ONNX

As explained in Deep Learning with R, Third Edition

ONNX is an open format and ecosystem for representing and exchanging machine-learning models across frameworks and runtimes.

model |> export_savedmodel("path/to/location", format = "onnx")  #1reticulate::py_require("onnxruntime") #1

onnxruntime <- reticulate::import("onnxruntime")
ort_session <- onnxruntime$InferenceSession("path/to/location")
predictions <- ort_session$run(NULL, input_data)

How Deep Learning with R, Third Edition covers it

How deep it goes

Explains the export-and-runtime workflow but stops short of detailing ONNX internals or optimization techniques.

Examples

Uses Keras export_savedmodel(), onnxruntime InferenceSession, and prediction calls as the working deployment example.

What it assumes

Assumes a trained Keras model and input data while demonstrating the deployment steps from export through inference.

What you can do after

Operate/configure ONNX serving by exporting a Keras model and running predictions through onnxruntime.

How it connects

Connects model export with Keras, REST APIs, TensorFlow Serving, inference, and onnxruntime.

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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.
• 

  liveBookmanning.com
  Server-Side WebAssemblyONNX is an open model representation and interoperability standard for exchanging machine-learning models between frameworks and runtimes.
• 

  liveBookmanning.com
  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 Python, Third EditionAn open format and runtime ecosystem for representing and executing trained machine-learning models outside the original training framework.
• 

  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 R, Third Edition. The section links point to the parts of the book it draws on.