def get_machine_memory():
if ray.is_initialized(): # using ray cluster
@ray.remote(num_gpus=1)
def get_remote_gpu():
gpus = GPUtil.getGPUs()
total_mem_mb = gpus[0].memory_total
return total_mem_mb * BYTES_PER_MiB
@ray.remote(num_cpus=1)
def get_remote_cpu():
total_mem = psutil.virtual_memory().total
return total_mem
resources = get_available_resources() # check if cluster has GPUS
if resources["gpu"] > 0:
machine_mem = ray.get(get_remote_gpu.remote())
else:
machine_mem = ray.get(get_remote_cpu.remote())
else: # not using ray cluster
if GPUtil.getGPUs():
machine_mem = GPUtil.getGPUs()[0].memory_total * BYTES_PER_MiB
else:
machine_mem = psutil.virtual_memory().total
return machine_mem
def create_auto_config(
dataset: Union[str, pd.DataFrame, dd.core.DataFrame, DatasetInfo],
target: Union[str, List[str]],
time_limit_s: Union[int, float],
tune_for_memory: bool,
user_config: Dict = None,
random_seed: int = default_random_seed,
use_reference_config: bool = False,
) -> dict:
"""Returns an auto-generated Ludwig config with the intent of training the best model on given given dataset /
target in the given time limit.
# Inputs
:param dataset: (str, pd.DataFrame, dd.core.DataFrame, DatasetInfo) data source to train over.
:param target: (str, List[str]) name of target feature
:param time_limit_s: (int, float) total time allocated to auto_train. acts
as the stopping parameter
:param tune_for_memory: (bool) refine hyperopt search space for available
host / GPU memory
:param user_config: (dict) override automatic selection of specified config items
:param random_seed: (int, default: `42`) a random seed that will be used anywhere
there is a call to a random number generator, including
hyperparameter search sampling, as well as data splitting,
parameter initialization and training set shuffling
:param use_reference_config: (bool) refine hyperopt search space by setting first
search point from reference model config, if any
# Return
:return: (dict) selected model configuration
"""
default_configs = _create_default_config(dataset, target, time_limit_s,
random_seed)
model_config, model_category, row_count = _model_select(
dataset, default_configs, user_config, use_reference_config)
if tune_for_memory:
if ray.is_initialized():
resources = get_available_resources() # check if cluster has GPUS
if resources["gpu"] > 0:
model_config, fits_in_memory = ray.get(
ray.remote(num_gpus=1, num_cpus=1,
max_calls=1)(memory_tune_config).remote(
model_config, dataset, model_category,
row_count))
else:
model_config, fits_in_memory = ray.get(
ray.remote(num_cpus=1)(memory_tune_config).remote(
model_config, dataset, model_category, row_count))
else:
model_config, fits_in_memory = memory_tune_config(
model_config, dataset, model_category, row_count)
if not fits_in_memory:
warnings.warn(
"AutoML with tune_for_memory enabled did not return estimation that model will fit in memory. "
"If out-of-memory occurs, consider setting AutoML user_config to reduce model memory footprint. "
)
return model_config
def _create_default_config(dataset: Union[str, dd.core.DataFrame, pd.DataFrame,
DatasetInfo],
target_name: str = None,
time_limit_s: Union[int, float] = None) -> dict:
"""
Returns auto_train configs for three available combiner models.
Coordinates the following tasks:
- extracts fields and generates list of FieldInfo objects
- gets field metadata (i.e avg. words, total non-null entries)
- builds input_features and output_feautures section of config
- for each combiner, adds default training, hyperopt
- infers resource constraints and adds gpu and cpu resource allocation per
trial
# Inputs
:param dataset: (str) filepath to dataset.
:param target_name: (str) name of target feature
:param time_limit_s: (int, float) total time allocated to auto_train. acts
as the stopping parameter
# Return
:return: (dict) dictionaries contain auto train config files for all available
combiner types
"""
_ray_init()
resources = get_available_resources()
experiment_resources = allocate_experiment_resources(resources)
dataset_info = dataset
if not isinstance(dataset, DatasetInfo):
dataset_info = get_dataset_info(dataset)
input_and_output_feature_config = get_features_config(
dataset_info.fields, dataset_info.row_count, resources, target_name)
model_configs = {}
for model_name, path_to_defaults in model_defaults.items():
default_model_config = load_yaml(path_to_defaults)
default_model_config.update(input_and_output_feature_config)
default_model_config['hyperopt']['executor'].update(
experiment_resources)
default_model_config['hyperopt']['executor'][
'time_budget_s'] = time_limit_s
model_configs[model_name] = default_model_config
return model_configs
def _create_default_config(
dataset: Union[str, dd.core.DataFrame, pd.DataFrame, DatasetInfo],
target_name: Union[str, List[str]] = None,
time_limit_s: Union[int, float] = None,
) -> dict:
"""Returns auto_train configs for three available combiner models. Coordinates the following tasks:
- extracts fields and generates list of FieldInfo objects
- gets field metadata (i.e avg. words, total non-null entries)
- builds input_features and output_features section of config
- for each combiner, adds default training, hyperopt
- infers resource constraints and adds gpu and cpu resource allocation per
trial
# Inputs
:param dataset: (str) filepath to dataset.
:param target_name: (str, List[str]) name of target feature
:param time_limit_s: (int, float) total time allocated to auto_train. acts
as the stopping parameter
# Return
:return: (dict) dictionaries contain auto train config files for all available
combiner types
"""
_ray_init()
resources = get_available_resources()
experiment_resources = allocate_experiment_resources(resources)
dataset_info = dataset
if not isinstance(dataset, DatasetInfo):
dataset_info = get_dataset_info(dataset)
input_and_output_feature_config = get_features_config(
dataset_info.fields, dataset_info.row_count, resources, target_name)
model_configs = {}
# read in base config and update with experiment resources
base_automl_config = load_yaml(BASE_AUTOML_CONFIG)
base_automl_config["hyperopt"]["executor"].update(experiment_resources)
base_automl_config["hyperopt"]["executor"]["time_budget_s"] = time_limit_s
if time_limit_s is not None:
base_automl_config["hyperopt"]["sampler"]["scheduler"][
"max_t"] = time_limit_s
base_automl_config.update(input_and_output_feature_config)
model_configs["base_config"] = base_automl_config
# read in all encoder configs
for feat_type, default_configs in encoder_defaults.items():
if feat_type not in model_configs.keys():
model_configs[feat_type] = {}
else:
for encoder_name, encoder_config_path in default_configs.items():
model_configs[feat_type][encoder_name] = load_yaml(
encoder_config_path)
# read in all combiner configs
model_configs["combiner"] = {}
for combiner_type, default_config in combiner_defaults.items():
combiner_config = load_yaml(default_config)
model_configs["combiner"][combiner_type] = combiner_config
return model_configs
