Source code for h2o.estimators.deepwater

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_python.py
# Copyright 2016 H2O.ai;  Apache License Version 2.0 (see LICENSE for details)
#
from __future__ import absolute_import, division, print_function, unicode_literals

from h2o.estimators.estimator_base import H2OEstimator
from h2o.exceptions import H2OValueError
from h2o.frame import H2OFrame
from h2o.utils.typechecks import assert_is_type, Enum, numeric
import h2o


[docs]class H2ODeepWaterEstimator(H2OEstimator): """ Deep Water Build a Deep Learning model using multiple native GPU backends Builds a deep neural network on an H2OFrame containing various data sources """ algo = "deepwater" def __init__(self, **kwargs): super(H2ODeepWaterEstimator, self).__init__() self._parms = {} names_list = {"model_id", "checkpoint", "autoencoder", "training_frame", "validation_frame", "nfolds", "balance_classes", "max_after_balance_size", "class_sampling_factors", "keep_cross_validation_predictions", "keep_cross_validation_fold_assignment", "fold_assignment", "fold_column", "response_column", "offset_column", "weights_column", "ignored_columns", "score_each_iteration", "categorical_encoding", "overwrite_with_best_model", "epochs", "train_samples_per_iteration", "target_ratio_comm_to_comp", "seed", "standardize", "learning_rate", "learning_rate_annealing", "momentum_start", "momentum_ramp", "momentum_stable", "distribution", "score_interval", "score_training_samples", "score_validation_samples", "score_duty_cycle", "classification_stop", "regression_stop", "stopping_rounds", "stopping_metric", "stopping_tolerance", "max_runtime_secs", "ignore_const_cols", "shuffle_training_data", "mini_batch_size", "clip_gradient", "network", "backend", "image_shape", "channels", "sparse", "gpu", "device_id", "cache_data", "network_definition_file", "network_parameters_file", "mean_image_file", "export_native_parameters_prefix", "activation", "hidden", "input_dropout_ratio", "hidden_dropout_ratios", "problem_type"} if "Lambda" in kwargs: kwargs["lambda_"] = kwargs.pop("Lambda") for pname, pvalue in kwargs.items(): if pname == 'model_id': self._id = pvalue self._parms["model_id"] = pvalue elif pname in names_list: # Using setattr(...) will invoke type-checking of the arguments setattr(self, pname, pvalue) else: raise H2OValueError("Unknown parameter %s = %r" % (pname, pvalue)) @property def checkpoint(self): """ Model checkpoint to resume training with. Type: ``str``. """ return self._parms.get("checkpoint") @checkpoint.setter def checkpoint(self, checkpoint): assert_is_type(checkpoint, None, str, H2OEstimator) self._parms["checkpoint"] = checkpoint @property def autoencoder(self): """ Auto-Encoder. Type: ``bool`` (default: ``False``). """ return self._parms.get("autoencoder") @autoencoder.setter def autoencoder(self, autoencoder): assert_is_type(autoencoder, None, bool) self._parms["autoencoder"] = autoencoder @property def training_frame(self): """ Id of the training data frame. Type: ``H2OFrame``. """ return self._parms.get("training_frame") @training_frame.setter def training_frame(self, training_frame): assert_is_type(training_frame, None, H2OFrame) self._parms["training_frame"] = training_frame @property def validation_frame(self): """ Id of the validation data frame. Type: ``H2OFrame``. """ return self._parms.get("validation_frame") @validation_frame.setter def validation_frame(self, validation_frame): assert_is_type(validation_frame, None, H2OFrame) self._parms["validation_frame"] = validation_frame @property def nfolds(self): """ Number of folds for K-fold cross-validation (0 to disable or >= 2). Type: ``int`` (default: ``0``). """ return self._parms.get("nfolds") @nfolds.setter def nfolds(self, nfolds): assert_is_type(nfolds, None, int) self._parms["nfolds"] = nfolds @property def balance_classes(self): """ Balance training data class counts via over/under-sampling (for imbalanced data). Type: ``bool`` (default: ``False``). """ return self._parms.get("balance_classes") @balance_classes.setter def balance_classes(self, balance_classes): assert_is_type(balance_classes, None, bool) self._parms["balance_classes"] = balance_classes @property def max_after_balance_size(self): """ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Type: ``float`` (default: ``5``). """ return self._parms.get("max_after_balance_size") @max_after_balance_size.setter def max_after_balance_size(self, max_after_balance_size): assert_is_type(max_after_balance_size, None, float) self._parms["max_after_balance_size"] = max_after_balance_size @property def class_sampling_factors(self): """ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. Type: ``List[float]``. """ return self._parms.get("class_sampling_factors") @class_sampling_factors.setter def class_sampling_factors(self, class_sampling_factors): assert_is_type(class_sampling_factors, None, [float]) self._parms["class_sampling_factors"] = class_sampling_factors @property def keep_cross_validation_predictions(self): """ Whether to keep the predictions of the cross-validation models. Type: ``bool`` (default: ``False``). """ return self._parms.get("keep_cross_validation_predictions") @keep_cross_validation_predictions.setter def keep_cross_validation_predictions(self, keep_cross_validation_predictions): assert_is_type(keep_cross_validation_predictions, None, bool) self._parms["keep_cross_validation_predictions"] = keep_cross_validation_predictions @property def keep_cross_validation_fold_assignment(self): """ Whether to keep the cross-validation fold assignment. Type: ``bool`` (default: ``False``). """ return self._parms.get("keep_cross_validation_fold_assignment") @keep_cross_validation_fold_assignment.setter def keep_cross_validation_fold_assignment(self, keep_cross_validation_fold_assignment): assert_is_type(keep_cross_validation_fold_assignment, None, bool) self._parms["keep_cross_validation_fold_assignment"] = keep_cross_validation_fold_assignment @property def fold_assignment(self): """ Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. One of: ``"auto"``, ``"random"``, ``"modulo"``, ``"stratified"`` (default: ``"auto"``). """ return self._parms.get("fold_assignment") @fold_assignment.setter def fold_assignment(self, fold_assignment): assert_is_type(fold_assignment, None, Enum("auto", "random", "modulo", "stratified")) self._parms["fold_assignment"] = fold_assignment @property def fold_column(self): """ Column with cross-validation fold index assignment per observation. Type: ``str``. """ return self._parms.get("fold_column") @fold_column.setter def fold_column(self, fold_column): assert_is_type(fold_column, None, str) self._parms["fold_column"] = fold_column @property def response_column(self): """ Response variable column. Type: ``str``. """ return self._parms.get("response_column") @response_column.setter def response_column(self, response_column): assert_is_type(response_column, None, str) self._parms["response_column"] = response_column @property def offset_column(self): """ Offset column. This will be added to the combination of columns before applying the link function. Type: ``str``. """ return self._parms.get("offset_column") @offset_column.setter def offset_column(self, offset_column): assert_is_type(offset_column, None, str) self._parms["offset_column"] = offset_column @property def weights_column(self): """ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. Type: ``str``. """ return self._parms.get("weights_column") @weights_column.setter def weights_column(self, weights_column): assert_is_type(weights_column, None, str) self._parms["weights_column"] = weights_column @property def ignored_columns(self): """ Names of columns to ignore for training. Type: ``List[str]``. """ return self._parms.get("ignored_columns") @ignored_columns.setter def ignored_columns(self, ignored_columns): assert_is_type(ignored_columns, None, [str]) self._parms["ignored_columns"] = ignored_columns @property def score_each_iteration(self): """ Whether to score during each iteration of model training. Type: ``bool`` (default: ``False``). """ return self._parms.get("score_each_iteration") @score_each_iteration.setter def score_each_iteration(self, score_each_iteration): assert_is_type(score_each_iteration, None, bool) self._parms["score_each_iteration"] = score_each_iteration @property def categorical_encoding(self): """ Encoding scheme for categorical features One of: ``"auto"``, ``"enum"``, ``"one_hot_internal"``, ``"one_hot_explicit"``, ``"binary"``, ``"eigen"``, ``"label_encoder"``, ``"sort_by_response"``, ``"enum_limited"`` (default: ``"auto"``). """ return self._parms.get("categorical_encoding") @categorical_encoding.setter def categorical_encoding(self, categorical_encoding): assert_is_type(categorical_encoding, None, Enum("auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited")) self._parms["categorical_encoding"] = categorical_encoding @property def overwrite_with_best_model(self): """ If enabled, override the final model with the best model found during training. Type: ``bool`` (default: ``True``). """ return self._parms.get("overwrite_with_best_model") @overwrite_with_best_model.setter def overwrite_with_best_model(self, overwrite_with_best_model): assert_is_type(overwrite_with_best_model, None, bool) self._parms["overwrite_with_best_model"] = overwrite_with_best_model @property def epochs(self): """ How many times the dataset should be iterated (streamed), can be fractional. Type: ``float`` (default: ``10``). """ return self._parms.get("epochs") @epochs.setter def epochs(self, epochs): assert_is_type(epochs, None, numeric) self._parms["epochs"] = epochs @property def train_samples_per_iteration(self): """ Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic. Type: ``int`` (default: ``-2``). """ return self._parms.get("train_samples_per_iteration") @train_samples_per_iteration.setter def train_samples_per_iteration(self, train_samples_per_iteration): assert_is_type(train_samples_per_iteration, None, int) self._parms["train_samples_per_iteration"] = train_samples_per_iteration @property def target_ratio_comm_to_comp(self): """ Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning). Type: ``float`` (default: ``0.05``). """ return self._parms.get("target_ratio_comm_to_comp") @target_ratio_comm_to_comp.setter def target_ratio_comm_to_comp(self, target_ratio_comm_to_comp): assert_is_type(target_ratio_comm_to_comp, None, numeric) self._parms["target_ratio_comm_to_comp"] = target_ratio_comm_to_comp @property def seed(self): """ Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded. Type: ``int`` (default: ``-1``). """ return self._parms.get("seed") @seed.setter def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed @property def standardize(self): """ If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data. Type: ``bool`` (default: ``True``). """ return self._parms.get("standardize") @standardize.setter def standardize(self, standardize): assert_is_type(standardize, None, bool) self._parms["standardize"] = standardize @property def learning_rate(self): """ Learning rate (higher => less stable, lower => slower convergence). Type: ``float`` (default: ``0.001``). """ return self._parms.get("learning_rate") @learning_rate.setter def learning_rate(self, learning_rate): assert_is_type(learning_rate, None, numeric) self._parms["learning_rate"] = learning_rate @property def learning_rate_annealing(self): """ Learning rate annealing: rate / (1 + rate_annealing * samples). Type: ``float`` (default: ``1e-06``). """ return self._parms.get("learning_rate_annealing") @learning_rate_annealing.setter def learning_rate_annealing(self, learning_rate_annealing): assert_is_type(learning_rate_annealing, None, numeric) self._parms["learning_rate_annealing"] = learning_rate_annealing @property def momentum_start(self): """ Initial momentum at the beginning of training (try 0.5). Type: ``float`` (default: ``0.9``). """ return self._parms.get("momentum_start") @momentum_start.setter def momentum_start(self, momentum_start): assert_is_type(momentum_start, None, numeric) self._parms["momentum_start"] = momentum_start @property def momentum_ramp(self): """ Number of training samples for which momentum increases. Type: ``float`` (default: ``10000``). """ return self._parms.get("momentum_ramp") @momentum_ramp.setter def momentum_ramp(self, momentum_ramp): assert_is_type(momentum_ramp, None, numeric) self._parms["momentum_ramp"] = momentum_ramp @property def momentum_stable(self): """ Final momentum after the ramp is over (try 0.99). Type: ``float`` (default: ``0.9``). """ return self._parms.get("momentum_stable") @momentum_stable.setter def momentum_stable(self, momentum_stable): assert_is_type(momentum_stable, None, numeric) self._parms["momentum_stable"] = momentum_stable @property def distribution(self): """ Distribution function One of: ``"auto"``, ``"bernoulli"``, ``"multinomial"``, ``"gaussian"``, ``"poisson"``, ``"gamma"``, ``"tweedie"``, ``"laplace"``, ``"quantile"``, ``"huber"`` (default: ``"auto"``). """ return self._parms.get("distribution") @distribution.setter def distribution(self, distribution): assert_is_type(distribution, None, Enum("auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber")) self._parms["distribution"] = distribution @property def score_interval(self): """ Shortest time interval (in seconds) between model scoring. Type: ``float`` (default: ``5``). """ return self._parms.get("score_interval") @score_interval.setter def score_interval(self, score_interval): assert_is_type(score_interval, None, numeric) self._parms["score_interval"] = score_interval @property def score_training_samples(self): """ Number of training set samples for scoring (0 for all). Type: ``int`` (default: ``10000``). """ return self._parms.get("score_training_samples") @score_training_samples.setter def score_training_samples(self, score_training_samples): assert_is_type(score_training_samples, None, int) self._parms["score_training_samples"] = score_training_samples @property def score_validation_samples(self): """ Number of validation set samples for scoring (0 for all). Type: ``int`` (default: ``0``). """ return self._parms.get("score_validation_samples") @score_validation_samples.setter def score_validation_samples(self, score_validation_samples): assert_is_type(score_validation_samples, None, int) self._parms["score_validation_samples"] = score_validation_samples @property def score_duty_cycle(self): """ Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring). Type: ``float`` (default: ``0.1``). """ return self._parms.get("score_duty_cycle") @score_duty_cycle.setter def score_duty_cycle(self, score_duty_cycle): assert_is_type(score_duty_cycle, None, numeric) self._parms["score_duty_cycle"] = score_duty_cycle @property def classification_stop(self): """ Stopping criterion for classification error fraction on training data (-1 to disable). Type: ``float`` (default: ``0``). """ return self._parms.get("classification_stop") @classification_stop.setter def classification_stop(self, classification_stop): assert_is_type(classification_stop, None, numeric) self._parms["classification_stop"] = classification_stop @property def regression_stop(self): """ Stopping criterion for regression error (MSE) on training data (-1 to disable). Type: ``float`` (default: ``0``). """ return self._parms.get("regression_stop") @regression_stop.setter def regression_stop(self, regression_stop): assert_is_type(regression_stop, None, numeric) self._parms["regression_stop"] = regression_stop @property def stopping_rounds(self): """ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Type: ``int`` (default: ``5``). """ return self._parms.get("stopping_rounds") @stopping_rounds.setter def stopping_rounds(self, stopping_rounds): assert_is_type(stopping_rounds, None, int) self._parms["stopping_rounds"] = stopping_rounds @property def stopping_metric(self): """ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) One of: ``"auto"``, ``"deviance"``, ``"logloss"``, ``"mse"``, ``"rmse"``, ``"mae"``, ``"rmsle"``, ``"auc"``, ``"lift_top_group"``, ``"misclassification"``, ``"mean_per_class_error"`` (default: ``"auto"``). """ return self._parms.get("stopping_metric") @stopping_metric.setter def stopping_metric(self, stopping_metric): assert_is_type(stopping_metric, None, Enum("auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "lift_top_group", "misclassification", "mean_per_class_error")) self._parms["stopping_metric"] = stopping_metric @property def stopping_tolerance(self): """ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Type: ``float`` (default: ``0``). """ return self._parms.get("stopping_tolerance") @stopping_tolerance.setter def stopping_tolerance(self, stopping_tolerance): assert_is_type(stopping_tolerance, None, numeric) self._parms["stopping_tolerance"] = stopping_tolerance @property def max_runtime_secs(self): """ Maximum allowed runtime in seconds for model training. Use 0 to disable. Type: ``float`` (default: ``0``). """ return self._parms.get("max_runtime_secs") @max_runtime_secs.setter def max_runtime_secs(self, max_runtime_secs): assert_is_type(max_runtime_secs, None, numeric) self._parms["max_runtime_secs"] = max_runtime_secs @property def ignore_const_cols(self): """ Ignore constant columns. Type: ``bool`` (default: ``True``). """ return self._parms.get("ignore_const_cols") @ignore_const_cols.setter def ignore_const_cols(self, ignore_const_cols): assert_is_type(ignore_const_cols, None, bool) self._parms["ignore_const_cols"] = ignore_const_cols @property def shuffle_training_data(self): """ Enable global shuffling of training data. Type: ``bool`` (default: ``True``). """ return self._parms.get("shuffle_training_data") @shuffle_training_data.setter def shuffle_training_data(self, shuffle_training_data): assert_is_type(shuffle_training_data, None, bool) self._parms["shuffle_training_data"] = shuffle_training_data @property def mini_batch_size(self): """ Mini-batch size (smaller leads to better fit, larger can speed up and generalize better). Type: ``int`` (default: ``32``). """ return self._parms.get("mini_batch_size") @mini_batch_size.setter def mini_batch_size(self, mini_batch_size): assert_is_type(mini_batch_size, None, int) self._parms["mini_batch_size"] = mini_batch_size @property def clip_gradient(self): """ Clip gradients once their absolute value is larger than this value. Type: ``float`` (default: ``10``). """ return self._parms.get("clip_gradient") @clip_gradient.setter def clip_gradient(self, clip_gradient): assert_is_type(clip_gradient, None, numeric) self._parms["clip_gradient"] = clip_gradient @property def network(self): """ Network architecture. One of: ``"auto"``, ``"user"``, ``"lenet"``, ``"alexnet"``, ``"vgg"``, ``"googlenet"``, ``"inception_bn"``, ``"resnet"`` (default: ``"auto"``). """ return self._parms.get("network") @network.setter def network(self, network): assert_is_type(network, None, Enum("auto", "user", "lenet", "alexnet", "vgg", "googlenet", "inception_bn", "resnet")) self._parms["network"] = network @property def backend(self): """ Deep Learning Backend. One of: ``"mxnet"``, ``"caffe"``, ``"tensorflow"`` (default: ``"mxnet"``). """ return self._parms.get("backend") @backend.setter def backend(self, backend): assert_is_type(backend, None, Enum("mxnet", "caffe", "tensorflow")) self._parms["backend"] = backend @property def image_shape(self): """ Width and height of image. Type: ``List[int]`` (default: ``[0, 0]``). """ return self._parms.get("image_shape") @image_shape.setter def image_shape(self, image_shape): assert_is_type(image_shape, None, [int]) self._parms["image_shape"] = image_shape @property def channels(self): """ Number of (color) channels. Type: ``int`` (default: ``3``). """ return self._parms.get("channels") @channels.setter def channels(self, channels): assert_is_type(channels, None, int) self._parms["channels"] = channels @property def sparse(self): """ Sparse data handling (more efficient for data with lots of 0 values). Type: ``bool`` (default: ``False``). """ return self._parms.get("sparse") @sparse.setter def sparse(self, sparse): assert_is_type(sparse, None, bool) self._parms["sparse"] = sparse @property def gpu(self): """ Whether to use a GPU (if available). Type: ``bool`` (default: ``True``). """ return self._parms.get("gpu") @gpu.setter def gpu(self, gpu): assert_is_type(gpu, None, bool) self._parms["gpu"] = gpu @property def device_id(self): """ Device IDs (which GPUs to use). Type: ``List[int]`` (default: ``[0]``). """ return self._parms.get("device_id") @device_id.setter def device_id(self, device_id): assert_is_type(device_id, None, [int]) self._parms["device_id"] = device_id @property def cache_data(self): """ Whether to cache the data in memory (automatically disabled if data size is too large). Type: ``bool`` (default: ``True``). """ return self._parms.get("cache_data") @cache_data.setter def cache_data(self, cache_data): assert_is_type(cache_data, None, bool) self._parms["cache_data"] = cache_data @property def network_definition_file(self): """ Path of file containing network definition (graph, architecture). Type: ``str``. """ return self._parms.get("network_definition_file") @network_definition_file.setter def network_definition_file(self, network_definition_file): assert_is_type(network_definition_file, None, str) self._parms["network_definition_file"] = network_definition_file @property def network_parameters_file(self): """ Path of file containing network (initial) parameters (weights, biases). Type: ``str``. """ return self._parms.get("network_parameters_file") @network_parameters_file.setter def network_parameters_file(self, network_parameters_file): assert_is_type(network_parameters_file, None, str) self._parms["network_parameters_file"] = network_parameters_file @property def mean_image_file(self): """ Path of file containing the mean image data for data normalization. Type: ``str``. """ return self._parms.get("mean_image_file") @mean_image_file.setter def mean_image_file(self, mean_image_file): assert_is_type(mean_image_file, None, str) self._parms["mean_image_file"] = mean_image_file @property def export_native_parameters_prefix(self): """ Path (prefix) where to export the native model parameters after every iteration. Type: ``str``. """ return self._parms.get("export_native_parameters_prefix") @export_native_parameters_prefix.setter def export_native_parameters_prefix(self, export_native_parameters_prefix): assert_is_type(export_native_parameters_prefix, None, str) self._parms["export_native_parameters_prefix"] = export_native_parameters_prefix @property def activation(self): """ Activation function. Only used if no user-defined network architecture file is provided, and only for problem_type=dataset. One of: ``"rectifier"``, ``"tanh"``. """ return self._parms.get("activation") @activation.setter def activation(self, activation): assert_is_type(activation, None, Enum("rectifier", "tanh")) self._parms["activation"] = activation @property def hidden(self): """ Hidden layer sizes (e.g. [200, 200]). Only used if no user-defined network architecture file is provided, and only for problem_type=dataset. Type: ``List[int]``. """ return self._parms.get("hidden") @hidden.setter def hidden(self, hidden): assert_is_type(hidden, None, [int]) self._parms["hidden"] = hidden @property def input_dropout_ratio(self): """ Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Type: ``float`` (default: ``0``). """ return self._parms.get("input_dropout_ratio") @input_dropout_ratio.setter def input_dropout_ratio(self, input_dropout_ratio): assert_is_type(input_dropout_ratio, None, numeric) self._parms["input_dropout_ratio"] = input_dropout_ratio @property def hidden_dropout_ratios(self): """ Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5. Type: ``List[float]``. """ return self._parms.get("hidden_dropout_ratios") @hidden_dropout_ratios.setter def hidden_dropout_ratios(self, hidden_dropout_ratios): assert_is_type(hidden_dropout_ratios, None, [numeric]) self._parms["hidden_dropout_ratios"] = hidden_dropout_ratios @property def problem_type(self): """ Problem type, auto-detected by default. If set to image, the H2OFrame must contain a string column containing the path (URI or URL) to the images in the first column. If set to text, the H2OFrame must contain a string column containing the text in the first column. If set to dataset, Deep Water behaves just like any other H2O Model and builds a model on the provided H2OFrame (non-String columns). One of: ``"auto"``, ``"image"``, ``"dataset"`` (default: ``"auto"``). """ return self._parms.get("problem_type") @problem_type.setter def problem_type(self, problem_type): assert_is_type(problem_type, None, Enum("auto", "image", "dataset")) self._parms["problem_type"] = problem_type # Ask the H2O server whether a Deep Water model can be built (depends on availability of native backends)
[docs] @staticmethod def available(): """Returns True if a deep water model can be built, or False otherwise.""" builder_json = h2o.api("GET /3/ModelBuilders", data={"algo": "deepwater"}) visibility = builder_json["model_builders"]["deepwater"]["visibility"] if visibility == "Experimental": print("Cannot build a Deep Water model - no backend found.") return False else: return True