{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"},{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Lithops is a Python framework for running unmodified code at scale across cloud providers, HPC, and on-premise platforms using serverless compute and storage backends.","skillfed_tags":["serverless","multi-cloud","parallel-computing"],"use_cases":["Run Monte Carlo simulations or financial modeling across hundreds of parallel functions on AWS Lambda without managing infrastructure.","Process large geospatial or metabolomics datasets by distributing computation across Kubernetes pods or cloud VMs.","Migrate existing Python multiprocessing code to serverless or cloud backends by swapping Pool() with lithops.multiprocessing.Pool().","Execute the same data pipeline on multiple cloud providers (AWS, GCP, Azure) by changing configuration without rewriting code.","Reduce costs for batch workloads by running them on Spot Instances via AWS Batch while keeping the same Python code."],"what_it_does":"Lithops abstracts away cloud infrastructure management by letting you write ordinary Python functions and run them in parallel across multiple cloud providers, Kubernetes clusters, or on-premise systems without code changes. It supports both a Futures-style API (map/reduce patterns) and a Multiprocessing API (drop-in replacement for Python's multiprocessing.Pool), plus cloud-native storage backends for reading and writing data. The framework handles deployment, scaling, and orchestration transparently\u2014you configure a compute backend (Lambda, Cloud Run, Batch, Kubernetes, etc.) and a storage backend (S3, GCS, Azure Blob, etc.), then submit your workload.\n\nThe primary use case is highly parallel workloads: Monte Carlo simulations, machine learning preprocessing, geospatial analytics, and metabolomics. You can optimize for speed using serverless functions that launch hundreds of tasks in milliseconds, or for cost by running the same code on batch systems with spot instances. It supports major cloud providers (AWS, Google Cloud, Azure, IBM, Aliyun, Oracle) and on-premise platforms (Kubernetes, OpenStack, Ceph, MinIO), making it useful for hybrid or multi-cloud scenarios where code portability matters.","worth_installing":"Yes, if you have highly parallel workloads and want to avoid cloud-specific code. The framework is actively maintained, has no known vulnerabilities, and supports a wide range of backends. Install friction is low. The main gotcha is that you must configure a compute and storage backend before running anything; it is not a drop-in replacement for local parallelism without setup. Best for teams already using cloud or Kubernetes and wanting code portability across providers."},"id":"lithops","links":{"html":"https://skillfed.io/packages/lithops","md":"https://skillfed.io/packages/lithops.md","pypi":"https://pypi.org/project/lithops/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":null,"license_treatment":"permissive","name":"lithops","python_support":"supports_current","summary":"Lithops lets you transparently run your Python applications in the Cloud"},"popularity":{"monthly_downloads":233113,"position":9045,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.7.0"}