Lithops is a Python multi-cloud distributed computing framework that lets you run unmodified Python code at massive scale across cloud, HPC, and on-premise platforms. It supports major cloud providers and Kubernetes platforms, running your code transparently without requiring you to manage deployment or infrastructure.
Lithops is ideal for highly parallel workloads—such as Monte Carlo simulations, machine learning, metabolomics, or geospatial analytics—and lets you tailor execution to your priorities: you can optimize for performance using AWS Lambda to launch hundreds of functions in milliseconds, or reduce costs by running the same code on AWS Batch with Spot Instances.
-
Install Lithops from the PyPi repository:
pip install lithops
-
Execute a Hello World function:
lithops hello
Lithops provides an extensible backend architecture (compute, storage) designed to work with various cloud providers and on-premise platforms. You can write your code in Python and run it unmodified across major cloud providers and Kubernetes environments.
Follow these instructions to configure your compute and storage backends
Lithops is shipped with 2 different high-level Compute APIs, and 2 high-level Storage APIs
|
|
|
|---|---|
from lithops import FunctionExecutor
def double(i):
return i * 2
with FunctionExecutor() as fexec:
f = fexec.map(double, [1, 2, 3, 4])
print(f.result()) |
from lithops.multiprocessing import Pool
def double(i):
return i * 2
with Pool() as pool:
result = pool.map(double, [1, 2, 3, 4])
print(result) |
|
|
|
|---|---|
from lithops import Storage
if __name__ == "__main__":
st = Storage()
st.put_object(bucket='mybucket',
key='test.txt',
body='Hello World')
print(st.get_object(bucket='lithops',
key='test.txt')) |
from lithops.storage.cloud_proxy import os
if __name__ == "__main__":
filepath = 'bar/foo.txt'
with os.open(filepath, 'w') as f:
f.write('Hello world!')
dirname = os.path.dirname(filepath)
print(os.listdir(dirname))
os.remove(filepath) |
You can find more usage examples in the examples folder.
For documentation on using Lithops, see latest release documentation or current github docs.
If you are interested in contributing, see CONTRIBUTING.md.
- Simplify the developer experience with OpenShift for Big Data processing by using Lithops framework
- Speed-up your Python applications using Lithops and Serverless Cloud resources
- Serverless Without Constraints
- Lithops, a Multi-cloud Serverless Programming Framework
- CNCF Webinar - Toward Hybrid Cloud Serverless Transparency with Lithops Framework
- Using Serverless to Run Your Python Code on 1000 Cores by Changing Two Lines of Code
- Decoding dark molecular matter in spatial metabolomics with IBM Cloud Functions
- Your easy move to serverless computing and radically simplified data processing Strata Data Conference, NY 2019. See video of Lithops usage here and the example of Monte Carlo here
- Speed up data pre-processing with Lithops in deep learning
- Predicting the future with Monte Carlo simulations over IBM Cloud Functions
- Process large data sets at massive scale with Lithops over IBM Cloud Functions
- Industrial project in Technion on Lithops
- Outsourcing Data Processing Jobs with Lithops - IEEE Transactions on Cloud Computing 2022
- Towards Multicloud Access Transparency in Serverless Computing - IEEE Software 2021
- Primula: a Practical Shuffle/Sort Operator for Serverless Computing - ACM/IFIP International Middleware Conference 2020. See presentation here
- Bringing scaling transparency to Proteomics applications with serverless computing - 6th International Workshop on Serverless Computing (WoSC6) 2020. See presentation here
- Serverless data analytics in the IBM Cloud - ACM/IFIP International Middleware Conference 2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825184 (CloudButton).