{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Helper library for integrating Python code with the Valohai machine learning platform, handling parameters, inputs, outputs, metrics logging, and distributed task coordination.","skillfed_tags":["ml-platform-integration","experiment-management","distributed-computing"],"use_cases":["Define hyperparameters and inputs in Python, then override them from Valohai's web UI without code changes","Download training data from cloud storage and process it locally for quick iteration before cloud deployment","Log training metrics (loss, accuracy, epoch) in a format Valohai renders as interactive graphs","Run distributed training across multiple workers with automatic master/worker discovery and coordination","Compress and upload large output datasets (images, models) to Valohai storage after training completes"],"what_it_does":"Valohai-utils is a Python helper library that bridges your local machine learning code and the Valohai cloud platform. It abstracts away platform-specific details so you can write code that runs identically in both environments\u2014locally during development and in the cloud during production runs. The library handles the plumbing: parsing command-line parameters, downloading input files from cloud storage (S3, Azure, GCS), managing output directories, and logging metrics in a format Valohai can visualize.\n\nThe package provides a straightforward API for defining experiment parameters and inputs as Python dictionaries, then accessing them at runtime through a unified interface. It also includes utilities for compressing outputs, handling distributed workloads across multiple workers, and querying execution metadata. A companion CLI tool can auto-generate the valohai.yaml configuration file by inspecting your Python code, reducing boilerplate.","worth_installing":"Yes, if you are actively using Valohai for ML experiments. The library significantly reduces boilerplate and ensures code parity between local and cloud execution. However, if you are not using Valohai, this package has no value. The aging maintenance status (511 days since last release) suggests slower bug fixes, so evaluate whether your use case tolerates that risk."},"id":"valohai-utils","links":{"html":"https://skillfed.io/packages/valohai-utils","md":"https://skillfed.io/packages/valohai-utils.md","pypi":"https://pypi.org/project/valohai-utils/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-03-21","license_spdx":"MIT","license_treatment":"permissive","name":"valohai-utils","python_support":"supports_current","summary":null},"popularity":{"monthly_downloads":88205,"position":13744,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.0"}