Scale Python across 1000 computers in 1 second. Using one line of code.

Burla is a package with only one function. Here's how it works:

from burla import remote_parallel_map

my_inputs = list(range(1000))

def my_function(x):
    print(f"I'm running on my own separate computer in the cloud! #{x}")

remote_parallel_map(my_function, my_inputs)

This runs my_function on 1000 vm's in the cloud, in 1 second:

☝️ Try this example now in Google Colab 🔗

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 with remote_parallel_map:

• Anything they print appears locally (and inside Burla's dashboard).

• Any exceptions are thrown locally.

• Any packages or local modules they use 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 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 big hardware to some functions, and less to others.

Convert any workload into a scalable data-pipeline:

Have a workload that takes forever to run?

By injecting many remote_parallel_map calls into their code, Data-Scientists, ML-Engineers, and Analysts have created programs that handle terabytes of data, and finish running in minutes.

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

Burla only takes 2 minutes to try!

Try Burla for free

1. ☝️ Sign in using your Google or Microsoft account.

2. Click the ⏻ Start button to boot some computers.

3. Scale Python over 1,000 CPU's in this Google Colab notebook!

Quick reminder: Burla is open-source and easy to self-host. Click here to deploy Burla in your Cloud.

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