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| Original file line number | Diff line number | Diff line change |
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| Utilities Module | ||
| ================ | ||
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| This module contains utility functions for logging setup, search space creation, and random seed setup, designed for compatibility with TensorFlow, PyTorch, and scikit-learn. | ||
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| Functions | ||
| --------- | ||
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| create_search_space | ||
| ------------------- | ||
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| This function creates a hyperparameter search space based on provided ranges, supporting integers, floats, booleans, and strings. | ||
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| **Code:** | ||
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| .. code-block:: python | ||
| import inspect | ||
| import logging | ||
| import os | ||
| import random | ||
| import numpy as np | ||
| import sklearn | ||
| import tensorflow as tf | ||
| import torch | ||
| from packaging import version | ||
| from skopt.space import Real, Categorical, Integer | ||
| def create_search_space(hp_ranges, logger): | ||
| def isint(v): | ||
| return type(v) is int | ||
| def isfloat(v): | ||
| return type(v) is float | ||
| def isbool(v): | ||
| return type(v) is bool | ||
| def isstr(v): | ||
| return type(v) is str | ||
| search_space = {} | ||
| for key, value in hp_ranges.items(): | ||
| logger.info(f"Before key {key} value {value}") | ||
| if version.parse(sklearn.__version__) < version.parse("0.25.0"): | ||
| if key == "criterion" and "squared_error" in value: | ||
| value = ["friedman_mse", "mse"] | ||
| if isint(value[0]) and isint(value[1]): | ||
| search_space[key] = Integer(value[0], value[1]) | ||
| if isfloat(value[0]) and isfloat(value[1]): | ||
| if len(value) == 3: | ||
| search_space[key] = Real(value[0], value[1], prior=value[2]) | ||
| if (isbool(value[0]) and isbool(value[1])) or (isstr(value[0]) and isstr(value[1])): | ||
| search_space[key] = Categorical(value) | ||
| logger.info(f"key {key} value {value}") | ||
| return search_space | ||
| setup_logging | ||
| ------------- | ||
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| Sets up logging for experiments, allowing control over log file location and verbosity. | ||
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| **Code:** | ||
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| .. code-block:: python | ||
| def setup_logging(log_path=None, level=logging.INFO): | ||
| """Function setup as many logging for the experiments.""" | ||
| if log_path is None: | ||
| dirname = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) | ||
| dirname = os.path.dirname(dirname) | ||
| log_path = os.path.join(dirname, "logs", "logs.log") | ||
| logging.basicConfig( | ||
| filename=log_path, | ||
| level=level, | ||
| format="%(asctime)s %(name)s %(levelname)-8s %(message)s", | ||
| datefmt="%Y-%m-%d %H:%M:%S", | ||
| force=True, | ||
| ) | ||
| logger = logging.getLogger("SetupLogging") # root logger | ||
| logger.info("log file path: {}".format(log_path)) | ||
| os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # Suppresses INFO, WARNING, and ERROR logs | ||
| # Additional TensorFlow setting to disable GPU usage explicitly | ||
| tf.config.set_visible_devices([], "GPU") | ||
| logging.captureWarnings(False) | ||
| import warnings | ||
| warnings.filterwarnings("ignore") | ||
| warnings.filterwarnings("ignore", category=DeprecationWarning) | ||
| logging.getLogger("matplotlib").setLevel(logging.ERROR) | ||
| logging.getLogger("tensorflow").setLevel(logging.ERROR) | ||
| logging.getLogger("pytorch").setLevel(logging.ERROR) | ||
| logging.getLogger("torch").setLevel(logging.ERROR) | ||
| logging.getLogger("urllib3.connectionpool").setLevel(logging.ERROR) | ||
| setup_random_seed | ||
| ----------------- | ||
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| Sets up a random seed across TensorFlow, PyTorch, NumPy, and Python’s `random` module, while also configuring CPU and GPU usage. | ||
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| **Code:** | ||
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| .. code-block:: python | ||
| def setup_random_seed(random_state=1234): | ||
| logger = logging.getLogger("Setup Logging") | ||
| random_state = check_random_state(random_state) | ||
| seed = random_state.randint(2**31, dtype="uint32") | ||
| torch.manual_seed(seed) | ||
| logger.info(f"Total number of torch threads {torch.get_num_threads()}") | ||
| if torch.get_num_threads() <= 2: | ||
| n_cpus = 1 | ||
| else: | ||
| n_cpus = torch.get_num_threads() - 2 | ||
| if "pc2" in os.environ["HOME"]: | ||
| n_cpus = 4 | ||
| logger.info(f"Torch threads set {n_cpus}") | ||
| torch.set_num_threads(n_cpus) | ||
| tf.random.set_seed(seed) | ||
| seed = random_state.randint(2**31, dtype="uint32") | ||
| np.random.seed(seed) | ||
| random.seed(seed) | ||
| os.environ["KERAS_BACKEND"] = "tensorflow" | ||
| devices = tf.config.list_physical_devices("GPU") | ||
| logger.info("Keras Devices {}".format(devices)) | ||
| n_gpus = len(devices) | ||
| logger.info("Keras GPU {}".format(n_gpus)) | ||
| if n_gpus == 0: | ||
| cpu_count = multiprocessing.cpu_count() | ||
| tf.config.threading.set_inter_op_parallelism_threads(1) | ||
| tf.config.threading.set_intra_op_parallelism_threads(1) | ||
| if cpu_count > 2: | ||
| pass | ||
| else: | ||
| for gpu in tf.config.list_physical_devices("GPU"): | ||
| tf.config.experimental.set_memory_growth(gpu, True) | ||
| torch_gpu = torch.device("cuda" if torch.cuda.is_available() else "cpu") | ||
| logger.info("Torch GPU device {}".format(torch_gpu)) |