isat-tuner 0.9.2

ISAT: Inference Stack Auto-Tuner — 56-command CLI to convert any model to ONNX, auto-detect hardware, generate inference scripts, tune, profile, prune, deploy, and monitor on any GPU.

ISAT -- Inference Stack Auto-Tuner

One command to convert any model to ONNX, detect your hardware, and auto-tune inference -- on any GPU from any vendor.

ISAT is a production-grade CLI toolkit for ONNX inference optimization. It converts models from any framework (PyTorch, TensorFlow, JAX, HuggingFace, TFLite, SafeTensors) to ONNX, auto-detects your hardware (AMD, NVIDIA, Intel, Apple, Qualcomm), classifies it (iGPU/dGPU/APU/SoC), and generates copy-paste-ready inference configurations with runnable Python scripts. Then it jointly searches across memory strategy, kernel backend, precision, graph transforms, batch size, and thread tuning -- benchmarking each combination with thermal-aware cooldowns, statistical rigor, and Bayesian optimization.

pip install isat-tuner

# Convert any model to ONNX + auto-detect hardware + generate inference script:
isat onnx google/vit-base-patch16-224
isat onnx facebook/opt-1.3b
isat onnx model.pt --input-shape 1,3,224,224

# Detect your hardware and get instant recommendations:
isat tune

# Detect + recommend + auto-tune a specific model:
isat tune model.onnx

# Full production tuning with cloud profile:
isat tune model.onnx --profile cloud

Install note: On modern Linux (Ubuntu 23.04+, Debian 12+), bare pip install is blocked by PEP 668. Use pipx install isat-tuner instead -- it creates an isolated environment and puts isat on your PATH automatically. If you don't have pipx: sudo apt install pipx && pipx ensurepath.

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Why ISAT?

Deploying an ONNX model today means manually tweaking dozens of settings:

Setting	Choices	Impact
HSA_XNACK	0 or 1	Up to 30% on APUs
MIGRAPHX_DISABLE_MLIR	0 or 1	5-15% GEMM performance
MIGRAPHX_SET_GEMM_PROVIDER	default, rocblas, hipblaslt	10-25% on GEMM-heavy models
Precision	FP32, FP16, INT8	2-4x throughput
Batch size	1 to 256	Linear throughput scaling
Graph optimization level	0-99	5-20% latency reduction
Inter/intra op threads	1 to N	CPU-side parallelism

A single wrong choice can leave 40%+ performance on the table. With 6 dimensions and 4+ choices each, there are thousands of combinations. Nobody has time to test them all manually.

ISAT does it automatically.

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All 56 Commands

Model Conversion

Command	What it does
isat onnx	Convert any model (PyTorch, TF, JAX, HuggingFace, TFLite, SafeTensors) to ONNX + auto-tune

Auto-Tuning & Search

Command	What it does
isat tune	Auto-detect hardware + recommend + tune (works with or without a model)
isat profiles	List available tuning profiles (edge, cloud, latency, etc.)
isat init	Generate a default isat.yaml config file
isat batch	Find optimal batch size (latency vs throughput tradeoff)
isat shapes	Benchmark model across dynamic input shapes

Model Analysis & Inspection

Command	What it does
isat inspect	Deep fingerprint a model without benchmarking
isat diff	Structural diff between two ONNX models
isat fusion	Analyze operator fusion (fused vs unfused ops)
isat attention	Profile attention heads in transformer models
isat weight-sharing	Detect shared/tied weights across layers
isat visualize	Visualize ONNX graph (DOT, ASCII, histogram)
isat scan	Security and compliance scan of ONNX model
isat compat-matrix	Operator compatibility across providers

Benchmarking & Profiling

Command	What it does
isat profile	Decompose latency into load/compile/inference phases
isat llm-bench	LLM token throughput (TPS, TTFT, ITL with P95)
isat compiler-compare	Benchmark same model across ALL execution providers
isat stress	Sustained/burst/ramp stress testing
isat leak-check	Detect memory leaks during inference
isat power	Profile power efficiency (perf/watt, energy/inference)
isat thermal	Thermal throttle detection during inference
isat gpu-frag	GPU memory fragmentation analysis
isat warmup	Analyze warmup behavior, find optimal iterations

Model Optimization

Command	What it does
isat optimize	Optimize ONNX model (simplify, quantize, export)
isat prune	Prune model weights (magnitude/percentage/global)
isat surgery	ONNX graph surgery (remove/rename/extract nodes)
isat quant-sensitivity	Per-layer quantization sensitivity analysis
isat distill	Knowledge distillation planning for teacher models

Production Deployment

Command	What it does
isat serve	Launch REST API server (FastAPI)
isat triton	Generate Triton Inference Server config
isat canary	Canary deployment between two model versions
isat ensemble	Run model ensemble with aggregation
isat guard	Validate inference inputs against model schema
isat codegen	Generate standalone C++ inference code

Monitoring & Operations

Command	What it does
isat alerts	Inference alert rules engine (P99, error rate, GPU temp)
isat trace	OpenTelemetry-compatible request tracing
isat drift	Monitor output quality and detect confidence drift
isat regression	Performance regression detection across versions
isat replay	Record or replay inference requests

Planning & Cost

Command	What it does
isat cost	Estimate cloud inference cost
isat sla	Validate inference against SLA requirements
isat recommend	Hardware recommendation for a model
isat migrate	Generate migration plan between providers
isat memory	Estimate memory usage and predict OOM risk

Infrastructure & Utilities

Command	What it does
isat hwinfo	Print hardware fingerprint
isat doctor	Pre-flight system health and compatibility check
isat history	Show past tuning results from database
isat export	Re-generate reports from database
isat compare	Compare two configs with significance testing
isat abtest	A/B test two models with statistical rigor
isat snapshot	Capture environment state for reproducibility
isat cache	Manage compilation cache (MIGraphX/ORT)
isat zoo	List pre-tuned model configurations
isat download	Download ONNX model by name or URL
isat registry	Model version registry (register, promote, diff)
isat pipeline	Profile multi-model inference pipeline

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Supported Conversion Formats (isat onnx)

Source Format	Extensions / IDs	Conversion Backend
HuggingFace	org/model-name, hf://...	optimum.exporters.onnx (primary), torch.onnx.export (fallback)
PyTorch	.pt, .pth, .bin	torch.onnx.export
TensorFlow	.pb, SavedModel/ dirs	tf2onnx
TFLite	.tflite	tflite2onnx / tf2onnx
JAX	.jax, .msgpack	jax2onnx / jax2tf + tf2onnx
SafeTensors	.safetensors	safetensors + torch.onnx.export
ONNX	.onnx	Passthrough (optional onnxsim simplification)

Validated Models

Model	Type	Params	Status
google/vit-base-patch16-224	Vision Transformer	86.6M	PASS
openai/clip-vit-base-patch32	Multimodal (CLIP)	151.3M	PASS
facebook/detr-resnet-50	Object Detection	41.6M	PASS
Salesforce/blip-image-captioning-base	Image Captioning	196.2M	PASS
distilgpt2	LLM (small)	81.9M	PASS
facebook/opt-1.3b	LLM (1.3B)	1,315.7M	PASS

---

Installation

# From PyPI
pip install isat-tuner

# From GitHub (latest)
pip install git+https://github.com/SID-Devu/isat-tuner.git

# With all optional features
pip install "isat-tuner[all]"

# Model conversion (HuggingFace, PyTorch, TensorFlow)
pip install "isat-tuner[convert]"       # All conversion backends
pip install "isat-tuner[convert-hf]"    # HuggingFace only (optimum)
pip install "isat-tuner[convert-pt]"    # PyTorch only
pip install "isat-tuner[convert-tf]"    # TensorFlow only

# Platform-specific
pip install "isat-tuner[rocm]"      # ROCm GPU support
pip install "isat-tuner[cuda]"      # NVIDIA CUDA support
pip install "isat-tuner[server]"    # REST API server
pip install "isat-tuner[bayesian]"  # Bayesian optimization (scipy)

# Development
git clone https://github.com/SID-Devu/isat-tuner.git
cd isat && pip install -e ".[dev,all]"

---

Quick Start

Convert Any Model to ONNX

# HuggingFace models (auto-detects architecture)
isat onnx google/vit-base-patch16-224               # Vision Transformer
isat onnx openai/clip-vit-base-patch32               # Multimodal (CLIP)
isat onnx facebook/detr-resnet-50                    # Object Detection
isat onnx Salesforce/blip-image-captioning-base      # Image Captioning
isat onnx distilgpt2                                 # LLM (small)
isat onnx facebook/opt-1.3b                          # LLM (1.3B params)

# Local models
isat onnx model.pt --input-shape 1,3,224,224         # PyTorch
isat onnx saved_model/                               # TensorFlow SavedModel
isat onnx model.tflite                               # TFLite
isat onnx weights.safetensors --input-shape 1,3,224,224  # SafeTensors

# Convert only (skip auto-tune)
isat onnx facebook/opt-1.3b --no-tune

# Simplify ONNX graph after conversion
isat onnx model.pt --simplify --input-shape 1,3,224,224

Auto-Tune & Benchmark

# One-command auto-tune
isat tune model.onnx --warmup 3 --runs 5 --cooldown 60

# Use a deployment profile
isat tune model.onnx --profile edge
isat tune model.onnx --profile cloud

# Bayesian optimization (smarter than grid search)
isat tune model.onnx --bayesian --max-configs 20

# Hardware-only detection (no model needed)
isat tune

Analyze & Optimize

# Inspect model
isat inspect model.onnx

# Check your hardware
isat hwinfo

# System health check
isat doctor

# LLM token benchmarking
isat llm-bench model.onnx --seq-lengths 32,64,128,256

# Compare across all available providers
isat compiler-compare model.onnx

# Prune a model
isat prune model.onnx --strategy magnitude --sparsity 0.5

# Analyze operator fusion
isat fusion model.onnx

# Generate C++ inference code
isat codegen model.onnx --output-dir cpp_build/

Deploy & Monitor

# Canary deployment (safe model rollout)
isat canary baseline.onnx candidate.onnx

# Monitor output drift
isat drift model.onnx

# Graph surgery (remove Identity/Dropout nodes)
isat surgery model.onnx --remove-op Identity --remove-op Dropout

# Launch REST API
isat serve --port 8000

---

Search Dimensions

1. Memory Strategy

Config	Environment	When to use
xnack0_default	HSA_XNACK=0	Discrete GPUs, no demand paging
xnack1_default	HSA_XNACK=1	APUs, unified memory
xnack1_coarse_grain	XNACK=1 + coarse-grain	Large models on APU
xnack1_oversubscribe	XNACK=1 + queue limit	Models exceeding VRAM

2. Kernel Backend

Config	Environment	When to use
mlir_default	(default)	General-purpose, fused kernels
rocblas_explicit	MIGRAPHX_DISABLE_MLIR=1	GEMM-heavy models
hipblaslt_explicit	MIGRAPHX_SET_GEMM_PROVIDER=hipblaslt	Latest GEMM tuning

3. Precision

Config	Method	Typical speedup
fp32_native	Original	Baseline
fp16_migraphx	MIGraphX built-in	1.5-2x
int8_qdq	ORT static quantization	2-4x

4. Graph Transforms

Config	Transform	Effect
raw_opt99	None + full ORT opt	Default
sim_opt99	onnxsim + full ORT opt	Remove dead ops
pinned_opt99	Freeze dynamic dims	Better kernel selection

5. Batch Size

Auto-explores powers of 2 up to GPU memory limit.

6. Thread Tuning

Explores inter/intra thread counts and sequential vs parallel execution modes.

7. Execution Provider (Multi-Platform)

Auto-detects available providers: MIGraphX, CUDA, TensorRT, OpenVINO, ROCm, DirectML, CPU.

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Deployment Profiles

Profile	Warmup	Runs	Cooldown	Priority	Use case
edge	3	10	30s	Latency	IoT, mobile, embedded
cloud	5	20	120s	Throughput	Serving, batch processing
latency	5	30	60s	P99	Real-time inference
throughput	3	15	120s	FPS	Max batch throughput
power	3	10	60s	Perf/watt	Battery, thermal-constrained
quick	1	3	15s	Latency	Fast exploration
exhaustive	5	50	180s	Latency	Leave no stone unturned
apu	3	10	60s	Latency	APU-specific optimization

---

Output & Reports

File	Description
isat_report.html	Interactive HTML dashboard
isat_report.json	Machine-readable results for automation
best_config.sh	Shell script -- source it to apply best env vars
isat_results.db	SQLite database of all historical results
config.pbtxt	Triton Inference Server config
isat.prom	Prometheus metrics
traces_*.json	OpenTelemetry-compatible trace export
isat_inference.cpp	Generated C++ inference code

---

REST API Server

isat serve --port 8000

Endpoint	Method	Description
/api/v1/tune	POST	Submit a tuning job
/api/v1/jobs	GET	List all jobs
/api/v1/jobs/{id}	GET	Get job status + results
/api/v1/jobs/{id}/report	GET	Get JSON report
/api/v1/jobs/{id}/report/html	GET	Get HTML dashboard
/api/v1/inspect	POST	Fingerprint a model
/api/v1/hardware	GET	Get hardware fingerprint
/api/v1/history	GET	Query historical results
/health	GET	Health check

---

Docker

docker-compose up -d

# Or standalone
docker build -t isat .
docker run --device /dev/kfd --device /dev/dri --group-add video \
  -v ./models:/models isat tune /models/model.onnx

---

Using as a Library

from isat.converter.engine import convert, detect_format
from isat.auto_detect.detector import detect_hardware
from isat.auto_detect.recommender import generate_recommendations, format_report
from isat.auto_detect.script_gen import save_script
from isat.fingerprint import fingerprint_hardware, fingerprint_model
from isat.search import SearchEngine
from isat.pruning.pruner import ModelPruner
from isat.fusion.analyzer import FusionAnalyzer
from isat.guard.validator import InputGuard
from isat.inference_cache.cache import InferenceCache

# Convert any model to ONNX
result = convert("google/vit-base-patch16-224", output_dir="./output")
print(result.onnx_path, result.size_mb)

# Auto-detect hardware + generate recommendations
hw = detect_hardware()
report = generate_recommendations(hw, result.onnx_path)
print(format_report(report))

# Generate runnable inference script
script_path = save_script(hw, result.onnx_path, "./output")

# Auto-tune
hw_fp = fingerprint_hardware()
model_fp = fingerprint_model("model.onnx")
engine = SearchEngine(hw_fp, model_fp, warmup=3, runs=5, cooldown=60)
candidates = engine.generate_candidates()

# Prune a model
pruner = ModelPruner("model.onnx")
result = pruner.prune(strategy="magnitude", sparsity=0.5)

# Analyze fusion
analyzer = FusionAnalyzer("model.onnx")
report = analyzer.analyze()

# Validate inputs before inference
guard = InputGuard(model_path="model.onnx")
result = guard.validate({"input": my_tensor})

# Cache inference results
cache = InferenceCache(max_memory_entries=1000, disk_cache_dir="./cache")

---

CI/CD Integration

# Fail CI if latency > 50ms or throughput < 100 fps
isat tune model.onnx --gate-latency 50 --gate-throughput 100
echo $?  # 0 = pass, 1 = fail

---

Architecture

isat/
├── cli.py                 # 56 subcommands
├── converter/             # Universal model-to-ONNX conversion engine
│   ├── engine.py          #   Format detection + dispatch
│   └── backends.py        #   HuggingFace, PyTorch, TF, JAX, TFLite, SafeTensors
├── auto_detect/           # Hardware auto-detection + inference recommendations
│   ├── detector.py        #   Cross-platform GPU/CPU detection
│   ├── recommender.py     #   Vendor-specific recipe generation
│   └── script_gen.py      #   Runnable Python inference script generator
├── fingerprint/           # Hardware + model fingerprinting
├── search/                # 7-dimension search engine + Bayesian optimization
├── benchmark/             # Runner, stats, thermal monitoring, multi-GPU
├── analysis/              # Outliers, significance, Pareto, regression
├── pruning/               # Magnitude/percentage/global weight pruning
├── distillation/          # Knowledge distillation planning
├── fusion/                # Operator fusion analysis
├── attention/             # Transformer attention head profiling
├── surgery/               # ONNX graph surgery (remove/rename/extract)
├── guard/                 # Input validation and schema enforcement
├── ensemble/              # Multi-model ensemble with aggregation
├── canary/                # Canary deployment with auto-rollback
├── alerts/                # Alert rules engine (P99, error rate, temp)
├── tracing/               # OpenTelemetry-compatible request tracing
├── inference_cache/       # LRU + disk inference result caching
├── replay/                # Record and replay inference requests
├── output_monitor/        # Confidence drift detection (KS test)
├── llm_bench/             # LLM token throughput (TPS, TTFT, ITL)
├── compiler_compare/      # Cross-provider benchmark comparison
├── codegen/               # ONNX to C++ code generator
├── weight_analysis/       # Weight sharing detection
├── continuous_profiler/   # Always-on production profiling
├── gpu_frag/              # GPU memory fragmentation analysis
├── batching/              # Dynamic request batching engine
├── scanner/               # ONNX security/compliance scanner
├── compat_matrix/         # Operator compatibility matrix
├── thermal/               # Thermal throttle detection
├── quant_sensitivity/     # Per-layer quantization sensitivity
├── pipeline/              # Multi-model pipeline optimizer
├── recommend/             # Hardware recommendation engine
├── registry/              # Model version registry
├── regression/            # Performance regression detector
├── optimizer/             # Graph transforms + quantization
├── profiler/              # Latency decomposition
├── cost/                  # Cloud cost estimation
├── sla/                   # SLA validation
├── memory/                # Memory planning + OOM prediction
├── power/                 # Power efficiency profiling
├── health/                # System health checks
├── cache/                 # Compilation cache management
├── migration/             # Provider migration planning
├── warmup/                # Warmup analysis
├── shapes/                # Dynamic shape benchmarking
├── hub/                   # Model download from HuggingFace/ONNX Zoo
├── scheduler/             # Adaptive batch scheduling
├── snapshot/              # Environment snapshotting
├── abtesting/             # A/B testing framework
├── visualizer/            # Graph visualization (DOT, ASCII)
├── stress/                # Stress testing + memory leak detection
├── notifications/         # Webhook, Slack, console notifications
├── server/                # FastAPI REST API
├── integrations/          # Triton, Prometheus, CI/CD
├── database/              # SQLite results database
├── report/                # JSON, HTML, console reports
├── config/                # YAML/JSON config loader
├── profiles/              # 8 deployment profiles
├── model_zoo.py           # Pre-tuned model configurations
├── plugins.py             # Plugin system with lifecycle hooks
├── retry.py               # Exponential backoff retry logic
└── utils/                 # sysfs, rocm, onnx utilities

---

Requirements

• Python >= 3.9
• onnxruntime (CPU), onnxruntime-rocm (ROCm), or onnxruntime-gpu (CUDA)
• onnx, numpy

Optional:

• Conversion: optimum[exporters], torch, tensorflow, tf2onnx, safetensors, onnxsim
• Server: fastapi, uvicorn
• Optimization: scipy, onnxsim
• Monitoring: prometheus-client, pyyaml, jinja2

---

Version History

Version	Date	Highlights
v0.9.1	May 2026	Universal model converter (isat onnx), 6-model E2E validation, PyTorch 2.9+ compat, multi-modal export (56 commands)
v0.8.x	Apr 2026	Auto-detect hardware, generate inference scripts, Windows DirectML + MIGraphX via WinML, cross-platform GPU detection
v0.7.x	Apr 2026	Pruning, distillation, fusion analysis, LLM bench, compiler comparison, replay, drift monitor, codegen (55 commands)
v0.6.0	Apr 2026	Tracing, canary deploy, alerts, graph surgery, caching, input guard, ensemble, GPU frag (45 commands)
v0.5.0	Apr 2026	Regression detector, security scanner, compat matrix, thermal monitor, quant sensitivity, pipeline optimizer, HW recommender, model registry (38 commands)
v0.4.0	Apr 2026	Dynamic shapes, model hub, power profiler, memory planner, A/B testing, graph visualizer, env snapshot, batch scheduler (30 commands)
v0.3.0	Apr 2026	Latency profiler, cost estimator, SLA validator, health checker, migration tool, notifications (22 commands)
v0.2.0	Apr 2026	Config system, model optimization, stress testing, plugin system, model zoo (14 commands)
v0.1.0	Apr 2026	Initial release: auto-tuning, Bayesian search, multi-provider support (9 commands)

---

Citation

@software{isat_tuner,
  author = {Sudheer Ibrahim Daniel Devu},
  title = {ISAT: Inference Stack Auto-Tuner},
  year = {2026},
  version = {0.9.1},
  url = {https://github.com/SID-Devu/isat-tuner},
  license = {Apache-2.0}
}

---

License

Apache 2.0 -- see LICENSE

Copyright 2026 Sudheer Ibrahim Daniel Devu