# Initialize various utilities that will make our lives easier
studio_utils = WatsonStudioUtils(region="us-south")
studio_utils.configure_utilities_from_file()
project_utils = ProjectUtils(studio_utils)
# Initialize our experiment
experiment = Experiment("Fashion MNIST-dropout tests",
"Test two different dropout values", "tensorflow",
"1.5", "python", "3.5", studio_utils, project_utils)
# Add two training runs to determine which dropout is best: 0.4 or 0.9
run_1a_path = os.path.join("experiment_zips", "dropout_0.4.zip")
run_1b_path = os.path.join("experiment_zips", "dropout_0.6.zip")
# Specify different GPU types as "k80", "k80x2", "k80x4", "p100", ...
experiment.add_training_run("Run #1", "python3 experiment.py", run_1a_path,
"k80")
experiment.add_training_run("Run #2", "python3 experiment.py", run_1b_path,
"k80")
# Execute experiment
experiment.execute()
# Print the current status of the Experiment.
experiment.print_experiment_summary()
# Now you'll want to continuously monitor your experiment. To do that from the command line,
# you should use the WML CLI.
# Initialize various utilities that will make our lives easier
studio_utils = WatsonStudioUtils(region="us-south")
studio_utils.configure_utilities_from_file()
project_utils = ProjectUtils(studio_utils)
# Initialize our experiment
experiment = Experiment("Fashion MNIST-Random", "Perform random grid search",
"tensorflow", "1.5", "python", "3.5", studio_utils,
project_utils)
# Create random parameters to search then create a training run for each
search = create_random_search()
experiment_zip = os.path.join("zips", "fashion_mnist_random_search.zip")
for index, run_params in enumerate(search):
run_name = "run_%d" % (index + 1)
experiment.add_training_run(run_name, run_params, "python3 experiment.py",
experiment_zip, "k80")
# Execute experiment
experiment.execute()
# Print the current status of the Experiment.
experiment.print_experiment_summary()
# Now you'll want to continuously monitor your experiment. To do that from the command line,
# you should use the WML CLI: bx ml monitor training-runs TRAINING_RUN_ID
else:
framework = "tensorflow"
version = "1.5"
experiment_zip = "dynamic_hyperparms_tf.zip"
experiment_zip = os.path.join("experiment_zips", experiment_zip)
# Initialize our experiment
gpu_type = "k80"
experiment = Experiment(
"Fashion MNIST-RBFOpt HPO-{}-{}".format(framework,
gpu_type), "Perform RBFOpt HPO",
framework, version, "python", "3.5", studio_utils, project_utils)
# Run RBFOpt to search through the hyperparameters
rbfopt_config = get_rbfopt_config()
experiment.set_rbfopt_config(rbfopt_config)
# Specify different GPU types as "k80", "k80x2", "k80x4", "p100", ...
experiment.add_training_run("RBFOpt search", "python3 experiment.py",
experiment_zip, gpu_type)
# Execute experiment
experiment.execute()
# Print the current status of the Experiment.
#experiment.print_experiment_summary()
# Now you'll want to continuously monitor your experiment. To do that from the command line,
# you should use the WML CLI: bx ml monitor training-runs TRAINING_RUN_ID
"python",
"3.5",
studio_utils,
project_utils)
experiment_zip = os.path.join("experiment_zips", experiment_zip)
# Create random parameters to search then create a training run for each
search = create_random_search()
for index, run_params in enumerate(search):
# Append hyperparameters to the command
command = "python3 experiment.py"
# Specify different GPU types as "k80", "k80x2", "k80x4", "p100", ...
run_name = "run_%d" % (index + 1)
# Add the hyperparameters to the experiment.zip (in config.json)
updated_experiment_zip = experiment.save_hyperparameters_config(run_params, experiment_zip)
experiment.add_training_run(run_name, command, updated_experiment_zip, gpu_type)
# Execute experiment
experiment.execute()
# Print the current status of the Experiment.
#experiment.print_experiment_summary()
# Now you'll want to continuously monitor your experiment. To do that from the command line,
# you should use the WML CLI: bx ml monitor training-runs TRAINING_RUN_ID
run_count = 25
rbfopt_search = get_rbfopt_search()
# Note: "accuracy" is the objective variable being provided to RBFOpt. For RBFOpt to function properly
# then, we must also store the "accuracy" values to "val_dict_list.json" as shown at the end of the experiment.py file
# in the .zip experiment.
#
# To use a different objective metric, pass the object name here plus update the
# experiment.py file to pass the correct values and corresponding name to the "val_dict_list.json"
#
# Likewise if you want to use iterations instead of epochs, then you must also change that value both here as well
# as in the "val_dict_list.json".
experiment_zip = os.path.join("zips", "fashion_mnist_rbfopt.zip")
rbfopt_experiment_zip = rbfopt_search.save_rbfopt_hpo(
experiment_zip, run_count, "accuracy", HPOUtils.TIME_INTERVAL_EPOCH,
HPOUtils.GOAL_MAXIMIZE)
# Note: We don't pass hyperparameters for this run as RBFOpt will determine the hyperparameters to pass for
# each training run as it intelligently explores the hyperparameter space for us.
experiment.add_training_run("RBFOpt search", None, "python3 experiment.py",
rbfopt_experiment_zip, "k80")
# Execute experiment
experiment.execute()
# Print the current status of the Experiment.
experiment.print_experiment_summary()
# Now you'll want to continuously monitor your experiment. To do that from the command line,
# you should use the WML CLI: bx ml monitor training-runs TRAINING_RUN_ID