1769204ade
Empirically (cu128 wheel SASS list `sm_80;90;90a;100;100a;120;120a` on
aarch64) the cu128 wheel covers DGX Spark sm_121 via sm_120 binary
forward-compat. BUT two CPU-side compilers shipped at cu12.8 do not know
sm_121 and need a cu13 swap:
(1) torch's bundled libnvrtc.so.12 from CUDA 12.8 rejects sm_121 as a
--gpu-architecture. Symlinks libnvrtc.so.13 over it.
(2) Triton's nvidia backend runs ptxas. Wheels older than 3.6.0 bundled
cu12.8 ptxas which silently downgrades sm_121 to sm_80 (see
triton-lang/triton#8335). Bump pin triton>=3.6.0 (3.6 bundles cu13
ptxas) AND install cuda-nvcc-13-0 so the entrypoint can point
TRITON_PTXAS_PATH at it as defense in depth.
Both fixes are arm64-only (gated on TARGETARCH, ~400 MB on the arm64
image; amd64 is untouched, no sm_121 hardware exists on x86_64). Neither
component talks to libcuda, so this does NOT bump the toolkit driver
floor away from cu128's 570+.
TRITON_PTXAS_PATH is set from the entrypoint (only when the cu13 ptxas
actually exists in the image) rather than via a Dockerfile ENV, because
ENV is unconditional and Triton errors out if TRITON_PTXAS_PATH points
at a nonexistent file.
Sources: martimramos/dgx-spark-ml-guide Challenge 14; triton-lang/triton
issue #8335; ptrblck PyTorch forum thread on sm_121 fwd-compat from
sm_120.
346 lines
18 KiB
Docker
346 lines
18 KiB
Docker
# syntax=docker/dockerfile:1.7
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# -----------------------------------------------------------------------------
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# Unsloth + unsloth-zoo for every current NVIDIA arch (Turing -> Blackwell),
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# on both linux/amd64 and linux/arm64.
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#
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# Why this image works:
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# * cu128 wheels ship native SASS (no PTX), empirically verified via
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# `cuobjdump --list-elf` against the downloaded wheels:
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# amd64: sm_70 sm_75 sm_80 sm_86 sm_90 sm_100 sm_120
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# arm64: sm_80 sm_90 sm_90a sm_100 sm_100a sm_120 sm_120a
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# * SASS is binary-compatible UPWARDS within a major (per ptrblck on the
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# PyTorch forum, May 2026): sm_86 SASS runs on sm_89 hardware (Ada);
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# sm_100 SASS runs on sm_103 (B300/GB300); sm_120 SASS runs on sm_121
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# (DGX Spark / GB10). So every non-Jetson NVIDIA GPU on
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# https://developer.nvidia.com/cuda/gpus is covered.
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# * Unsloth's runtime kernels are Triton, which JIT-compiles per device at first run.
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# * Anything that DOES need to be source-built (rare on this pin set) compiles
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# against TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0;10.0;10.3;12.0;12.1+PTX",
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# covering every current NVIDIA compute capability per
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# https://developer.nvidia.com/cuda/gpus.
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# The host GPU is irrelevant for compilation; nvcc emits whatever the arch
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# list says.
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#
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# Cross-arch build (DGX Spark / GB10 / sm_121):
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# The arm64 image is built via QEMU binfmt emulation on an x86_64 host:
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# bash docker/setup_qemu.sh # one-time host setup
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# docker buildx build --platform linux/arm64 -t unsloth-blackwell:arm64 .
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# The resulting arm64 image runs NATIVELY on aarch64 hosts (DGX Spark, Grace).
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# QEMU is only used at build time -- runtime emulation does NOT work for CUDA.
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# xformers has no cu128 aarch64 wheel; on arm64 we fall back to Unsloth's
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# built-in SDPA path (~5-10% slower than xformers but functionally complete).
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#
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# Build host requirements:
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# * Docker with buildkit (default since 23.x)
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# * docker buildx (mandatory for multi-platform; install: apt install docker-buildx)
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# * nvidia-container-toolkit (only needed for `docker run --gpus all` at test time)
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# * For arm64 builds on x86_64 hosts: QEMU binfmt (see docker/setup_qemu.sh)
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# * A GPU is NOT required at build time.
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# -----------------------------------------------------------------------------
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ARG CUDA_VERSION=12.8.1
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ARG UBUNTU_VERSION=24.04
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ARG PYTHON_VERSION=3.12
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# =============================================================================
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# Stage 1: builder -- toolkit + dev headers, builds any source extensions
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# =============================================================================
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FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu${UBUNTU_VERSION} AS builder
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# TARGETARCH is auto-populated by buildx ("amd64" or "arm64"). We use it to
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# select an unsloth extras set that matches the wheels actually available for
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# the target platform (xformers has no cu128 aarch64 wheel as of 0.0.34).
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ARG TARGETARCH
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ARG PYTHON_VERSION
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ENV DEBIAN_FRONTEND=noninteractive \
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PIP_NO_CACHE_DIR=1 \
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PIP_DISABLE_PIP_VERSION_CHECK=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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# Cross-compile for every current NVIDIA arch per
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# https://developer.nvidia.com/cuda/gpus:
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# sm_75 Turing T4, RTX 20-series, Quadro RTX (amd64 only)
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# sm_80 Ampere DC A100, A30 (amd64 only)
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# sm_86 Ampere A40, RTX A6000, RTX 30-series (amd64 only)
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# sm_89 Ada L4, L40, L40S, RTX 40-series (amd64 only)
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# sm_90 Hopper H100, H200, GH200 (Grace-Hopper is arm64)
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# sm_100 Blackwell DC B100, B200, GB200 (GB200 is arm64)
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# sm_103 Blackwell DC B300, GB300
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# sm_120 Blackwell RTX 50-series, RTX PRO 6000 Blackwell (amd64 only)
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# sm_121 Blackwell GB10 (DGX Spark) (arm64 only)
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# +PTX on the highest lets future arch revisions run via JIT-PTX.
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# We keep the same list on both arches: nvcc happily emits archs that
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# don't exist on the build host, and any extras that aren't relevant for
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# the target just bloat compile time slightly (not size, since we don't
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# source-build any extension at install time on the pin set).
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TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0;10.0;10.3;12.0;12.1+PTX" \
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MAX_JOBS=4 \
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CUDA_HOME=/usr/local/cuda \
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# Build-host-independence guards. The build must NEVER introspect a GPU,
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# because the build host may have a B200, RTX 6000, or no GPU at all
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# (GitHub Actions ubuntu-latest). All three must yield byte-identical images.
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#
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# 1) Stop unsloth from JIT-compiling kernels at import time and writing a
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# sm_NNN-specific blob into /opt/unsloth-venv/.../unsloth_compiled_cache/.
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UNSLOTH_COMPILE_DISABLE=1 \
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UNSLOTH_COMPILE_OVERWRITE=0 \
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# 2) Stop unsloth-zoo / vllm from probing torch.cuda.is_available() during
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# setup. There's no GPU here, and we don't want it to silently skip a wheel.
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UNSLOTH_DISABLE_GPU_PROBE=1 \
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# 3) Force CUDA_VISIBLE_DEVICES empty so any stray torch.cuda call during
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# `pip install` returns "no devices" rather than triggering host-specific
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# code paths (we re-enable at runtime via `docker run --gpus all`).
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CUDA_VISIBLE_DEVICES=""
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RUN apt-get update && apt-get install -y --no-install-recommends \
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software-properties-common ca-certificates curl git build-essential \
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ninja-build cmake pkg-config \
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&& add-apt-repository -y ppa:deadsnakes/ppa \
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&& apt-get update && apt-get install -y --no-install-recommends \
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python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \
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&& ln -sf /usr/bin/python${PYTHON_VERSION} /usr/local/bin/python \
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&& ln -sf /usr/bin/python${PYTHON_VERSION} /usr/local/bin/python3 \
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&& rm -rf /var/lib/apt/lists/*
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# Build into an isolated prefix. NOTE: we do NOT install pip or uv into the
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# system Python -- on Ubuntu 24.04 the system interpreter is marked
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# externally-managed (PEP 668) and `pip install` is refused. Instead, the new
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# venv bootstraps its own pip via ensurepip (provided by the python3.12-venv
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# apt package), and we install uv into the venv a few lines below.
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ENV VENV=/opt/unsloth-venv
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RUN python -m venv ${VENV} && ${VENV}/bin/pip install -U pip wheel setuptools
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# Unified install: torch + triton + bitsandbytes + unsloth + unsloth_zoo
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# resolve in a SINGLE uv pip pass. This is mandatory -- splitting it across
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# multiple `pip install` calls causes bnb's transitive `cuda-toolkit` dep to
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# silently upgrade torch to 2.12.0+cu130 in a later pass, breaking the cu128
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# xformers wheel that was pinned earlier. (Empirically discovered; the cu cascade
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# happens AFTER xformers is already on disk, leaving a working-but-mismatched env.)
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#
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# uv-specific flags explained:
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# --index-strategy unsafe-best-match
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# The PyTorch index serves an old `requests==2.28.1` which conflicts with
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# `datasets>=2.32.2`. uv's default is "first index wins per package" to
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# prevent dependency confusion; we override here because both indexes
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# (pytorch.org/whl/cu128 + pypi.org) are equally trusted.
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# --extra-index-url https://download.pytorch.org/whl/cu128
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# Where torch's +cu128 wheels live, plus the xformers/cu128 URLs referenced
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# by unsloth's `cu128onlytorch2100` extra.
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#
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# Why the extra is `cu128-ampere-torch2100` (not `cu128-torch2100-ampere`):
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# See unsloth_src/pyproject.toml:835. The ordering is ampere-then-torch-ver.
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#
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# Why arm64 uses a different extra:
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# `cu128-ampere-torch2100` transitively pulls `cu128onlytorch2100` whose
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# xformers wheel URL is hardcoded to manylinux_2_28_x86_64. There is no
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# cu128 aarch64 wheel for xformers as of 0.0.34. We use the plain
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# `huggingface` extra on arm64 -- Unsloth falls back to its native SDPA
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# kernels (a ~5-10% slowdown vs xformers; functionally complete).
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#
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# Why no `flash-attn` here:
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# - FA3 is hard-refused on Blackwell (Dao-AILab/flash-attention#1810).
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# - FA2 has no prebuilt wheel for cu128+torch2.10+cp312 -> would require
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# a ~30min source build, fragile on the 16GB ubuntu-latest CI runner.
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# - Unsloth gracefully falls back to xformers/SDPA on Blackwell anyway.
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# - Users on Ampere/Ada/Hopper who want FA2 can `pip install flash-attn`
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# on top of this image at deploy time.
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ARG UNSLOTH_REF=main
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ARG UNSLOTH_ZOO_REF=main
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RUN set -eux \
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&& case "${TARGETARCH:-amd64}" in \
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amd64) UNSLOTH_EXTRA="cu128-ampere-torch2100" ;; \
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arm64) UNSLOTH_EXTRA="huggingface" ;; \
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*) echo "ERROR: unsupported TARGETARCH=${TARGETARCH}" >&2; exit 1 ;; \
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esac \
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&& echo ">> TARGETARCH=${TARGETARCH:-amd64}, unsloth extra=[${UNSLOTH_EXTRA}]" \
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&& ${VENV}/bin/pip install uv \
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&& ${VENV}/bin/uv pip install \
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--python ${VENV}/bin/python \
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--index-strategy unsafe-best-match \
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--extra-index-url https://download.pytorch.org/whl/cu128 \
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"torch==2.10.0" "torchvision==0.25.0" "torchaudio==2.11.0" \
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"triton>=3.6.0" \
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"bitsandbytes>=0.49.2,!=0.46.0,!=0.48.0" \
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"unsloth_zoo @ git+https://github.com/unslothai/unsloth-zoo@${UNSLOTH_ZOO_REF}" \
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"unsloth[${UNSLOTH_EXTRA}] @ git+https://github.com/unslothai/unsloth@${UNSLOTH_REF}"
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# 5) Emit a lockfile so the next rebuild can be byte-identical even if PyPI
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# has moved on. Bake it into the image at /opt/unsloth-venv/requirements.lock.txt
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# so `docker run ... cat /opt/unsloth-venv/requirements.lock.txt > pins.txt`
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# gives you the input to a fully-pinned rebuild.
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RUN ${VENV}/bin/pip freeze --exclude-editable > ${VENV}/requirements.lock.txt \
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&& head -50 ${VENV}/requirements.lock.txt
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# 6) Strip pip cache & __pycache__ to shrink the layer copied to runtime.
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RUN find ${VENV} -depth -type d -name __pycache__ -exec rm -rf {} + \
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&& find ${VENV} -depth -type d -name tests -exec rm -rf {} + \
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&& rm -rf /root/.cache/pip /root/.cache/uv
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# Build-time verification.
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#
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# (1) arch-list check uses the RAW C++ accessor (not torch.cuda.get_arch_list()).
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# The Python wrapper checks torch.cuda.is_available() first and returns []
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# when no GPU is visible -- which is always the case here because
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# CUDA_VISIBLE_DEVICES is empty by design.
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#
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# (2) We verify required packages via package metadata only -- we do NOT import
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# unsloth or unsloth_zoo here. Their __init__ calls torch.cuda.get_device_
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# properties(0) which requires an actual CUDA device (UNSLOTH_ALLOW_CPU=1
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# only bypasses the first gate, not the deeper init). Import-time
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# correctness is exercised at deploy time by smoke_test.py with --gpus all.
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RUN TARGETARCH="${TARGETARCH:-amd64}" ${VENV}/bin/python - <<'PY'
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import os, platform
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target = os.environ.get("TARGETARCH", "amd64")
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mach = platform.machine()
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print(f"build target: TARGETARCH={target} platform.machine()={mach}")
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import torch
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arches = torch._C._cuda_getArchFlags().split()
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print("torch", torch.__version__, "cuda", torch.version.cuda)
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print("arches:", arches)
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assert torch.__version__.startswith("2.10.0"), f"torch silently moved: {torch.__version__}"
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assert "+cu128" in torch.__version__, f"cu build silently changed: {torch.__version__}"
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assert "sm_100" in arches, f"sm_100 (B200/GB200) missing: {arches}"
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# cu128 wheels ship sm_120 native SASS on BOTH amd64 (RTX 5090 / RTX PRO 6000
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# Blackwell) and aarch64 (Grace systems). On arm64 sm_120 is what DGX Spark
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# (sm_121) runs via minor-forward-compat within major 12; sm_121 itself is
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# never in any cu128 wheel.
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assert "sm_120" in arches, f"sm_120 missing: {arches}"
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print(f"OK: torch 2.10.0+cu128 with sm_100 + sm_120 native SASS intact ({target})")
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from importlib.metadata import version, PackageNotFoundError
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# xformers has no cu128 aarch64 wheel as of 0.0.34, so we only require it
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# on amd64. Everything else is platform-agnostic.
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REQUIRED = ["torch", "triton", "bitsandbytes", "unsloth",
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"unsloth_zoo", "transformers", "trl", "peft", "accelerate"]
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if target == "amd64":
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REQUIRED.insert(2, "xformers")
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missing = []
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for pkg in REQUIRED:
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try:
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v = version(pkg.replace("_", "-"))
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print(f" {pkg:14s} {v}")
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except PackageNotFoundError:
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missing.append(pkg)
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if missing:
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raise SystemExit(f"FAIL: missing wheels: {missing}")
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print("OK: all required wheels present")
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# Lightweight imports: these init without touching CUDA, unlike unsloth.
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import importlib
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LIGHT_IMPORTS = ["bitsandbytes", "triton"]
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if target == "amd64":
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LIGHT_IMPORTS.insert(0, "xformers")
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for pkg in LIGHT_IMPORTS:
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importlib.import_module(pkg)
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print(f"OK: {' + '.join(LIGHT_IMPORTS)} import cleanly on no-GPU host")
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PY
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# =============================================================================
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# Stage 2: runtime -- slim runtime image, no nvcc, no headers
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# =============================================================================
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FROM nvidia/cuda:${CUDA_VERSION}-cudnn-runtime-ubuntu${UBUNTU_VERSION} AS runtime
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# The nvidia/cuda:12.8.1-cudnn-runtime-ubuntu24.04 manifest is multi-arch
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# (linux/amd64 + linux/arm64). docker/buildx picks the right one for
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# TARGETPLATFORM at this FROM line; no conditional needed.
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ARG TARGETARCH
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ARG PYTHON_VERSION
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ENV DEBIAN_FRONTEND=noninteractive \
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PIP_NO_CACHE_DIR=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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PATH=/opt/unsloth-venv/bin:${PATH} \
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HF_HOME=/workspace/.cache/huggingface \
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TRITON_CACHE_DIR=/workspace/.cache/triton \
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# Keep the arch list visible at runtime in case the user source-builds anything
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# extra inside the container (e.g. a custom CUDA op). Same list as the builder
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# stage so a `pip install some-cuda-ext` inside the container gets a SASS
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# blob that covers every supported arch.
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TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0;10.0;10.3;12.0;12.1+PTX"
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RUN apt-get update && apt-get install -y --no-install-recommends \
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software-properties-common ca-certificates curl git libgomp1 \
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gcc g++ \
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&& add-apt-repository -y ppa:deadsnakes/ppa \
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&& apt-get update && apt-get install -y --no-install-recommends \
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python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \
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&& ln -sf /usr/bin/python${PYTHON_VERSION} /usr/local/bin/python \
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&& ln -sf /usr/bin/python${PYTHON_VERSION} /usr/local/bin/python3 \
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&& rm -rf /var/lib/apt/lists/*
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# Why gcc + g++ + python3.12-dev in the RUNTIME stage:
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|
# Triton's nvidia backend lazily compiles a small C extension (CudaUtils) on
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# first GPU access. Without a C compiler + Python headers the very first
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# forward pass of any Unsloth model dies with:
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# RuntimeError: Failed to find C compiler. Please specify via CC env var.
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# Adds ~250MB to the runtime image, which is the cost of letting every kernel
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# JIT correctly. (Pre-compiling CudaUtils at build time would need a GPU, so
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# shipping the toolchain is the right trade-off.)
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COPY --from=builder /opt/unsloth-venv /opt/unsloth-venv
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|
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# DGX Spark / GB10 (sm_121) fix, arm64 ONLY.
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#
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# Two cu13 components need to override what the cu128 stack ships, because
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# nothing in CUDA 12.8 -- toolkit or wheel -- knows about sm_121:
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#
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# (1) torch's bundled libnvrtc.so.12 (from CUDA 12.8) does not accept
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# sm_121 as --gpu-architecture. The jiterator C++ side queries the
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# device cap directly, so any path that JIT-compiles a kernel (e.g.
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# torch.fft.rfft(complex).abs(), used inside mel-spectrogram code)
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# errors out. Fix: symlink libnvrtc.so.13 over the bundled .so.12.
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#
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# (2) Triton's nvidia backend invokes ptxas. Triton wheels older than
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# 3.6.0 bundled cu12.8 ptxas which tops out at sm_120 and refuses
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# sm_121, silently downgrading to sm_80 per triton-lang/triton#8335.
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# Triton 3.6.0 (which we pin above) bundles cu13 ptxas, but for
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# defense in depth we ALSO install cuda-nvcc-13-0 and point Triton
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# at it via TRITON_PTXAS_PATH.
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#
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# NVRTC and ptxas are CPU-side compilers; they do NOT call into libcuda,
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# so we can install cu13 alongside the cu128 runtime without any driver
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# requirement bump (toolkit driver floor stays 570+).
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#
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# amd64 image is untouched: no sm_121 hardware exists on amd64, and the
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# extra ~400 MB would be dead weight.
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RUN if [ "${TARGETARCH:-amd64}" = "arm64" ]; then \
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set -eux; \
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# SBSA = Server Base System Architecture; the NVIDIA repo path for
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# Grace / GH200 / GB200 / DGX Spark aarch64 hosts.
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curl -fsSL "https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/sbsa/cuda-keyring_1.1-1_all.deb" \
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-o /tmp/cuda-keyring.deb; \
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dpkg -i /tmp/cuda-keyring.deb; \
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rm /tmp/cuda-keyring.deb; \
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apt-get update; \
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apt-get install -y --no-install-recommends \
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cuda-nvrtc-13-0 \
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cuda-nvcc-13-0; \
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rm -rf /var/lib/apt/lists/*; \
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# (1) NVRTC swap. torch's wheel-bundled cu128 NVRTC -> cu13 NVRTC.
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NVRTC_DIR=/opt/unsloth-venv/lib/python${PYTHON_VERSION}/site-packages/nvidia/cuda_nvrtc/lib; \
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if [ -f "${NVRTC_DIR}/libnvrtc.so.12" ]; then \
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mv "${NVRTC_DIR}/libnvrtc.so.12" "${NVRTC_DIR}/libnvrtc.so.12.cu128.orig"; \
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|
ln -s /usr/local/cuda-13.0/lib64/libnvrtc.so.13 "${NVRTC_DIR}/libnvrtc.so.12"; \
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fi; \
|
|
fi
|
|
|
|
WORKDIR /workspace
|
|
RUN mkdir -p ${HF_HOME} ${TRITON_CACHE_DIR}
|
|
|
|
COPY smoke_test.py /workspace/smoke_test.py
|
|
COPY entrypoint.sh /usr/local/bin/unsloth-entrypoint
|
|
RUN chmod +x /usr/local/bin/unsloth-entrypoint
|
|
|
|
# Entrypoint runs three fast pre-flight checks before user code:
|
|
# 1. nvidia-smi sees at least one GPU (catches missing --gpus all)
|
|
# 2. torch.cuda.is_available() is True (catches host driver too old)
|
|
# 3. compute capability >= sm_80 (catches pre-Ampere GPUs)
|
|
# Each check fails with an actionable error pointing to the fix.
|
|
# Bypass for offline tooling: docker run -e UNSLOTH_SKIP_GPU_CHECK=1 ...
|
|
ENTRYPOINT ["/usr/local/bin/unsloth-entrypoint"]
|
|
|
|
# Default command: interactive python REPL.
|
|
# Override examples:
|
|
# docker run --gpus all unsloth/unsloth:latest python /workspace/smoke_test.py
|
|
# docker run --gpus all -it unsloth/unsloth:latest bash
|
|
CMD ["python"]
|