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Run any Python function on 1000 computers in 1 second.

Burla is the world's simplest cluster compute software. It's open-source, works with any hardware, and any docker container.

A fully fledged data-platform any team can learn in minutes:

Schedule jobs, create pipelines, scale your machine learning system, or your research. Burla is simple enough to learn in minutes, and can be installed with a single command.

How it works:

Burla only has one function:

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)

With Burla, running code in the cloud 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 pretty quick, you can call a million simple functions in a couple seconds.

Attach big hardware to functions that need it:

Zero config files, just simple arguments like func_cpu & func_ram.

from xgboost import XGBClassifier

def train_model(hyper_parameters):
    model = XGBClassifier(n_jobs=64, **hyper_parameters)
    model.fit(training_inputs, training_targets)
    
remote_parallel_map(train_model, parameter_grid, func_cpu=64, func_ram=256)

Simple, flexible pipelines:

Nest remote_parallel_map calls to build simple, massively parallel pipelines. Use background=True to fire and forget code, then monitor progress from the dashboard.

from burla import remote_parallel_map

def process_record(record):
    # Pretend this does some math per-record!
    return result

def process_file(file):
    results = remote_parallel_map(process_record, split_into_records(file))
    upload_results(results)

def process_files(files):
    remote_parallel_map(process_file, files, func_ram=16)
    

remote_parallel_map(process_files, [files], background=True)

Run code in any docker image, on any hardware:

Public or private, just paste a link to your image and hit start.

Get started now:

(Burla is currently Google Cloud only!)

  1. pip install burla

  2. burla install

See our Getting Started guide for more info:

LogoGetting Started | Burladocs.burla.dev

Stay Up to Date:

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