anycloud Python SDK
Submit jobs, run workloads on any cloud.
Install
pip install anycloud-sdk
Quick start
import anycloud
ac = anycloud.Client()
job = ac.submit("my-training:latest", gpu="h100:8", env={"LR": "0.01"})
job.wait()
print(job.logs())
Chaining jobs
prep = ac.submit("prep:latest")
prep.wait()
train = ac.submit("train:latest", gpu="h100:8", env={"LR": "0.01"})
train.wait()
eval_job = ac.submit("eval:latest")
eval_job.wait()
Fan-out / fan-in
from anycloud import Submission
split = ac.submit("split:latest")
split.wait()
shards = ac.submit_many([
Submission(image="worker:latest", env={"LR": lr})
for lr in ["0.1", "0.01", "0.001"]
])
shards.wait()
merge = ac.submit("merge:latest")
merge.wait()
Decorator (run a Python function remotely)
Skip the image-build loop — decorate a function, and AnyCloud clones your repo on the remote VM:
@anycloud.function(image="ghcr.io/acme/trainer:latest", gpu="h100:8")
def train(lr: float):
...
jobs = train.map([0.1, 0.01, 0.001]) # fan out across args
jobs.wait()
Decorator deployments clone your committed, pushed GitHub repo on the remote VM. Private repos work after anycloud login, or with GITHUB_TOKEN set to a token with repo read access. The image must include git. See reference.
Serve decorator (run a long-lived daemon)
Use @anycloud.serve for one long-running server process with a stable URL:
import os
import anycloud
@anycloud.serve(image="ghcr.io/acme/inference:latest", gpu="L40S:1")
def node():
import uvicorn
from myapp import app
uvicorn.run(app, host="0.0.0.0", port=int(os.environ["PORT"]))
server = node.start(id="model-a-001")
server.wait_running(timeout=600)
print(server.url)
The entrypoint must block. AnyCloud sets PORT=8088 by default; override it with env={"PORT": "<port>"}. The public URL is https://<deployment-id>.anycloud.sh. @anycloud.serve uses the same private-repo git-clone authentication as @anycloud.function.
The live SDK e2e suite includes serve-decorator coverage:
bash test/integration/e2e.sh sdk-python-serve
When a remote function fails, DeploymentFailedError.logs includes the labeled bootstrap error:
from anycloud import DeploymentFailedError
try:
train.submit(0.01).wait()
except DeploymentFailedError as e:
print(e.state, e.logs)
Buckets
Chain data between jobs using bucket handles. Output buckets are auto-created by the server; upload() auto-creates input buckets.
data = ac.bucket("training-data")
model = ac.bucket("model-output")
data.upload("~/datasets/imagenet") # auto-creates bucket
prep = ac.submit("prep:latest", input=data, output=model)
prep.wait()
train = ac.submit("train:latest", input=model, output=model, gpu="h100:8")
train.wait()
model.download("~/checkpoints/")
Bucket I/O streams through the local anycloud api server using a credential registered with anycloud credentials new — no extra installs needed. Use Client(credentials="<name>") so the SDK knows which credential to pass.
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