def _training_data_to_numpy(X, y):
"""Convert input training data into numpy format"""
if isinstance(X, np.ndarray):
X_np = X
y_np = y
elif isinstance(X, cudf.DataFrame):
X_np = X.as_gpu_matrix().copy_to_host()
y_np = y.to_gpu_array().copy_to_host()
elif cuda.devicearray.is_cuda_ndarray(X):
X_np = X.copy_to_host()
y_np = y.copy_to_host()
elif isinstance(X, (pd.DataFrame, pd.Series)):
X_np = datagen._convert_to_numpy(X)
y_np = datagen._convert_to_numpy(y)
else:
raise TypeError("Received unsupported input type")
return X_np, y_np
def _benchmark_algo(
benchmarker,
algo_name,
bench_step,
dataset,
setup_kwargs={},
training_kwargs={},
inference_kwargs={},
client=None
):
"""
Benchmark utility
Parameters
----------
benchmarker :
Pytest benchmark function, allows to enclose the code
that should be benchmarked
algo_name :
Algorithm/model name, can be found in the algorithms.py file
bench_step :
Either 'training' or 'inference', describe the algorithm/model
step to be benchmarked
dataset :
Tuple with the data and a dictionnary that describes how it was built.
The dictionnary can be later used during the NVTX benchmark.
setup_kwargs :
Algorithm/model setup kwargs
training_kwargs :
Algorithm/model training kwargs
inference_kwargs :
Algorithm/model inference kwargs
client :
Dask client used in MNMG settings
"""
# Get data and dict describing how it was built
dataset, data_kwargs = dataset
# The presence of a Dask client signifies MNMG mode
MNMG_mode = client is not None
# Distribute data in MNMG settings
if MNMG_mode:
# Add the client to the setup kwargs used by model instantiation
setup_kwargs['client'] = client
# Exception : data is scattered by the MNMG DBSCAN model itself
if algo_name != 'MNMG.DBSCAN':
# Distribute data
dataset = [distribute(client, d) for d in dataset]
# Search AlgorithmPair instance by name
algo = algorithms.algorithm_by_name(algo_name)
# Setup the AlgorithmPair and the model to be ready for benchmark on GPU
cuml_setup = setup_bench('cuml', algo, bench_step, dataset,
setup_kwargs, training_kwargs)
# Pytest benchmark
if bench_step == 'training':
benchmarker(algo.run_cuml, dataset, bench_args=training_kwargs,
**cuml_setup)
elif bench_step == 'inference':
benchmarker(algo.run_cuml, dataset, bench_args=inference_kwargs,
**cuml_setup)
# CPU benchmark and NVTX benchmark (only in SG mode)
if not MNMG_mode:
# Check that the cuML model has a CPU equivalency
if algo.cpu_class:
# Convert sataset to a Numpy array
cpu_dataset = datagen._convert_to_numpy(dataset)
# Setup the AlgorithmPair and the model
# to be ready for benchmark on CPU
cpu_setup = setup_bench('cpu', algo, bench_step, cpu_dataset,
setup_kwargs, training_kwargs)
# CPU benchmark
cpu_bench(algo, bench_step, cpu_dataset, inference_kwargs,
cpu_setup)
# NVTX benchmark performs both the training and inference at once
# but only when bench_step == 'inference'
if bench_step == 'inference':
# NVTX benchmark
nvtx_profiling(algo_name, data_kwargs, setup_kwargs,
training_kwargs, inference_kwargs)