def main(args):
"""Multi-GPU Criteo/DLRM Preprocessing Benchmark
This benchmark is designed to measure the time required to preprocess
the Criteo (1TB) dataset for Facebookâs DLRM model. The user must specify
the path of the raw dataset (using the `--data-path` flag), as well as the
output directory for all temporary/final data (using the `--out-path` flag)
Example Usage
-------------
python dask-nvtabular-criteo-benchmark.py
--data-path /path/to/criteo_parquet --out-path /out/dir/`
Dataset Requirements (Parquet)
------------------------------
This benchmark is designed with a parquet-formatted dataset in mind.
While a CSV-formatted dataset can be processed by NVTabular, converting
to parquet will yield significantly better performance. To convert your
dataset, try using the `optimize_criteo.ipynb` notebook (also located
in `NVTabular/examples/`)
For a detailed parameter overview see `NVTabular/examples/MultiGPUBench.md`
"""
# Input
data_path = args.data_path
freq_limit = args.freq_limit
out_files_per_proc = args.out_files_per_proc
high_card_columns = args.high_cards.split(",")
dashboard_port = args.dashboard_port
if args.protocol == "ucx":
UCX_TLS = os.environ.get("UCX_TLS", "tcp,cuda_copy,cuda_ipc,sockcm")
os.environ["UCX_TLS"] = UCX_TLS
# Cleanup output directory
BASE_DIR = args.out_path
dask_workdir = os.path.join(BASE_DIR, "workdir")
output_path = os.path.join(BASE_DIR, "output")
stats_path = os.path.join(BASE_DIR, "stats")
if not os.path.isdir(BASE_DIR):
os.mkdir(BASE_DIR)
for dir_path in (dask_workdir, output_path, stats_path):
if os.path.isdir(dir_path):
shutil.rmtree(dir_path)
os.mkdir(dir_path)
# Use Criteo dataset by default (for now)
cont_names = (args.cont_names.split(",")
if args.cont_names else ["I" + str(x) for x in range(1, 14)])
cat_names = (args.cat_names.split(",")
if args.cat_names else ["C" + str(x) for x in range(1, 27)])
label_name = ["label"]
# Specify Categorify/GroupbyStatistics options
tree_width = {}
cat_cache = {}
for col in cat_names:
if col in high_card_columns:
tree_width[col] = args.tree_width
cat_cache[col] = args.cat_cache_high
else:
tree_width[col] = 1
cat_cache[col] = args.cat_cache_low
# Use total device size to calculate args.device_limit_frac
device_size = device_mem_size(kind="total")
device_limit = int(args.device_limit_frac * device_size)
device_pool_size = int(args.device_pool_frac * device_size)
part_size = int(args.part_mem_frac * device_size)
# Parse shuffle option
shuffle = None
if args.shuffle == "PER_WORKER":
shuffle = nvt_io.Shuffle.PER_WORKER
elif args.shuffle == "PER_PARTITION":
shuffle = nvt_io.Shuffle.PER_PARTITION
# Check if any device memory is already occupied
for dev in args.devices.split(","):
fmem = _pynvml_mem_size(kind="free", index=int(dev))
used = (device_size - fmem) / 1e9
if used > 1.0:
warnings.warn(
f"BEWARE - {used} GB is already occupied on device {int(dev)}!"
)
# Setup LocalCUDACluster
if args.protocol == "tcp":
cluster = LocalCUDACluster(
protocol=args.protocol,
n_workers=args.n_workers,
CUDA_VISIBLE_DEVICES=args.devices,
device_memory_limit=device_limit,
local_directory=dask_workdir,
dashboard_address=":" + dashboard_port,
)
else:
cluster = LocalCUDACluster(
protocol=args.protocol,
n_workers=args.n_workers,
CUDA_VISIBLE_DEVICES=args.devices,
enable_nvlink=True,
device_memory_limit=device_limit,
local_directory=dask_workdir,
dashboard_address=":" + dashboard_port,
)
client = Client(cluster)
# Setup RMM pool
if args.device_pool_frac > 0.01:
setup_rmm_pool(client, device_pool_size)
# Define Dask NVTabular "Workflow"
processor = Workflow(cat_names=cat_names,
cont_names=cont_names,
label_name=label_name,
client=client)
if args.normalize:
processor.add_feature([ops.FillMissing(), ops.Normalize()])
else:
processor.add_feature(
[ops.FillMissing(),
ops.Clip(min_value=0),
ops.LogOp()])
processor.add_preprocess(
ops.Categorify(
out_path=stats_path,
tree_width=tree_width,
cat_cache=cat_cache,
freq_threshold=freq_limit,
search_sorted=not freq_limit,
on_host=not args.cats_on_device,
))
processor.finalize()
dataset = Dataset(data_path, "parquet", part_size=part_size)
# Execute the dask graph
runtime = time.time()
if args.profile is not None:
with performance_report(filename=args.profile):
processor.apply(
dataset,
shuffle=shuffle,
out_files_per_proc=out_files_per_proc,
output_path=output_path,
num_io_threads=args.num_io_threads,
)
else:
processor.apply(
dataset,
num_io_threads=args.num_io_threads,
shuffle=shuffle,
out_files_per_proc=out_files_per_proc,
output_path=output_path,
)
runtime = time.time() - runtime
print("\nDask-NVTabular DLRM/Criteo benchmark")
print("--------------------------------------")
print(f"partition size | {part_size}")
print(f"protocol | {args.protocol}")
print(f"device(s) | {args.devices}")
print(f"rmm-pool-frac | {(args.device_pool_frac)}")
print(f"out-files-per-proc | {args.out_files_per_proc}")
print(f"num_io_threads | {args.num_io_threads}")
print(f"shuffle | {args.shuffle}")
print(f"cats-on-device | {args.cats_on_device}")
print("======================================")
print(f"Runtime[s] | {runtime}")
print("======================================\n")
client.close()
def run_preprocessing(input_path, base_dir, num_train_days, num_val_days,
num_gpus):
# Define paths to save artifacts
dask_workdir = os.path.join(base_dir, "test_dask/workdir")
output_path = os.path.join(base_dir, "test_dask/output")
stats_path = os.path.join(base_dir, "test_dask/stats")
logging.info(f"Dask Workdir: {dask_workdir}")
logging.info(f"Output Path: {output_path}")
# Make sure we have a clean worker space for Dask
if os.path.isdir(dask_workdir):
shutil.rmtree(dask_workdir)
os.makedirs(dask_workdir)
# Make sure we have a clean stats space for Dask
if os.path.isdir(stats_path):
shutil.rmtree(stats_path)
os.mkdir(stats_path)
# Make sure we have a clean output path
if os.path.isdir(output_path):
shutil.rmtree(output_path)
os.mkdir(output_path)
logging.info("Created output directories..")
# This requires the data to be in this specific format eg. day_0.parquet, day_2.parquet etc.
fname = 'day_{}.parquet'
num_days = len([
i for i in os.listdir(input_path)
if re.match(fname.format('[0-9]{1,2}'), i) is not None
])
train_paths = [
os.path.join(input_path, fname.format(day))
for day in range(num_train_days)
]
valid_paths = [
os.path.join(input_path, fname.format(day))
for day in range(num_train_days, num_train_days + num_val_days)
]
logging.info(f"Training data: {train_paths}")
logging.info(f"Validation data: {valid_paths}")
# Deploy a Dask Distributed Cluster
# Single-Machine Multi-GPU Cluster
protocol = "tcp" # "tcp" or "ucx"
visible_devices = ",".join([str(n) for n in num_gpus
]) # Delect devices to place workers
device_limit_frac = 0.4 # Spill GPU-Worker memory to host at this limit.
device_pool_frac = 0.5
part_mem_frac = 0.05 # Desired maximum size of each partition as a fraction of total GPU memory.
# Use total device size to calculate args.device_limit_frac
device_size = device_mem_size(kind="total")
part_size = int(part_mem_frac * device_size)
logging.info(f"Partition size: {part_size}")
# Deploy Dask Distributed cluster only if asked for multiple GPUs
if len(num_gpus) > 1:
device_limit = int(device_limit_frac * device_size)
device_pool_size = int(device_pool_frac * device_size)
logging.info("Checking if any device memory is already occupied..")
# Check if any device memory is already occupied
for dev in visible_devices.split(","):
fmem = _pynvml_mem_size(kind="free", index=int(dev))
used = (device_size - fmem) / 1e9
if used > 1.0:
warnings.warn(
f"BEWARE - {used} GB is already occupied on device {int(dev)}!"
)
cluster = None # (Optional) Specify existing scheduler port
if cluster is None:
cluster = LocalCUDACluster(protocol=protocol,
n_workers=len(
visible_devices.split(",")),
CUDA_VISIBLE_DEVICES=visible_devices,
device_memory_limit=device_limit,
local_directory=dask_workdir)
logging.info("Create the distributed client..")
# Create the distributed client
client = Client(cluster)
logging.info("Initialize memory pools..")
# Initialize RMM pool on ALL workers
def _rmm_pool():
rmm.reinitialize(
# RMM may require the pool size to be a multiple of 256.
pool_allocator=True,
initial_pool_size=(device_pool_size // 256) * 256,
)
client.run(_rmm_pool)
# Preprocessing
CONTINUOUS_COLUMNS = ['I' + str(x) for x in range(1, 14)]
CATEGORICAL_COLUMNS = ['C' + str(x) for x in range(1, 27)]
LABEL_COLUMNS = ['label']
COLUMNS = CONTINUOUS_COLUMNS + CATEGORICAL_COLUMNS + LABEL_COLUMNS
cat_features = CATEGORICAL_COLUMNS >> Categorify(out_path=stats_path)
cont_features = CONTINUOUS_COLUMNS >> FillMissing() >> Clip(
min_value=0) >> Normalize()
features = cat_features + cont_features + LABEL_COLUMNS
logging.info("Defining a workflow object..")
if len(num_gpus) > 1:
workflow = nvt.Workflow(features, client=client)
else:
workflow = nvt.Workflow(features)
dict_dtypes = {}
for col in CATEGORICAL_COLUMNS:
dict_dtypes[col] = np.int64
for col in CONTINUOUS_COLUMNS:
dict_dtypes[col] = np.float32
for col in LABEL_COLUMNS:
dict_dtypes[col] = np.float32
train_dataset = nvt.Dataset(train_paths,
engine='parquet',
part_size=part_size)
valid_dataset = nvt.Dataset(valid_paths,
engine='parquet',
part_size=part_size)
output_train_dir = os.path.join(output_path, 'train/')
logging.info(f"Creating train/ directory at: {output_train_dir}")
if not os.path.exists(output_train_dir):
os.makedirs(output_train_dir)
output_valid_dir = os.path.join(output_path, 'valid/')
logging.info(f"Creating valid/ directory at: {output_valid_dir}")
if not os.path.exists(output_valid_dir):
os.makedirs(output_valid_dir)
logging.info("Workflow Fit..")
workflow.fit(train_dataset)
logging.info("Transform Training data..")
workflow.transform(train_dataset).to_parquet(
output_path=output_train_dir,
shuffle=nvt.io.Shuffle.PER_PARTITION,
dtypes=dict_dtypes,
cats=CATEGORICAL_COLUMNS,
conts=CONTINUOUS_COLUMNS,
labels=LABEL_COLUMNS)
logging.info("Transform Validation data..")
workflow.transform(valid_dataset).to_parquet(output_path=output_valid_dir,
dtypes=dict_dtypes,
cats=CATEGORICAL_COLUMNS,
conts=CONTINUOUS_COLUMNS,
labels=LABEL_COLUMNS)
# use these printed out cardinalities list in the "slot_size_array" in the HugeCTR training "dcn_parquet.json"
cardinalities = []
for col in CATEGORICAL_COLUMNS:
cardinalities.append(nvt.ops.get_embedding_sizes(workflow)[col][0])
logging.info(
f"Cardinalities for configuring slot_size_array: {cardinalities}")
logging.info(f"Saving workflow object at: {output_path + '/workflow'}")
workflow.save(output_path + '/workflow')
logging.info("Done!")
def run_preprocessing(input_train_path, workflow_path, output_path,
dask_workdir, num_gpus):
fname = '{}.parquet'
train_files = [
i for i in os.listdir(input_train_path)
if re.match(fname.format('.*'), i) is not None
]
train_paths = [
os.path.join(input_train_path, filename) for filename in train_files
]
# Deploy a Dask Distributed Cluster
# Single-Machine Multi-GPU Cluster
protocol = "tcp" # "tcp" or "ucx"
visible_devices = ",".join([str(n) for n in num_gpus
]) # Delect devices to place workers
device_limit_frac = 0.4 # Spill GPU-Worker memory to host at this limit.
device_pool_frac = 0.5
part_mem_frac = 0.05
# Use total device size to calculate args.device_limit_frac
device_size = device_mem_size(kind="total")
part_size = int(part_mem_frac * device_size)
logging.info(f"Partition size: {part_size}")
# Deploy Dask Distributed cluster only if asked for multiple GPUs
if len(num_gpus) > 1:
logging.info("Deploy Dask Distributed cluster...")
device_limit = int(device_limit_frac * device_size)
device_pool_size = int(device_pool_frac * device_size)
logging.info("Checking if any device memory is already occupied...")
# Check if any device memory is already occupied
for dev in visible_devices.split(","):
fmem = _pynvml_mem_size(kind="free", index=int(dev))
used = (device_size - fmem) / 1e9
if used > 1.0:
warnings.warn(
f"BEWARE - {used} GB is already occupied on device {int(dev)}!"
)
cluster = None # (Optional) Specify existing scheduler port
if cluster is None:
cluster = LocalCUDACluster(protocol=protocol,
n_workers=len(
visible_devices.split(",")),
CUDA_VISIBLE_DEVICES=visible_devices,
device_memory_limit=device_limit,
local_directory=dask_workdir)
logging.info("Create the distributed client...")
# Create the distributed client
client = Client(cluster)
logging.info("Initialize memory pools...")
# Initialize RMM pool on ALL workers
def _rmm_pool():
rmm.reinitialize(
# RMM may require the pool size to be a multiple of 256.
pool_allocator=True,
initial_pool_size=(device_pool_size // 256) *
256, # Use default size
)
client.run(_rmm_pool)
# Import the test .parquet
logging.info("Importing Data...")
test_dataset = nvt.Dataset(train_paths,
engine='parquet',
part_size=part_size)
logging.info("Loading workflow object...")
workflow = nvt.Workflow.load(workflow_path)
# Specify the columns IDs: this part should exactly the columns while preproc. train, valid datasets
CONTINUOUS_COLUMNS = ['I' + str(x) for x in range(1, 14)]
CATEGORICAL_COLUMNS = ['C' + str(x) for x in range(1, 27)]
LABEL_COLUMNS = ['label']
dict_dtypes = {}
for col in CATEGORICAL_COLUMNS:
dict_dtypes[col] = np.int64
for col in CONTINUOUS_COLUMNS:
dict_dtypes[col] = np.float32
for col in LABEL_COLUMNS:
dict_dtypes[col] = np.float32
# Create output directory for test data
output_test_dir = os.path.join(output_path, 'train/')
if not os.path.exists(output_test_dir):
logging.info(f"Creating train/ directory at: {output_test_dir}")
os.makedirs(output_test_dir)
logging.info("Preprocessing Data...")
workflow.transform(test_dataset).to_parquet(output_path=output_test_dir,
dtypes=dict_dtypes,
cats=CATEGORICAL_COLUMNS,
conts=CONTINUOUS_COLUMNS,
labels=LABEL_COLUMNS)
logging.info("Done!")
def nvt_etl(
data_path,
out_path,
devices,
protocol,
device_limit_frac,
device_pool_frac,
part_mem_frac,
cats,
conts,
labels,
out_files_per_proc,
):
# Set up data paths
input_path = data_path[:-1] if data_path[-1] == "/" else data_path
base_dir = out_path[:-1] if out_path[-1] == "/" else out_path
dask_workdir = os.path.join(base_dir, "workdir")
output_path = os.path.join(base_dir, "output")
stats_path = os.path.join(base_dir, "stats")
output_train_dir = os.path.join(output_path, "train/")
output_valid_dir = os.path.join(output_path, "valid/")
# Make sure we have a clean worker space for Dask
if os.path.isdir(dask_workdir):
shutil.rmtree(dask_workdir)
os.makedirs(dask_workdir)
# Make sure we have a clean stats space for Dask
if os.path.isdir(stats_path):
shutil.rmtree(stats_path)
os.mkdir(stats_path)
# Make sure we have a clean output path
if os.path.isdir(output_path):
shutil.rmtree(output_path)
os.mkdir(output_path)
os.mkdir(output_train_dir)
os.mkdir(output_valid_dir)
# Get train/valid files
train_paths = [
os.path.join(input_path, f) for f in os.listdir(input_path)
if os.path.isfile(os.path.join(input_path, f))
]
n_files = int(len(train_paths) * 0.9)
valid_paths = train_paths[n_files:]
train_paths = train_paths[:n_files]
# Force dtypes for HugeCTR usage
dict_dtypes = {}
for col in cats:
dict_dtypes[col] = np.int64
for col in conts:
dict_dtypes[col] = np.float32
for col in labels:
dict_dtypes[col] = np.float32
# Use total device size to calculate args.device_limit_frac
device_size = device_mem_size(kind="total")
device_limit = int(device_limit_frac * device_size)
device_pool_size = int(device_pool_frac * device_size)
part_size = int(part_mem_frac * device_size)
# Check if any device memory is already occupied
for dev in devices.split(","):
fmem = _pynvml_mem_size(kind="free", index=int(dev))
used = (device_size - fmem) / 1e9
if used > 1.0:
warnings.warn(
f"BEWARE - {used} GB is already occupied on device {int(dev)}!"
)
# Setup dask cluster and perform ETL
with managed_client(dask_workdir, devices, device_limit,
protocol) as client:
# Setup RMM pool
if device_pool_frac > 0.01:
setup_rmm_pool(client, device_pool_size)
# Define Dask NVTabular "Workflow"
cont_features = conts >> ops.FillMissing() >> ops.Clip(
min_value=0) >> ops.LogOp()
cat_features = cats >> ops.Categorify(out_path=stats_path,
max_size=10000000)
workflow = Workflow(cat_features + cont_features + labels,
client=client)
train_dataset = Dataset(train_paths,
engine="parquet",
part_size=part_size)
valid_dataset = Dataset(valid_paths,
engine="parquet",
part_size=part_size)
workflow.fit(train_dataset)
workflow.transform(train_dataset).to_parquet(
output_path=output_train_dir,
shuffle=nvt_io.Shuffle.PER_WORKER,
dtypes=dict_dtypes,
cats=cats,
conts=conts,
labels=labels,
out_files_per_proc=out_files_per_proc,
)
workflow.transform(valid_dataset).to_parquet(
output_path=output_valid_dir,
shuffle=nvt_io.Shuffle.PER_WORKER,
dtypes=dict_dtypes,
cats=cats,
conts=conts,
labels=labels,
out_files_per_proc=out_files_per_proc,
)
workflow.save(os.path.join(output_path, "workflow"))
return workflow