import neptune neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/scikit-optimize-integration') ## Step 2: Create an Experiment neptune.create_experiment(name='skopt-sweep') ## Step 3: Run skopt with the Neptune Callback # Create Neptune Callback import neptunecontrib.monitoring.skopt as skopt_utils neptune_callback = skopt_utils.NeptuneCallback() # Run the skopt minimize function with the Neptune Callback results = skopt.forest_minimize(objective, space, n_calls=25, n_random_starts=10, callback=[neptune_callback]) ## Step 4: Log best parameter configuration, best score and diagnostic plots skopt_utils.log_results(results) ## Step 5: Stop logging and Explore results in the Neptune UI # tests
neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/scikit-optimize-integration') # Quickstart ## Step 1: Create an Experiment neptune.create_experiment(name='skopt-sweep') ## Step 2: Run skopt with the Neptune Callback # Create Neptune Callback import neptunecontrib.monitoring.skopt as skopt_utils neptune_callback = skopt_utils.NeptuneCallback() # Run the skopt minimize function with the Neptune Callback results = skopt.forest_minimize(objective, space, n_calls=25, n_random_starts=10, callback=[neptune_callback]) ## Step 3: Log best parameter configuration, best score and diagnostic plots skopt_utils.log_results(results) ## Step 4: Stop logging and Explore results in the Neptune UI neptune.stop()