feat: mlperf example

examples/mlflow/requirenments.txt

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+alembic==1.13.1
+blinker==1.7.0
+certifi==2023.11.17
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpickle==3.0.0
+contourpy==1.1.1
+cycler==0.12.1
+databricks-cli==0.18.0
+docker==6.1.3
+entrypoints==0.4
+Flask==3.0.0
+fonttools==4.47.0
+gitdb==4.0.11
+GitPython==3.1.40
+greenlet==3.0.3
+gunicorn==21.2.0
+h5py==3.10.0
+idna==3.6
+importlib-metadata==7.0.1
+importlib-resources==6.1.1
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.3.2
+kiwisolver==1.4.5
+Mako==1.3.0
+Markdown==3.5.1
+MarkupSafe==2.1.3
+matplotlib==3.7.4
+mlflow==2.9.2
+mpi4py==3.1.5
+numpy==1.24.4
+nvidia-ml-py==12.535.133
+oauthlib==3.2.2
+packaging==23.2
+pandas==2.0.3
+-e git+ssh://[email protected]/Helmholtz-AI-Energy/perun.git@e2f23885dc8207838961a4a036583fdc5bd2e2bc#egg=perun
+Pillow==10.1.0
+protobuf==4.25.1
+psutil==5.9.7
+py-cpuinfo==9.0.0
+pyarrow==14.0.2
+PyJWT==2.8.0
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+PyYAML==6.0.1
+querystring-parser==1.2.4
+requests==2.31.0
+scikit-learn==1.3.2
+scipy==1.10.1
+six==1.16.0
+smmap==5.0.1
+SQLAlchemy==2.0.24
+sqlparse==0.4.4
+tabulate==0.9.0
+threadpoolctl==3.2.0
+typing_extensions==4.9.0
+tzdata==2023.3
+urllib3==2.1.0
+websocket-client==1.7.0
+Werkzeug==3.0.1
+zipp==3.17.0

examples/mlflow/train.py

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+import logging
+import sys
+import warnings
+from urllib.parse import urlparse
+
+import mlflow
+import mlflow.sklearn
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import ElasticNet
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.model_selection import train_test_split
+
+from perun import monitor, register_callback
+
+logging.basicConfig(level=logging.WARN)
+logger = logging.getLogger(__name__)
+
+
+@monitor()
+def eval_metrics(actual, pred):
+    rmse = np.sqrt(mean_squared_error(actual, pred))
+    mae = mean_absolute_error(actual, pred)
+    r2 = r2_score(actual, pred)
+    return rmse, mae, r2
+
+
+@monitor()
+def train(data):
+    train, test = train_test_split(data)
+
+    # The predicted column is "quality" which is a scalar from [3, 9]
+    train_x = train.drop(["quality"], axis=1)
+    test_x = test.drop(["quality"], axis=1)
+    train_y = train[["quality"]]
+    test_y = test[["quality"]]
+
+    alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
+    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
+
+    with mlflow.start_run() as active_run:
+
+        @register_callback
+        def perun2mlflow(node):
+            mlflow.start_run(active_run.info.run_id)
+            for metricType, metric in node.metrics.items():
+                name = f"{metric.type.value}"
+                mlflow.log_metric(name, metric.value)
+
+        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
+        lr.fit(train_x, train_y)
+
+        predicted_qualities = lr.predict(test_x)
+
+        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
+
+        print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
+        print("  RMSE: %s" % rmse)
+        print("  MAE: %s" % mae)
+        print("  R2: %s" % r2)
+
+        mlflow.log_param("alpha", alpha)
+        mlflow.log_param("l1_ratio", l1_ratio)
+        mlflow.log_metric("rmse", rmse)
+        mlflow.log_metric("r2", r2)
+        mlflow.log_metric("mae", mae)
+
+        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
+
+        # Model registry does not work with file store
+        if tracking_url_type_store != "file":
+            # Register the model
+            # There are other ways to use the Model Registry, which depends on the use case,
+            # please refer to the doc for more information:
+            # https://mlflow.org/docs/latest/model-registry.html#api-workflow
+            mlflow.sklearn.log_model(
+                lr, "model", registered_model_name="ElasticnetWineModel"
+            )
+        else:
+            mlflow.sklearn.log_model(lr, "model")
+
+
+if __name__ == "__main__":
+    warnings.filterwarnings("ignore")
+    np.random.seed(40)
+
+    # Read the wine-quality csv file from the URL
+    csv_url = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
+    try:
+        data = pd.read_csv(csv_url, sep=";")
+    except Exception as e:
+        logger.exception(
+            "Unable to download training & test CSV, check your internet connection. Error: %s",
+            e,
+        )
+    train(data)