gpu-container-setup-flagos（GPU 容器设置 flagos）

GPU Container Setup Skill

This skill automates multi-vendor GPU container setup for PyTorch workloads.

Supported GPU Vendors

Execution Flow

When invoked, follow these steps:

Step 1: Parse Arguments

Check if user provided:

• --vendor <name> - Force specific vendor (skip detection)
• --image <image> - Force specific container image
• --data <path> - Force specific data mount path
• --name <name> - Container name (default: pytorch-gpu)

Step 2: Detect GPU Vendor

Run the detection script:

python3 .claude/skills/gpu-container-setup/scripts/detect_gpu.py

Expected output:

{"vendor": "ascend", "devices": ["Ascend 910B"], "count": 8}

If detection fails and no --vendor flag provided, ask user which vendor to use.

Step 3: Find Data Disk

Run the data disk detection:

python3 .claude/skills/gpu-container-setup/scripts/find_data_disk.py

Expected output:

{"data_disk": "/mnt/data", "found": true, "size": "2.0T", "available": "1.5T"}

If no suitable disk found, ask user for data mount path.

Step 4: Find Container Image

Follow strict priority order (only proceed to next if current fails):

1. Primary Vendor Hub (hardcoded) → 2. BAAI Harbor → 3. Web Search → 4. Local Images → 5. Ask User

Step 4.1: Primary Vendor Hub (hardcoded URLs)

# Example: Query NGC for latest NVIDIA PyTorch
TAG=$(curl -s "https://api.ngc.nvidia.com/v2/repos/nvidia/pytorch/tags" | jq -r '.tags[].name' | grep -E '^[0-9]{2}\.[0-9]{2}-py3$' | sort -rV | head -1)
IMAGE="nvcr.io/nvidia/pytorch:${TAG}"

Step 4.2: BAAI Harbor (fallback)

Only if Step 4.1 fails (unreachable, no image, pull fails).

# Query BAAI Harbor
curl -s "https://harbor.baai.ac.cn/api/v2.0/projects/flagrelease-public/repositories?page_size=100" | jq -r '.[].name' | grep "flagrelease-<vendor>"

Step 4.3: Web Search (fallback)

Only if Steps 4.1 and 4.2 fail. Search for "<vendor> pytorch docker official".

Step 4.4: Local Images (fallback)

Only if Steps 4.1-4.3 fail. Check docker images | grep pytorch.

Test Before Use

docker pull "${IMAGE}" && docker run --rm "${IMAGE}" python -c "import torch; print(torch.__version__)"

If test fails, try next source. If all fail, ask user for image.

Step 4.5: Update Skill (self-improvement)

IMPORTANT: If image found via Web Search (Step 4.3) passes all tests, update references/image-sources.md to add the newly discovered vendor hub as a primary source. This makes future lookups faster.

# After successful web search discovery:
# 1. Verify image works (pull + pytorch test + GPU test)
# 2. Extract registry URL pattern
# 3. Update references/image-sources.md Step 1 section with new vendor hub

Step 5: Build Docker Command

Refer to references/mount-requirements.md for vendor-specific requirements.

NVIDIA:

docker run -d --gpus all \
  --name pytorch-gpu \
  --shm-size=16g \
  -v <data_disk>:/data \
  <image> sleep infinity

AMD/ROCm:

docker run -d \
  --device=/dev/kfd --device=/dev/dri \
  --group-add video --group-add render \
  --name pytorch-gpu \
  --shm-size=16g \
  -v <data_disk>:/data \
  <image> sleep infinity

Ascend:

docker run -d \
  --device=/dev/davinci0 --device=/dev/davinci1 ... \
  --device=/dev/davinci_manager \
  --device=/dev/devmm_svm \
  --device=/dev/hisi_hdc \
  -v /usr/local/Ascend:/usr/local/Ascend:ro \
  -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi:ro \
  --name pytorch-gpu \
  --shm-size=16g \
  -v <data_disk>:/data \
  <image> sleep infinity

Metax:

docker run -d \
  --device=/dev/mx0 --device=/dev/mx1 ... \
  -v /opt/metax:/opt/metax:ro \
  --name pytorch-gpu \
  --shm-size=16g \
  -v <data_disk>:/data \
  <image> sleep infinity

Iluvatar:

docker run -d \
  --device=/dev/bi0 --device=/dev/bi1 ... \
  -v /opt/iluvatar:/opt/iluvatar:ro \
  --name pytorch-gpu \
  --shm-size=16g \
  -v <data_disk>:/data \
  <image> sleep infinity

Step 6: Start Container

Execute the docker run command. If container with same name exists:

1. Check if it's running - offer to use existing or replace
2. If stopped - offer to restart or replace

Step 7: Validate PyTorch GPU

Copy and run validation script inside container:

docker cp .claude/skills/gpu-container-setup/scripts/validate_pytorch.py pytorch-gpu:/tmp/
docker exec pytorch-gpu python3 /tmp/validate_pytorch.py

Expected output:

{
  "status": "PASS",
  "backend": "npu",
  "device_count": 8,
  "device_names": ["Ascend 910B", ...],
  "tests": {
    "device_detection": true,
    "tensor_creation": true,
    "matrix_multiply": true,
    "gpu_to_cpu_transfer": true
  }
}

Step 8: Report Results

Summarize to user:

• GPU vendor and devices detected
• Container name and image used
• Data mount path
• Validation status
• How to access: docker exec -it pytorch-gpu bash

Error Handling

Reference Files

• references/gpu-detection.md - Detection methods by vendor
• references/image-sources.md - Image discovery guide (registry APIs, priority order, selection criteria)
• references/mount-requirements.md - Vendor mount specifications

Example Usage

User: /gpu-container-setup
User: setup a pytorch container
User: start container with ascend GPU
User: /gpu-container-setup --image nvcr.io/nvidia/pytorch:24.01-py3
User: /gpu-container-setup --image harbor.baai.ac.cn/flagrelease-public/ngctorch:2601