npu-docker-launcher

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:

1. Execute the command using Bash tool
2. Verify container is running:

   docker ps | grep <container-name>
   

3. 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 --privileged for 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:

1. Check environment (driver, NPU, Docker)
2. List available images
3. Recommend appropriate image based on task
4. Use defaults (privileged, host network, all NPUs)
5. 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>