krauncher 0.1.1

Run Python functions and notebook cells on the cheapest suitable remote GPU — per-task billing, no instances

Krauncher

Run your training script on a remote GPU. Nothing more.

Krauncher is a minimal Python library for researchers who have a working local script and need a GPU — not a platform.

Website & API keys: krauncher.com

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Quickstart

pip install krauncher
export CAS_API_KEY="cas_..."        # krauncher.com → Account → API Keys

Requires Python 3.11+.

import asyncio
from krauncher import KrauncherClient

client = KrauncherClient()           # reads CAS_API_KEY / CAS_BROKER_URL from env or .env

@client.task(vram_gb=1, timeout=120)
def multiply(size: int):
    import numpy as np               # imports go INSIDE the function
    a, b = np.random.rand(size, size), np.random.rand(size, size)
    return {"mean": float((a @ b).mean())}

async def main():
    handle = await multiply(size=1000)   # submit → TaskHandle
    print("task:", handle.task_id)
    result = await handle                # await the handle → TaskResult
    print("output:", result.output)
    print("gpu:", result.actual_gpu, "·", f"{result.execution_time_sec:.1f}s")

asyncio.run(main())

The decorated function becomes async: calling it submits the task and returns a TaskHandle; awaiting the handle (or await handle.wait(...)) returns a TaskResult.

Using an LLM / coding agent? Read AGENTS.md — a single accurate reference of the API, parameters, result fields, errors and constraints. Runnable examples live in tutorial/.

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The problem with serverless ML platforms

Serverless orchestration platforms are genuinely impressive pieces of infrastructure. They handle container builds, secret management, artifact storage, scheduling, persistent volumes, and team dashboards.

They also charge you for all of it — whether you use it or not.

If you're fine-tuning a small model, running ablations, or iterating on a research experiment with a dataset under 2 GB, you're likely paying for an orchestration layer you don't need.

Krauncher does less, on purpose. It runs your existing Python function on a remote GPU, returns the result, and gets out of the way.

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What Krauncher is (and isn't)

Good fit:

• Fine-tuning, LoRA, small-scale experiments with training datasets up to ~2 GB
• Researchers who already have a working local script
• Anyone tired of rewriting their code to fit a platform's abstractions
• Teams where "infrastructure" means one person and a credit card

Not the right tool if:

• You need managed versioned artifact storage
• Your team requires persistent shared volumes across runs
• Your dataset is hundreds of GBs with complex multi-node sharding
• You want a UI dashboard for experiment tracking

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How it works

Add a decorator. Await your function. Get a result. Your existing code doesn't change — no base images, no volume mounts, no platform imports.

import asyncio
from krauncher import KrauncherClient

client = KrauncherClient()

@client.task(gpu_name="RTX4090", group_id="mistral-run", timeout=3600)
def finetune():
    from transformers import AutoModelForCausalLM, Trainer, TrainingArguments
    from datasets import load_dataset

    # Weights download to worker storage on first run (~15 GB for 7B);
    # later runs in the same group_id reuse the cached weights.
    model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
    dataset = load_dataset("tatsu-lab/alpaca", split="train[:2000]")

    # ... your training logic, unchanged from local ...

    model.save_pretrained("/tmp/output")
    # Worker storage is ephemeral — sync checkpoints out before returning.
    upload_to_s3("/tmp/output", "my-checkpoints/run-1")
    return {"status": "done", "checkpoint": "s3://my-checkpoints/run-1"}

async def main():
    result = await finetune()        # submit and wait
    print(result.output)

asyncio.run(main())

The decorated function is async — always call it from an async context and await the handle (which submits and waits). See the Quickstart for the canonical shape.

Choosing a GPU

Decorator argument	Effect
vram_gb=24	Require at least 24 GB VRAM
gpu_name="H100"	Require a specific model (case-insensitive substring)
gpu_arch="Ada"	Require a GPU architecture
(omit vram_gb)	Auto-classify: the analyzer inspects your code and picks the VRAM tier for you

Leaving vram_gb unset is the recommended default — Krauncher analyzes your code statically and sizes the GPU automatically.

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Security model

Krauncher doesn't store anything. Your API key and training code are encrypted on your machine before leaving it, and decrypted only inside the ephemeral worker. The relay that routes your jobs cannot read the payload — it doesn't have the keys.

What	Visible to Krauncher
Your storage credentials	No
Your training code	No
Your model weights/outputs	No
Job timing and GPU type	Yes

This isn't a feature we added. It's a consequence of not wanting to be in the data custody business. E2E encryption is on by default (CAS_ENCRYPT=true).

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Data locality

Tasks with the same group_id are routed to the same physical host, so whatever your first run downloaded to local NVMe is still there for the next.

@client.task(gpu_name="RTX4090", group_id="my-experiment-v1")
def train_epoch(epoch: int):
    import os
    cache_path = "/tmp/dataset.bin"
    if not os.path.exists(cache_path):
        download_from_s3("my-bucket", "dataset.bin", cache_path)
        # subsequent tasks in this group skip this step
    run_training(cache_path, epoch=epoch)
    return {"epoch": epoch, "status": "complete"}

async def main():
    for epoch in range(10):
        await train_epoch(epoch=epoch)

For larger or registered datasets, use the data bridge (data_urls= / data=), which downloads into /data inside the sandbox — see tutorial/06 and tutorial/15.

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Inspecting a finished task

After a task completes, the broker keeps a structured record — the same one the web UI renders on the task detail page.

task   = await client.get_task(task_id)         # what GET /tasks/{id} returns
report = await client.get_task_report(task_id)  # task + extended report

get_task returns status, timing breakdown (queue / download / pip / setup / execution), classification, costs, GPU and worker specs, and the result.

get_task_report adds an extended report field: peak/average GPU utilization, peak VRAM, the actual GPU's hardware specs, and an estimated time/cost comparison across all known GPUs at the worker's measured host capabilities. It is intended as feedback for an LLM author of the user code — pure data, no interpretation.

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Examples

Numbered, runnable tutorials in tutorial/:

#	File	Demonstrates
01	01_remote_simple.py	Minimal submit + await
02	02_remote_with_deps.py	pip= dependencies in the sandbox
03	03_error_handling.py	Catching TaskError / remote tracebacks
04	04_timeout.py	Execution timeout behaviour
05	05_task_groups.py	group_id host affinity
06	06_data_bridge.py	data_urls= downloads into /data
09	09_streaming_logs.py	Live logs via wait(on_log=...)
10	10_progress_bar.py	Progress reporting
11	11_e2e_encryption.py	End-to-end encryption
12	12_helper_functions.py	Shipping helper functions with the task
13	13_bert_finetune.py	Real ML code → analyzer classification
15	15_data_sources_s3.py	Registered S3 data sources
17	17_multiphase_training.py	Multi-phase training in one group
18	18_resnet152_food101.py	ResNet-152 on Food-101
19	19_huggingface_dataset.py	HuggingFace dataset bridge
20	20_bert_imdb.py	BERT fine-tuning on IMDB
21	21_qwen25_7b_lora_alpaca.py	Qwen2.5-7B LoRA fine-tuning
22	22_qwen25_7b_inference_gsm8k.py	Qwen2.5-7B inference
23	23_gnn_node_classification_cora.py	GCN node classification
30+	30_…–36_…	LLM inference and batched inference

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Install

pip install krauncher
export CAS_API_KEY="your_api_key"

Requires Python 3.11+.

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License

MIT