Burla is the world's simplest distributed computing framework.
Easily scale ML‑pipelines, AI-inference, batch processing, or any other program.

Documentation · Getting started · API reference · Examples · Website

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Burla runs Python functions in parallel across thousands of CPUs or GPUs in your cloud using one function:

from burla import remote_parallel_map

def double(x):
    return x * 2

results = remote_parallel_map(double, range(1000), grow=True)

This code calls double on every item in range(1000), each in a separate container (1-CPU each) in your current cloud provider.

Build fully distributed applications in plain Python.

Specify different hardware, or a custom Docker image, for each function call at runtime.
remote_parallel_map can be nested to create composable distributed applications:

from burla import remote_parallel_map
 
def build_index(day):
    docs = remote_parallel_map(parse, pdfs(day), func_cpu=64)
    vecs = remote_parallel_map(embed, docs, func_gpu="A100", image="pytorch")
    return remote_parallel_map(index, [vecs])
 
remote_parallel_map(build_index, last_30_days, detach=True)

With detach=True this pipeline will run independently in the cloud.

Key features

• Fast iteration: Scale to 1,000 CPUs or GPUs in under a second on a warm cluster. Prints, exceptions, and results appear locally.
• Automatic env replication: Your local Python environment is automatically cloned on all remote workers in seconds.
• Up to 50% more efficient: Burla continuously adjusts concurrency keeping every machine saturated so jobs finish faster and cost less.
• Pipelines: Burla builds a live DAG showing how infrastructure changes throughout your distributed application.
• Your cloud: Run in your own AWS, Google Cloud, or Azure account. Share a deployed cluster with your team.

Monitor distributed workloads in the dashboard:

Track every function call, inspect logs and tracebacks, spot resource bottlenecks, and manage workloads across your entire team.

Get started

With Python 3.12+ & having signed into your cloud provider's CLI (aws, gcloud, az):

pip install burla
burla dashboard

Run the Python example above. grow=True starts VMs in your cloud and removes them when the job finishes.
To share dashboard access, or use background jobs, run burla deploy.

Setup guide · Examples · API reference

Contributing

Bug reports, feature requests, and contribution proposals are welcome in GitHub issues. Report security issues to security@burla.dev.

License

Licensed under FSL-1.1-Apache-2.0. Free to use except for competing commercial offerings; each version becomes Apache 2.0 after two years.

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Questions? Email jake@burla.dev or book a call, we're always happy to talk.