train-in-silence 0.1.0

LLM fine-tuning hardware planner with CLI and API.

Train in Silence

Stop comparing GPU prices. Start training.

中文

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You want to fine-tune an LLM. You open Vast.ai, RunPod, AWS -- three tabs, three pricing models, three different ways to describe a GPU. An hour later you're still in a spreadsheet and haven't written a single line of training code.

Train in Silence does that homework for you. Describe your workload once, and it returns the cheapest, fastest, and most balanced hardware options across cloud providers -- in seconds.

Quickstart

Option A: Ask Claude (recommended)

Install the library and register it as a tool in Claude Code:

pip install train-in-silence
claude mcp add tis --scope user -- tis-mcp

Then just ask in natural language:

> I want to QLoRA fine-tune Llama-13B on 100M tokens, budget under $20.
  Find me the best GPU options across Vast.ai, RunPod, and AWS.

Claude calls TIS behind the scenes and returns a structured recommendation -- no YAML, no config files, no manual comparison.

Option B: CLI

pip install train-in-silence
tis recommend examples/request.yaml

$ tis recommend examples/request.yaml

  Found 5 viable configurations
  Lowest cost: $4.32 | Fastest runtime: 2.1 hours

  #1 [cheapest]  RunPod 1x A6000 (48 GB)    $4.32 / 6.8 h
  #2 [fastest]   Vast.ai 2x A100 (80 GB)    $9.10 / 2.1 h
  #3 [balanced]  RunPod 1x A100 (80 GB)     $6.40 / 3.2 h
  ...

Note: Output above is illustrative. Actual results depend on live market data.

Use It Your Way

Channel	Command	Docs
CLI	tis recommend request.yaml	CLI Guide
REST API	uvicorn tis.api.server:app	API Reference
Claude Code	claude mcp add tis --scope user -- tis-mcp	MCP Guide
Claude Desktop	Add tis-mcp to claude_desktop_config.json	MCP Guide

Market Providers

Live pricing from three providers -- no manual data entry:

Provider	Source	Auth Required
Vast.ai	REST API	VAST_API_KEY
RunPod	GraphQL API	RUNPOD_API_KEY
AWS	Public EC2 Price List	None

If a provider is unreachable, TIS gracefully falls back to bundled sample data and marks the result accordingly. -> Provider details

Architecture at a Glance

YAML request -> Estimator -> Market Aggregator -> Optimizer -> Pareto Frontier -> Ranked Output
                  |                |                 |
              VRAM/FLOPs     Vast+RunPod+AWS    Cost vs. Time

Each recommendation shows where the data came from (live or sample) and flags any estimated fields -- no silent guesswork. -> Architecture deep-dive

Known Limitations

• Estimation model is fixed with no built-in calibration; future versions will calibrate using real runtimes.
• AWS availability uses an approximation method due to the lack of a real-time instance list API (flagged transparently).
• Upstream Provider API schema changes will require synchronized mapping updates.