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dcnv2-training

Train and evaluate DCNv2 CTR models with FuxiCTR via configuration files.

2|Updated Apr 10, 2026
Search Tags:#model-training#hyperparameter-tuning#ctr#multi-gpu#dcnv2#fuxictr#dataset-configuration
One-click install
npx skills add https://github.com/raoxuan98-hash/open_unimixer_skills --skill dcnv2-training
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dcnv2-training
Source: https://github.com/raoxuan98-hash/open_unimixer_skills/tree/main/.kimi/skills/dcnv2-training
Command: npx skills add https://github.com/raoxuan98-hash/open_unimixer_skills --skill dcnv2-training

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the configuration, training, and evaluation of DCNv2 models within the FuxiCTR framework for click-through-rate prediction, reducing manual setup, configuration errors, and common runtime issues.

Core Features & Use Cases

  • End-to-end training: Provides guidance for model_config.yaml and dataset_config.yaml to run experiments end-to-end.
  • Multi-environment support: Supports CPU and single- or multi-GPU training options and DataParallel execution.
  • Configuration templates & tuning: Exposes key hyperparameters such as embedding_dim, num_cross_layers, batch_size, and learning_rate for benchmarking and hyperparameter sweeps.
  • Troubleshooting: Documents common fixes for NumPy compatibility, OOM errors, and invalid data paths.
  • Use Case: Run reproducible DCNv2 experiments across MovielensLatest_x1 and other CTR datasets to compare AUC and logloss across model variants.

Quick Start

Run the dcnv2-training skill to train DCNv2 on a prepared dataset by pointing model_config.yaml and dataset_config.yaml to your data directory and executing run_expid with the desired GPU device.

Frequently Asked Questions about dcnv2-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train DCNv2 models for CTR prediction using FuxiCTR?▼

Run DCNv2 CTR training by pointing model_config.yaml and dataset_config.yaml to your prepared data directory and executing run_expid with the desired GPU device for end-to-end model training and evaluation.

Can I use multi-GPU DataParallel for DCNv2 CTR training?▼

Multi-GPU training for DCNv2 CTR is supported using DataParallel execution, alongside standard CPU and single-GPU options, allowing scalable model training across different hardware environments.

How do I tune hyperparameters for DCNv2 on CTR benchmark datasets?▼

Tune DCNv2 hyperparameters including embedding_dim, num_cross_layers, batch_size, and learning_rate via configuration files to perform benchmarking and model comparison on datasets like MovielensLatest_x1 and Frappe_x1.

Why does my FuxiCTR training fail with NumPy compatibility or OOM errors?▼

Resolve FuxiCTR training failures caused by NumPy compatibility or OOM errors by applying documented runtime fixes, which also address invalid data path issues to restore smooth execution.

What datasets are supported for DCNv2 CTR benchmarking experiments?▼

Supported datasets for DCNv2 CTR benchmarking include MovielensLatest_x1, Frappe_x1, and TaobaoAd_x1, enabling reproducible experiments to compare AUC and logloss across model variants.

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