[tests, docker, docs, scripts, ops] chore: remove internal-only assets and references

Drop internal build/CI assets and replace internal-infrastructure defaults
with neutral placeholders so a fresh clone is self-contained for external
users.

Packaging / infra:
- drop the internal pip index and --trusted-host from ops/requirements.txt
- remove the internal XPU docker assets and the P800 install tutorial
  (both the English and Chinese copies), and drop the toctree entries and
  cross-links that pointed at it
- ignore local AI-assistant rule directories

Examples / configs:
- default the Kimi NVFP4 blobfile install to the public PyPI index, with
  BLOBFILE_PIP_INDEX / BLOBFILE_PIP_TRUSTED_HOST as opt-in mirror overrides
- replace internal cluster paths and dataset locations in examples, configs
  and tests/embodied/config/prepare.sh with placeholders

LLM/VLM E2E suite:
- remove prepare_env.sh / download_datasets.sh, which bootstrapped models
  and datasets from internal object storage, plus their invocations in
  main_start.sh and pipeline_runner.sh
- drop the --ak / --sk credential passthrough and --skip_env
- remove per-model bos_path entries from the test configs
- remove the internal docker build assets and unreleased-chip baselines
- remove the internal pipeline launcher (ipipe_start.sh), its PyTorchJob
  yaml templates, and the internal notification helpers
  (utils/send_msg.sh, utils/HiRobotApi.py), along with the k8s/pipeline
  plumbing they required in common/common.sh and main.py

Change-Id: I161b8ef486803c2b248ac2b96ef4ef566fe18668

Author: zhangqiu01

.gitignore

@@ -96,3 +96,6 @@ nsight_*/
 
 # Launch / marketing kit (local only, not for publishing)
 /launch-kit/
+
+# Local AI coding assistant rules
+/.comate/

CLAUDE.md

@@ -41,15 +41,12 @@ sh build.sh    # Creates output tarball
 
 E2E tests use a custom YAML-driven framework (`tests/llm_vlm/main.py`), not pytest.
 
-```bash
-# Download test datasets first
-bash tests/llm_vlm/download_datasets.sh
+The entry script does NOT download artifacts. Provision the datasets, HuggingFace base
+models, and pre-converted checkpoints referenced by the selected configs first, then run:
 
-# Run default CI test suite (all models in tests/llm_vlm/configs/)
+```bash
+# Run the default CI suite (all models in tests/llm_vlm/configs/)
 bash tests/llm_vlm/main_start.sh
-
-# Run optional regression tests (tests/llm_vlm/optional_configs/)
-bash tests/llm_vlm/main_start.sh --optional
 ```
 
 ### Running a Single Model Test

README.md

@@ -72,7 +72,6 @@ See the full documentation for installation, tutorials, and advanced usage — [
 
 **1. Install** — using **prebuilt Docker images** or **source build**:
 - **NVIDIA GPU**: [Installation Guide](https://loongforge.readthedocs.io/en/latest/get_started/installation.html)
-- **Kunlun XPU**: [Installation Guide](https://loongforge.readthedocs.io/en/latest/kunlun_tutorial/install_p800.html)
 
 **2. Launch your first training run** — follow a tutorial for your target hardware and modality:
 - **NVIDIA GPU**: [LLM](https://loongforge.readthedocs.io/en/latest/llm_tutorial/quick_start_llm_pretrain.html) · [VLM](https://loongforge.readthedocs.io/en/latest/vlm_tutorial/quick_start_vlm_pretrain.html) · [VLA & WAM](https://loongforge.readthedocs.io/en/latest/embodied_tutorial/overview.html) · [Diffusion](https://loongforge.readthedocs.io/en/latest/wan_tutorial/quick_start_wan_training.html)
@@ -240,7 +239,7 @@ LoongForge/
 ├── tools/                        # Checkpoint conversion, data preprocessing
 ├── ops/                          # Custom fused operators (incl. open-sourced TileLang)
 ├── patches/                      # TransformerEngine patches
-├── docker/                       # Dockerfiles (GPU & XPU)
+├── docker/                       # Dockerfile (GPU)
 ├── tests/                        # E2E test suite (YAML-driven)
 └── docs/                         # Documentation
 ```

README_zh.md

@@ -72,7 +72,6 @@
 
 **1. 安装** —— 可使用 **预构建镜像** 或 **源码构建**：
 - **NVIDIA GPU**：[安装指南](https://loongforge.readthedocs.io/zh-cn/latest/get_started/installation.html)
-- **昆仑芯 XPU**：[安装指南](https://loongforge.readthedocs.io/zh-cn/latest/kunlun_tutorial/install_p800.html)
 
 **2. 启动你的第一个训练任务** —— 根据目标硬件与模态选择教程：
 - **NVIDIA GPU**：[LLM](https://loongforge.readthedocs.io/zh-cn/latest/llm_tutorial/quick_start_llm_pretrain.html) · [VLM](https://loongforge.readthedocs.io/zh-cn/latest/vlm_tutorial/quick_start_vlm_pretrain.html) · [VLA & WAM](https://loongforge.readthedocs.io/zh-cn/latest/embodied_tutorial/overview.html) · [Diffusion (WAN)](https://loongforge.readthedocs.io/zh-cn/latest/wan_tutorial/quick_start_wan_training.html)
@@ -239,7 +238,7 @@ LoongForge/
 ├── tools/                        # Checkpoint 转换、数据预处理
 ├── ops/                          # 自定义融合算子（含开源的 TileLang 版本）
 ├── patches/                      # TransformerEngine 补丁
-├── docker/                       # Dockerfile（GPU & XPU）
+├── docker/                       # Dockerfile（GPU）
 ├── tests/                        # 端到端测试（YAML 驱动）
 └── docs/                         # 文档
 ```

configs/models/deepseek4/deepseek_v4_flash_lite_4l.yaml

@@ -1,5 +1,5 @@
 # DeepSeek-V4 Flash-Base lite (4 layers + 1 MTP) — matches
-# /ssd3/weizhihao/ckpt/DeepSeek-V4-Flash-Base-lite-lay4L-mtp1/config.json
+# DeepSeek-V4-Flash-Base-lite-lay4L-mtp1/config.json
 _target_: loongforge.models.foundation.deepseek_v4.DeepseekV4Config
 
 num_layers: 4

docker/Dockerfile.xpu

@@ -14,8 +14,8 @@
 - **CUDA Toolkit**: >= 12.1
 - **OS**: Linux (Ubuntu 22.04 / 24.04 recommended)
 
-Note: For Kunlun XPU installation, see the
-[Kunlun Installation Guide](../kunlun_tutorial/install_p800.md).
+Note: For Kunlun XPU, see the
+[Kunlun XPU tutorials](../kunlun_tutorial/README.md).
 
 ## Prerequisites