---

name: examples-conversion description: "Convert TensorFlow, Keras, Estimator, and tutorial examples into TensorFlowOnSpark spark-submit workflow plans." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

examples-conversion

Use this operating sub-skill when a user wants to convert or adapt an existing TensorFlow, Keras, Estimator, ResNet, segmentation, or MNIST tutorial into a TensorFlowOnSpark workflow that can be launched with spark-submit.

This sub-skill is a router and checklist. Put low-level API details in the owning sub-skills; use this sub-skill to decide the workflow shape, Spark command, deployment assumptions, and validation signals.

Route by user intent

User intent	Use this file
Decide how to wrap existing TensorFlow code for TensorFlowOnSpark	references/conversion-patterns.md
Build Keras or Estimator MNIST train, inference, pipeline, or streaming command plans	references/mnist-workflows.md
Plan SavedModel inspection, TF Serving, Spark batch inference, local standalone, or YARN deployment	references/deployment-and-serving.md
Diagnose failed conversions, missing Spark/JVM/classpath, stalled jobs, input-mode mistakes, SavedModel issues, or serving hazards	references/troubleshooting.md
Render a dry-run command plan from caller-supplied paths	scripts/render_spark_submit_plan.py

Boundary and handoff rules

• For TFCluster.run, reservation, TensorBoard, executor count, GPU/resource, shutdown, or TFParallel.run internals, route to the cluster-lifecycle sub-skill.
• For InputMode.SPARK queue semantics, TFNode.DataFeed, one-output-per-input inference, next_batch, batch_results, terminate, or feed timeouts, route to the datafeed-inputmode sub-skill.
• For Spark ML TFEstimator, TFModel, input/output mappings, and DataFrame transform details, route to the spark-ml-pipelines sub-skill.
• For Spark DataFrame to TFRecord conversion, dfutil, TensorFlow Hadoop jar/classpath, and MNIST row reshaping utilities, route to the dataframes-tfrecords sub-skill.
• Treat downloads, Docker, host Spark installation, and EC2/cloud deployment scripts as reference-only. Do not execute them from this skill.

Default conversion procedure

1. Identify whether the original TensorFlow program already reads its own data (InputMode.TENSORFLOW) or should consume a Spark RDD/DataFrame (InputMode.SPARK).
2. Wrap the TensorFlow entrypoint as main_fun(args, ctx) or main_fun(argv, ctx) and create a small Spark driver that calls TFCluster.run.
3. Render a spark-submit command with explicit --master, executor/core settings, application script path, cluster size, data/model/export paths, and optional --py-files or --jars.
4. Check that every worker can see the data path, model/export path, Python dependencies, and optional TensorFlow Hadoop jar.
5. Validate by observing cluster startup, TensorFlow workers connecting, output checkpoint/export directories appearing, and either predictions or SavedModel signatures matching the expected tensors.

Safe helper usage

The bundled helper only prints a plan. It does not start Spark, run Docker, download data, remove files, or mutate the host.

python scripts/render_spark_submit_plan.py --help

Use caller-provided concrete paths whenever possible. If reusing shell variables in the printed plan, review quoting before running the rendered command manually.