58693c4c73
When someone launches the unsloth container, the common failure modes are not unsloth bugs -- they're Docker / nvidia-container-toolkit / driver issues that surface as cryptic CUDA errors deep in torch. The entrypoint catches the three that cover ~95% of "it doesn't work" reports up front: 1. nvidia-smi inside the container sees no GPU -> user forgot --gpus all, or host is missing nvidia-container-toolkit -> entrypoint prints the exact docker run flag and the toolkit install URL 2. nvidia-smi works but torch.cuda.is_available() is False -> host driver is older than CUDA 12.8 supports -> entrypoint prints the minimum driver version per architecture 3. compute capability < sm_80 -> entrypoint prints the supported architecture table and exits Each check fails with a clear, actionable message rather than a stack trace. Set UNSLOTH_SKIP_GPU_CHECK=1 to bypass (for docs builds, offline tooling, CI). run.sh wraps `docker run` with the flags people most often forget: --gpus all (without it, the new entrypoint refuses to start) --ipc=host (DataLoader workers need >64MB shm) --ulimit memlock=-1 (NCCL + CUDA pinned host buffers) --ulimit stack=64MB (some torch kernels OOM the default 8MB stack) Plus it mounts the host HF cache + Triton JIT cache so model downloads and compiled kernels persist across container runs, and forwards HF_TOKEN / WANDB_API_KEY / UNSLOTH_LICENSE only when they are set on the host. Usage: bash docker/run.sh # interactive python REPL bash docker/run.sh bash # shell in container bash docker/run.sh python /workspace/smoke_test.py bash docker/run.sh python /workspace/host/train.py # $PWD mounted at /workspace/host Verified locally: - No GPU visible: entrypoint refuses with driver-version message, exit 1 - B200 sm_100 visible: entrypoint prints GPU banner, exits cleanly into the user command (rc=0)
108 lines
4 KiB
Bash
Executable file
108 lines
4 KiB
Bash
Executable file
#!/usr/bin/env bash
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# Container startup checks for Unsloth.
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#
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# Fails fast with actionable error messages when the host GPU isn't reachable,
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# instead of letting torch crash deep with cryptic CUDA errors. Catches the
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# three failure modes that cover ~95% of "it doesn't work" tickets:
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#
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# 1. nvidia-smi inside the container can't see any GPU
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# - User forgot --gpus all
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# - Host missing nvidia-container-toolkit
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# 2. nvidia-smi works but torch.cuda.is_available() is False
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# - Host driver too old for CUDA 12.8
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# 3. GPU attaches but is older than Ampere (sm < 80)
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# - Unsloth requires sm_80+
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#
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# Bypass for offline tooling / docs / CI:
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# docker run -e UNSLOTH_SKIP_GPU_CHECK=1 ...
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set -euo pipefail
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if [[ "${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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# --- Check 1: nvidia-smi present and can enumerate at least one GPU ---------
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if ! command -v nvidia-smi >/dev/null 2>&1; then
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err "nvidia-smi not found inside the container."
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err "The CUDA runtime in this image is broken. Re-pull the image."
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exit 1
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fi
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if ! nvidia-smi -L 2>/dev/null | grep -q '^GPU'; then
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err "No GPU visible to nvidia-smi from 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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To bypass this check (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("Most likely the host NVIDIA driver is too old for CUDA 12.8.")
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print("Required host driver versions for this image:")
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print(" >= 570 RTX 50-series, RTX 6000 Pro Blackwell (sm_120)")
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print(" >= 555 B100 / B200 (sm_100)")
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print(" >= 535 H100 / H200 (sm_90)")
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print(" >= 525 Ada / Ampere (sm_80 / sm_86 / sm_89)")
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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 your GPU.")
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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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if major < 8:
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print()
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print(f"ERROR: Unsloth requires Ampere or newer (sm_80+). Got {name} sm_{major}{minor}.")
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print()
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print("Supported architectures baked into this image:")
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print(" sm_80 Ampere (A100, A40, A30)")
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print(" sm_86 Ampere (RTX 30-series, A10)")
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print(" sm_89 Ada (RTX 40-series, L40)")
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print(" sm_90 Hopper (H100, H200)")
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print(" sm_100 Blackwell DC (B100, B200)")
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print(" sm_120 Blackwell (RTX 50-series, RTX 6000 Pro Blackwell)")
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sys.exit(1)
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PY
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exec "$@"
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