Burla is a python package that makes it easy to run code on (lots of) other computers.
Burla only has one function: remote_parallel_map.
This function requires just two arguments, here's how it works:
from burla import remote_parallel_map
# Arg 1: Any python function:
def my_function(my_input):
...
# Arg 2: List of inputs for `my_function`
my_inputs = [1, 2, 3, ...]
# Calls `my_function` on every input in `my_inputs`,
# at the same time, each on a separate computer in the cloud.
remote_parallel_map(my_function, my_inputs)- Burla is fast and scalable.
Code starts running within 1 second, on up to 1000 CPU's. - Running code remotely with Burla feels like local development. This means that:
- Errors thrown on remote computers are raised on your local machine.
- Anything you print appears in the terminal on your local machine.
- Your python environment is automaticaly cloned on all remote computers.
This allows you to call any local python package in a function sent toremote_parallel_map.
After installing once, environments are cached to keep latency below 1 second.
- Burla is easy to install.
Try our managed service with two commands. Install Burla in your cloud with three commands. - Burla supports custom resource requirements.
Allocate up to 96 CPUs and 360G of ram to each individual function call with two simple arguments. - Burla supports GPU's.
Just add one argument:remote_parallel_map(my_function, my_inputs, gpu="A100") - Burla supports custom Docker images.
Just add one argument:remote_parallel_map(my_function, my_inputs, dockerfile="./Dockerfile")
After building once, images are cached to keep latency below 1 second. - Burla offers simple network storage.
By default, all remote machines are attached to the same persistent network disk.
Upload & download files to this disk through a simple CLI:> burla nas upload / download / ls / rm ...
To join our mailing list go to burla.dev.
If you have any questions, email me at: jake@burla.dev, or join us on Discord.