What platforms does remote-gpu-trainer support?

It supports AutoDL, RunPod, vast.ai, Lambda, Paperspace, Chinese platforms, and bare SSH/Slurm/K8s boxes. Each platform has its own profile that defines exact paths, billing semantics, and spot preemption behavior.

When should I NOT use remote-gpu-trainer?

Don't use it for local single-GPU training or in-box multi-GPU DDP (use torchrun/accelerate instead), managed multi-cloud price-shopping (use SkyPilot), or zero-ops serverless inference (use Modal).

How does remote-gpu-trainer ensure results survive instance termination?

It uses a 6-phase workflow that includes checkpoint-to-durable-location plus unconditional load-latest-on-startup, which survives SSH drops, Slurm walltime kills, K8s reschedules, and spot preemption. An "Iron Law" blocks any stop/terminate action until checkpoints are pulled to local and verified by load with explicit user approval.

What are the key invariants for using remote-gpu-trainer?

Key invariants include: smoke-test on CPU before renting, trust loaded artifacts over log lines, know which storage survives stop vs. terminate per platform, monitor inode usage (df -i), and always checkpoint to durable location with unconditional load-on-startup.

Does remote-gpu-trainer handle multi-instance training?

Yes, it supports single or multi-instance deployments for training, evaluation, ablation sweeps, batch inference, and large data processing jobs on rented GPU instances.

Skill

Orchestrate GPU training jobs across cloud platforms end-to-end

Name: Orchestrate GPU training jobs across cloud platforms end-to-end
Availability: OnlineOnly
Author: Hanyuyuan6

Runs and babysits long GPU jobs on rented boxes: detach the work, keep results after preemption and stop the billing meter safely.

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Works with autodlrunpodvastailambdapaperspace

Hanyuyuan6

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Spark score

out of 100

Updated 13 days ago

Source checked Oct 28, 2026

Version 20.1.0

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Why it matters

Run deep learning experiments on owned or rented GPUs through the complete lifecycle-launch jobs that survive spot interruptions and billing traps, verify results are real effects rather than bugs or noise, and deliver reproducible figures with provenance locked to immutable evidence.

Outcomes

What it gets done

Launch detached training jobs on 8+ GPU platforms with platform-specific billing, disk, and spot handling

Monitor long-running jobs through four-layer durability checks and safe teardown sequences

Verify experimental results using 14-probe methodology to distinguish real effects from bugs and noise

Organize deliverables so every shipped number traces to single-source immutable evidence

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-remote-gpu-trainer | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Remote GPU Trainer helps deploy and babysit long-running GPU jobs on rented machines you do not own. It detaches the work, keeps results after preemption, and stops the meter safely, using a 6-phase lifecycle and one profile file per platform. Use it for long GPU jobs on rented or preemptible boxes where results must outlive the instance. It is not meant for provisioning a cluster.

What it does

Remote GPU Trainer deploys and babysits long-running GPU jobs on rented machines you do not own, on any platform, and gets the result off the machine before the billing meter or a preemption ends it. Its core idea is that you are a short-term tenant on someone else's hardware. The job is therefore to detach the work from your session, make the result outlive the instance, and stop the meter safely, rather than to provision a cluster.

The skill is platform-agnostic at its core and platform-specific only at the edges. The core is a fixed set of operating principles and a 6-phase lifecycle that hold everywhere. Each platform then gets its own profile file, profiles/<platform>.md, which owns every concrete path, proxy, billing verb and spot-instance behavior for that provider.

Its distinctive value is the ground that larger orchestration tools skip: Chinese cgroup-isolated rentals, cheap bare-SSH boxes, and the practical realities of disk budgeting, monitoring and teardown that make up most of the work on metered hardware.

When to use - and when NOT to

Use it when a long training or compute job must run on a rented or preemptible GPU box and the result has to survive the instance being stopped, preempted or billed out. It is not built for provisioning or managing a cluster; it targets short-term tenancy on machines you rent, where cost control and getting output off the box matter most.

Source README

Deploy and babysit long-running GPU jobs on rented boxes you don't own, across any platform, and get the result off the box before the meter or a preemption kills it. The core insight: you are a short-term tenant on someone else's machine - so the job is to detach the work, make the result outlive the instance, and stop the meter safely, not to provision a cluster.

This skill is platform-agnostic at the core, platform-specific at the edges: a fixed set of operating principles + a 6-phase lifecycle that hold everywhere, plus one profile per platform (profiles/<platform>.md) that owns every concrete path, proxy, billing verb, and spot semantic. Its defensible value is the union the big orchestrators skip: Chinese cgroup-isolated rentals + bare-SSH cheap boxes + the disk-budget / monitoring / teardown reality that is the job on metered hardware.

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