SDK Python : les GPU comme gestionnaire de contexte

pip install lowcostgpu            # Python ≥3.10, zero heavy deps
export LOWCOSTGPU_API_KEY=pg_live_…

import lowcostgpu

client = lowcostgpu.Client()                     # reads LOWCOSTGPU_API_KEY

# the public price sheet — no key needed for this call
for g in client.gpus():
    print(g.slug, g.price.on_demand)

# find capacity and deploy
offer = client.offers(gpu="rtx-4090", num_min=1)[0]
inst  = client.instances.create(
    machine_id=offer.machine_id,
    template="pytorch",
    disk_gb=60,
    type="interruptible",
)
inst.wait("running")
print(inst.hostname, inst.price_hr)

inst.stop()                                    # billing ends this second

with client.ephemeral(gpu="a100-sxm4", template="axolotl",
                      volume="ckpts:/ckpts") as gpu:
    gpu.exec("axolotl train qlora.yml")        # streams output
    gpu.download("/ckpts/adapter", "./out")    # billed at $0.01/GB
# ← destroyed here, success or crash — no forgotten $2/hr instances

import asyncio, lowcostgpu

async def frame(n: int):
    async with lowcostgpu.AsyncClient().ephemeral(
            gpu="rtx-4090", template="blender",
            volume="scene:/scene:ro") as gpu:
        await gpu.exec(f"blender -b /scene/shot.blend -f {n}")

# 8 frames in parallel — per-second billing makes this cost the same
asyncio.run(asyncio.gather(*[frame(n) for n in range(1, 9)]))