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Signed-off-by: Ubospica <[email protected]>
Summary of ChangesHello @Ubospica, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the development and benchmarking workflow for Highlights
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📝 WalkthroughWalkthroughAdds Docker infrastructure for FlashInfer-Bench: a GPU-enabled Dockerfile, a docker-compose service for local development, and ignore rules (.gitignore and docker/.dockerignore) to exclude local and build artifacts. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~15 minutes Poem
🚥 Pre-merge checks | ✅ 3✅ Passed checks (3 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches🧪 Generate unit tests (beta)
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Code Review
This pull request introduces Docker support for flashinfer-bench, which is a great addition for ensuring a consistent development and benchmarking environment. The Dockerfile is comprehensive, setting up a full development environment with CUDA, Python, and necessary tools. The compose.yaml file is also well-configured, using modern Docker features for GPU access and providing excellent caching for better performance across runs. I've provided a couple of suggestions to further refine the Docker setup for better optimization and consistency. Overall, this is a solid contribution.
| RUN --mount=type=cache,target=/root/.cache/pip \ | ||
| python3 -m pip install --upgrade pip setuptools wheel \ | ||
| # Fast package manager | ||
| && python3 -m pip install uv \ | ||
| # IPython for interactive development | ||
| && python3 -m pip install ipython | ||
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| # Install PyTorch and FlashInfer | ||
| RUN --mount=type=cache,target=/root/.cache/pip \ | ||
| case "$CUDA_VERSION" in \ | ||
| 12.6.*) CUINDEX=126 ;; \ | ||
| 12.8.*) CUINDEX=128 ;; \ | ||
| 12.9.*) CUINDEX=129 ;; \ | ||
| 13.0.*) CUINDEX=130 ;; \ | ||
| *) echo "Unsupported CUDA version: $CUDA_VERSION" && exit 1 ;; \ | ||
| esac \ | ||
| && python3 -m pip install torch --index-url https://download.pytorch.org/whl/cu${CUINDEX} \ | ||
| && python3 -m pip install flashinfer-python==${FLASHINFER_VERSION} flashinfer-cubin \ | ||
| && python3 -m pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu${CUINDEX} | ||
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| # Install flashinfer-bench (editable install will be done via volume mount) | ||
| # For now, install dependencies | ||
| RUN --mount=type=cache,target=/root/.cache/pip \ | ||
| python3 -m pip install \ | ||
| pydantic>=2.0.0 \ | ||
| safetensors>=0.5.0 \ | ||
| apache-tvm-ffi>=0.1.2 \ | ||
| docstring-parser>=0.16 \ | ||
| # Dev dependencies | ||
| pytest>=7.0.0 \ | ||
| pytest-cov>=4.0.0 \ | ||
| black>=22.0.0 \ | ||
| isort>=5.0.0 \ | ||
| ruff>=0.1.0 \ | ||
| pre-commit>=3.0.0 \ | ||
| setuptools |
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To optimize the Docker image, it's a good practice to combine related RUN instructions into a single layer. You can merge the three pip install blocks into a single RUN command. This reduces the number of layers in the final image, making it slightly smaller and potentially faster to pull.
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install --upgrade pip setuptools wheel \
# Fast package manager
&& python3 -m pip install uv \
# IPython for interactive development
&& python3 -m pip install ipython \
# Install PyTorch and FlashInfer
&& case "$CUDA_VERSION" in \
12.6.*) CUINDEX=126 ;; \
12.8.*) CUINDEX=128 ;; \
12.9.*) CUINDEX=129 ;; \
13.0.*) CUINDEX=130 ;; \
*) echo "Unsupported CUDA version: $CUDA_VERSION" && exit 1 ;; \
esac \
&& python3 -m pip install torch --index-url https://download.pytorch.org/whl/cu${CUINDEX} \
&& python3 -m pip install flashinfer-python==${FLASHINFER_VERSION} flashinfer-cubin \
&& python3 -m pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu${CUINDEX} \
# Install flashinfer-bench (editable install will be done via volume mount)
# For now, install dependencies
&& python3 -m pip install \
pydantic>=2.0.0 \
safetensors>=0.5.0 \
apache-tvm-ffi>=0.1.2 \
docstring-parser>=0.16 \
# Dev dependencies
pytest>=7.0.0 \
pytest-cov>=4.0.0 \
black>=22.0.0 \
isort>=5.0.0 \
ruff>=0.1.0 \
pre-commit>=3.0.0 \
setuptools
| volumes: | ||
| # Mount local code (editable) | ||
| - ..:/workspace/flashinfer-bench | ||
| # HuggingFace cache | ||
| - ${HOME}/.cache/huggingface:/root/.cache/huggingface | ||
| # FlashInfer JIT cache | ||
| - ${HOME}/.cache/flashinfer:/root/.cache/flashinfer | ||
| # FlashInfer-Bench compile cache | ||
| - ${HOME}/.cache/flashinfer_bench:/root/.cache/flashinfer_bench | ||
| # Triton compile cache | ||
| - ${HOME}/.triton:/root/.triton | ||
| # Dataset (configurable via FIB_HOST_DATASET_PATH) | ||
| - ${FIB_HOST_DATASET_PATH:-${HOME}/.cache/flashinfer_bench/dataset}:/data/flashinfer-trace | ||
| working_dir: /workspace/flashinfer-bench |
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There's a slight inconsistency between the WORKDIR in the Dockerfile (which is /workspace) and the volume mount path and working_dir in compose.yaml (/workspace/flashinfer-bench). For better clarity and consistency, it's recommended to use /workspace for both the mount path and the working directory. This simplifies the paths and aligns the docker compose environment with what a user would expect from a standalone docker run command.
volumes:
# Mount local code (editable)
- ..:/workspace
# HuggingFace cache
- ${HOME}/.cache/huggingface:/root/.cache/huggingface
# FlashInfer JIT cache
- ${HOME}/.cache/flashinfer:/root/.cache/flashinfer
# FlashInfer-Bench compile cache
- ${HOME}/.cache/flashinfer_bench:/root/.cache/flashinfer_bench
# Triton compile cache
- ${HOME}/.triton:/root/.triton
# Dataset (configurable via FIB_HOST_DATASET_PATH)
- ${FIB_HOST_DATASET_PATH:-${HOME}/.cache/flashinfer_bench/dataset}:/data/flashinfer-trace
working_dir: /workspace
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yzh119
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Can we reuse any of the docker files provided in https://github.com/flashinfer-ai/flashinfer/tree/main/docker ?
docker/compose.yaml
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| services: | ||
| flashinfer-bench: | ||
| image: flashinfer/flashinfer-bench:cu129 |
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I would encourage using cu131 at least, some DSLs such as cutile relies on cu131 as minimal version.
docker/Dockerfile
Outdated
| # docker build --build-arg CUDA_VERSION=12.6.3 -t flashinfer-bench:cu126 . | ||
| # docker build --build-arg CUDA_VERSION=13.0.1 -t flashinfer-bench:cu130 . | ||
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| ARG CUDA_VERSION=12.9.1 |
| FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu24.04 | ||
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| ARG CUDA_VERSION | ||
| ARG FLASHINFER_VERSION=0.6.1 |
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why do we hardcode flashinfer version?
| # Install PyTorch and FlashInfer | ||
| RUN --mount=type=cache,target=/root/.cache/pip \ | ||
| case "$CUDA_VERSION" in \ | ||
| 12.6.*) CUINDEX=126 ;; \ |
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I suppose there should be only one choice here?
Signed-off-by: Ubospica <[email protected]>
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 3
🤖 Fix all issues with AI agents
In `@docker/compose.yaml`:
- Line 19: Remove the fixed container_name setting to avoid name collisions when
running concurrent instances (e.g., using docker compose run --rm); specifically
delete the container_name: flashinfer-bench entry from the service definition so
Compose will auto-generate unique container names for the service.
In `@docker/Dockerfile`:
- Around line 97-110: The RUN pip install command in the Dockerfile is using
unquoted version specifiers like pydantic>=2.0.0 which the shell interprets as
redirections; update the RUN --mount=type=cache,target=/root/.cache/pip python3
-m pip install block so every package with a version constraint is quoted (e.g.,
'pydantic>=2.0.0', 'safetensors>=0.5.0', etc.), ensuring the shell passes them
intact to pip and avoids creating stray files; leave non-constrained names
(setuptools) unquoted if desired but quote all "name>=version" entries.
- Around line 82-93: The CUDA->cu index mapping in the Dockerfile RUN case uses
"13.1.*) CUINDEX=131" which is invalid because PyTorch publishes cu130 for CUDA
13.x; update the case branch in the RUN block that sets CUINDEX based on
CUDA_VERSION (the case handling for 13.1.*) to map to CUINDEX=130 instead of 131
(or remove the 13.1.* arm entirely) so the python3 -m pip install torch
--index-url https://download.pytorch.org/whl/cu${CUINDEX} resolves to a valid
wheel.
🧹 Nitpick comments (4)
docker/compose.yaml (1)
54-55:shm_sizeis redundant whenipc: hostis set.With
ipc: host, the container shares the host's entire IPC namespace (including/dev/shm), making theshm_size: "2gb"limit ineffective. This is harmless but could be confusing — consider removingshm_sizeor adding a comment clarifying it's a fallback ifipc: hostis ever removed.docker/Dockerfile (3)
62-69: Add--no-install-recommendsto the CUDA toolkit install.The first
apt-get installblock uses--no-install-recommends, but this one doesn't. CUDA toolkit recommended packages can add significant image bloat.Suggested fix
RUN --mount=type=cache,target=/var/cache/apt \ CUDA_VER="${CUDA_VERSION}" \ && CUDA_MAJOR="${CUDA_VER%%.*}" \ && CUDA_MINOR="$(echo "${CUDA_VER}" | cut -d. -f2)" \ && apt-get update \ - && apt-get install -y \ + && apt-get install -y --no-install-recommends \ cuda-toolkit-${CUDA_MAJOR}-${CUDA_MINOR} \ && rm -rf /var/lib/apt/lists/*
115-118: Oh My Zsh install fetches from the network at build time.This is common for dev containers but creates a non-reproducible build. If reproducibility matters, consider pinning to a specific commit or bundling the install script. Low priority for a dev image.
1-124: No non-rootUSERinstruction (Trivy DS002).The entire image runs as root. This is typical and often necessary for GPU development containers that need to install packages and access NVIDIA devices at runtime, so likely acceptable here. Noting for awareness.
| services: | ||
| flashinfer-bench: | ||
| image: flashinfer/flashinfer-bench:cu131 | ||
| container_name: flashinfer-bench |
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container_name conflicts with docker compose run --rm.
Setting a fixed container_name prevents running multiple concurrent instances of this service (e.g., two docker compose run --rm flashinfer-bench invocations will clash). Since the usage instructions promote run --rm, consider removing this line — Compose will auto-generate unique names.
Suggested fix
flashinfer-bench:
image: flashinfer/flashinfer-bench:cu131
- container_name: flashinfer-bench
build:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| container_name: flashinfer-bench | |
| flashinfer-bench: | |
| image: flashinfer/flashinfer-bench:cu131 | |
| build: |
🤖 Prompt for AI Agents
In `@docker/compose.yaml` at line 19, Remove the fixed container_name setting to
avoid name collisions when running concurrent instances (e.g., using docker
compose run --rm); specifically delete the container_name: flashinfer-bench
entry from the service definition so Compose will auto-generate unique container
names for the service.
| RUN --mount=type=cache,target=/root/.cache/pip \ | ||
| case "$CUDA_VERSION" in \ | ||
| 12.6.*) CUINDEX=126 ;; \ | ||
| 12.8.*) CUINDEX=128 ;; \ | ||
| 12.9.*) CUINDEX=129 ;; \ | ||
| 13.0.*) CUINDEX=130 ;; \ | ||
| 13.1.*) CUINDEX=131 ;; \ | ||
| *) echo "Unsupported CUDA version: $CUDA_VERSION" && exit 1 ;; \ | ||
| esac \ | ||
| && python3 -m pip install torch --index-url https://download.pytorch.org/whl/cu${CUINDEX} \ | ||
| && python3 -m pip install flashinfer-python==${FLASHINFER_VERSION} flashinfer-cubin \ | ||
| && python3 -m pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu${CUINDEX} |
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🧩 Analysis chain
🌐 Web query:
PyTorch CUDA wheels cu130 cu131 2025 2026 availability
💡 Result:
Summary (as of Feb 6, 2026)
-
cu130(CUDA 13.0) wheels are available:- Stable releases: PyTorch 2.9.0 and 2.9.1 provide official
cu130pip installs via thecu130index URL (and the directory containstorch-2.9.1+cu130wheels). [1][2] - Nightly builds: a
cu130nightly wheel index exists (.../whl/nightly/cu130). [3] - PyTorch 2.10.0 packaging explicitly lists support for CUDA 13.0 (alongside 12.6/12.8). [4]
- Stable releases: PyTorch 2.9.0 and 2.9.1 provide official
-
cu131(CUDA 13.1) wheels are not published (no official index)- The official nightly wheel root lists
cu130but does not listcu131as a supported wheel directory/variant. [3]
- The official nightly wheel root lists
Practical takeaway
If you need “CUDA 13” PyTorch wheels today, use cu130 (stable 2.9.x or newer / nightly). There isn’t an official cu131 wheel channel published yet. [1][3][4]
Sources: [1] [2] [3] [4]
Remove or fix the CUDA 13.1→cu131 mapping; PyTorch does not provide cu131 wheels.
The case statement maps CUDA 13.1 to cu131, but PyTorch only publishes official wheels for cu130 (CUDA 13.0). If CUDA 13.1 is detected, the pip install will fail with no matching wheel index. Either remove the 13.1.* case or map it to cu130.
🤖 Prompt for AI Agents
In `@docker/Dockerfile` around lines 82 - 93, The CUDA->cu index mapping in the
Dockerfile RUN case uses "13.1.*) CUINDEX=131" which is invalid because PyTorch
publishes cu130 for CUDA 13.x; update the case branch in the RUN block that sets
CUINDEX based on CUDA_VERSION (the case handling for 13.1.*) to map to
CUINDEX=130 instead of 131 (or remove the 13.1.* arm entirely) so the python3 -m
pip install torch --index-url https://download.pytorch.org/whl/cu${CUINDEX}
resolves to a valid wheel.
| RUN --mount=type=cache,target=/root/.cache/pip \ | ||
| python3 -m pip install \ | ||
| pydantic>=2.0.0 \ | ||
| safetensors>=0.5.0 \ | ||
| apache-tvm-ffi>=0.1.2 \ | ||
| docstring-parser>=0.16 \ | ||
| # Dev dependencies | ||
| pytest>=7.0.0 \ | ||
| pytest-cov>=4.0.0 \ | ||
| black>=22.0.0 \ | ||
| isort>=5.0.0 \ | ||
| ruff>=0.1.0 \ | ||
| pre-commit>=3.0.0 \ | ||
| setuptools |
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Critical: Unquoted >= version specifiers are interpreted as shell redirections.
In the shell executed by RUN, > is a redirection operator. For example, pydantic>=2.0.0 is parsed as: argument pydantic, redirect stdout to file =2.0.0. This means:
- Version constraints are silently ignored — pip installs the latest versions.
- Junk files (e.g.,
=2.0.0,=0.5.0) are created in the working directory.
This is confirmed by Hadolint SC2261: "Multiple redirections compete for stdout."
Quote every version-constrained package specifier with single quotes.
Suggested fix
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install \
- pydantic>=2.0.0 \
- safetensors>=0.5.0 \
- apache-tvm-ffi>=0.1.2 \
- docstring-parser>=0.16 \
+ 'pydantic>=2.0.0' \
+ 'safetensors>=0.5.0' \
+ 'apache-tvm-ffi>=0.1.2' \
+ 'docstring-parser>=0.16' \
# Dev dependencies
- pytest>=7.0.0 \
- pytest-cov>=4.0.0 \
- black>=22.0.0 \
- isort>=5.0.0 \
- ruff>=0.1.0 \
- pre-commit>=3.0.0 \
+ 'pytest>=7.0.0' \
+ 'pytest-cov>=4.0.0' \
+ 'black>=22.0.0' \
+ 'isort>=5.0.0' \
+ 'ruff>=0.1.0' \
+ 'pre-commit>=3.0.0' \
setuptools📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| RUN --mount=type=cache,target=/root/.cache/pip \ | |
| python3 -m pip install \ | |
| pydantic>=2.0.0 \ | |
| safetensors>=0.5.0 \ | |
| apache-tvm-ffi>=0.1.2 \ | |
| docstring-parser>=0.16 \ | |
| # Dev dependencies | |
| pytest>=7.0.0 \ | |
| pytest-cov>=4.0.0 \ | |
| black>=22.0.0 \ | |
| isort>=5.0.0 \ | |
| ruff>=0.1.0 \ | |
| pre-commit>=3.0.0 \ | |
| setuptools | |
| RUN --mount=type=cache,target=/root/.cache/pip \ | |
| python3 -m pip install \ | |
| 'pydantic>=2.0.0' \ | |
| 'safetensors>=0.5.0' \ | |
| 'apache-tvm-ffi>=0.1.2' \ | |
| 'docstring-parser>=0.16' \ | |
| # Dev dependencies | |
| 'pytest>=7.0.0' \ | |
| 'pytest-cov>=4.0.0' \ | |
| 'black>=22.0.0' \ | |
| 'isort>=5.0.0' \ | |
| 'ruff>=0.1.0' \ | |
| 'pre-commit>=3.0.0' \ | |
| setuptools |
🧰 Tools
🪛 Hadolint (2.14.0)
[error] 97-97: Multiple redirections compete for stdout. Use cat, tee, or pass filenames instead.
(SC2261)
🤖 Prompt for AI Agents
In `@docker/Dockerfile` around lines 97 - 110, The RUN pip install command in the
Dockerfile is using unquoted version specifiers like pydantic>=2.0.0 which the
shell interprets as redirections; update the RUN
--mount=type=cache,target=/root/.cache/pip python3 -m pip install block so every
package with a version constraint is quoted (e.g., 'pydantic>=2.0.0',
'safetensors>=0.5.0', etc.), ensuring the shell passes them intact to pip and
avoids creating stray files; leave non-constrained names (setuptools) unquoted
if desired but quote all "name>=version" entries.
2 participants
Signed-off-by: Ubospica [email protected]
Summary by CodeRabbit