||||
"\n",
|
||||
"src.run_config.framework = 'python'\n",
|
||||
"src.run_config.environment = conda_env\n",
|
||||
@@ -533,7 +536,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Register Datasets\n",
|
||||
"### Register datasets\n",
|
||||
"Use the register() method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script."
|
||||
]
|
||||
},
|
||||
@@ -553,10 +556,10 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register models with Datasets\n",
|
||||
"## Register models with datasets\n",
|
||||
"The last step in the training script wrote the model files in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n",
|
||||
"\n",
|
||||
"You can register models with Datasets for reproducibility and auditing purpose."
|
||||
"You can register models with datasets for reproducibility and auditing purpose."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -642,9 +645,11 @@
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"Dataset"
|
||||
"Dataset",
|
||||
"Estimator",
|
||||
"ScriptRun"
|
||||
],
|
||||
"task": "Filtering"
|
||||
"task": "Train"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
8
index.md
8
index.md
@@ -36,7 +36,11 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
|
||||
| :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries |
|
||||
|
||||
| :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets.ipynb) | Filtering | Iris, Diabetes | Remote | None | Azure ML | Dataset |
|
||||
| :star:[Introduction to labeled datasets](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.ipynb) | Train | | Remote | None | Azure ML | Dataset, label, Estimator |
|
||||
|
||||
| :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun |
|
||||
|
||||
| :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun |
|
||||
|
||||
| [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals |
|
||||
|
||||
@@ -138,6 +142,8 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
|
||||
| :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
|
||||
|
||||
| :star:[Deploy models to AKS using controlled roll out](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Kubernetes Service | Scikit-learn | None |
|
||||
|
||||
| [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
|
||||
|
||||
| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
|
||||
@@ -102,7 +102,7 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.0.76.1 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.0.79 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -15,6 +15,13 @@
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../contrib/batch_inferencing/) for examples on how to get started."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
Reference in New Issue
Block a user