Runpod Flash

Write code locally, test with flash run (dev server at localhost:8888), and flash automatically provisions and deploys to remote GPUs/CPUs in the cloud. Endpoint handles everything.

Setup

pip install runpod-flash                 # requires Python >=3.10

# auth option 1: browser-based login (saves token locally)
flash login

# auth option 2: API key via environment variable
export RUNPOD_API_KEY=your_key

flash init my-project                    # scaffold a new project in ./my-project

CLI

flash run                                # start local dev server at localhost:8888
flash run --auto-provision               # same, but pre-provision endpoints (no cold start)
flash build                              # package artifact for deployment (500MB limit)
flash build --exclude pkg1,pkg2          # exclude packages from build
flash deploy                             # build + deploy (auto-selects env if only one)
flash deploy --env staging               # build + deploy to "staging" environment
flash deploy --app my-app --env prod     # deploy a specific app to an environment
flash deploy --preview                   # build + launch local preview in Docker
flash env list                           # list deployment environments
flash env create staging                 # create "staging" environment
flash env get staging                    # show environment details + resources
flash env delete staging                 # delete environment + tear down resources
flash undeploy list                      # list all active endpoints
flash undeploy my-endpoint               # remove a specific endpoint

Endpoint: Three Modes

Mode 1: Your Code (Queue-Based Decorator)

One function = one endpoint with its own workers.

from runpod_flash import Endpoint, GpuGroup

@Endpoint(name="my-worker", gpu=GpuGroup.AMPERE_80, workers=5, dependencies=["torch"])
async def compute(data):
    import torch  # MUST import inside function (cloudpickle)
    return {"sum": torch.tensor(data, device="cuda").sum().item()}

result = await compute([1, 2, 3])

Mode 2: Your Code (Load-Balanced Routes)

Multiple HTTP routes share one pool of workers.

from runpod_flash import Endpoint, GpuGroup

api = Endpoint(name="my-api", gpu=GpuGroup.ADA_24, workers=(1, 5), dependencies=["torch"])

@api.post("/predict")
async def predict(data: list[float]):
    import torch
    return {"result": torch.tensor(data, device="cuda").sum().item()}

@api.get("/health")
async def health():
    return {"status": "ok"}

Mode 3: External Image (Client)

Deploy a pre-built Docker image and call it via HTTP.

from runpod_flash import Endpoint, GpuGroup, PodTemplate

server = Endpoint(
    name="my-server",
    image="my-org/my-image:latest",
    gpu=GpuGroup.AMPERE_80,
    workers=1,
    env={"HF_TOKEN": "xxx"},
    template=PodTemplate(containerDiskInGb=100),
)

# LB-style
result = await server.post("/v1/completions", {"prompt": "hello"})
models = await server.get("/v1/models")

# QB-style
job = await server.run({"prompt": "hello"})
await job.wait()
print(job.output)

Connect to an existing endpoint by ID (no provisioning):

ep = Endpoint(id="abc123")
job = await ep.runsync({"input": "hello"})
print(job.output)

How Mode Is Determined

Parameters	Mode
`name=` only	Decorator (your code)
`image=` set	Client (deploys image, then HTTP calls)
`id=` set	Client (connects to existing, no provisioning)

Endpoint Constructor

Endpoint(
    name="endpoint-name",                  # required (unless id= set)
    id=None,                               # connect to existing endpoint
    gpu=GpuGroup.AMPERE_80,               # single GPU type (default: ANY)
    gpu=[GpuGroup.ADA_24, GpuGroup.AMPERE_80],  # or list for auto-select by supply
    cpu=CpuInstanceType.CPU5C_4_8,        # CPU type (mutually exclusive with gpu)
    workers=5,                             # shorthand for (0, 5)
    workers=(1, 5),                        # explicit (min, max)
    idle_timeout=60,                       # seconds before scale-down (default: 60)
    dependencies=["torch"],                # pip packages for remote exec
    system_dependencies=["ffmpeg"],        # apt-get packages
    image="org/image:tag",                 # pre-built Docker image (client mode)
    env={"KEY": "val"},                    # environment variables
    volume=NetworkVolume(...),             # persistent storage
    gpu_count=1,                           # GPUs per worker
    template=PodTemplate(containerDiskInGb=100),
    flashboot=True,                        # fast cold starts
    execution_timeout_ms=0,                # max execution time (0 = unlimited)
)

gpu= and cpu= are mutually exclusive
workers=5 means (0, 5). Default is (0, 1)
idle_timeout default is 60 seconds
flashboot=True (default) -- enables fast cold starts via snapshot restore
gpu_count -- GPUs per worker (default 1), use >1 for multi-GPU models

NetworkVolume

NetworkVolume(name="my-vol", size=100)  # size in GB, default 100

PodTemplate

PodTemplate(
    containerDiskInGb=64,    # container disk size (default 64)
    dockerArgs="",           # extra docker arguments
    ports="",                # exposed ports
    startScript="",          # script to run on start
)

EndpointJob

Returned by ep.run() and ep.runsync() in client mode.

job = await ep.run({"data": [1, 2, 3]})
await job.wait(timeout=120)        # poll until done
print(job.id, job.output, job.error, job.done)
await job.cancel()

GPU Types (GpuGroup)

Enum	GPU	VRAM
`ANY`	any	varies
`AMPERE_16`	RTX A4000	16GB
`AMPERE_24`	RTX A5000/L4	24GB
`AMPERE_48`	A40/A6000	48GB
`AMPERE_80`	A100	80GB
`ADA_24`	RTX 4090	24GB
`ADA_32_PRO`	RTX 5090	32GB
`ADA_48_PRO`	RTX 6000 Ada	48GB
`ADA_80_PRO`	H100 PCIe (80GB) / H100 HBM3 (80GB) / H100 NVL (94GB)	80GB+
`HOPPER_141`	H200	141GB

CPU Types (CpuInstanceType)

Enum	vCPU	RAM	Max Disk	Type
`CPU3G_1_4`	1	4GB	10GB	General
`CPU3G_2_8`	2	8GB	20GB	General
`CPU3G_4_16`	4	16GB	40GB	General
`CPU3G_8_32`	8	32GB	80GB	General
`CPU3C_1_2`	1	2GB	10GB	Compute
`CPU3C_2_4`	2	4GB	20GB	Compute
`CPU3C_4_8`	4	8GB	40GB	Compute
`CPU3C_8_16`	8	16GB	80GB	Compute
`CPU5C_1_2`	1	2GB	15GB	Compute (5th gen)
`CPU5C_2_4`	2	4GB	30GB	Compute (5th gen)
`CPU5C_4_8`	4	8GB	60GB	Compute (5th gen)
`CPU5C_8_16`	8	16GB	120GB	Compute (5th gen)

from runpod_flash import Endpoint, CpuInstanceType

@Endpoint(name="cpu-work", cpu=CpuInstanceType.CPU5C_4_8, workers=5, dependencies=["pandas"])
async def process(data):
    import pandas as pd
    return pd.DataFrame(data).describe().to_dict()

Common Patterns

CPU + GPU Pipeline

from runpod_flash import Endpoint, GpuGroup, CpuInstanceType

@Endpoint(name="preprocess", cpu=CpuInstanceType.CPU5C_4_8, workers=5, dependencies=["pandas"])
async def preprocess(raw):
    import pandas as pd
    return pd.DataFrame(raw).to_dict("records")

@Endpoint(name="infer", gpu=GpuGroup.AMPERE_80, workers=5, dependencies=["torch"])
async def infer(clean):
    import torch
    t = torch.tensor([[v for v in r.values()] for r in clean], device="cuda")
    return {"predictions": t.mean(dim=1).tolist()}

async def pipeline(data):
    return await infer(await preprocess(data))

Parallel Execution

import asyncio
results = await asyncio.gather(compute(a), compute(b), compute(c))

Gotchas

Imports outside function -- most common error. Everything inside the decorated function.
Forgetting await -- all decorated functions and client methods need await.
Missing dependencies -- must list in dependencies=[].
gpu/cpu are exclusive -- pick one per Endpoint.
idle_timeout is seconds -- default 60s, not minutes.
10MB payload limit -- pass URLs, not large objects.
Client vs decorator -- image=/id= = client. Otherwise = decorator.
Auto GPU switching requires workers >= 5 -- pass a list of GPU types (e.g. gpu=[GpuGroup.ADA_24, GpuGroup.AMPERE_80]) and set workers=5 or higher. The platform only auto-switches GPU types based on supply when max workers is at least 5.
runsync timeout is 60s -- cold starts can exceed 60s. Use ep.runsync(data, timeout=120) for first requests or use ep.run() + job.wait() instead.