tunefit 0.1.0

Preflight-check whether a machine can train an AI workload before the run starts.

TuneFit

TuneFit is the preflight check for AI training.

TuneFit predicts whether a local machine can train a target AI workload before the real run starts.

It is built for the point before a profiler helps and before a failed training run wastes time.

What TuneFit Does

• scans the local machine and runtime
• estimates whether a workload is trainable on that machine
• predicts peak VRAM, host RAM, and rough step time
• returns PASS, WARN, or FAIL
• points to likely bottlenecks
• suggests the next change to try

Why It Exists

Many training runs fail too late:

• out-of-memory on the first step
• unsupported CUDA, MPS, or precision combinations
• framework and driver mismatches
• technically valid runs that are too slow to be useful

TuneFit moves that answer forward.

Quick Example

tunefit scan --output system.json
tunefit estimate --spec workload.yaml --system system.json

Typical output:

TuneFit verdict: PASS
Target: mps on Apple Silicon Integrated GPU
Peak VRAM: 824.0 MB
Peak RAM: 563.2 MB
Step time: 215.4 ms
Throughput: 1188.59 tokens/sec

Current Scope

• CLI-first
• local-only
• CPU, NVIDIA CUDA, and Apple MPS
• PyTorch-first validation
• CNN and transformer workload templates

Commands

• tunefit scan
• tunefit estimate
• tunefit validate
• tunefit calibration list
• tunefit calibration prune

Source And Documentation

• Source: https://github.com/erdoganxarda/my-portfolio
• Product spec: https://github.com/erdoganxarda/my-portfolio/blob/main/docs/spec_v1.md
• Architecture: https://github.com/erdoganxarda/my-portfolio/blob/main/docs/architecture.md

Notes

TuneFit is intentionally lightweight and heuristic. It is designed to improve preflight decisions, not to replace a real profiler or guarantee exact runtime behavior for arbitrary custom training code.