Hyperparameterafstemming upgraden naar SDK v2
In SDK v2 worden hyperparameters samengevoegd in taken.
Een taak heeft een type. De meeste taken zijn opdrachttaken die een command, zoals python main.py. Wat in een taak wordt uitgevoerd, is agnostisch voor elke programmeertaal, zodat u scripts kunt uitvoeren, interpreters kunt aanroepen bash python , een aantal curl opdrachten kunt uitvoeren of iets anders.
Een sweep-taak is een ander type taak, dat de instellingen voor opruimen definieert en kan worden gestart door de sweep-methode van opdracht aan te roepen.
Als u een upgrade wilt uitvoeren, moet u uw code wijzigen voor het definiëren en verzenden van uw hyperparameterafstemmingsexperiment naar SDK v2. Wat u binnen de taak uitvoert, hoeft niet te worden bijgewerkt naar SDK v2. Het is echter raadzaam om code te verwijderen die specifiek is voor Azure Machine Learning uit uw modeltrainingsscripts. Deze scheiding maakt een eenvoudigere overgang tussen lokale en cloud mogelijk en wordt beschouwd als best practice voor volwassen MLOps. In de praktijk betekent dit dat regels code worden verwijderd azureml.* . Modelregistratie- en traceringscode moet worden vervangen door MLflow. Zie voor meer informatie hoe u MLflow gebruikt in v2.
Dit artikel bevat een vergelijking van scenario('s) in SDK v1 en SDK v2.
Hyperparameterafstemming uitvoeren in een experiment
SDK v1
from azureml.core import ScriptRunConfig, Experiment, Workspace from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal from azureml.train.hyperdrive import choice, loguniform dataset = Dataset.get_by_name(ws, 'mnist-dataset') # list the files referenced by mnist dataset dataset.to_path() #define the search space for your hyperparameters param_sampling = RandomParameterSampling( { '--batch-size': choice(25, 50, 100), '--first-layer-neurons': choice(10, 50, 200, 300, 500), '--second-layer-neurons': choice(10, 50, 200, 500), '--learning-rate': loguniform(-6, -1) } ) args = ['--data-folder', dataset.as_named_input('mnist').as_mount()] #Set up your script run src = ScriptRunConfig(source_directory=script_folder, script='keras_mnist.py', arguments=args, compute_target=compute_target, environment=keras_env) # Set early stopping on this one early_termination_policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1) # Define the configurations for your hyperparameter tuning experiment hyperdrive_config = HyperDriveConfig(run_config=src, hyperparameter_sampling=param_sampling, policy=early_termination_policy, primary_metric_name='Accuracy', primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, max_total_runs=20, max_concurrent_runs=4) # Specify your experiment details experiment = Experiment(workspace, experiment_name) hyperdrive_run = experiment.submit(hyperdrive_config) #Find the best model best_run = hyperdrive_run.get_best_run_by_primary_metric()SDK v2
from azure.ai.ml import MLClient from azure.ai.ml import command, Input from azure.ai.ml.sweep import Choice, Uniform, MedianStoppingPolicy from azure.identity import DefaultAzureCredential # Create your command command_job_for_sweep = command( code="./src", command="python main.py --iris-csv ${{inputs.iris_csv}} --learning-rate ${{inputs.learning_rate}} --boosting ${{inputs.boosting}}", environment="AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu@latest", inputs={ "iris_csv": Input( type="uri_file", path="https://azuremlexamples.blob.core.windows.net/datasets/iris.csv", ), #define the search space for your hyperparameters "learning_rate": Uniform(min_value=0.01, max_value=0.9), "boosting": Choice(values=["gbdt", "dart"]), }, compute="cpu-cluster", ) # Call sweep() on your command job to sweep over your parameter expressions sweep_job = command_job_for_sweep.sweep( compute="cpu-cluster", sampling_algorithm="random", primary_metric="test-multi_logloss", goal="Minimize", ) # Define the limits for this sweep sweep_job.set_limits(max_total_trials=20, max_concurrent_trials=10, timeout=7200) # Set early stopping on this one sweep_job.early_termination = MedianStoppingPolicy(delay_evaluation=5, evaluation_interval=2) # Specify your experiment details sweep_job.display_name = "lightgbm-iris-sweep-example" sweep_job.experiment_name = "lightgbm-iris-sweep-example" sweep_job.description = "Run a hyperparameter sweep job for LightGBM on Iris dataset." # submit the sweep returned_sweep_job = ml_client.create_or_update(sweep_job) # get a URL for the status of the job returned_sweep_job.services["Studio"].endpoint # Download best trial model output ml_client.jobs.download(returned_sweep_job.name, output_name="model")
Hyperparameterafstemming uitvoeren in een pijplijn
SDK v1
tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU') data_folder = dataset.as_mount() src = ScriptRunConfig(source_directory=script_folder, script='tf_mnist.py', arguments=['--data-folder', data_folder], compute_target=compute_target, environment=tf_env) #Define HyperDrive configs ps = RandomParameterSampling( { '--batch-size': choice(25, 50, 100), '--first-layer-neurons': choice(10, 50, 200, 300, 500), '--second-layer-neurons': choice(10, 50, 200, 500), '--learning-rate': loguniform(-6, -1) } ) early_termination_policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1) hd_config = HyperDriveConfig(run_config=src, hyperparameter_sampling=ps, policy=early_termination_policy, primary_metric_name='validation_acc', primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, max_total_runs=4, max_concurrent_runs=4) metrics_output_name = 'metrics_output' metrics_data = PipelineData(name='metrics_data', datastore=datastore, pipeline_output_name=metrics_output_name, training_output=TrainingOutput("Metrics")) model_output_name = 'model_output' saved_model = PipelineData(name='saved_model', datastore=datastore, pipeline_output_name=model_output_name, training_output=TrainingOutput("Model", model_file="outputs/model/saved_model.pb")) #Create HyperDriveStep hd_step_name='hd_step01' hd_step = HyperDriveStep( name=hd_step_name, hyperdrive_config=hd_config, inputs=[data_folder], outputs=[metrics_data, saved_model]) #Find and register best model conda_dep = CondaDependencies() conda_dep.add_pip_package("azureml-sdk") rcfg = RunConfiguration(conda_dependencies=conda_dep) register_model_step = PythonScriptStep(script_name='register_model.py', name="register_model_step01", inputs=[saved_model], compute_target=cpu_cluster, arguments=["--saved-model", saved_model], allow_reuse=True, runconfig=rcfg) register_model_step.run_after(hd_step) #Run the pipeline pipeline = Pipeline(workspace=ws, steps=[hd_step, register_model_step]) pipeline_run = exp.submit(pipeline)SDK v2
train_component_func = load_component(path="./train.yml") score_component_func = load_component(path="./predict.yml") # define a pipeline @pipeline() def pipeline_with_hyperparameter_sweep(): """Tune hyperparameters using sample components.""" train_model = train_component_func( data=Input( type="uri_file", path="wasbs://datasets@azuremlexamples.blob.core.windows.net/iris.csv", ), c_value=Uniform(min_value=0.5, max_value=0.9), kernel=Choice(["rbf", "linear", "poly"]), coef0=Uniform(min_value=0.1, max_value=1), degree=3, gamma="scale", shrinking=False, probability=False, tol=0.001, cache_size=1024, verbose=False, max_iter=-1, decision_function_shape="ovr", break_ties=False, random_state=42, ) sweep_step = train_model.sweep( primary_metric="training_f1_score", goal="minimize", sampling_algorithm="random", compute="cpu-cluster", ) sweep_step.set_limits(max_total_trials=20, max_concurrent_trials=10, timeout=7200) score_data = score_component_func( model=sweep_step.outputs.model_output, test_data=sweep_step.outputs.test_data ) pipeline_job = pipeline_with_hyperparameter_sweep() # set pipeline level compute pipeline_job.settings.default_compute = "cpu-cluster" # submit job to workspace pipeline_job = ml_client.jobs.create_or_update( pipeline_job, experiment_name="pipeline_samples" ) pipeline_job
Toewijzing van belangrijke functionaliteit in SDK v1 en SDK v2
| Functionaliteit in SDK v1 | Ruwe toewijzing in SDK v2 |
|---|---|
| HyperDriveRunConfig() | SweepJob() |
| hyperdrive-pakket | opruimen pakket |
Volgende stappen
Zie voor meer informatie: