Skip to main content

ComputeFence

Pre-flight validation for GPU training runs on rented infrastructure. Built specifically for RunPod, Vast.ai, Lambda, and similar providers.

Install

pip install computefence

Usage

computefence doctor computefence doctor --dataset train.csv

What it checks

  • CUDA and GPU availability
  • HuggingFace cache volume path (catches the RunPod /root vs /workspace conflict)
  • Accelerate GPU count vs config
  • Dataset duplicates and missing values

What it does not yet check

  • Training script correctness
  • Model architecture compatibility
  • Learning rate or hyperparameter safety
  • Runtime monitoring during the job

Why this exists

I burned ~£1,000 on GPU training runs that failed silently. CUDA fell back to CPU with no error. Class weights caused loss collapse. My dataset had 28,432 duplicate rows and 312 conflicting labels I only found during the rebuild.

Nothing existed that caught these before the job started. So I built it.

Metadata

Release files for computefence 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for computefence 0.1.2
File Size Uploaded
computefence-0.1.2.tar.gz 8.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for computefence 0.1.2
File Interpreter ABI Platform
computefence-0.1.2-py3-none-any.whl Python 3 none any Details

Total release size: 17.2 kB

Release files / computefence-0.1.2.tar.gz

Download URL computefence-0.1.2.tar.gz
Size 8.1 kB
Tags Source
SHA-256 checksum
How to use checksums
d7fef7787d08e6fc1b68ea1b8d613616440d254b943b818b4f8fdf78c3a377db
BLAKE2b-256 checksum
How to use checksums
21088547963a29efeff29bc153776f7e6c7deae2b08241ddfe4889971ea52ff2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.3

Release files / computefence-0.1.2-py3-none-any.whl

Download URL computefence-0.1.2-py3-none-any.whl
Size 9.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
873c1859cfac2ed029f92c63fb56354fe2b42997ef6a4a344306c2e083d6dc33
BLAKE2b-256 checksum
How to use checksums
a1418587b7333e46bf9fe21a79cc4fecd2006f7b98bc4ecb7d85850141bf6715
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.3

Release history Release notifications | RSS feed

0.2.6

2 release files

0.2.5

2 release files

0.2.4

2 release files

0.2.3

2 release files

0.2.1

2 release files

0.2.0

2 release files

This release

0.1.2 This release

2 release files

0.1.1

2 release files

0.1.0

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page