sidnarsipur/onnx-inference

ONNX-Predict Dataset

This dataset is derived from ONNX-Predict, a dataset of ONNX model inference performance measurements collected from models hosted on the ONNX Model Zoo.

It contains inference data for 2,300+ ONNX models. Each model was optimized into four variants, and inference measurements were collected across 16 machines with different CPU core counts, memory sizes, cache configurations, and CPU providers.

Dataset Overview

Each row in the dataset combines three groups of features:

1. Graph features describing the ONNX model structure and estimated data movement
2. Hardware features describing the machine used for inference
3. Inference measurements describing observed runtime performance

Features

The dataset includes:

• Graph features: 133 features

  • Node counts: 127 features
  • Data movement and compute estimates: 6 features

• Hardware features: 9 features

• Inference data: 4 target/measurement columns

Graph Features

Graph features describe the structure and computational characteristics of each ONNX model.

Node Count Features

There are 127 node-count features, grouped by operation type.

Convolution Operations

Examples include:

• Conv
• QLinearConv
• ConvTranspose

Matrix Multiplication Operations

Examples include:

• MatMul
• Gemm

Elementwise Operations

Examples include:

• Relu
• Sigmoid
• Tanh
• Mul
• Div

Reduction and Pooling Operations

Examples include:

• MaxPool
• AveragePool
• Other reduction-style operators

Normalization Operations

Examples include:

• Softmax
• BatchNormalization

Data Movement Operations

Examples include:

• Transpose
• Reshape
• Other tensor layout or shape transformation operations

Other Operations

This group includes operators related to:

• Control flow
• Constants
• Shape transformations
• Boolean logic
• Miscellaneous ONNX graph operations

Compute and Data Movement Features

The dataset also includes estimated compute and data movement features:

• conv_flops
• matmul_flops
• elementwise_mb
• reduction_mb
• movement_mb
• normalization_mb

These features provide additional information about the expected computational and memory behavior of each model.

Hardware Features

Hardware features describe the machine used to run inference.

Available hardware features include:

• memory_mb
• l1d_cache_kb
• l1i_cache_kb
• l2_cache_kb
• base_clock_mhz
• num_cores
• memory_bandwidth_gbs
• cpu_provider

The cpu_provider field identifies the CPU vendor, such as:

• amd
• intel

Inference Data

Inference measurements summarize observed runtime latency for each model, optimization variant, and hardware configuration.

The dataset includes the following inference columns:

• average_ms
• stddev_ms
• min_ms
• max_ms

All latency measurements are reported in milliseconds.

Source

The dataset is based on data from the ONNX-Predict project:

https://github.com/sidnarsipur/onnx-predict

License

This dataset is released under the MIT License.