ONNX runtime
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
What is ONNX runtime?
ONNX Runtime is a cross-platform inference and training accelerator for machine learning models in the ONNX format. It enables faster model execution and lower deployment costs by optimizing performance across different hardware platforms while supporting models from popular frameworks like PyTorch, TensorFlow, and scikit-learn.
Machine learning engineers and developers who need to deploy trained models in production environments with high performance requirements across diverse hardware configurations.
Developers choose ONNX Runtime for its exceptional performance optimizations, broad framework compatibility, and ability to leverage hardware accelerators without rewriting model code, making it ideal for production ML deployments.
Overview
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Use Cases
Best For
- Deploying PyTorch or TensorFlow models to production with minimal latency
- Running machine learning inference on edge devices with hardware accelerators
- Accelerating transformer model training on multi-GPU systems
- Standardizing model deployment across different frameworks using ONNX format
- Optimizing inference performance for classical ML models like XGBoost and LightGBM
- Building cross-platform ML applications that run on Windows, Linux, and macOS
Not Ideal For
- Projects exclusively using frameworks with built-in serving solutions like TensorFlow Serving or TorchServe, without cross-framework needs
- Rapid prototyping or research environments where model conversion to ONNX adds unnecessary overhead
- Edge deployments on highly constrained devices where the runtime's footprint might be too large
- Teams requiring frequent model updates without conversion steps, as ONNX models need re-exporting and reloading
Pros & Cons
Pros
Runs on Windows, Linux, macOS and leverages various hardware accelerators like NVIDIA GPUs, enabling optimal performance across diverse environments as stated in the README.
Supports models from PyTorch, TensorFlow, scikit-learn, LightGBM, and XGBoost, allowing deployment without rewriting code for different frameworks, highlighting its versatility.
Reduces inference latency and costs through graph optimizations and hardware acceleration, with documentation emphasizing faster customer experiences.
Speeds up transformer training on multi-node NVIDIA GPUs with a one-line addition in PyTorch scripts, as mentioned in the README for minimal code changes.
Cons
Models must be converted to ONNX format, which can be complex and may not support all operations or latest framework features, adding a step to deployment pipelines.
While broad, support for non-standard or custom models might require additional work, and not all ML libraries export seamlessly to ONNX, potentially causing compatibility issues.
Integrating ONNX Runtime involves learning its APIs and managing dependencies for different hardware providers, which can be steep for simple or existing deployments without clear plug-and-play solutions.
Frequently Asked Questions
Related Projects
OpenCVOpen Source Computer Vision Library
FAISSA library for efficient similarity search and clustering of dense vectors.
XGBoostScalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
DarknetConvolutional Neural Networks
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