env-install

Install MindIE-SD and third-party inference frameworks on Ascend NPU environments.

14|5|Updated Mar 30, 2026
One-click install
npx skills add https://github.com/Ascend/MindIE-SD --skill env-install-ascend
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: env-install
Source: https://github.com/Ascend/MindIE-SD/tree/main/.agents/skills/env-install
Command: npx skills add https://github.com/Ascend/MindIE-SD --skill env-install-ascend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires paramiko, and includes scripts (resource) and references (resource) components.

What problem does it solve? Setting up a working Ascend NPU inference environment involves compiling MindIE-SD from source, building the full vLLM-Omni stack (torch, torch_npu, vllm, vllm-ascend, vllm-omni), deploying LightX2V and DiffSynth-Engine, and downloading large model weights to remote containers. This Skill consolidates those installation paths, version compatibility matrices, and known pitfalls into one guided workflow. ## Core Features & Use Cases - MindIE-SD Compile & Install: Local Ascend direct install, SSH push to a remote Docker container via an incremental deploy script, or direct use of official prebuilt images, with compatibility prechecks (PyTorch, TorchNPU, CANN, Python) and build workarounds such as the build_tik_ops.sh fix. - Third-Party Framework Full-Stack Builds: Source builds of the vLLM-Omni stack when official images do not cover the target architecture, plus DiffSynth-Engine and LightX2V editable deployments with the required PLATFORM=ascend_npu setup. - Model Weight Preparation: Confirm whether weights already exist remotely before downloading, then fetch partitions from ModelScope (default) or HuggingFace with integrity verification. - Use Case: You need to push modified MindIE-SD code from a local dev machine into a remote Ascend container, compile it, and prepare MiniMax-H3 weights for vLLM-Omni serving. The Skill walks you through credential confirmation, incremental transfer, in-container build, and weight validation. ## Quick Start Ask the assistant to install MindIE-SD and the vLLM-Omni stack into your remote Ascend container and prepare the required model weights.

Frequently Asked Questions about env-install

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I install MindIE-SD on a remote Ascend NPU container?▼

Use the deploy_to_remote.py script with host, user, workspace, container, and local-root arguments. It incrementally transfers source files over SFTP, converts CRLF line endings, then runs python setup.py build_py and pip install -e . inside the container after sourcing the CANN environment.

How do I build the vLLM-Omni full stack from source for Ascend?▼

When official images do not cover your architecture, install torch 2.11.0+cpu and matching torch_npu, then build vllm with VLLM_TARGET_DEVICE=empty, vllm-ascend with --no-deps --no-build-isolation, and vllm-omni with VLLM_OMNI_TARGET_DEVICE=npu. Finish by building mindiesd with triton-ascend 3.2.1.

Why does importing lightx2v fail with ERR99999 on Ascend?▼

The error occurs because PLATFORM=ascend_npu was not exported before importing lightx2v, so device initialization follows the default platform path. Export PLATFORM=ascend_npu first; it is an environment configuration issue, not a code problem.

Should I download model weights from ModelScope or HuggingFace?▼

ModelScope is the default because it is reachable domestically without a proxy and its mirrors of HF gated repos usually need no authentication. Use HuggingFace with a token only when ModelScope lacks the repo or a specific HF revision is required.

Why does vllm multi-card startup fail with hcclCommInitRootInfoConfig error code 4?▼

The container is missing the HCCL ranktable directory. Mount or copy /usr/local/Ascend/driver/topo into the container at the same path, either at docker run time or afterward with docker cp.

What are the limits of this environment installation workflow?▼

It stops at installation completion: successful mindiesd import, matched versions, and weights in place. Service startup, feature enablement, performance tier selection, accuracy judgment, and SSH transport troubleshooting are delegated to separate skills.