ascend-ai-coding NPU Docker Container Launcher
Overview
This skill directly launches Docker containers on Ascend NPU servers. When invoked, perform pre-launch checks, gather user requirements, and execute the appropriate docker run command.
Workflow
1. Pre-Launch Environment Checks
MUST perform these checks before proceeding:
1.1 Check NPU Driver Availability
# Check if Ascend driver is installed
ls -l /usr/local/Ascend/driver/
# Check driver version (optional)
cat /usr/local/Ascend/driver/version.info 2>/dev/null || echo "Driver version info not found"
If driver directory doesn't exist, STOP and inform user: "Ascend driver not found at /usr/local/Ascend/driver/. Please install the driver first."
1.2 Check NPU Device Availability
# List NPU devices
ls -l /dev/davinci* 2>/dev/null
# Check NPU status using npu-smi
npu-smi info 2>/dev/null || echo "npu-smi not available"
If no /dev/davinci* devices found, STOP and inform user: "No NPU devices detected. Please check hardware and driver installation."
1.3 Check Docker Service
# Check if Docker is running
docker info > /dev/null 2>&1 && echo "Docker is running" || echo "Docker is not running"
If Docker is not running, STOP and inform user.
2. Query Available Docker Images
Query and display available images before asking user:
# List all local Docker images
docker images --format "table {{.Repository}}\t{{.Tag}}\t{{.Size}}\t{{.CreatedAt}}"
Display the images to the user in a formatted table. Then ask about their task and recommend appropriate images based on:
- PyTorch/TensorFlow training: Look for images with "pytorch", "tensorflow", "ascend-pytorch", "ascend-tf"
- MindSpore training: Look for images with "mindspore"
- Inference: Look for images with "inference", "serving", "vllm", "mindie"
- Development: Look for images with "dev", "devel", or general framework images
3. Gather Requirements
After showing available images and getting user's task, ask:
## Docker Container Configuration
1. Container name: (e.g., my-pytorch-env)
2. Privileged mode: (default: yes) - Grants full device access [yes/no]
3. Network mode: (default: host) or specify port mappings [host/custom]
4. NPU cards: Which cards to use? (default: all, or specific like "0,1,2") [all/specific]
5. Mount directories: (format: host:container, comma-separated)
Example: /home/user:/home/user,/data:/data
6. Work directory inside container: (e.g., /home/user)
7. Environment variables: (space-separated, e.g., "VAR1=value1 VAR2=value2")
8. Additional options: (e.g., auto-remove --rm, shm-size)
4. Build Docker Run Command
Default command structure:
docker run -it -d \
--name <container-name> \
--privileged \
--network host \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin \
-v <user-mounts> \
-w <work-dir> \
[-e ENV_VARS...] \
<image> \
/bin/bash
Key defaults:
-it -d: Interactive terminal + detached mode (ALWAYS use both)/bin/bash: Default command to run (ALWAYS include at the end)--privileged: Default for NPU access--network host: Default network mode
Command Components:
Required Arguments:
| Argument | Default | Description |
|---|---|---|
-it -d |
Always | Interactive + detached |
--name |
Required | Container name |
--privileged |
yes | Full device access for NPU |
--network host |
host | Network mode |
Driver Mounts (ALWAYS include):
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin
Optional Arguments:
| Argument | When to Use |
|---|---|
--device=/dev/davinci0 |
Specific NPU card (instead of --privileged) |
-p host:container |
Port mapping (instead of --network host) |
--shm-size=16g |
Large shared memory for distributed training |
--rm |
Auto-remove container on exit |
-e VAR=value |
Environment variables |
5. Show Command and Confirm
Display the complete command and ask for confirmation:
📋 Generated Docker Run Command:
docker run -it -d \
--name my-container \
--privileged \
--network host \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin \
-v /home/user:/home/user \
-w /home/user \
pytorch/pytorch:latest \
/bin/bash
Proceed with container launch? [yes/no]
6. Execute and Verify
After user confirms:
- Execute the command using Bash tool
- Verify container is running:
docker ps | grep <container-name> - Report status with access instructions
7. Report Status
✅ Container started successfully
- Container ID: abc123def456
- Container name: my-container
- Image: pytorch/pytorch:latest
- Status: Running
📌 Access Instructions:
- Enter container: docker exec -it my-container bash
- View logs: docker logs -f my-container
- Stop container: docker stop my-container
- Remove container: docker rm my-container
Decision Rules
Privileged Mode vs Device Mounting
| Approach | When to Use | Command |
|---|---|---|
| Privileged (default) | Development, multiple NPU cards, quick setup | --privileged |
| Device Mounting | Production, specific cards, security required | --device=/dev/davinci0 |
Rule: Default to --privileged unless user explicitly requests device mounting.
Network Mode
| Mode | When to Use | Command |
|---|---|---|
| Host (default) | Development, local access, no port conflicts | --network host |
| Bridge | Production, need port isolation, service deployment | --network bridge -p 8080:80 |
Rule: Default to --network host for NPU servers.
NPU Card Selection
- all (default): Use
--privilegedfor all NPU access - specific: Use
--device=/dev/davinci{i}for each card
Common Scenarios
Scenario 1: Development Environment (Most Common)
User request: "帮我启动一个开发容器" or "I want a dev container"
Steps:
- Check environment (driver, NPU, Docker)
- List available images
- Recommend appropriate image based on task
- Use defaults (privileged, host network, all NPUs)
- Mount home directory
Command:
docker run -it -d \
--name pytorch-dev \
--privileged \
--network host \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin \
-v /home/user:/home/user \
-w /home/user \
pytorch/pytorch:latest \
/bin/bash
Scenario 2: Single NPU Card Training
User request: "我只用卡0训练"
Command:
docker run -it -d \
--name train-card0 \
--network host \
--device=/dev/davinci0 \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin \
-v /data:/data \
-w /data \
-e ASCEND_DEVICE_ID=0 \
ascend-pytorch:latest \
/bin/bash
Scenario 3: Multi-Card Distributed Training
User request: "我要用所有卡进行分布式训练"
Command:
docker run -it -d \
--name distributed-train \
--privileged \
--network host \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin \
-v /home/user:/workspace \
-w /workspace \
--shm-size=16g \
-e ASCEND_VISIBLE_DEVICES=0,1,2,3 \
-e RANK_SIZE=4 \
ascend-mindspore:latest \
/bin/bash
Scenario 4: Inference Service with Port Mapping
User request: "我需要启动一个推理服务，端口8080"
Command:
docker run -it -d \
--name inference-svc \
--network bridge \
-p 8080:8000 \
--device=/dev/davinci0 \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/sbin:/usr/local/sbin \
-v /models:/models \
-w /models \
ascend-inference:latest \
/bin/bash
Pre-Launch Checklist
Before executing docker run, verify:
- Ascend driver exists at
/usr/local/Ascend/driver/ - NPU devices available (
/dev/davinci*) - Docker service is running
- Image exists locally or can be pulled
- Container name doesn't conflict with existing container
- User confirmed the command
Error Handling
| Error | Solution |
|---|---|
| "driver not found" | Ask user to install Ascend driver |
| "No NPU devices" | Check hardware connection and driver |
| "Docker not running" | Start Docker: systemctl start docker |
| "Image not found" | Pull image or use different image |
| "Container name exists" | Remove old: docker rm -f <name> or use new name |
| "Permission denied" | Check user is in docker group or use sudo |
Reference Files
- npu-devices.md - NPU device querying and mounting details
- examples.md - More detailed examples
Quick Reference Commands
# List local images
docker images
# Check NPU devices
ls -l /dev/davinci* && npu-smi info
# List running containers
docker ps
# Enter container
docker exec -it <name> bash
# View logs
docker logs -f <name>
# Stop container
docker stop <name>
# Remove container
docker rm <name>
# Force remove container
docker rm -f <name>