Merge pull request #1024 from kvcache-ai/mian-update-doc

delete sudo install

doc/en/balance-serve.md

 # Balance Serve backend (multi-concurrency) for ktransformers
 
 ## KTransformers v0.2.4 Release Notes
+
 We are excited to announce the official release of the long-awaited **KTransformers v0.2.4**!
 In this version, we’ve added highly desired **multi-concurrency** support to the community through a major refactor of the whole architecture, updating more than 10,000 lines of code.
 By drawing inspiration from the excellent architecture of sglang, we have implemented high-performance asynchronous concurrent scheduling in C++, including features like continuous batching, chunked prefill, and more. Thanks to GPU sharing in concurrent scenarios, overall throughput is also improved to a certain extent. The following is a demonstration:
 
-
-
 https://github.com/user-attachments/assets/faa3bda2-928b-45a7-b44f-21e12ec84b8a
 
 </p>
 
 ### 🚀 Key Updates
+
 1. Multi-Concurrency Support
    - Added capability to handle multiple concurrent inference requests. Supports receiving and executing multiple tasks simultaneously.
    - We implemented [custom_flashinfer](https://github.com/kvcache-ai/custom_flashinfer/tree/fix-precision-mla-merge-main) based on the high-performance and highly flexible operator library [flashinfer](https://github.com/flashinfer-ai/flashinfer/), and achieved a variable batch size CUDA Graph, which further enhances flexibility while reducing memory and padding overhead.
    - In our benchmarks, overall throughput improved by approximately 130% under 4-way concurrency.
    - With support from Intel, we tested KTransformers v0.2.4 on the latest Xeon6 + MRDIMM-8800 platform. By increasing concurrency, the total output throughput increased from 17 tokens/s to 40 tokens/s. We observed that the bottleneck has now shifted to the GPU. Using a higher-end GPU than the 4090D could further improve performance.
 2. Engine Architecture Optimization
- ![image](https://github.com/user-attachments/assets/f5f001fa-dca7-4377-a01a-32192902aa47)
-  Inspired by the scheduling framework of sglang, we refactored KTransformers with a clearer three-layer architecture through an update of 11,000 lines of code, now supporting full multi-concurrency:
+   ![image](https://github.com/user-attachments/assets/f5f001fa-dca7-4377-a01a-32192902aa47)
+   Inspired by the scheduling framework of sglang, we refactored KTransformers with a clearer three-layer architecture through an update of 11,000 lines of code, now supporting full multi-concurrency:
    - Server：Handles user requests and serves the OpenAI-compatible API.
    - Inference Engine：Executes model inference and supports chunked prefill.
    - Scheduler：Manages task scheduling and requests orchestration. Supports continuous batching by organizing queued requests into batches in a FCFS manner and sending them to the inference engine.
 3. Project Structure Reorganization
-All C/C++ code is now centralized under the /csrc directory.
+   All C/C++ code is now centralized under the /csrc directory.
 4. Parameter Adjustments
-Removed some legacy and deprecated launch parameters for a cleaner configuration experience.
-We plan to provide a complete parameter list and detailed documentation in future releases to facilitate flexible configuration and debugging.
+   Removed some legacy and deprecated launch parameters for a cleaner configuration experience.
+   We plan to provide a complete parameter list and detailed documentation in future releases to facilitate flexible configuration and debugging.
+
 ### 📚 Upgrade Notes
+
 - Due to parameter changes, users who have installed previous versions are advised to delete the ~/.ktransformers directory and reinitialize.
 - To enable multi-concurrency, please refer to the latest documentation for configuration examples.
-### What's Changed
-Implemented **custom_flashinfer** @Atream @ovowei @qiyuxinlin
-Implemented **balance_serve** engine based on **FlashInfer** @qiyuxinlin @ovowei 
-Implemented a **continuous batching** scheduler in C++ @ErvinXie 
-release: bump version v0.2.4 by @Atream @Azure-Tang @ErvinXie  @qiyuxinlin @ovowei @KMSorSMS @SkqLiao 
 
+### What's Changed
 
+Implemented **custom_flashinfer** @Atream @ovowei @qiyuxinlin
+Implemented **balance_serve** engine based on **FlashInfer** @qiyuxinlin @ovowei
+Implemented a **continuous batching** scheduler in C++ @ErvinXie
+release: bump version v0.2.4 by @Atream @Azure-Tang @ErvinXie  @qiyuxinlin @ovowei @KMSorSMS @SkqLiao
 
 ## Installation Guide
+
 ⚠️ Please note that installing this project will replace flashinfer in your environment. It is strongly recommended to create a new conda environment!!!
 
 ⚠️ Please note that installing this project will replace flashinfer in your environment. It is strongly recommended to create a new conda environment!!!
 
 ⚠️ Please note that installing this project will replace flashinfer in your environment. It is strongly recommended to create a new conda environment!!!
+
 ### 1. Set Up Conda Environment
 
 We recommend using Miniconda3/Anaconda3 for environment management:
@@ -82,9 +86,9 @@ git submodule update --init --recursive
 
 
 # Install single NUMA dependencies
-sudo env USE_BALANCE_SERVE=1 PYTHONPATH="$(which python)" PATH="$(dirname $(which python)):$PATH" bash ./install.sh
+USE_BALANCE_SERVE=1  bash ./install.sh
 # Or Install Dual NUMA dependencies
-sudo env USE_BALANCE_SERVE=1 USE_NUMA=1 PYTHONPATH="$(which python)" PATH="$(dirname $(which python)):$PATH" bash ./install.sh
+USE_BALANCE_SERVE=1 USE_NUMA=1 bash ./install.sh
 ```
 
 ## Running DeepSeek-R1-Q4KM Models
@@ -116,6 +120,7 @@ It features the following arguments:
 - `--backend_type`: `balance_serve` is a multi-concurrency backend engine introduced in version v0.2.4. The original single-concurrency engine is `ktransformers`.
 
 ### 2. access server
+
 ```
 curl -X POST http://localhost:10002/v1/chat/completions \
   -H "accept: application/json" \

doc/en/install.md

@@ -87,47 +87,47 @@ sudo apt install libtbb-dev libssl-dev libcurl4-openssl-dev libaio1 libaio-dev l
    
    for windows we prepare a pre compiled whl package on [ktransformers-0.2.0+cu125torch24avx2-cp312-cp312-win_amd64.whl](https://github.com/kvcache-ai/ktransformers/releases/download/v0.2.0/ktransformers-0.2.0+cu125torch24avx2-cp312-cp312-win_amd64.whl), which require cuda-12.5, torch-2.4, python-3.11, more pre compiled package are being produced.  -->
 
-
 Download source code and compile:
 
-  - init source code
+- init source code
+
+  ```sh
+  git clone https://github.com/kvcache-ai/ktransformers.git
+  cd ktransformers
+  git submodule update --init --recursive
+  ```
+- [Optional] If you want to run with website, please [compile the website](./api/server/website.md) before execute ``bash install.sh``
+- For Linux
+
+  - For simple install:
+
+    ```shell
+    bash install.sh
+    ```
+  - For those who have two cpu and 1T RAM:
+
+    ```shell
+    # Make sure your system has dual sockets and double size RAM than the model's size (e.g. 1T RAM for 512G model)
+     apt install libnuma-dev
+     export USE_NUMA=1
+     bash install.sh # or #make dev_install
+    ```
+  - For Multi-concurrency with 500G RAM:
 
-    ```sh
-    git clone https://github.com/kvcache-ai/ktransformers.git
-    cd ktransformers
-    git submodule update --init --recursive
+    ```shell
+    USE_BALANCE_SERVE=1 bash ./install.sh
     ```
-  - [Optional] If you want to run with website, please [compile the website](./api/server/website.md) before execute ``bash install.sh``
-  - For Linux
-
-    - For simple install:
-
-      ```shell
-      bash install.sh
-      ```
-    - For those who have two cpu and 1T RAM:
-
-      ```shell
-      # Make sure your system has dual sockets and double size RAM than the model's size (e.g. 1T RAM for 512G model)
-       apt install libnuma-dev
-       export USE_NUMA=1
-       bash install.sh # or #make dev_install
-      ```
-    - For Multi-concurrency with 500G RAM:
-
-      ```shell
-      sudo env USE_BALANCE_SERVE=1 PYTHONPATH="\$(which python)" PATH="\$(dirname \$(which python)):\$PATH" bash ./install.sh
-      ```
-    - For Multi-concurrency with two cpu and 1T RAM:
-
-      ```shell
-      sudo env USE_BALANCE_SERVE=1 USE_NUMA=1 PYTHONPATH="\$(which python)" PATH="\$(dirname \$(which python)):\$PATH" bash ./install.sh
-      ```
-  - For Windows (Windows native temprarily deprecated, please try WSL)
+  - For Multi-concurrency with two cpu and 1T RAM:
 
     ```shell
-    install.bat
+    USE_BALANCE_SERVE=1 USE_NUMA=1 bash ./install.sh
     ```
+- For Windows (Windows native temprarily deprecated, please try WSL)
+
+  ```shell
+  install.bat
+  ```
+
 * If you are developer, you can make use of the makefile to compile and format the code. <br> the detailed usage of makefile is [here](./makefile_usage.md)
 
 <h3>Local Chat</h3>
@@ -157,6 +157,7 @@ python -m ktransformers.local_chat --model_path deepseek-ai/DeepSeek-V2-Lite-Cha
 # GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite
 # python  ktransformers.local_chat --model_path ./DeepSeek-V2-Lite --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
 ```
+
 It features the following arguments:
 
 - `--model_path` (required): Name of the model (such as "deepseek-ai/DeepSeek-V2-Lite-Chat" which will automatically download configs from [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite)). Or if you already got local files  you may directly use that path to initialize the model.
@@ -174,6 +175,7 @@ We provide a server script, which supports multi-concurrency functionality in ve
 ```
 python ktransformers/server/main.py --model_path /mnt/data/models/DeepSeek-V3 --gguf_path /mnt/data/models/DeepSeek-V3-GGUF/DeepSeek-V3-Q4_K_M/ --cpu_infer 62 --optimize_config_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-serve.yaml --port 10002 --chunk_size 256 --max_new_tokens 1024 --max_batch_size 4 --port 10002 --cache_lens 32768 --backend_type balance_serve
 ```
+
 It features the following arguments:
 
 - `--chunk_size`: Maximum number of tokens processed in a single run by the engine.
@@ -301,16 +303,19 @@ Start without website:
 ```sh
 ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002
 ```
+
 Start with website:
 
 ```sh
 ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF  --port 10002 --web True
 ```
+
 Or you want to start server with transformers, the model_path should include safetensors
 
 ```bash
 ktransformers --type transformers --model_path /mnt/data/model/Qwen2-0.5B-Instruct --port 10002 --web True
 ```
+
 Access website with url [http://localhost:10002/web/index.html#/chat](http://localhost:10002/web/index.html#/chat) :
 
 <p align="center">

doc/zh/DeepseekR1_V3_tutorial_zh.md

@@ -126,9 +126,9 @@ git clone https://github.com/kvcache-ai/ktransformers.git
 cd ktransformers
 git submodule update --init --recursive
 # 如果使用双 numa 版本
-sudo env USE_BALANCE_SERVE=1 USE_NUMA=1 PYTHONzPATH="$(which python)" PATH="$(dirname $(which python)):$PATH" bash ./install.sh
+USE_BALANCE_SERVE=1 USE_NUMA=1 bash ./install.sh
 # 如果使用单 numa 版本
-sudo env USE_BALANCE_SERVE=1 PYTHONzPATH="$(which python)" PATH="$(dirname $(which python)):$PATH" bash ./install.sh
+USE_BALANCE_SERVE=1 bash ./install.sh
 # 启动命令
 python ktransformers/server/main.py --model_path <your model path> --gguf_path <your gguf path> --cpu_infer 62 --optimize_config_path <inject rule path> --port 10002 --chunk_size 256 --max_new_tokens 1024 --max_batch_size 4 --port 10002 --cache_lens 32768 --backend_type balance_serve
 ```