---
id: valohai-utils
version: "0.7.0"
license: MIT
license_treatment: permissive
maintenance: aging
---

# valohai-utils

License: permissive · Maintenance: aging · Downloads: 88.2K/mo

## What it is and 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—locally 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.

The 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.

Use it for:

- 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

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Helper library for integrating Python code with the Valohai machine learning platform, handling parameters, inputs, outputs, metrics logging, and distributed task coordination.

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.

## Install

pip install valohai-utils
uv add valohai-utils
poetry add valohai-utils

## Installing valohai-utils

Before you install: Low install friction with a pure Python wheel. Maintenance status is aging (511 days since last release), so expect slower response to issues, though the package remains functional for its intended use.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it safe to adopt in most projects.

Quickstart:

pip install valohai-utils

import valohai

default_parameters = {'iterations': 10}
valohai.prepare(step="example", default_parameters=default_parameters)

for i in range(valohai.parameters('iterations').value):
    print(f"Iteration {i}")

Requires Python 3.8 or later. Code runs locally without Valohai platform, but cloud features require a Valohai account and execution context.

Verify before relying:

- Whether the package is actively maintained or in maintenance-only mode given the 511-day gap since last release
- Compatibility with recent versions of valohai-papi and valohai-yaml dependencies
- Whether distributed workload features work reliably in production environments

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 88.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

valohai integration library, ml platform helper, experiment parameter handling, distributed machine learning tasks, ml metrics logging, cloud ml execution, experiment input output management, ml-platform-integration, experiment-management, distributed-computing

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