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cpu-mode-assertions-torch

Validate CPU-only PyTorch execution by checking torch.cuda.is_available() and environment variables.

Updated Aug 23, 2026
Search Tags:#docker#pytorch#cuda#cpu#torch#ci-testing
One-click install
npx skills add https://github.com/jfriisj/real-time-speech-translation-mvp --skill cpu-mode-assertions-torch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cpu-mode-assertions-torch
Source: https://github.com/jfriisj/real-time-speech-translation-mvp/tree/main/.github/skills/cpu-mode-assertions-torch
Command: npx skills add https://github.com/jfriisj/real-time-speech-translation-mvp --skill cpu-mode-assertions-torch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enforces CPU-only execution inside a container for PyTorch workloads, preventing accidental GPU usage during CPU-focused tests.

Core Features & Use Cases

  • CPU-only validation: confirms that torch.cuda.is_available() is False to avoid GPU paths.
  • Optional environment variable checks: validates a provided env var matches an expected value when supplied.
  • CI/QA reliability: ensures CPU-only runtimes in smoke tests and debugging sessions.

Quick Start

Use the cpu-mode-assertions-torch skill to verify CPU-only runtime inside your container by checking that torch.cuda.is_available() is False and, if needed, validating a specific environment variable against an expected value.

Frequently Asked Questions about cpu-mode-assertions-torch

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce CPU-only PyTorch execution in a Docker container?▼

Enforce CPU-only PyTorch execution by validating that torch.cuda.is_available() returns False inside the container. This prevents accidental GPU usage during CPU-focused smoke tests and debugging sessions where GPU acceleration must be avoided.

Can I validate environment variables to ensure PyTorch stays on CPU mode?▼

Yes, you can validate environment variables for CPU mode by checking a provided variable against an expected value. This optional environment variable check supplements the core torch.cuda.is_available() False assertion to confirm runtime configuration.

Why does my PyTorch CI test still use GPU paths when running in a CPU container?▼

PyTorch CI tests use GPU paths when torch.cuda.is_available() returns True inside the container. Asserting that this function returns False enforces CPU-only execution, ensuring CI and QA reliability for CPU-focused smoke tests.

What is the best way to prevent accidental GPU acceleration during PyTorch troubleshooting?▼

The best way to prevent accidental GPU acceleration during PyTorch troubleshooting is to assert torch.cuda.is_available() is False. This validation enforces CPU-only execution inside containers, blocking unintended GPU code paths during debugging sessions.

Does this CPU-only validation approach require CUDA dependencies or GPU drivers installed?▼

No, CPU-only validation does not require CUDA dependencies or GPU drivers. The approach checks that torch.cuda.is_available() returns False, confirming the PyTorch workload runs entirely on CPU without accessing GPU acceleration resources.

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