ONNX

As explained in Deep Learning with Python, Third Edition

An open format and runtime ecosystem for representing and executing trained machine-learning models outside the original training framework.

model.export("path/to/location", format="onnx")

ort_session = onnxruntime.InferenceSession("path/to/location")
predictions = ort_session.run(None, input_data)

How Deep Learning with Python, Third Edition covers it

How deep it goes

Shows export and runtime execution, but defers detailed optimization and implementation internals.

Examples

Uses Keras export, ONNX Runtime session loading, and prediction execution as the concrete workflow.

What it assumes

Assumes a trained Keras model and basic familiarity with serving predictions.

What you can do after

Operate exported models through ONNX Runtime for production prediction.

How it connects

Connects model deployment with inference, computation graphs, REST APIs, Keras, and ONNX Runtime.

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Also explained in

• 

  liveBookmanning.com
  Domain-Specific Small Language ModelsA serialized machine-learning model represented in the ONNX format for portable execution outside the training framework.
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  Server-Side WebAssemblyONNX is an open model representation and interoperability standard for exchanging machine-learning models between frameworks and runtimes.
• 

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  LLMs in ProductionONNX is an open model format and ecosystem for representing neural-network models across different frameworks, tools, and hardware environments.
• 

  liveBookmanning.com
  Machine Learning System DesignONNX is an open format and interoperability standard for representing machine learning models across frameworks and runtimes.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionONNX is an open format and ecosystem for representing and exchanging machine-learning models across frameworks and runtimes.
• 

  liveBookmanning.com
  Deep Learning with PyTorch, Second EditionA vendor-neutral standard and file format for representing neural network computations so models can be executed in different runtimes and devices.

Compare how 7 books explain ONNX →

Related concepts

• ONNX Runtimeused by · ONNX Runtime is an open-source, cross-platform inference engine for executing and optimizing machine-learning models represented in the ONNX format.
• model exportproduced by · Model export is the process of converting a trained machine-learning model into a portable representation suitable for another format, framework, runtime, or deployment environment.
• TensorFlow Servingalternative to · TensorFlow Serving is an open-source system for deploying trained machine-learning models and exposing them through production inference APIs.
• artifact packagingused by · Artifact packaging is the preparation of a trained model and its associated files and requirements as a deployable unit.
• Optimumproduced by · Optimum is a Hugging Face library for optimizing and deploying machine-learning models across inference engines and hardware platforms.
• language modelused by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.