Pros of models

• Extensive collection of pre-trained models for various tasks
• Well-documented and maintained by Google's TensorFlow team
• Includes tutorials and examples for easy implementation

Cons of models

• Primarily focused on TensorFlow, limiting flexibility for other frameworks
• May have a steeper learning curve for beginners due to its comprehensive nature

Code Comparison

models:

import tensorflow as tf
from official.nlp import bert
model = bert.BertModel(config=bert_config)
outputs = model(input_ids, attention_mask=input_mask)

inference:

import mlperf_loadgen as lg
settings = lg.TestSettings()
settings.scenario = lg.TestScenario.SingleStream
sut = SystemUnderTest()
sut.issue_queries(query_samples)

Key Differences

• models focuses on providing pre-trained models and implementations
• inference emphasizes benchmarking and performance evaluation
• models is TensorFlow-centric, while inference is framework-agnostic
• inference is designed for standardized ML performance testing
• models offers a wider range of model architectures and applications

Both repositories serve different purposes in the machine learning ecosystem, with models being more suitable for model development and implementation, while inference is tailored for standardized performance benchmarking across different hardware and software configurations.

Pros of examples

• Broader range of examples covering various PyTorch use cases
• More beginner-friendly with simpler implementations
• Regularly updated with new PyTorch features and best practices

Cons of examples

• Less focused on benchmarking and performance optimization
• May not include industry-standard inference scenarios
• Limited emphasis on cross-platform compatibility

Code Comparison

examples:

import torch
import torchvision.models as models

model = models.resnet18(pretrained=True)
input_tensor = torch.randn(1, 3, 224, 224)
output = model(input_tensor)

inference:

import mlperf_loadgen as lg
import numpy as np

settings = lg.TestSettings()
settings.scenario = lg.TestScenario.SingleStream
settings.mode = lg.TestMode.PerformanceOnly
sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)

The examples code showcases a simple model inference, while inference focuses on benchmarking setup using MLPerf LoadGen.

Pros of DeepLearningExamples

• Broader range of deep learning models and applications
• More detailed documentation and tutorials for each example
• Optimized for NVIDIA hardware, potentially offering better performance

Cons of DeepLearningExamples

• Limited to NVIDIA-specific implementations and optimizations
• May not provide standardized benchmarking across different hardware platforms
• Less focus on inference-specific optimizations and techniques

Code Comparison

DeepLearningExamples (PyTorch ResNet50 inference):

model = torchvision.models.resnet50(pretrained=True).cuda().eval()
input_tensor = torch.randn(1, 3, 224, 224).cuda()
with torch.no_grad():
    output = model(input_tensor)

inference (MLPerf ResNet50 v1.5 inference):

model = tf.keras.applications.resnet50.ResNet50(weights='imagenet')
input_tensor = tf.random.normal([1, 224, 224, 3])
warmup_iterations = 10
for _ in range(warmup_iterations):
    _ = model(input_tensor)

Both repositories provide examples for deep learning inference, but DeepLearningExamples offers a wider range of models and applications, while inference focuses on standardized benchmarking and cross-platform compatibility. DeepLearningExamples is more tailored to NVIDIA hardware, potentially offering better performance on those platforms, but may be less suitable for cross-vendor comparisons.

Pros of ONNX Runtime

• Broader scope: Supports a wide range of ML frameworks and hardware accelerators
• Production-ready: Optimized for performance and deployment in various environments
• Active development: Frequent updates and extensive documentation

Cons of ONNX Runtime

• Steeper learning curve: More complex API due to its broader feature set
• Larger footprint: Heavier library with more dependencies

Code Comparison

ONNX Runtime:

import onnxruntime as ort

session = ort.InferenceSession("model.onnx")
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: input_data})

MLCommons Inference:

from mlperf_loadgen import TestSettings, PerformanceOnly, LoadGenerator
from backend import Backend

settings = TestSettings()
settings.scenario = PerformanceOnly()
lg = LoadGenerator(settings)
lg.load(Backend())

Summary

ONNX Runtime offers a more comprehensive solution for ML model deployment across various frameworks and hardware, while MLCommons Inference focuses on benchmarking and standardizing ML inference performance. ONNX Runtime is better suited for production environments, while MLCommons Inference is ideal for performance testing and comparisons across different ML systems.

Pros of ONNX

• Broader ecosystem support and wider adoption across various frameworks and tools
• More comprehensive model representation, supporting a wider range of operations and architectures
• Active development with frequent updates and improvements

Cons of ONNX

• Steeper learning curve due to its more complex architecture and extensive feature set
• May introduce overhead for simpler use cases or when working with specific frameworks

Code Comparison

ONNX example:

import onnx

# Create an ONNX model
model = onnx.ModelProto()
# Add nodes, inputs, outputs, etc.
onnx.save(model, "model.onnx")

MLCommons Inference example:

from mlperf_inference_src.loadgen import *

# Configure and run inference
settings = TestSettings()
scenario = TestScenario.SingleStream
load_generator = GenerateTest(settings, scenario)

While ONNX focuses on model representation and interoperability, MLCommons Inference emphasizes benchmarking and standardized testing for inference workloads. ONNX provides a more versatile format for exchanging models between different frameworks, while MLCommons Inference offers a structured approach to measuring and comparing inference performance across various hardware and software configurations.

Pros of transformers

• Extensive library of pre-trained models for various NLP tasks
• User-friendly API with easy-to-use abstractions for fine-tuning and inference
• Active community and frequent updates with state-of-the-art models

Cons of transformers

• Primarily focused on NLP tasks, less versatile for other domains
• Can be resource-intensive for large models, requiring significant computational power

Code comparison

transformers:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)

inference:

import mlperf_loadgen as lg

settings = lg.TestSettings()
settings.scenario = lg.TestScenario.SingleStream
settings.mode = lg.TestMode.PerformanceOnly
sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)
qsl = lg.ConstructQSL(total_sample_count, perf_sample_count, load_query_samples, unload_query_samples)
lg.StartTest(sut, qsl, settings)

The transformers library provides a high-level API for working with pre-trained models, while inference focuses on benchmarking and performance testing for machine learning models across various scenarios.