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Narrow GPU Python Dockerfiles can often be migrated from conda to uv for faster, lock-friendly installs.
PropertyValue
SeverityInfo
CategoryBest-practices
DefaultEnabled (experimental)
Auto-fixYes (AI AutoFix, unsafe)

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Description

This rule fires on Dockerfiles that:
  • Use a GPU/PyTorch-oriented base image (nvidia/cuda:*, nvcr.io/nvidia/*, pytorch/pytorch:*cuda*, or a stage that inherits CUDA from one of these).
  • Install Python/ML packages (e.g. torch, torchvision, transformers, flash-attn, xformers) via conda, mamba, or micromamba.
  • Do not rely on a heavy conda environment-management workflow (no conda env create, no environment.yml / conda-lock.yml copied into the image).
For that narrow, migratable slice the rule suggests an AI-assisted conversion to uv, which offers faster resolution, explicit CUDA wheel index support, and lock-friendly reproducibility.

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Why this matters

  • Conda resolution on GPU images is slow and can pull in unused Anaconda channels.
  • Many GPU images use conda only as a Python package installer — uv is a closer fit with pip install uv && uv pip install --index-url https://download.pytorch.org/whl/cuXYZ ....
  • uv supports explicit CUDA wheel indexes, which makes the CUDA alignment story (see also tally/gpu/cuda-version-mismatch) simpler and more auditable.

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Examples

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Violation

FROM nvidia/cuda:12.1.0-devel-ubuntu22.04
RUN conda install -y pytorch pytorch-cuda=12.1 -c pytorch -c nvidia
CMD ["python"]

FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel
RUN mamba install -y numpy transformers

FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
RUN micromamba install -y flash-attn xformers

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No violation

# Already uses uv.
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04
RUN pip install uv && uv pip install --system --index-url https://download.pytorch.org/whl/cu121 torch

# Heavy conda environment workflow; migration is out of scope.
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04
COPY environment.yml /app/
RUN conda env create -f /app/environment.yml

# CPU base image; rule does not fire.
FROM ubuntu:22.04
RUN conda install -y numpy

# conda installs only system packages; not a Python package workflow.
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04
RUN conda install -y gcc cmake

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Auto-fix

When the rule fires, it attaches an unsafe, async SuggestedFix backed by the AI AutoFix resolver. The AI agent is asked to:
  • Replace conda/mamba/micromamba Python/ML installs with uv pip install ....
  • Install uv before its first use (via pip install uv or the official installer).
  • Preserve the base image and OS package installs.
  • Preserve runtime invariants in the final stage (CMD, ENTRYPOINT, USER, WORKDIR, ENV, LABEL, EXPOSE, HEALTHCHECK).
  • Output NO_CHANGE if the file is better left alone (e.g. an environment.yml-driven workflow snuck past detection).
Applying the fix requires:
  • --fix --fix-unsafe
  • A configured ACP-capable agent in the config file (see the top-level [ai] section)
The resolver returns a single edit that replaces the entire Dockerfile content.

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Applicability

The rule fires when all of the following hold for one stage:
  1. The stage is GPU-oriented:
    • base image resolves to nvidia/cuda:* (or nvcr.io/nvidia/*, nvidia/cudagl:*), or
    • base image resolves to pytorch/pytorch:* (or nvcr.io/nvidia/pytorch:*), or
    • StageFacts.CUDAMajor > 0 (inherited via a stage reference).
  2. A RUN invokes conda, mamba, or micromamba with an install subcommand that lists at least one known Python/ML package.
  3. The Dockerfile as a whole does not show signs of a heavier conda workflow:
    • no conda env create / mamba env create / micromamba env create in any RUN
    • no environment.yml, environment.yaml, conda-lock.yml, or conda-lock.yaml in the build context
Violations are emitted at most once per stage; the AI AutoFix rewrite is file-scoped.

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Configuration

This rule has no rule-specific options. 
[rules.tally."gpu/prefer-uv-over-conda"]
severity = "info"
fix = "explicit"

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References