f4e378e8b5
entrypoint.sh: a container started without a GPU request has no
nvidia-smi at all (the toolkit injects it), so the old check 1 reported
'CUDA runtime in this image is broken, re-pull' for the most common user
error. Fold the missing-binary case into the actionable 'No GPU visible'
message and document the CPU-only option (UNSLOTH_ALLOW_CPU=1).
run.sh / test_locally.sh: guard empty-array expansions with the
${arr[@]+...} form; bash 3.2 (macOS /bin/bash) treats "${empty[@]}"
as unbound under set -u, which broke the documented macOS CPU path.
studio_launch.sh: exclude *_TOKEN, *_API_KEY, *_PASSWORD, *_SECRET,
*_LICENSE from the env snapshot written for SSH sessions; secrets stay
in process env only, never on disk.
supervisord.conf / Dockerfile.studio: pin HOME=/root for the studio and
jupyter programs (jupyter would silently fall back to token auth if HOME
were unset), default JUPYTER_PORT and UNSLOTH_ENABLE_SSHD at the image
level so a direct supervisord invocation cannot hit a bad %(ENV_*)s
expansion, and document the root-services decision (non-root parity with
the previous production image is a tracked follow-up).
docker_confirm.ps1: mirror the bash script's GPU selector translation so
GPUS=0 / 0,1 select devices instead of silently using all GPUs.
docker-publish.yml: studio cache scope moves to mode=min; a mode=max
cache of a ~24GB image would evict everything else in the 10GB GHA
quota for no hit-rate gain.
149 lines
6.3 KiB
Bash
Executable file
149 lines
6.3 KiB
Bash
Executable file
#!/usr/bin/env bash
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# Container startup checks for Unsloth.
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#
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# Fails fast with actionable error messages when the host GPU isn't reachable,
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# instead of letting torch crash deep with cryptic CUDA errors. Catches the
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# three failure modes that cover ~95% of "it doesn't work" tickets:
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#
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# 1. nvidia-smi inside the container can't see any GPU
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# - User forgot --gpus all
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# - Host missing nvidia-container-toolkit
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# 2. nvidia-smi works but torch.cuda.is_available() is False
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# - Host driver too old for CUDA 12.8
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# 3. GPU attaches but is older than Ampere (sm < 80)
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# - Unsloth requires sm_80+
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#
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# Bypass for offline tooling / docs / CI:
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# docker run -e UNSLOTH_SKIP_GPU_CHECK=1 ...
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set -euo pipefail
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# DGX Spark fix, arm64 image only: prefer the cu13 ptxas we baked into the
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# image at /usr/local/cuda-13.0/bin/ptxas over Triton's bundled tools. The
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# file only exists on the arm64 variant; amd64 images skip this and use
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# Triton's own ptxas (cu13 in triton>=3.6.0).
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if [[ -x /usr/local/cuda-13.0/bin/ptxas ]] && [[ -z "${TRITON_PTXAS_PATH:-}" ]]; then
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export TRITON_PTXAS_PATH=/usr/local/cuda-13.0/bin/ptxas
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fi
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if [[ "${UNSLOTH_SKIP_GPU_CHECK:-0}" == "1" ]]; then
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exec "$@"
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fi
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err() { printf "\033[1;31mERROR:\033[0m %s\n" "$*" >&2; }
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warn() { printf "\033[1;33mWARN:\033[0m %s\n" "$*" >&2; }
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# CPU mode for hosts that cannot pass a GPU into a Linux container at all:
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# Docker Desktop on macOS (no Metal passthrough), Docker Desktop on Windows
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# without WSL2 GPU support, plain CPU Linux boxes, and CI runners. Training
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# needs an NVIDIA GPU, but Jupyter, GGUF tooling (the baked llama.cpp), and
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# Studio chat / Data Recipes all work on CPU. With UNSLOTH_ALLOW_CPU=1 a
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# missing GPU degrades to a warning instead of the hard pre-flight failure;
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# when a GPU IS visible the normal checks below still run so a broken GPU
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# setup is not silently ignored.
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if [[ "${UNSLOTH_ALLOW_CPU:-0}" == "1" ]]; then
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if ! command -v nvidia-smi >/dev/null 2>&1 || ! nvidia-smi -L 2>/dev/null | grep -q '^GPU'; then
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warn "UNSLOTH_ALLOW_CPU=1 and no GPU visible -- continuing on CPU."
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warn "Training requires an NVIDIA GPU. CPU mode covers Jupyter, GGUF tooling and Studio chat."
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exec "$@"
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fi
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fi
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# --- Check 1: nvidia-smi present and can enumerate at least one GPU ---------
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# nvidia-smi is injected by nvidia-container-toolkit when the container is
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# started with a GPU request; it is NOT baked into the image. A missing
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# binary therefore means "no GPU was attached", the same failure class as
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# an empty -L listing, not a broken image.
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if ! command -v nvidia-smi >/dev/null 2>&1 || ! nvidia-smi -L 2>/dev/null | grep -q '^GPU'; then
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err "No GPU visible inside the container."
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cat >&2 <<'MSG'
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Likely causes (in order of frequency):
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1. You started the container without --gpus all.
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Re-launch with:
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docker run --gpus all <other-flags> unsloth/unsloth:latest <cmd>
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Or use the bundled wrapper:
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bash docker/run.sh <cmd>
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2. Host is missing nvidia-container-toolkit.
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Install: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
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Then: sudo systemctl restart docker
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3. nvidia-container-toolkit is installed but the Docker daemon was not
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restarted after install. Run:
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sudo systemctl restart docker
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4. You are using Podman / Kubernetes / a managed container service that
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needs a different GPU flag than --gpus all. See the relevant docs:
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podman: --device nvidia.com/gpu=all
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k8s: nvidia.com/gpu resource request + GPU operator
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5. This host has no NVIDIA GPU at all (Docker Desktop on macOS, Windows
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without WSL2 GPU support, CPU-only Linux). Training needs a GPU, but
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Jupyter, GGUF tooling and Studio chat work on CPU:
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docker run -e UNSLOTH_ALLOW_CPU=1 ...
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To bypass this check entirely (e.g. offline tooling), set UNSLOTH_SKIP_GPU_CHECK=1.
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MSG
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exit 1
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fi
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# --- Check 2: torch can actually use the GPU --------------------------------
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# This catches host-driver-too-old (the GPU enumerates via nvidia-smi but
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# the kernel module rejects CUDA contexts).
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python - >&2 <<'PY' || exit 1
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import sys
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import torch
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if torch.cuda.is_available():
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sys.exit(0)
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print("ERROR: torch.cuda.is_available() is False despite nvidia-smi working.")
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print()
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print("This image bakes in CUDA 12.8, so the host driver MUST be:")
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print(" >= 570.26 (toolkit floor for cu128, applies to every GPU)")
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print()
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print("Two GPUs need an even newer driver because their launch driver was")
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print("released after cu128's:")
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print(" >= 580 B300 / GB300 (sm_103)")
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print(" >= 580 GB10 / DGX Spark (sm_121)")
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print()
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print("Check the host (NOT the container) with: nvidia-smi")
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print("Then upgrade the driver to match.")
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sys.exit(1)
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PY
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# --- Check 3: compute capability is supported -------------------------------
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python - >&2 <<'PY' || exit 1
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import sys
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import torch
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major, minor = torch.cuda.get_device_capability(0)
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name = torch.cuda.get_device_name(0)
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n = torch.cuda.device_count()
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print(f"Unsloth container: {n} GPU(s). Primary: {name} sm_{major}{minor} bf16={torch.cuda.is_bf16_supported()}")
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# Image targets every current x86_64 NVIDIA arch from Turing onward, per
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# https://developer.nvidia.com/cuda/gpus.
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SUPPORTED = (
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("sm_75", "Turing", "T4, RTX 20-series, Quadro RTX"),
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("sm_80", "Ampere DC", "A100, A30"),
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("sm_86", "Ampere", "A40, RTX A6000, RTX 30-series"),
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("sm_89", "Ada", "L4, L40, L40S, RTX 40-series"),
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("sm_90", "Hopper", "H100, H200, GH200"),
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("sm_100", "Blackwell DC", "B100, B200, GB200"),
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("sm_103", "Blackwell DC", "B300, GB300"),
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("sm_120", "Blackwell", "RTX 50-series, RTX PRO 6000 Blackwell"),
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("sm_121", "Blackwell", "GB10 (DGX Spark)"),
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)
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if major < 7 or (major == 7 and minor < 5):
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print()
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print(f"ERROR: Unsloth image requires Turing or newer (sm_75+). Got {name} sm_{major}{minor}.")
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print()
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print("Supported architectures in this image:")
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for arch, fam, ex in SUPPORTED:
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print(f" {arch:7s} {fam:13s} ({ex})")
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sys.exit(1)
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if major < 8:
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print(f"NOTE: {name} is Turing (sm_{major}{minor}) -- bfloat16 is not supported.")
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print(" Unsloth will fall back to fp16. Training works but is slightly slower.")
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PY
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exec "$@"
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