Valohai TensorFlow Examples

This repository serves as an example for the Valohai MLOps platform. It implements handwritten digit detection using TensorFlow, based on TensorFlow's example.

The project describes a machine learning pipeline with five unique steps:

• Preprocess data
• Train model
• Batch inference
• Compare predictions
• Online inference deployment

If you are just starting out, it is recommended to follow the learning path in the Valohai documentation. This learning path recreates the Train model step of this example.

Running on Valohai

Login to the Valohai app and create a new project.

In the project's settings, configure this repository as the project's repository.

Now you are ready to run executions, tasks and pipelines. After you've trained a model, you can also deploy it for online inference!

Running Locally

You can run all the steps of the pipeline locally. This requires Python 3.9 and specific packages, which you can install with:

pip install -r requirements.txt

The steps require different inputs to run, so you need to run them in order.

Preprocess data has all the required inputs defined as defaults and can be run with:

python preprocess_dataset.py

Train model requires the preprocessed dataset, but that is also defined as a default, so you can run:

python train_model.py

Batch inference requires both a model and some new data. The new data has default values, but the model needs to be provided, for example from an earlier train model run:

python batch_inference.py --model .valohai/outputs/{local_run_id}/train-model/model-{suffix}.h5

Compare predictions requires two or more batch inference results and optionally the corresponding models. We can run it for example like this:

python compare_predictions.py --predictions .valohai/outputs/{local_run_id}/batch-inference/predictions-{suffix}.json .valohai/outputs/{local_run_id}/batch-inference/predictions-{suffix}.json