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name: deep-ctr-torch description: "Use DeepCTR-Torch for PyTorch CTR/recommender feature columns, single-task models, DIN/DIEN sequence models, and multi-task learning workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepCTR-Torch

Use this repo skill when a task names DeepCTR-Torch, deepctr_torch, deepctr-torch, PyTorch CTR prediction, recommender ranking models, SparseFeat/DenseFeat, DeepFM-style models, DIN/DIEN behavior histories, or SharedBottom/ESMM/MMOE/PLE multi-task CTR workflows.

DeepCTR-Torch is a PyTorch package for deep-learning based CTR and recommender models. It exposes feature-column classes, model constructors, and Keras-like compile/fit/predict/evaluate methods.

Install and import

Prefer an isolated Python environment. Minimal package install:

python -m pip install -U deepctr-torch
python - <<'PY'
import deepctr_torch
from deepctr_torch.inputs import SparseFeat, DenseFeat, VarLenSparseFeat, get_feature_names
from deepctr_torch.models import DeepFM
print(deepctr_torch.__version__)
PY

If import deepctr_torch fails with ModuleNotFoundError: requests, install requests explicitly; the package imports it for a best-effort version check even though the distribution metadata may not declare it.

Run the bundled environment checker when starting from an unfamiliar environment:

python scripts/check_deepctr_torch_env.py --quick

Route map

User task	Load
Build SparseFeat, DenseFeat, VarLenSparseFeat, feature_names, or model_input dictionaries from tabular data	feature-column-inputs
Validate sparse ids, dense vector widths, sequence padding, length_name, shared embedding_name, or batch sizes	feature-column-inputs
Train or predict with DeepFM, WDL, xDeepFM, AFM, AFN, AutoInt, DCN, DCNMix, FiBiNET, IFM, DIFM, MLR, NFM, ONN, PNN, or CCPM	single-task-modeling
Convert a binary CTR example to regression, choose losses/metrics, use callbacks, save/load weights, or debug single-target training	single-task-modeling and training API
Build DIN/DIEN behavior-history models, align hist_* features, share embeddings, set seq_length, or use DIEN negative sampling	sequence-and-interest-models
Use pooled multi-value inputs such as genre lists without DIN/DIEN attention	feature-column-inputs, then sequence-and-interest-models if sequence-specific behavior is needed
Train SharedBottom, ESMM, MMOE, or PLE with multiple targets	multitask-modeling
Troubleshoot install/import, offline version checks, GPU selection, data shapes, callbacks, metrics, or PyTorch compatibility	troubleshooting
Check whether this skill matches a repository checkout or package version	repo provenance

Common operating pattern

1. Build and validate feature columns in feature-column-inputs.
2. Choose the model route: single-task, sequence-interest, or multi-task.
3. Compile with supported optimizer/loss/metric strings from training API.
4. Fit on a model_input dictionary whose keys exactly match get_feature_names(...).
5. Predict and evaluate by task type; for multi-task outputs, evaluate each prediction column against the matching task_names entry.
6. Use bundled smoke scripts from the owning sub-skill to verify installation or reproduce a minimal pattern before scaling to real data.

Backend notes

DeepCTR-Torch supports CPU workflows and optional PyTorch CUDA devices via device='cuda:0' and, for DataParallel, gpus=[0, 1]. The generated skill was verified for CPU package inspection and tiny CPU training/prediction smokes. Treat GPU and multi-GPU execution as optional unless a user explicitly requests backend verification.

Boundaries

This skill teaches package use, not model-quality benchmarking or recommender-system theory. It does not replace feature engineering, data leakage checks, calibration, or production serving validation. It does not cover arbitrary custom multi-task graph architectures beyond the four package classes.