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Scaling Criteo: ETL with NVTabular#

This notebook is created using the latest stable merlin-hugectr, merlin-tensorflow, or merlin-pytorch container.


NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems. It provides a high level abstraction to simplify code and accelerates computation on the GPU using the RAPIDS cuDF library.

In this notebook, we will use NVTabular with multiple GPUs. The notebook can run on single GPU, as well. Prerequisite is to be familiar with NVTabular and its API. You can read more NVTabular and its API in our Documentation or check out our NVTabular Examples.

The full Criteo 1TB Click Logs dataset contains ~1.3 TB of uncompressed click logs containing over four billion samples spanning 24 days. In our benchmarks, we are able to preprocess and engineer features in 13.8min with 1x NVIDIA A100 GPU and 1.9min with 8x NVIDIA A100 GPUs. This is a speed-up of 100x-10000x in comparison to different CPU versions, You can read more in our blog.

Our pipeline will be representative with most common preprocessing transformation for deep learning recommender models.

  • Categorical input features are Categorified to be continuous integers (0, …, |C|) for the embedding layers

  • Missing values of continuous input features are filled with 0. Afterwards the continuous features are clipped and normalized.

Learning objectives#

In this notebook, we learn how to to scale ETLs with NVTabular

  • Learn to use larger than GPU/host memory datasets

  • Use multi-GPU or multi node for ETL

  • Apply common deep learning ETL workflow

ETL with NVTabular#

Here we’ll show how to use NVTabular first as a preprocessing library to prepare the Criteo 1TB Click Logs dataset dataset. The following notebooks can use the output to train a deep learning model.

Data Prep#

The previous notebook 01-Download-Convert converted the tsv data published by Criteo into the parquet format that our accelerated readers prefer. Accelerating these pipelines on new hardware like GPUs may require us to make new choices about the representations we use to store that data, and parquet represents a strong alternative.

We load the required libraries.

# Standard Libraries
import os
import re
import shutil
import warnings

# External Dependencies
import numpy as np
import numba
from dask_cuda import LocalCUDACluster
from dask.distributed import Client

# NVTabular
import nvtabular as nvt
from nvtabular.ops import (
from nvtabular.utils import pynvml_mem_size, device_mem_size
from merlin.schema.tags import Tags

Once our data is ready, we’ll define some high level parameters to describe where our data is and what it “looks like” at a high level.

# define some information about where to get our data
BASE_DIR = os.environ.get("BASE_DIR", "/raid/data/criteo")
INPUT_DATA_DIR = os.environ.get("INPUT_DATA_DIR", BASE_DIR + "/converted/criteo")
OUTPUT_DATA_DIR = os.environ.get("OUTPUT_DATA_DIR", BASE_DIR + "/test_dask/output")
USE_HUGECTR = bool(os.environ.get("USE_HUGECTR", ""))
stats_path = os.path.join(OUTPUT_DATA_DIR, "test_dask/stats")
dask_workdir = os.path.join(OUTPUT_DATA_DIR, "test_dask/workdir")

# Make sure we have a clean worker space for Dask
if os.path.isdir(dask_workdir):

# Make sure we have a clean stats space for Dask
if os.path.isdir(stats_path):

# Make sure we have a clean output path
if os.path.isdir(OUTPUT_DATA_DIR):

We use the last day as validation dataset and the remaining days as training dataset.

fname = "day_{}.parquet"
num_days = len(
    [i for i in os.listdir(INPUT_DATA_DIR) if re.match(fname.format("[0-9]{1,2}"), i) is not None]
train_paths = [os.path.join(INPUT_DATA_DIR, fname.format(day)) for day in range(num_days - 1)]
valid_paths = [
    os.path.join(INPUT_DATA_DIR, fname.format(day)) for day in range(num_days - 1, num_days)

Deploy a Distributed-Dask Cluster#

Now we configure and deploy a Dask Cluster. Please, read this document to know how to set the parameters.

# Dask dashboard
dashboard_port = "8787"

# Deploy a Single-Machine Multi-GPU Cluster
protocol = "tcp"  # "tcp" or "ucx"
if numba.cuda.is_available():
    NUM_GPUS = list(range(len(numba.cuda.gpus)))
    NUM_GPUS = []
visible_devices = ",".join([str(n) for n in NUM_GPUS])  # Select devices to place workers
device_limit_frac = 0.7  # Spill GPU-Worker memory to host at this limit.
device_pool_frac = 0.8
part_mem_frac = 0.15

# 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 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(
        dashboard_address=":" + dashboard_port,
        rmm_pool_size=(device_pool_size // 256) * 256

# Create the distributed client
client = Client(cluster)
2022-12-06 10:52:32,108 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize
2022-12-06 10:52:32,242 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize



Connection method: Cluster object Cluster type: dask_cuda.LocalCUDACluster

Cluster Info

That’s it. We initialized our Dask cluster and NVTabular will execute the workflow on multiple GPUs. Similar, we could define a cluster with multiple nodes.

Defining our Preprocessing Pipeline#

At this point, our data still isn’t in a form that’s ideal for consumption by neural networks. The most pressing issues are missing values and the fact that our categorical variables are still represented by random, discrete identifiers, and need to be transformed into contiguous indices that can be leveraged by a learned embedding. Less pressing, but still important for learning dynamics, are the distributions of our continuous variables, which are distributed across multiple orders of magnitude and are uncentered (i.e. E[x] != 0).

We can fix these issues in a concise and GPU-accelerated manner with an NVTabular Workflow. More information about NVTabular’s API can be found in its Documentation.

Frequency Thresholding#

One interesting thing worth pointing out is that we’re using frequency thresholding in our Categorify op. This handy functionality will map all categories which occur in the dataset with some threshold level of infrequency (which we’ve set here to be 15 occurrences throughout the dataset) to the same index, keeping the model from overfitting to sparse signals.

# define our dataset schema
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"]

num_buckets = 10000000
categorify_op = Categorify(out_path=stats_path, max_size=num_buckets, dtype='int32')
cat_features = CATEGORICAL_COLUMNS >> categorify_op
cont_features = CONTINUOUS_COLUMNS >> FillMissing() >> Clip(min_value=0) >> Normalize(out_dtype='float32')
label_features = LABEL_COLUMNS >> AddMetadata(
    tags=[str(Tags.BINARY_CLASSIFICATION), "target"]

features = cat_features + cont_features + label_features

workflow = nvt.Workflow(features)

We need to enforce the required HugeCTR data types, so we set them in a dictionary and give as an argument when creating our dataset. The dictionary defines the output datatypes of our datasets.

dict_dtypes = {}

# The environment variable USE_HUGECTR defines, if we want to use the output for HugeCTR or another framework
    dict_dtypes[col] = np.int64 if USE_HUGECTR else np.int32

    dict_dtypes[col] = np.float32

for col in LABEL_COLUMNS:
    dict_dtypes[col] = np.int32

Now instantiate dataset iterators to loop through our dataset (which we couldn’t fit into GPU memory).

train_dataset = nvt.Dataset(train_paths, engine="parquet", part_size=part_size)
valid_dataset = nvt.Dataset(valid_paths, engine="parquet", part_size=part_size)

Now run them through our workflows to collect statistics on the train set, then transform and save to parquet files.

output_train_dir = os.path.join(OUTPUT_DATA_DIR, "train/")
output_valid_dir = os.path.join(OUTPUT_DATA_DIR, "valid/")
! mkdir -p $output_train_dir
! mkdir -p $output_valid_dir

For reference, let’s time it to see how long it takes…

CPU times: user 2.38 s, sys: 260 ms, total: 2.64 s
Wall time: 12.6 s
<nvtabular.workflow.workflow.Workflow at 0x7fdacec4fdc0>

CPU times: user 980 ms, sys: 202 ms, total: 1.18 s
Wall time: 19.5 s

CPU times: user 801 ms, sys: 203 ms, total: 1 s
Wall time: 15.4 s

In the next notebooks, we will train a deep learning model. Our training pipeline requires information about the data schema to define the neural network architecture. We will save the NVTabular workflow to disk so that we can restore it in the next notebooks.

workflow.save(os.path.join(OUTPUT_DATA_DIR, "workflow"))