Scale Python across 1,000's of computers using one line of code.

Burla is an open-source Python package with only one function: remote_parallel_map Here's an example:

This realtime example runs my_function on 1,000 separate computers in one second!

Enable anyone to process terabytes of data in minutes, not days.

Burla is simple enough for anyone to learn, yet extremely scalable, and flexible:

• Scalable: See our demo where we process 2.4TB in 76s using 10,000 CPUs!

• Flexible: Runs any code, inside any Docker container, on any hardware like GPU's or TPU's.

Easily monitor long-running workloads, or manage compute resources in the dashboard.

How it works:

With Burla, running code in the cloud feels the same as coding on your laptop:

return_values = remote_parallel_map(my_function, my_inputs)

When functions are run using remote_parallel_map:

• Anything they print appears in your local terminal, and inside Burla's dashboard.

• Any exceptions in your code are thrown locally.

• Your local Python environment is quickly cloned on all remote workers.

• Code begins 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 in every remote machine where your code runs.

🐋 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 ./shared in every container.

⚙️ Variable Hardware Per-Function

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

Convert any workload into a fast, scalable data-pipeline:

Have a workload that takes forever to run?

By injecting many remote_parallel_map calls into your code, data-science teams have created programs that handle more data, and finish running in minutes instead of days.

The network filesystem at ./shared makes it trivial to process your data stored in cloud storage.

from burla import remote_parallel_map

# Run `process_file` on many small machines
results = remote_parallel_map(process_file, files)

# Combine results on one big machine
result = remote_parallel_map(combine_results, [results], func_cpu=64)

The above example demonstrates a basic map-reduce operation.

Get started in 2 minutes, with $50 in free compute.

There are two ways to host Burla:

1. In your cloud. Burla is open-source, and can be deployed with one command (currently Google-Cloud only). Click here to get started with self-hosted Burla.

2. In our cloud. First $1,000 in compute spend is free, try it now 👇

Signup | Burladocs.burla.dev

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Questions? Schedule a call, or email [email protected]. We're always happy to talk.