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name: estimator-workflows description: "Use legacy TensorFlow Estimator DeepCTR workflows for TFRecord or Pandas input when tf.estimator is available." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Estimator Workflows

Use this sub-skill when the task is specifically about DeepCTR's legacy TensorFlow Estimator API: large-scale or distributed-style CTR training with tf.estimator, tf.feature_column, input_fn_tfrecord, input_fn_pandas, RunConfig, and Estimator checkpoint directories.

Route by Need

• Can this runtime run Estimators? Run scripts/check_estimator_runtime.py. It reports TensorFlow/DeepCTR versions, top-level tf.estimator availability, DeepCTR Estimator imports, the native DeepCTR version gate, and optional constructor/input-function checks.
• Build TFRecord or Pandas inputs: Use references/tfrecord-and-pandas.md for Criteo-style FixedLenFeature schemas, matching tf.feature_column objects, safe Pandas preprocessing, and expected Estimator outputs.
• Choose an Estimator constructor or feature-column pattern: Use references/api-reference.md for the Estimator model catalog, constructor parameters, runtime/version facts, optimizer defaults, model_dir, and RunConfig notes.
• Diagnose failures: Use references/troubleshooting.md for missing tf.estimator in modern TensorFlow/Keras 3 stacks, dtype/schema mismatches, label-key errors, Pandas missing-value handling, distributed-data expectations, and checkpoint cleanup.

Boundaries

• This sub-skill uses TensorFlow tf.feature_column objects directly. For DeepCTR Keras SparseFeat, DenseFeat, VarLenSparseFeat, and get_feature_names, route to ../data-and-feature-columns/SKILL.md.
• For Keras-style DeepCTR models such as DeepFM(...), xDeepFM(...), DIN(...), BST(...), compile/fit/predict, or Keras serialization, route to ../keras-model-workflows/SKILL.md.
• For multi-task Keras models such as MMOE, PLE, ESMM, or SharedBottom, route to ../multitask-models/SKILL.md.
• Do not assume Estimators work just because deepctr imports. The constructors call top-level tf.estimator; modern TensorFlow builds may omit it.

Minimum Safe Flow

1. Probe the environment before writing training code:

   python sub-skills/estimator-workflows/scripts/check_estimator_runtime.py --construct-estimator
   

2. If the probe reports unsupported, use a Keras workflow instead or install a TensorFlow release with top-level tf.estimator support. Do not keep debugging DeepCTR feature columns until the runtime gate passes.

3. For Criteo-style TFRecord data, match every serialized feature to both a tf.io.FixedLenFeature entry and a compatible tf.feature_column entry. Sparse IDs should be int64 and dense values should be float32.

4. Create a fresh model_dir for each changed model/feature schema. Reusing checkpoints after changing feature names, bucket sizes, embedding dimensions, or task type commonly causes opaque restore errors.