---

name: "tensorflow-template-application" description: "Route TensorFlow template application workflows for data preparation, TF1 training/export, and serving/client tasks." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

TensorFlow Template Application

This skill routes the TensorFlow 1.x template application's public workflows. Use it when the user asks about CSV or LIBSVM conversion, TFRecords, dense or sparse training, checkpointing, SavedModel export, TensorBoard, TensorFlow Serving requests, or the repo's legacy client examples.

Start here

• Read references/installation.md for the TF1-compatible package set and optional serving add-ons.
• Run scripts/check_tf1_environment.py after installation to confirm the core TF1 symbols, trainer import, and the TFRecord smoke check.
• Read references/troubleshooting.md when a task fails because of TF2, flags, shapes, checkpoints, or serving mismatches.

Route map

sub-skills/data-preparation/

Use this route for:

• dense CSV to TFRecords conversion
• sparse LIBSVM to TFRecords conversion
• TFRecords inspection and schema checks
• tiny iris fixture generation from sklearn
• questions about dense features versus sparse ids and values

Read next:

• sub-skills/data-preparation/references/data-formats.md
• sub-skills/data-preparation/references/workflows.md
• sub-skills/data-preparation/references/troubleshooting.md

sub-skills/training-and-export/

Use this route for:

• dense and sparse training commands
• checkpoint restore and SavedModel export
• local inference from checkpoints or exported models
• model, optimizer, and loss flag questions
• TensorBoard event inspection
• legacy queue or distributed training notes

Read next:

• sub-skills/training-and-export/references/model-overview.md
• sub-skills/training-and-export/references/cli-reference.md
• sub-skills/training-and-export/references/workflows.md
• sub-skills/training-and-export/references/troubleshooting.md

sub-skills/serving-and-clients/

Use this route for:

• dense and sparse TensorFlow Serving PredictRequest payloads
• Python gRPC client helpers
• minimal-model latency and QPS benchmarks
• the legacy Django HTTP wrapper
• alternate Java, Go, C++, Android, and iOS client examples as reference-only

Read next:

• sub-skills/serving-and-clients/references/python-grpc-client.md
• sub-skills/serving-and-clients/references/minimal-benchmark.md
• sub-skills/serving-and-clients/references/http-service.md
• sub-skills/serving-and-clients/references/alternate-clients.md
• sub-skills/serving-and-clients/references/troubleshooting.md

Quick rules

• The installed distribution name is trainer, not the repository name.
• TensorFlow 2.x is not the default fit here because the source relies on tf.contrib, tf.app.flags, and tf.python_io.
• Dense and sparse trainer modules register global flags at import time, so run them in separate Python processes.
• Use the bundled scripts under each sub-skill instead of pointing future agents back to the original checkout.

Helpful root files

• references/repo-provenance.md records the source snapshot and refresh baseline.
• references/repo-routing-metadata.json places this skill in the router.
• references/installation.md captures the public install guidance.
• references/troubleshooting.md collects cross-cutting failure modes.

Minimal smoke check

When you only need a quick environment check, run:

python scripts/check_tf1_environment.py

Add --check-serving when the serving extras are installed and you want to verify the Python serving imports too.

Common task routing cues

• CSV, LIBSVM, TFRecords, iris fixture, or DICOM labels file questions usually belong to data-preparation.
• Model architecture, optimizer, checkpoint, export, or TensorBoard questions usually belong to training-and-export.
• PredictRequest construction, benchmark mode, or HTTP wrapper questions usually belong to serving-and-clients.

If a task spans routes, start with the most concrete file or payload shape and then follow the linked sub-skill references.