Enable anyone to process terabytes of data in minutes.

Burla makes it trivial to run Python functions on thousands of computers in the cloud. It's a package that only has one function:

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

The full power of the cloud, in an easy to use, open platform:

Burla enables anyone, even total beginners, to harness the full power of the cloud:

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

• Flexibility: Runs any Python function, inside any Docker container, on any hardware.

• Simplicity: Burla is just one function, with two required arguments.

Easily monitor workloads, and manage infrastructure from our open-source web dashboard:

How it works:

Burla only has one function: remote_parallel_map When called, it runs the given function, on every input in the given list, each on a separate computer.

from burla import remote_parallel_map

my_inputs = [1, 2, 3]

def my_function(my_input):
    print("I'm running on my own separate computer in the cloud!")
    return my_input
    
return_values = remote_parallel_map(my_function, my_inputs)

Running code in the cloud with Burla feels the same as coding locally:

• Anything you print appears in your local terminal.

• Exceptions thrown in your code are thrown on your local machine.

• Responses are quick, you run a million function calls in a couple seconds!

Features:

📦 Automatic Package Sync

Burla clusters automatically (and very quickly) install any missing python packages into all containers in the cluster.

🐋 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, unlocking new ways to simplify pipelines and architecture.

Build scalable data-pipelines using plain Python:

Fan code across thousands of machines, then combine results on one big machine. The network filesystem mounted at ./shared makes it easy to pass big data between steps.

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_ram=256)

The above example demonstrates a basic map-reduce operation.

Demo:

Try it out today:

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.