ascend-docker
Ascend Docker Container
Create Docker containers configured for Huawei Ascend NPU development.
Quick Start
# Privileged mode (default, auto-detect all devices)
./scripts/run-ascend-container.sh <image> <container_name>
# Basic mode with specific devices
./scripts/run-ascend-container.sh <image> <container_name> --mode basic
# Full mode with selected devices
./scripts/run-ascend-container.sh <image> <container_name> --mode full --device-list "0,1,2,3"
Device Selection
More from ascend-ai-coding/awesome-ascend-skills
npu-smi
Huawei Ascend NPU npu-smi command reference. Use for device queries (health, temperature, power, memory, processes, ECC), configuration (thresholds, modes, fan), firmware upgrades (MCU, bootloader, VRD), virtualization (vNPU), and certificate management.
67atc-model-converter
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.
55hccl-test
HCCL (Huawei Collective Communication Library) performance testing for Ascend NPU clusters. Use for testing distributed communication bandwidth, verifying HCCL functionality, and benchmarking collective operations like AllReduce, AllGather. Covers MPI installation, multi-node pre-flight checks (SSH/CANN version/NPU health), and production testing workflows.
54ascendc
AscendC transformer/GMM/MoE 算子与 Matmul/Cube Kernel 的统一开发规范。用于在 ops-transformer 下新增或修改 op_host、tiling/infershape、op_kernel（含 MatmulImpl/Cube 调用）、以及对应的 CANN aclnn 示例和单测。
51msmodelslim
Huawei Ascend NPU model compression tool (msModelSlim). Use for LLM quantization (W4A8, W8A8, W8A8S, W8A16), MoE model compression, multimodal model compression (Qwen-VL, InternVL, HunyuanVideo, FLUX, SD3), calibration data preparation, precision auto-tuning, sensitive layer analysis, custom model integration, and deployment in MindIE/vLLM-Ascend. Supports Qwen, LLaMA, DeepSeek, GLM, Kimi, InternLM and more.
44vllm-ascend
vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU. Use for offline batch inference, API server deployment, quantization inference (with msmodelslim quantized models), tensor/pipeline parallelism for distributed serving, and OpenAI-compatible API endpoints. Supports Qwen, DeepSeek, GLM, LLaMA models with Ascend-optimized kernels.
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