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Tensorflow Template Application

Tensorflow Template Application is an open-source Python reference codebase for training TensorFlow classification and regression models, exporting them, and connecting prediction clients to serving examples.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tensorflow Template Application — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a code-level starting point for adapting TensorFlow model training and connecting exported models to documented serving and prediction-client examples.

Who it is for

  • Developers adapting TensorFlow training examples to dense or sparse tabular datasets.
  • Teams assembling a model workflow spanning data conversion, training, export, and inference.
  • Developers exploring prediction-client examples across server, Android, and iOS environments.

Documented uses

  • Convert CSV or LIBSVM datasets to TFRecords.
  • Train and validate logistic regression, neural-network, convolutional, wide-and-deep, or regression examples.
  • Export a trained model and run inference or serve it with TensorFlow Serving.
  • Use provided prediction-client examples, including gRPC clients in several languages.
Before you choose

Requirements & limits

The public GitHub repository is identified as Apache-2.0 licensed.

  • The codebase is a reference application, so adapting datasets and configuring dimensions are part of using it.
  • Input examples cover CSV, LIBSVM, and TFRecords; Spark is referenced for large-dataset preparation.
  • Training and serving are separate stages, with TensorFlow Serving and client examples supplied for inference.
  • The repository includes distributed-training examples alongside checkpoints, TensorBoard output, and model-export features.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare tabular data and train a classifier

Convert CSV or LIBSVM input to TFRecords and train a dense or sparse classifier using the repository examples.

Before you start

  • The repository's data conversion scripts and classifier scripts.
  • A dataset in CSV or LIBSVM format; its fields must match the feature and label dimensions used for training.

Steps

  1. For CSV data, run the conversion script from the relevant data directory, as shown for ./data/cancer/.

  2. For LIBSVM data, run the conversion script from the relevant data directory, as shown for ./data/a8a/.

  3. Run ./dense_classifier.py or ./sparse_classifier.py, supplying training and validation file paths and the feature and label sizes for the dataset.

Expected outcome

TFRecords can be generated from the example CSV or LIBSVM inputs, and the classifier scripts can be configured to train and validate against dataset files.

Guide limitations

  • The README's commands use repository example data and dimensions; target data must match the expected formats and configured dimensions.
  • Spark is mentioned for processing large datasets, but no specific Spark command is provided in the documented steps.
Documentation 1 ↗
Source-based practical guide

Export and serve a trained model with a prediction client

Use the documented SavedModel export, TensorFlow Serving setup, and gRPC client example to connect a trained model to inference requests.

Before you start

  • A model trained with the repository example.
  • TensorFlow Serving and a prediction client from the repository; serving environment details are deployment-dependent.

Steps

  1. Export the model with the training script's automatic export or run ./dense_classifier.py --mode savedmodel for manual export.

  2. Start tensorflow_model_server with the documented port, model name, and model base path.

  3. Run the Python prediction client with the server host, port, model name, and version arguments.

Expected outcome

The documented flow exposes the exported model through TensorFlow Serving and provides a Python gRPC client example for prediction requests.

Guide limitations

  • The README's server command assumes a TensorFlow Serving executable and a model directory at the specified path.
  • Deployment configuration depends on the serving environment; the example is not a deployment compatibility guarantee.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Tensorflow Template ApplicationConvert CSV or LIBSVM datasets to TFRecords.Android, iOS, ServerThe public GitHub repository is identified as Apache-2.0 licensed.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
tobegit3hub/tensorflow_template_application · Official homepage title · retained in its source language
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