slightly different versions of the same dataset. All _l variables are local indices (meaning they range. Porting the model to use the FP16 data type where appropriate. With the model ready to go, Triton Server can be set up with the following steps. Those divergences in availability between systems, as well as the fact that there is no centralized definition of done for a specific date/partition, can lead to some nasty consequences and more generally to the introduction of several hacks and workarounds to support particular edge cases. The Criteo Terabyte click logs public dataset, one of the largest public datasets for recommendation tasks, offers a rare glimpse into the scale of real enterprise data. With automatic mixed precision training on NVIDIA Tensor Core GPUs, an optimized data loader and a custom embedding CUDA kernel, on a single Tesla V100 GPU, you can train a DLRM model on the Criteo Terabyte dataset in just 44 minutes, compared to 36.5 hours on 96-CPU threads. num_rows (int): number of rows to get from the npy file. It contains ~1.3 TB of uncompressed click logs containing over four billion samples spanning 24 days, and can be used to train recommender system models that . DL-based recommendation models are often too large to fit onto a single device memory. Enterprises try to leverage as much historical data as feasible, for this generally translates into better accuracy. It was first described in Hence, they must be transformed, together. are then fed into the "dot interaction" operation. Medium sized dataset. The Criteo Engine targets all shoppers who are valuable to you. A tag already exists with the provided branch name. They are a critical component for driving user engagement on many online platforms. For example, ImageNet 3232 The original Facebook DLRM code base comes with a data preprocessing utility to preprocess the data. Many recommendation models contain very large embedding tables. We have however been able to solve this issue for partition table lineage in a very reliable and automated way, without having to infer imperative programs. While investigating such an issue could take one hour or more to an analyst or data engineer before, it is now as trivial as simply looking at our lineage graph: the blocking node is very apparent (the first red one in the flow), and the delay cascades from it to its children. The following features were implemented in this model: This model supports the following features: Automatic Mixed Precision (AMP) - enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable. Based on this example, we can speculate that the model has three input channels: numeric_inputs, categorical_user_inputs, Automatically detecting which dataset should and should not be exposed in the catalog is not as trivial as it sounds, in particular for systems like HDFS where there is no clear distinction between a random file, and user-specific or test dataset, and a production one. We use the following heuristic for dividing the work between the GPUs: Please refer to the "Preprocessing" section for a detailed description of the Apache Spark 3.0 and NVTabular GPU functionality. If you dont want to experiment on the full set of 24 files, you can download a subset of files and modify the data preprocessing scripts to work on these files only. The Bottom MLP is placed on GPU-0 and no embedding tables are placed on this device. For instance, one job can rely on the daily data outputted by another job, but this latter job produces its outputs by day/country partitions. There are 26 anonymous categorical fields and 13 continuous fields in Criteo dataset. DLRM accepts two types of features: categorical and numerical. When investigating the existing data catalog/data discovery/metadata engine, we came to the conclusion that our own attempt, named DataDoc, was better tailored to Criteos needs, especially as we wanted to push it further than only a data discovery tool. input_dir_sparse (str): Input directory of sparse npy files. Preprocessing on GPU with NVTabular - Criteo dataset preprocessing can be conducted using NVTabular. This model uses a slightly different preprocessing procedure than the one found in the original implementation. part of the population is prevented from being targeted by advertising. In this experiment, we set the individual per-user request batch size to 1024, and Triton maximum and preferred batch size to 65536. Prior to joining Criteo, he taught computers to read handwriting at A2iA. For DLRM, AMP offers a 2.37x speed up compared to FP32 training. Thanks to the Criteo engineers, Anton Lin and Jean-Benoit Joujoute, who are reviewers of this post and also the main contributors to this application. Multi-GPU training with PyTorch distributed - our model uses torch.distributed to implement efficient multi-GPU training with NCCL. Now lets take a look at the predictive variables in the eCPM formula, which are calculated by the Criteo Engine. One example of such a feature would be the work we already started around resource usage and its monitoring. The layout for training data has been chosen arbitrarily to showcase the flexibility. To review, open the file in an editor that reveals hidden Unicode characters. # Iterate through each column in each file and map the sparse ids to contiguous ids. Next, we discuss several details of this training pipeline. Describes a dictionary of features, where key is feature name and values are features' characteristics such as dtype and other metadata (for example, cardinalities for categorical features), Specification of model's inputs and outputs (channel_spec). dense_paths=[template.format(0, "dense"), template.format(1, "dense")]. Flavian Vasile is a Principal ML Architect in the Criteo AI Lab, leveraging over 15 years of expertise in Machine Learning applications for Online Advertising. This article aims at providing you with an in-depth guide to help you set up Criteo Reporting with ease. For the "criteo_kaggle" test set, we set the labels to -1 representing filler data, because label data is not included in the "criteo_kaggle" test set. We explained the NVTabular API in Getting Started with Movielens notebooks and hope you are familiar with the syntax. The dense features enter the model and are transformed by a # To be consistent with dense and sparse. rank reads only the data for the portion of the dataset it is responsible for. This could be easily solved by training in a model-parallel way, using either the CPU or other GPUs as "memory donors". Among others, it enables: This lineage information can be captured at different granularities, mainly table and field ones: for each table/column, knowing what are the input and output dependencies (respectively, which tables/columns are used to compute this table/column, and which tables/columns are using this table/column as inputs). Source: AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System. It probably peaked the day when our data engineering manager got called during his holidays because a critical client-facing dashboard was not updated for the last days, and he and his teams struggled during several days just so they could find what was the datasets lineage leading to the production of this final dashboard. In order to train any Recommendation model in NVIDIA Deep Learning Examples one can follow one of three possible ways: One delivers already preprocessed dataset in the Intermediary Format supported by data loader used by the training script We improved the data preprocessing process with Spark to make use of all available CPU threads. This reduces embedding table size and avoids embedding entries that would not be sufficiently updated during training from their random initializations. The benefit is two-fold: the search helps users leverage already existing data whilst reducing the waste of building pipelines producing datasets that to a great extent overlap with already existing datasets. Explore on Papers With Code Three shoppers who have all previously browsed your website are now individually browsing a publisher website. eCPM = (CPC or COS or CPO, pCTR, pCR, pAOV, , , ). ({'exposure': 'exposure', 'f0': 'f0', 'f1': 'f1', 'f10': 'f10', 'f11': The train mapping consists of two chunks. Applications such as Amundsen (Lyft), DataHub (Linkedin), Databook (Uber) or Metacat (Netflix) are, among others, some of the answers big tech companies have developed to tackle this issue. Concurrency level is a parameter of perf_client that allows you to control the latency-throughput trade-off. We adopted a common practice to map all rare categorical values to a special missing category value (here, any category that occurs fewer than 15 times in the dataset is treated as a missing category). to measure out the performance of each section of your targetted audience and marketing . The result is a vast reduction in CPU overhead. In the remainder of this article we will dive into its main features and the specific problems that they help to solve: At Criteo, data analytics and data science is an integral part of our business. The predicted Click-Through Rate (pCTR) is how the Criteo Engine measures the likelihood of shopper engagement, in line with the click-based sales attribution systems most advertisers use. # Maintain a buffer that can contain up to batch_size rows. The server provides an inference service using an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. Dataset construction: The training dataset consists of a portion of Criteo's traffic over a period of 24 days. The output of "dot interaction" is then concatenated with the features resulting from the bottom MLP and fed into the "top MLP" which is also a series of dense layers with activations. Objectives. Shuffle the dataset. as_supervised doc): Mixed precision is the use of multiple numerical precisions, such as FP32 and FP16, in a computing procedure. At Criteo, most datasets are computed in a time series fashion, meaning that each week/day/hour, new partition (or subpartition) will be computed and added to the existing dataset. row_mapper (Optional[Callable[[List[str]], Any]]): function to apply to each split TSV line. In this section, you will find the data loading implementations (using DataPipes) of various popular datasets across different research domains. At a batch size of 8192, a V100 32-GB GPU reduces the latency by 19x compared to an 80-thread CPU inference. What is Adobe Firefly? out_labels_file (str): Output labels npy file path. Lab), Massih-Reza Amini (LIG, Grenoble INP). Under the hood, AMP is provided by the NVIDIA APEX library, which enables mixed precision training by changing only three lines of your script. # Find where range (rank_left_g, rank_right_g) intersects each file's range. We hope this dataset will serve as a large-scale standardized test-bed for the evaluation of counterfactual learning methods. sparse_columns (int): Total number of categorical columns. Real-world situations are considerably more complex, with Predictive Biddings machine learning technology using a vast dataset and real-time shopping signals to calculate the formulas predictive variables and additional parameters. If we reach a dataset that is exposed to HDFS, we know that this is an actual production dataset (as used as input for another production table) and not a test-specific one, and it should thus be exposed on DataDoc. Let's break down the train source mapping. With the rapid growth in scale of industry datasets, deep learning (DL) recommender models, which capitalize on large amounts of training data, have started to show advantages over traditional methods. Format: dictionary (feature name) => (metadata name => metadata value), source_spec provides information necessary to extract features from the files that store them. Not all systems provide utility functions to get a grasp on this data availability, and even when provided (e.g. Given a rank, world_size, and the lengths (number of rows) for a list of files, return which files and which portions of those files (represented as row ranges, - all range indices are inclusive) should be handled by the rank. This, combined with overlapping data loading and host2device transfer with neural net computations, allows us to achieve high GPU utilization. When that response is received, perf_client immediately sends another request, and then repeats this process. The model outputs a single number which can be interpreted as a likelihood of a certain user clicking an ad. Transformations are made along the columns. categorical_item_inputs, and one output channel: label. Recommender systems help people find what theyre looking for among an exponentially growing number of options. In the example shown in this repository we train models of three sizes: "small" (~15 GB), "large" (~82 GB), and "xlarge" (~142 GB). By default, perf_client measures your models latency and throughput using the lowest possible load on the model at a request concurrency of 1. Please note that the advertiser ID is required in the request URL. The second chunk contains the remaining columns and is saved in a single file. Figure It is more robust than FP16 for models that require a high dynamic range for weights or activations. All datasets have been anonymized to confirm to privacy standards. The dataset contains 24 zipped files and require about 1 TB of disk storage for the data and another 2 TB for immediate results. Learn step by step how to use NVIDIA Omniverse to generate your own synthetic dataset. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. Those pairwise interactions are fed into a top-level MLP to compute the likelihood of interaction between a user and item pair. "/home/datasets/criteo_kaggle/train.txt", Utility functions used to preprocess, save, load, partition, etc. Long story short, the Criteo Engine uses advanced Predictive Bidding technology to accurately determine the value of shoppers, and ensure your ads target the shoppers who are likely to drive the results youre looking for. Mixed precision training is turned off by default. Latency is broken down into client send/receive time, server queue and compute time, networking, server send/receive time. As stated above, data lineage can help us doing transversal analysis, looking at multiple datasets at once. For instance, there exists a large number of tables centered around ad clicks at Criteo (being an adtech company), most of them, therefore, containing clicks in their name. The contiguous ints start at a value of 2 so that. The feature specification does not specify what happens further: names of these streams are only lookup constants defined by the model/script. The columns are retrieved from the input files, loaded, aggregated into channels and supplied to the model/training script.