# Log metrics, parameters, and MLflow models during training
<a name="mlflow-track-experiments-log-metrics"></a>

After connecting to your MLflow Tracking Server, you can use the MLflow SDK to log metrics, parameters, and MLflow models.

## Log training metrics
<a name="mlflow-track-experiments-log-metrics-example"></a>

Use `mlflow.log_metric` within an MLflow training run to track metrics. For more information about logging metrics using MLflow, see `[mlflow.log\$1metric](https://mlflow.org/docs/2.13.2/python_api/mlflow.html#mlflow.log_metric)`.

```
with mlflow.start_run():
    mlflow.log_metric("foo", 1)
    
print(mlflow.search_runs())
```

This script should create an experiment run and print out an output similar to the following:

```
run_id experiment_id status artifact_uri ... tags.mlflow.source.name tags.mlflow.user tags.mlflow.source.type tags.mlflow.runName
0 607eb5c558c148dea176d8929bd44869 0 FINISHED s3://dddd/0/607eb5c558c148dea176d8929bd44869/a... ... file.py user-id LOCAL experiment-code-name
```

Within the MLflow UI, this example should look similar to the following: 

![\[An experiment shown in the top-level MLflow Experiments menu.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-ui-experiments.png)


Choose **Run Name** to see more run details.

![\[An experiment parameter shown on an experiment run page in the MLflow UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-ui-foo.png)


## Log parameters and models
<a name="mlflow-track-experiments-log-params-models"></a>

**Note**  
The following example requires your environment to have `s3:PutObject` permissions. This permission should be associated with the IAM Role that the MLflow SDK user assumes when they log into or federate into their AWS account. For more information, see [User and role policy examples](https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-policies-s3.html).

The following example takes you through a basic model training workflow using SKLearn and shows you how to track that model in an MLflow experiment run. This example logs parameters, metrics, and model artifacts.

```
import mlflow

from mlflow.models import infer_signature

import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# This is the ARN of the MLflow Tracking Server you created
mlflow.set_tracking_uri(your-tracking-server-arn)
mlflow.set_experiment("some-experiment")

# Load the Iris dataset
X, y = datasets.load_iris(return_X_y=True)

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the model hyperparameters
params = {"solver": "lbfgs", "max_iter": 1000, "multi_class": "auto", "random_state": 8888}

# Train the model
lr = LogisticRegression(**params)
lr.fit(X_train, y_train)

# Predict on the test set
y_pred = lr.predict(X_test)

# Calculate accuracy as a target loss metric
accuracy = accuracy_score(y_test, y_pred)

# Start an MLflow run and log parameters, metrics, and model artifacts
with mlflow.start_run():
    # Log the hyperparameters
    mlflow.log_params(params)

    # Log the loss metric
    mlflow.log_metric("accuracy", accuracy)

    # Set a tag that we can use to remind ourselves what this run was for
    mlflow.set_tag("Training Info", "Basic LR model for iris data")

    # Infer the model signature
    signature = infer_signature(X_train, lr.predict(X_train))

    # Log the model
    model_info = mlflow.sklearn.log_model(
        sk_model=lr,
        name="iris_model", # Changed from artifact_path to name for MLflow 3.0
        signature=signature,
        input_example=X_train,
        registered_model_name="tracking-quickstart",
    )
```

Within the MLflow UI, choose the experiment name in the left navigation pane to explore all associated runs. Choose the **Run Name** to see more information about each run. For this example, your experiment run page for this run should look similar to the following. 

![\[Tracked parameters for an experiment run in the MLflow UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-ui-parameters.png)


This example logs the logistic regression model. Within the MLflow UI, you should also see the logged model artifacts.

![\[Tracked model artifacts for an experiment run in the MLflow UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-ui-model-artifacts.png)