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Softmax Output Example - Onnx Typing CST Test
Softmax Output Example — Onnx Code
Performing inference and applying softmax to ONNX model outputs.
import onnxruntime as ort
import numpy as np
# Load model
session = ort.InferenceSession('classification_model.onnx')
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1,10).astype(np.float32)
# Run inference
logits = session.run(None, {input_name: input_data})[0]
softmax = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
print('Softmax probabilities:', softmax)Onnx Language Guide
ONNX (Open Neural Network Exchange) is an open-source format and ecosystem for representing machine learning models, enabling interoperability between frameworks like PyTorch, TensorFlow, and scikit-learn, and allowing deployment across diverse platforms.
Primary Use Cases
- ▸Exporting models from PyTorch, TensorFlow, or other frameworks
- ▸Cross-framework deployment without retraining
- ▸Hardware-accelerated inference on CPUs, GPUs, and specialized accelerators
- ▸Optimizing models with ONNX Runtime for production
- ▸Edge AI and mobile deployment of ML models
Notable Features
- ▸Framework-agnostic model format
- ▸Supports both deep learning and classical ML operators
- ▸ONNX Runtime for high-performance inference
- ▸Quantization and optimization tools for deployment
- ▸Extensible operator set for custom layers
Origin & Creator
ONNX was co-developed by Microsoft and Facebook in 2017 to unify model representation and interoperability between deep learning frameworks.
Industrial Note
ONNX is widely used in production pipelines where models need to be transferred between frameworks, optimized for inference, or deployed on resource-constrained devices like mobile phones or edge servers.
Quick Explain
- ▸ONNX provides a standard format for models, allowing them to be trained in one framework and deployed in another.
- ▸It supports operators for deep learning, classical ML, and other computational graphs.
- ▸ONNX enables cross-platform deployment, including edge devices, mobile, and cloud inference environments.
Core Features
- ▸Interoperability between frameworks (PyTorch, TensorFlow, scikit-learn, etc.)
- ▸Graph-based computational representation
- ▸Model optimization and runtime acceleration
- ▸Cross-platform support for cloud, mobile, and edge
- ▸Extensible via custom operators for advanced use cases
Learning Path
- ▸Understand ML model training in PyTorch or TensorFlow
- ▸Learn ONNX model export and import
- ▸Practice inference using ONNX Runtime
- ▸Experiment with model optimization and quantization
- ▸Deploy ONNX models on edge and cloud platforms
Practical Examples
- ▸Convert a PyTorch CNN to ONNX and run inference on CPU
- ▸Optimize a BERT model using ONNX Runtime for GPU
- ▸Deploy a scikit-learn RandomForest model using ONNX
- ▸Run quantized ONNX model on mobile device for reduced memory
- ▸Integrate ONNX model into cloud-based inference service
Comparisons
- ▸ONNX vs PyTorch: PyTorch for training; ONNX for interoperable deployment
- ▸ONNX vs TensorFlow SavedModel: ONNX is cross-framework; TF SavedModel is TF-specific
- ▸ONNX vs CoreML: CoreML targets Apple devices; ONNX is cross-platform
- ▸ONNX vs TensorRT: TensorRT optimizes for NVIDIA hardware; ONNX is model format
- ▸ONNX vs TFLite: TFLite is for mobile; ONNX supports broader deployment targets
Strengths
- ▸Simplifies model transfer between different ML frameworks
- ▸Optimized inference using ONNX Runtime
- ▸Supports deployment on multiple hardware backends
- ▸Reduces need to rewrite models for different environments
- ▸Strong ecosystem with converter tools and runtime support
Limitations
- ▸Not all framework-specific features/operators are supported
- ▸Complex custom layers may require manual conversion
- ▸Primarily focused on inference; less used for training
- ▸Debugging model conversion issues can be tricky
- ▸Smaller community compared to primary frameworks like PyTorch/TensorFlow
When NOT to Use
- ▸Training new models (ONNX is primarily for inference)
- ▸Projects not requiring cross-framework deployment
- ▸When custom operators cannot be converted easily
- ▸For extremely small-scale local models where overhead is unnecessary
- ▸When using framework-native runtime is sufficient
Cheat Sheet
- ▸ModelProto = serialized ONNX model
- ▸Graph = computation graph of nodes
- ▸Node = operator in the graph
- ▸Tensor = multi-dimensional data array
- ▸ONNX Runtime = inference engine
FAQ
- ▸Is ONNX free?
- ▸Yes - open-source under MIT license.
- ▸Which frameworks support ONNX?
- ▸PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, and more.
- ▸Can ONNX models run on mobile devices?
- ▸Yes - supported via ONNX Runtime Mobile or other accelerators.
- ▸Does ONNX support GPU acceleration?
- ▸Yes - ONNX Runtime supports GPU, CUDA, TensorRT, and other backends.
- ▸Is ONNX used for training?
- ▸Primarily for model interoperability and inference, not training.
30-Day Skill Plan
- ▸Week 1: Export simple models to ONNX
- ▸Week 2: Validate ONNX model inference matches original framework
- ▸Week 3: Apply optimizations and quantization
- ▸Week 4: Deploy models on GPU/CPU backends
- ▸Week 5: Integrate ONNX models into production pipelines
Final Summary
- ▸ONNX standardizes ML model representation for cross-framework deployment.
- ▸Enables optimized, hardware-accelerated inference across CPU, GPU, and edge devices.
- ▸Supports deep learning and classical ML operators with extensibility.
- ▸Facilitates production-ready deployment without framework lock-in.
- ▸Widely adopted in enterprise, edge AI, and cloud ML pipelines.
Project Structure
- ▸scripts/ - model training and conversion scripts
- ▸models/ - exported ONNX model files
- ▸datasets/ - data used for testing inference
- ▸notebooks/ - experiments and validation
- ▸logs/ - inference performance metrics
Monetization
- ▸Cross-platform AI model deployment services
- ▸Enterprise AI solutions with ONNX Runtime
- ▸Optimization consulting for inference performance
- ▸Edge AI deployment for mobile/IoT
- ▸Commercial support and training for ONNX ecosystem
Productivity Tips
- ▸Use ONNX Runtime for optimized inference
- ▸Apply quantization to reduce model size
- ▸Validate models after each conversion
- ▸Use graph optimization tools for speed
- ▸Batch inputs to maximize throughput
Basic Concepts
- ▸ModelProto: ONNX serialized model format
- ▸Graph: computational graph representing model operations
- ▸Node: operator within the graph (e.g., Conv, Add, Relu)
- ▸Tensor: multi-dimensional array data flowing between nodes
- ▸OperatorSet: collection of supported operators