Lithops is a Python multi-cloud distributed computing framework. It allows to run unmodified local python code at massive scale in the main serverless computing platforms. Lithops delivers the user’s code into the cloud without requiring knowledge of how it is deployed and run. Moreover, its multicloud-agnostic architecture ensures portability across cloud providers, overcoming vendor lock-in.
Lithops provides value for a great variety of uses cases like big data analytics and embarrassingly parallel jobs. It is specially suited for highly-parallel programs with little or no need for communication between processes, but it also supports parallel applications that need to share state among processes. Examples of applications that run with Lithops include Monte Carlo simulations, deep learning and machine learning processes, metabolomics computations, and geospatial analytics, to name a few.
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Install Lithops from the PyPi repository:
$ pip install lithops
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Test Lithops by simply running the next command:
$ lithops test
Lithops provides an extensible backend architecture (compute, storage) that is designed to work with different Cloud providers and on-premise backends. In this sense, you can code in python and run it unmodified in IBM Cloud, AWS, Azure, Google Cloud and Alibaba Aliyun. Moreover, it provides support for some kubernetes serverless frameworks such as Knative.
Lithops is shipped with 2 different high-level Compute APIs, and 2 high-level Storage APIs
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from lithops import FunctionExecutor
def hello(name):
return 'Hello {}!'.format(name)
with FunctionExecutor() as fexec:
fut = fexec.call_async(hello, 'World')
print(fut.result()) |
from lithops.multiprocessing import Pool
def double(i):
return i * 2
with Pool() as pool:
result = pool.map(double, [1, 2, 3, 4, 5])
print(result) |
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from lithops import Storage
if __name__ == "__main__":
storage = Storage()
storage.put_object(bucket='my-bucket',
key='test.txt',
body='Hello World')
...
print(storage.get_object(bucket='my-bucket',
key='test.txt')) |
from lithops.storage.cloud_proxy import open, os
if __name__ == "__main__":
filepath = 'bar/foo.txt'
with 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.
Lithops is shipped with 3 different modes of execution. The execution mode allows you to decide where and how the functions are executed.
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This mode allows to run functions in your local machine, by using processes. This is the default mode of execution if no configuration is provided. |
This mode allows to run functions by using publicly accessible Serverless compute services, such as IBM Cloud Functions, Amazon Lambda or Google Cloud Functions, among others. In this mode of execution, each function invocation equals to a parallel task running in the cloud in an isolated environment. |
This mode allows to run functions by using one or multiple Virtual machines (VM), either in a private cluster or in the cloud. In each VM, functions run using parallel processes like in the Localhost mode. |
For documentation on using Lithops, see the User guide.
If you are interested in contributing, see CONTRIBUTING.md and DEVELOPMENT.md.
- 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
- Ants, serverless computing, and simplified data processing
- 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
- 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.