Burla

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Scale Python across 1000 CPUs or GPUs in 1 second.

Burla is a high-performance parallel processing library for data-teams that iterate quickly.
Run vector embeddings, inference, or preprocessing inside your cloud with instant feedback.

Burla only has one function:

from burla import remote_parallel_map

my_inputs = list(range(1000))

def my_function(x):
    print(f"[#{x}] running on separate computer")

remote_parallel_map(my_function, my_inputs)

This runs my_function on 1000 vms in less than one second:

The cleanest way to build data-pipelines that scale.

Zero special syntax. Change containers, hardware, or cluster-size automatically mid-workload.

Burla scales up to 10,000 CPUs in a single function call, supports GPUs, and any Docker container.
Pipelines built with Burla are simpler, more maintainable, faster, and more fun to develop!

remote_parallel_map(process, [...], image="osgeo/gdal:latest")
remote_parallel_map(aggregate, [...], func_cpu=64)
remote_parallel_map(predict, [...], func_gpu="A100")

This creates a pipeline like:

Monitor progress in the dashboard:

Cancel bad runs, filter logs to watch individual inputs, or monitor output files in the UI.

How it works:

Remote development, local feel. With Burla hardware is defined 

return_values = remote_parallel_map(my_function, my_inputs)

When functions are run with remote_parallel_map:

  • Anything they print appears locally (and inside the dashboard).
  • Any exceptions are thrown locally.
  • Any packages or local modules are (very quickly) cloned on remote machines.
  • Code starts running in under one second! Even with millions of inputs or thousands of machines.

Features:

📦 Automatic Package Sync

Burla automatically (and very quickly) clones your Python packages on every remote machine where code is executed.

🐋 Custom Containers

Easily run code in any Docker container.
Public or private, just paste an image URI in the settings, then hit start!

📂 Network Filesystem

Need to get big data into/out of the cluster? Burla automatically mounts a cloud storage bucket to a folder in every container.

⚙️ Variable Hardware Per-Function

The func_cpu and func_ram args make it possible to assign big hardware to some functions, and less to others.

Pricing:

Free for Hobbyists. Compute prices the same as Google Cloud.

Self-Hosted: (in your cloud)

Managed: (in Burla's cloud)

✔ $100/month per Enterprise User.✔ $100/month per Enterprise User.
✅ Free for all non-commercial use.✅ 100% Identical pricing to Google Cloud.
✅ $500 in free credits for qualified users.

Getting Started:

Self-Host with one command:

Run burla install to deploy our self-hosted web-service.
Learn more about permissions and installation in our getting started guide:

/landing-page/docs/quickstart#quickstart-self-hosted-runs-in-your-cloud /landing-page/docs/quickstart#quickstart-self-hosted-runs-in-your-cloud

Try the 1000-CPU Quickstart, it's free and takes 2 minutes:

  1. Sign in using your Google or Microsoft account.
  2. Run our quickstart in this Google Colab notebook:
Google Colab colab.research.google.com

Questions?
Schedule a call, or email jake@burla.dev. We're always happy to talk.