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ctr-training

Configure and run CTR prediction model training with FuxiCTR.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines configuring, running, and evaluating CTR prediction experiments so practitioners can compare models and reproduce results without manually stitching together dataset, model, and training steps.

Core Features & Use Cases

  • End-to-end training orchestration: Prepare model and dataset YAMLs, launch experiments, and manage GPU/CPU execution for models such as DeepFM, DCNv2, DIN, xDeepFM and AutoInt.
  • Config templates and tuning guidance: Provides dataset_config and model_config templates, recommended hyperparameters, and best-practice settings for different dataset scales.
  • Troubleshooting and evaluation: Includes common error fixes (NumPy compatibility, CUDA OOM), logging conventions, and guidance for monitoring AUC/logloss and saving best models.
  • Use Case: Run a unified benchmark to compare multiple CTR models across MovieLens, Frappe, TaobaoAd, and Criteo datasets and collect metrics for model selection.

Quick Start

Run the ctr-training workflow by preparing dataset and model config YAMLs in the appropriate FuxiCTR model_zoo/{MODEL}/config directory and executing the run_expid.py command with your desired expid and GPU flags.

Frequently Asked Questions about ctr-training

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

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

Train CTR prediction models using FuxiCTR by configuring dataset and model YAML files in the model_zoo/{MODEL}/config directory and executing run_expid.py with your specified expid and GPU flags.

What datasets are supported for CTR model benchmarking?▼

Supported datasets for CTR model benchmarking include MovieLens, Frappe, TaobaoAd, KuaiVideo, and Criteo, enabling model comparison, hyperparameter tuning, and batch experiments for evaluation metrics.

Which CTR models can I configure for training experiments?▼

Configurable CTR models for training experiments include DeepFM, DCNv2, DIN, xDeepFM, and AutoInt, utilizing provided config templates and recommended hyperparameters for reproducible results.

Do I need GPU resources to run CTR training experiments?▼

Executing CTR training experiments requires access to CPU or GPU resources alongside a FuxiCTR-compatible Python environment, prepared dataset files, and YAML configs.

Why does my CTR training experiment fail with CUDA OOM or NumPy errors?▼

CTR training experiments failing with CUDA OOM or NumPy compatibility errors can be resolved using included troubleshooting guidance for common error fixes, logging conventions, and best-practice settings.

What is the best way to compare multiple CTR models across different datasets?▼

Comparing multiple CTR models across datasets is best achieved by running a unified benchmark using FuxiCTR to collect AUC and logloss metrics for model selection and reproducible evaluation.

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