NVIDIA AITune Examples

This directory contains practical examples demonstrating how to use NVIDIA AITune to tune different types of AI models for inference performance.

ResNet

Computer Vision - Image Classification

Shows how to tune ResNet models for image classification tasks. This example demonstrates model tuning and inference tuning for convolutional neural networks.

• Location: ResNet
• Model: ResNet50 image classification
• Use Case: Optimizing CNN models for computer vision tasks
• Key Features:
  • Model tuning with AITune
  • Image classification inference
  • Performance comparison before/after tuning
• More Info:
  • https://huggingface.co/microsoft/resnet-50

StableDiffusion

Generative AI - Text-to-Image

Demonstrates tuning of Stable Diffusion models for text-to-image generation. This example shows how to tune diffusion models for faster and more efficient image generation.

• Location: StableDiffusion
• Model: Stable Diffusion 3 from HuggingFace
• Use Case: Optimizing text-to-image diffusion models
• Key Features:
  • Diffusion pipeline tuning
  • Customizable image generation parameters
  • Text prompt-based image synthesis
• More Info:
  • https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers

FLUX

Generative AI - Advanced Text-to-Image

Shows tuning of the FLUX text-to-image model, demonstrating advanced diffusion model tuning techniques for high-quality image generation.

• Location: FLUX
• Model: FLUX.1-dev from Black Forest Labs
• Use Case: Optimizing state-of-the-art text-to-image models
• Key Features:
  • Advanced diffusion model tuning
  • High-quality image generation
  • Efficient inference pipeline tuning
• More Info:
  • https://huggingface.co/black-forest-labs/FLUX.1-dev

ParakeetCTC

Speech AI - Automatic Speech Recognition

Demonstrates tuning of ASR (Automatic Speech Recognition) models using NVIDIA’s Parakeet CTC model for speech-to-text conversion.

• Location: ParakeetCTC
• Model: NVIDIA Parakeet CTC 0.6B
• Use Case: Optimizing speech recognition models
• Key Features:
  • ASR model tuning
  • Audio-to-text transcription
  • NVIDIA NeMo framework integration
• More Info:
  • https://huggingface.co/nvidia/parakeet-ctc-0.6b
  • https://docs.nvidia.com/nemo-framework/user-guide/24.09/nemotoolkit/asr/models.html

ParakeetRNNT

Speech AI - Automatic Speech Recognition

Demonstrates tuning of ASR (Automatic Speech Recognition) models using NVIDIA’s Parakeet RNNT model for speech-to-text conversion.

• Location: ParakeetRNNT
• Model: NVIDIA Parakeet RNNT 1.1B
• Use Case: Optimizing speech recognition models
• Key Features:
  • ASR model tuning
  • Audio-to-text transcription
  • NVIDIA NeMo framework integration
• More Info:
  • https://huggingface.co/nvidia/parakeet-rnnt-1.1b
  • https://docs.nvidia.com/nemo-framework/user-guide/24.09/nemotoolkit/asr/models.html

ESM2

Text AI - Advanced Text Embedding

Demonstrates tuning of ESM2 model for text embedding tasks.

• Location: ESM2
• Model: ESM2 from HuggingFace
• Use Case: Optimizing text embedding models
• Key Features:
  • Text embedding tuning
  • Text embedding inference
  • HuggingFace integration
• More Info:
  • https://huggingface.co/esm/esm2-t12-100M-UR50S

E5Large

Text AI - Advanced Text Embedding

Demonstrates tuning of E5Large model for text embedding tasks.

• Location: E5Large
• Model: E5Large from HuggingFace
• Use Case: Optimizing text embedding models
• Key Features:
  • Text embedding tuning
  • Text embedding inference
  • HuggingFace integration
• More Info:
  • https://huggingface.co/intfloat/e5-large-v2

LLM

Large Language Models - Text Generation

Demonstrates tuning of Large Language Models for text generation tasks. This example shows how to optimize LLMs for efficient inference with KV cache support.

• Location: LLM
• Model: Microsoft Phi-3-mini-4k-instruct from HuggingFace
• Use Case: Optimizing LLMs for text generation and inference
• Key Features:
  • LLM model tuning with AITune
  • Static and dynamic KV cache optimization
  • Prefill and decode phase optimization
  • HuggingFace integration
• More Info:
  • https://huggingface.co/microsoft/Phi-3-mini-4k-instruct

JIT Tuning

Just-In-Time Model Tuning

Demonstrates how to use NVIDIA AITune with JIT (Just-In-Time) tuning that requires no code changes. This example shows how to enable automatic tuning through environment variables without modifying existing code.

• Location: JitTuning
• Models: Various models including ResNet, Stable Diffusion 3, Stable Diffusion XL, and FLUX
• Use Case: Zero-code-change automatic model optimization
• Key Features:
  • No-code JIT tuning via environment variables
  • Automatic tuning without imports or code modifications
  • Support for multiple model types (ResNet, diffusion models)
  • Simple enable/disable through AUTOWRAPT_BOOTSTRAP variable
• More Info:
  • https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers
  • https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
  • https://huggingface.co/black-forest-labs/FLUX.1-dev

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Each example includes:

• Complete setup instructions
• Usage examples with CLI commands
• Model-specific tuning parameters
• AI Dynamo deployment instructions where supported

To get started, navigate to any example directory and follow the README instructions for that specific model type.