elizaos--eliza/packages/training/scripts/cloud

Cloud-GPU one-line runner — run-on-cloud.sh

One command to rent a GPU, run an Eliza-1 task on it, pull the evidence back into the repo, and tear the instance down. It fails closed: it will not provision a paid instance unless you pass --yes-i-will-pay and the relevant API-key env var is set. --dry-run prints the provisioning plan and spends nothing.

This wraps existing primitives — it does not duplicate them:

Provider	What it uses
vast	dispatch-vast.sh, the vastai CLI (pip install --user vastai), VAST_API_KEY
--task train --provider vast	delegates to ../train_vast.sh provision-and-train (its GPU mapping, checkpoint pull, teardown)
--task train --provider nebius	delegates to ../train_nebius.sh full — H200 (gpu-h200x1 for 2b/4b/9b, gpu-h200x2 + FSDP for 27b/27b-256k); requires NEBIUS_PROJECT_ID. Emergency fallback; Vast is canonical.
nebius + kernel-verify/bench	dispatch-nebius.sh, delegating VM lifecycle to ../train_nebius.sh

The existing cloud backend abstraction (../lib/backends/base.py, ../cloud_run.py) is the place to add Nebius/RunPod/Lambda for the provision/search/status/teardown primitives; run-on-cloud.sh is the task-oriented front door on top of it.

Required env vars

Var	Needed for	Notes
VAST_API_KEY	any vast provisioning	or run vastai set api-key <key> once (persists to ~/.config/vastai/vast_api_key)
SSH_PUBKEY	any vast provisioning	path to your ssh pubkey; default ~/.ssh/id_ed25519.pub (or --ssh-pubkey)
HF_TOKEN / HUGGING_FACE_HUB_TOKEN	--task train (gated dataset/model repos)	forwarded by train_vast.sh
NEBIUS_*	--task train --provider nebius (fallback)	see ../train_nebius.sh
ELIZA_MTP_SMOKE_MODEL	--task kernel-verify graph smoke (optional)	path to a smoke GGUF; without it the runner does fixture-parity only and the emitted JSON is passRecordable: false (NOT a runtime-ready record)

Literal invocations

# Build the linux-x64-cuda-fused runtime on an H100 (llama-server +
# libelizainference + ggml-cuda kernels), ldd-self-check, emit a small
# build-evidence JSON into packages/inference/verify/build-results/.
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider vast --task build --gpu h100 --yes-i-will-pay

# Kernel verification on an H100 — build linux-x64-cuda, cuda-verify +
# cuda-verify-fused fixture parity, then (if --smoke-model) cuda_runner.sh
# --report; pulls JSON into packages/inference/verify/hardware-results/.
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider vast --task kernel-verify --gpu h100 --yes-i-will-pay

# Same, with a graph-smoke model so the JSON is a recordable runtime-ready record:
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider vast --task kernel-verify --gpu h100 \
  --smoke-model /models/eliza-1-smoke.gguf --yes-i-will-pay

# CUDA e2e bench for the 2B tier on an RTX 4090:
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider vast --task bench --gpu rtx4090 --tier 2b --yes-i-will-pay

# Train the 27B tier on 2x B200 (delegates to train_vast.sh provision-and-train):
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider vast --task train --gpu b200 --tier 27b --yes-i-will-pay

# Train the 2B tier on a Nebius H200 (delegates to train_nebius.sh full):
NEBIUS_PROJECT_ID=project-… HUGGING_FACE_HUB_TOKEN=… \
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider nebius --task train --gpu h200 --tier 2b --yes-i-will-pay
# Plan only (no spend):
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider nebius --task train --gpu h200 --tier 2b --dry-run

# Plan only — prints what it WOULD provision, spends nothing:
bash packages/training/scripts/cloud/run-on-cloud.sh \
  --provider vast --task kernel-verify --gpu h100 --dry-run

Flags

Flag	Values	Default
--provider	vast | nebius	auto from tier-routing.json
--task	build | kernel-verify | bench | train	(required)
--gpu	h100 h200 a100 a100-80 rtx4090 rtx5090 l40s b200 blackwell6000	h100
--tier	2b 4b 9b 27b 27b-256k	2b
--ssh-pubkey	path	~/.ssh/id_ed25519.pub
--smoke-model	path to a GGUF	none (parity-only)
--yes-i-will-pay	(gate) — required for any real provisioning	off
--dry-run	print the plan, no spend	off

What lands back in the repo

Task	Output
kernel-verify	packages/inference/verify/hardware-results/cuda-linux-<gpu>-<date>.json
bench	packages/inference/verify/bench_results/cuda_<gpu>_<tier>_<date>.json
train	checkpoints pulled by train_vast.sh pull-checkpoints (see ../CLOUD_VAST.md)

Teardown / safety

• The runner sets an EXIT trap that calls vastai destroy instance <id>. If it dies hard, the instance id is written to packages/training/scripts/cloud/.run_on_cloud_instance_id — destroy it manually with vastai destroy instance "$(cat …/.run_on_cloud_instance_id)".
• --dry-run and the missing-API-key / missing---yes-i-will-pay paths exit non-zero before any vastai create.
• Image: nvidia/cuda:12.8.0-devel-ubuntu24.04 (12.8 toolkit → real sm_120 SASS for Blackwell; harmless on Hopper/Ampere).

When you DON'T need cloud

If this box's NVIDIA dGPU has been brought up by the operator, run the verify locally instead — see ../../inference/reports/porting/2026-05-11/cuda-bringup-operator-steps.md.