"ML Flow datacamp" Code Answer's
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ML Flow datacamp
Gentle Gaur
on May 24, 2021
# Prints "created an experiment with ID <id>mlflow experiments create face-detection# Set the ID via environment variablesexport MLFLOW_EXPERIMENT_ID=<id>Source: towardsdatascience.com
ML Flow datacamp
Gentle Gaur
on May 24, 2021
# Launch a run. The experiment ID is inferred from the MLFLOW_EXPERIMENT_ID environment variablewith mlflow.start_run(): mlflow.log_parameter("a", 1) mlflow.log_metric("b", 2)Source: towardsdatascience.com
ML Flow datacamp
Gentle Gaur
on May 24, 2021
Usage: mlflow [OPTIONS] COMMAND [ARGS]...Options: --version Show the version and exit. --help Show this message and exit.Commands: azureml Serve models on Azure ML. download Downloads the artifact at the specified DBFS... experiments Tracking APIs. pyfunc Serve Python models locally. run Run an MLflow project from the given URI. sagemaker Serve models on SageMaker. sklearn Serve SciKit-Learn models. ui Run the MLflow tracking UI.Source: towardsdatascience.com
ML Flow datacamp
Gentle Gaur
on May 24, 2021
import osfrom mlflow import log_metric, log_param, log_artifactif __name__ == "__main__": # Log a parameter (key-value pair) log_param("param1", 5) # Log a metric; metrics can be updated throughout the run log_metric("foo", 1) log_metric("foo", 2) log_metric("foo", 3) # Log an artifact (output file) with open("output.txt", "w") as f: f.write("Hello world!") log_artifact("output.txt")Source: towardsdatascience.com
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