Run any Python function on 1000 computers in 1 second.
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:
- Scalable: See our demo where we process 2.4TB in 76s using 10,000 CPUs.
- Flexible: Runs any Python function, inside any Docker container, on any hardware.
- Simple: 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:
https://www.youtube.com/watch?v=9d22y_kWjyE
Try it out today:
There are two ways to host Burla:
- 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. - In our cloud.
First $1,000 in compute spend is free, try it now: https://burla.dev/signup
Questions?
Schedule a call, or email jake@burla.dev. We're always happy to talk.