pip install beam-client- Extremely Fast: Launch containers in under a second using a custom container runtime
- Parallelization and Concurrency: Fan out workloads to 100s of containers
- First-Class Developer Experience: Hot-reloading, webhooks, and scheduled jobs
- Scale-to-Zero: Workloads are serverless by default
- Volume Storage: Mount distributed storage volumes
- GPU Support: Run on our cloud (4090s, H100s, and more) or bring your own GPUs
- Create an account at https://beam.cloud
- Follow our Getting Started Guide
Spin up isolated containers to run LLM-generated code:
from beam import Image, Sandbox
sandbox = Sandbox(image=Image()).create()
response = sandbox.process.run_code("print('I am running remotely')")
print(response.result)Create an autoscaling endpoint for your custom model:
from beam import Image, endpoint
from beam import QueueDepthAutoscaler
@endpoint(
image=Image(python_version="python3.11"),
gpu="A10G",
cpu=2,
memory="16Gi",
autoscaler=QueueDepthAutoscaler(max_containers=5, tasks_per_container=30)
)
def handler():
return {"label": "cat", "confidence": 0.97}Replace your Celery queue with a simple decorator:
from beam import Image, task_queue
@task_queue(
image=Image(python_version="python3.11"),
cpu=1,
memory=1024,
)
def handler(images):
for image in images:
# Do something
passBeta9 is the open-source engine powering Beam, our fully-managed cloud platform. You can self-host Beta9 for free or choose managed cloud hosting through Beam.
We welcome contributions big or small. These are the most helpful things for us:
- Submit a feature request or bug report
- Open a PR with a new feature or improvement