Babysitter tensorflow-trainer

TensorFlow/Keras model training skill with callbacks, distributed strategies, and TensorBoard integration.

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/data-science-ml/skills/tensorflow-trainer" ~/.claude/skills/a5c-ai-babysitter-tensorflow-trainer && rm -rf "$T"

manifest: library/specializations/data-science-ml/skills/tensorflow-trainer/SKILL.md

tensorflow-trainer

Overview

TensorFlow/Keras model training skill with callbacks, distributed strategies, TensorBoard integration, and production-ready model export capabilities.

Capabilities

Keras model training with callbacks
Custom training loops with tf.GradientTape
Distribution strategy configuration (MirroredStrategy, MultiWorkerMirroredStrategy, TPUStrategy)
TensorBoard logging and visualization
SavedModel export for TF Serving
TFLite conversion for edge deployment
Mixed precision training

Target Processes

Model Training Pipeline with Experiment Tracking
Distributed Training Orchestration
Model Deployment Pipeline

Tools and Libraries

TensorFlow
Keras
TensorBoard
TensorFlow Serving
TensorFlow Lite

Input Schema

{
  "type": "object",
  "required": ["modelConfig", "dataConfig", "trainingConfig"],
  "properties": {
    "modelConfig": {
      "type": "object",
      "properties": {
        "modelPath": { "type": "string" },
        "modelType": { "type": "string", "enum": ["sequential", "functional", "subclassed"] }
      }
    },
    "dataConfig": {
      "type": "object",
      "properties": {
        "trainPath": { "type": "string" },
        "valPath": { "type": "string" },
        "batchSize": { "type": "integer" },
        "prefetch": { "type": "boolean" }
      }
    },
    "trainingConfig": {
      "type": "object",
      "properties": {
        "epochs": { "type": "integer" },
        "optimizer": { "type": "string" },
        "learningRate": { "type": "number" },
        "loss": { "type": "string" },
        "metrics": { "type": "array", "items": { "type": "string" } },
        "callbacks": { "type": "array", "items": { "type": "string" } },
        "distributionStrategy": { "type": "string" }
      }
    },
    "exportConfig": {
      "type": "object",
      "properties": {
        "savedModelPath": { "type": "string" },
        "tflitePath": { "type": "string" },
        "servingSignatures": { "type": "array", "items": { "type": "string" } }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "metrics", "modelPath"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error", "early_stopped"]
    },
    "metrics": {
      "type": "object",
      "properties": {
        "loss": { "type": "number" },
        "valLoss": { "type": "number" },
        "accuracy": { "type": "number" },
        "valAccuracy": { "type": "number" },
        "epochsTrained": { "type": "integer" }
      }
    },
    "modelPath": {
      "type": "string"
    },
    "savedModelPath": {
      "type": "string"
    },
    "tensorboardLogDir": {
      "type": "string"
    },
    "history": {
      "type": "object",
      "description": "Training history with all metrics per epoch"
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Train TensorFlow model',
  skill: {
    name: 'tensorflow-trainer',
    context: {
      modelConfig: {
        modelPath: 'models/cnn_model.py',
        modelType: 'functional'
      },
      dataConfig: {
        trainPath: 'data/train',
        valPath: 'data/val',
        batchSize: 64,
        prefetch: true
      },
      trainingConfig: {
        epochs: 50,
        optimizer: 'adam',
        learningRate: 0.001,
        loss: 'sparse_categorical_crossentropy',
        metrics: ['accuracy'],
        callbacks: ['early_stopping', 'model_checkpoint', 'tensorboard']
      }
    }
  }
}