/
mock_gpu_test_model.py
108 lines (87 loc) · 3.93 KB
/
mock_gpu_test_model.py
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"""For GPU usage testing purposes."""
import os
import numpy as np
from h2oaicore.models import CustomModel
class CustomGpuCheck(CustomModel):
_regression = True
_binary = True
_multiclass = False # WIP
_can_use_gpu = True # if enabled, will use special job scheduler for GPUs
_get_gpu_lock = True # whether to lock GPUs for this model before fit and predict
_must_use_gpu = True # this recipe can only be used if have GPUs
_predict_on_same_gpus_as_fit = True # force predict to behave like fit, regardless of config.num_gpus_for_prediction
@staticmethod
def do_acceptance_test():
return False
def set_default_params(self,
accuracy=None, time_tolerance=None, interpretability=None,
**kwargs):
self.params = {}
def mutate_params(self,
**kwargs):
self.params = {}
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
try:
x = os.environ['CUDA_VISIBLE_DEVICES']
if x == '':
raise AssertionError(f'CUDA_VISIBLE_DEVICES = {x} should not be set.')
except KeyError:
pass
self.set_model_properties(model=[1],
features=list(X.names),
importances=([1.0] * len(list(X.names))),
iterations=0)
def predict(self, X, **kwargs):
"""
Returns: dt.Frame, np.ndarray or pd.DataFrame, containing predictions (target values or class probabilities)
Shape: (K, c) where c = 1 for regression or binary classification, and c>=3 for multi-class problems.
"""
try:
x = os.environ['CUDA_VISIBLE_DEVICES']
if x == '':
raise AssertionError(f'CUDA_VISIBLE_DEVICES = {x} should not be set.')
except KeyError:
pass
return np.random.randint(0, 2, (X.nrows, 1))
# Not sure if we need the same model again, blending may work with only one model type, too.
class CustomGpuCheck2(CustomModel):
_regression = True
_binary = True
_multiclass = False # WIP
_can_use_gpu = True # if enabled, will use special job scheduler for GPUs
_get_gpu_lock = True # whether to lock GPUs for this model before fit and predict
_must_use_gpu = True # this recipe can only be used if have GPUs
_predict_on_same_gpus_as_fit = True # force predict to behave like fit, regardless of config.num_gpus_for_prediction
@staticmethod
def do_acceptance_test():
return False
def set_default_params(self,
accuracy=None, time_tolerance=None, interpretability=None,
**kwargs):
self.params = {}
def mutate_params(self,
**kwargs):
self.params = {}
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
try:
x = os.environ['CUDA_VISIBLE_DEVICES']
if x == '':
raise AssertionError(f'CUDA_VISIBLE_DEVICES = {x} should not be set.')
except KeyError:
pass
self.set_model_properties(model=[1],
features=list(X.names),
importances=([1.0] * len(list(X.names))),
iterations=0)
def predict(self, X, **kwargs):
"""
Returns: dt.Frame, np.ndarray or pd.DataFrame, containing predictions (target values or class probabilities)
Shape: (K, c) where c = 1 for regression or binary classification, and c>=3 for multi-class problems.
"""
try:
x = os.environ['CUDA_VISIBLE_DEVICES']
if x == '':
raise AssertionError(f'CUDA_VISIBLE_DEVICES = {x} should not be set.')
except KeyError:
pass
return np.random.randint(0, 2, (X.nrows, 1))