tally/gpu/no-buildtime-gpu-queries

GPU hardware is not available during docker build; runtime GPU queries in RUN will fail or return misleading results.

Property	Value
Severity	Error
Category	Correctness
Default	Enabled
Auto-fix	No

Description

Detects RUN instructions that query GPU hardware at build time. GPU devices are not attached during a normal docker build, so commands like nvidia-smi and runtime framework checks like torch.cuda.is_available() will either fail outright or return misleading results (e.g., zero devices, False). This rule does not fire on CMD or ENTRYPOINT, where GPU queries are expected to run at container startup.

Why this matters

Build failures — nvidia-smi will exit non-zero when no GPU is present, breaking the build
Silent wrong results — torch.cuda.is_available() returns False at build time, which can cause downstream logic to skip GPU code paths or produce incorrect configuration
Misleading smoke tests — a passing build does not mean GPU support works; the check must happen at runtime
Official guidance — Hugging Face explicitly warns that GPU hardware is not available during docker build

Detected patterns

GPU query commands

Command	Description
`nvidia-smi`	NVIDIA System Management Interface (queries GPU hardware)
`nvidia-debugdump`	NVIDIA debug information dump
`nvidia-persistenced`	NVIDIA persistence daemon (requires GPU)

Python/ML framework runtime checks

Pattern	Description
`torch.cuda.is_available()`	PyTorch CUDA availability check
`torch.cuda.device_count()`	PyTorch CUDA device count
`torch.cuda.get_device_name()`	PyTorch CUDA device name query
`torch.cuda.current_device()`	PyTorch current CUDA device
`tf.test.is_gpu_available()`	TensorFlow GPU availability check
`tf.config.list_physical_devices()`	TensorFlow physical device listing

Examples

Violation

FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
RUN nvidia-smi

FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
RUN python3 -c "import torch; print(torch.cuda.is_available())"

FROM tensorflow/tensorflow:2.14.0-gpu
RUN python -c "import tensorflow as tf; print(tf.test.is_gpu_available())"

No violation

# GPU query in CMD runs at container startup — correct
FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04
CMD ["nvidia-smi", "--loop=10"]

# GPU query in ENTRYPOINT runs at container startup — correct
FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04
ENTRYPOINT ["python3", "-c", "import torch; print(torch.cuda.is_available())"]

# Normal package installation — no GPU query
FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04
RUN apt-get update && apt-get install -y python3 python3-pip

Configuration

This rule has no rule-specific options.

[rules.tally.gpu.no-buildtime-gpu-queries]
severity = "error"

References

Hugging Face Docker Spaces GPU docs — explicitly warns GPU is unavailable during build
NVIDIA Container Toolkit

​Description

​Why this matters

​Detected patterns

​GPU query commands

​Python/ML framework runtime checks

​Examples

​Violation

​No violation

​Configuration

​References