valohai-utils
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later.
- Code runs locally without Valohai platform, but cloud features require a Valohai account and execution context.
- Low install friction with a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it safe to adopt in most projects.
last release 2025-03-21 (511 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 88,205 downloads/mo, #13,744 on PyPI
Alternatives
Verify before relying
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}")- 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
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 on this page. Verify before relying on it.
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
valohai-utils on PyPI
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.
Requires Python 3.8 or later. Code runs locally without Valohai platform, but cloud features require a Valohai account and execution context.
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}")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 the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesrequestsvalohai-papivalohai-yaml |
| Maintenance | Aging 511 days since the last release |
| First released | |
| Downloads | 88,205 / month, #13,744 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: valohai_utils-0.7.0-py3-none-any.whl
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