Chen Yulin's Blog

Posted 2025-05-13Updated 2025-05-23Notea few seconds read (About 59 words)

https://bbs.archlinux.org/viewtopic.php?id=303177

使用mpm安装
安装后需要使用patch

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patchelf --clear-execstack /home/user/.MathWorks/ServiceHost/-mw_shared_installs/v2024.13.0.2/bin/glnxa64/libmwfoundation_crash_handling.so
patchelf --clear-execstack /home/user/.MathWorks/ServiceHost/-mw_shared_installs/v2024.13.0.2/bin/glnxa64/mathworksservicehost/rcf/matlabconnector/serviceprocess/rcf/service/libmwmshrcfservice.so

Posted 2025-05-07Updated 2025-05-23Notea few seconds read (About 9 words)

Write Latex in Neovim on Archlinux

https://www.youtube.com/watch?app=desktop&v=HVcTPeitxmw

Posted 2025-05-07Updated 2025-05-23Notea few seconds read (About 31 words)

Davinci-resolve on Archlinux

Download pkg from https://apps.cloud.blackmagicdesign.com/davinci-resolve

git clone https://aur.archlinux.org/davinci-resolve.git
cd davinci-resolve
mv ~/Downloads/DaVinci_Resolve_19.1.4_Linux.zip ./
makepkg -si

Posted 2025-04-07Updated 2025-05-23Note4 minutes read (About 616 words)

SJTU-HPC基本操作

官方文档

`LMOD`软件管理

https://lmod.readthedocs.io/en/latest/010_user.html

交大hpc使用lmod来管理用户软件，每一次重新登陆都会重置module，需要将软件重新load。
常用指令：

ml # list these loaded modules
ml miniconda3 # load the miniconda3 module
ml -miniconda3 # unload the miniconda3 module
module spider # inspect the possible modules that can be loaded
module spider cuda # show available cuda versions

`oh-my-zsh`更好的shell

hpc默认已安装zsh, 通过omzsh的安装脚本进行安装，并安装插件

export CHSH=no  # 避免它自动切换默认 shell（HPC 通常不允许）
sh -c "$(curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"
git clone https://github.com/zsh-users/zsh-syntax-highlighting.git \
  ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-syntax-highlighting
git clone https://github.com/zsh-users/zsh-autosuggestions \
  ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-autosuggestions
git clone --depth 1 https://github.com/junegunn/fzf.git ~/.fzf
~/.fzf/install
git clone https://github.com/psprint/zsh-navigation-tools ~/.zsh-navigation-tools

## vim add configuration in `.zshrc`
plugins=(
  git
  zsh-navigation-tools
  rails
  ruby
  zsh-syntax-highlighting
  zsh-autosuggestions
)
source ${(q-)ZSH_CUSTOM:-$ZSH/custom}/plugins/zsh-syntax-highlighting/zsh-syntax-highlighting.zsh

## :wq

`nvim` 更好的编辑器

因为在服务器上没有sudo权限所以需要手动编译

首先使用conda创建编译环境

1	conda create -n nvim-build cmake libtool gettext curl unzip ninja gcc gxx -y -c conda-forge

clone & build

git clone https://github.com/neovim/neovim.git
cd neovim
git checkout stable
make CMAKE_BUILD_TYPE=Release

Tricks

交互式计算资源

某些情况下需要进行并行编译，编译需要耗费大量资源，并不一定可以在登录节点使用：

在这种情况下使用提交作业的方式较为低效，可以申请交互式计算资源

1	srun -p cpu -n 4 --pty /bin/bash

example:

(base) chenyulin@pilogin2 ~> srun -p cpu -n 4 --pty /bin/bash
srun: job 43090290 queued and waiting for resources
srun: job 43090290 has been allocated resources
(base) chenyulin@cas242 ~> ls
condalist.txt  dinov2  log  test.slurm
(base) chenyulin@cas242 ~> cd dinov2        
(base) chenyulin@cas242 ~/dinov2> conda activate dinov2
(dinov2) chenyulin@cas242 ~/dinov2> export CC=$CONDA_PREFIX/bin/gcc                       main
export CXX=$CONDA_PREFIX/bin/g++
(dinov2) chenyulin@cas242 ~/dinov2> which g++                                             main
~/.conda/envs/dinov2/bin/g++
(dinov2) chenyulin@cas242 ~/dinov2> vim conda_cyl.yaml                                    main
(dinov2) chenyulin@cas242 ~/dinov2> conda env update -f conda_cyl.yaml                    main
Retrieving notices: ...working... done
Channels:
 - xformers
 - pytorch
 - nvidia
 - conda-forge
 - defaults
 - nvidia/label/cuda-11.7.0
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done
Installing pip dependencies: |

文件传输

本地向云端传输

使用hpc studio传输
https://studio.hpc.sjtu.edu.cn/pun/sys/dashboard/files/fs//lustre/home/acct-umjbyy/chenyulin/Data

云端之间互相传输

例如拷贝conda环境

1	cp -r /lustre/home/acct-umjbyy/chenyulin/.conda /dssg/home/acct-umjbyy/chenyulin/

Posted 2025-04-02Updated 2025-05-23Notea few seconds read (About 16 words)

Tuxguitar on Archlinux

Installation

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yay -S tuxguitar
yay -S jack2
yay -S libffado

No sound, need some fix

Posted 2025-03-31Updated 2025-05-23Note2 minutes read (About 236 words)

DINOv2 Repository Application

Repository:

Installation

official:

1	conda env create -f conda.yaml

不建议使用official的conda.yaml, 使用更改后的conda_cyl.yaml。

pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1

conda install nvidia/label/cuda-11.7.0::cuda-toolkit -c nvidia/label/cuda-11.7.0
conda install cudatoolkit
conda install -c conda-forge gcc=11.2.0
conda install -c conda-forge gxx=11.2.0

conda env config vars set LD_LIBRARY_PATH="/home/cyl/miniconda3/envs/dinov2/lib/"
conda env config vars set CPATH="/home/cyl/miniconda3/envs/dinov2/include/"
conda env config vars set CUDA_HOME="/home/cyl/miniconda3/envs/dinov2/"

export CC=$CONDA_PREFIX/bin/gcc
export CXX=$CONDA_PREFIX/bin/g++

# check with `which g++`

conda env update -f conda_cyl.yaml

pip3 install -U xformers==0.0.18

conda env config vars set PYTHONPATH="/home/cyl/Reconst/dinov2/"

Demo 🐱

官方提供了 depth estimation 和 segmentation 的 notebook，可以找时间理解一下

Train

使用的数据集为Imagenet-mini

imagenet-mini
├── labels.txt
├── train
└── val

Note: 需要额外添加一个label.txt

使用脚本生产数据集的meta data:

Posted 2025-03-24Updated 2025-05-23Note4 minutes read (About 541 words)

Semantic-SAM Repository Application

My repository: https://github.com/Chen-Yulin/Semantic-SAM
My venv: ssam

Installation

测试过的python版本：3.8,3.10
官方步骤：

pip3 install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113
python -m pip install 'git+https://github.com/MaureenZOU/detectron2-xyz.git'
pip install git+https://github.com/cocodataset/panopticapi.git
git clone https://github.com/UX-Decoder/Semantic-SAM
cd Semantic-SAM
python -m pip install -r requirements.txt

export DATASET=/pth/to/dataset  # path to your coco data

一些绊脚石 ^ ^

1

根据[[Cuda+Torch]]，需要先安装cudatoolkit和cuda-toolkit

conda install nvidia/label/cuda-11.7.0::cuda-toolkit -c nvidia/label/cuda-11.7.0 
conda install cudatoolkit # no need to specify version
conda env config vars set LD_LIBRARY_PATH="/home/cyl/miniconda3/envs/<name>/lib/"
conda env config vars set CPATH="/home/cyl/miniconda3/envs/<name>/include/:/usr/include" # `/usr/include`for missing `crypt.h`
conda env config vars set CUDA_HOME="/home/cyl/miniconda3/envs/<name>/"

然后按照torch官网的安装指令：

1	conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

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第二行直接运行可能会报错，提示系统gcc版本过高，安装gcc=11.2.0

conda install -c conda-forge gcc=11.2.0
conda install -c conda-forge gxx=11.2.0

# 指定编译器路径
export CC=$CONDA_PREFIX/bin/gcc
export CXX=$CONDA_PREFIX/bin/g++

# 找不到crypt.h的情况
sudo pacman -S libxcrypt-compat

export CXXFLAGS="${CXXFLAGS} -fuse-ld=/usr/bin/ld"

如果编译时出现ld: cannot find -lcudart: No such file or directory collect2: error: ld returned 1 exit status 报错，只是因为没有安装cudatoolkit ^ ^

3

安装完成后直接import semantic_sam会报错ModuleNotFoundError: No module named 'MultiScaleDeformableAttention' ^ ^
提示：

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Please compile MultiScaleDeformableAttention CUDA op with the following commands:
	`cd mask2former[/modeling/pixel_decoder/ops](http://127.0.0.1:8888/modeling/pixel_decoder/ops)`
	`sh make.sh`

需要手动make一下 Mask2Former:

1 2	cd Mask2Former/mask2former/modeling/pixel_decoder/ops/ sh make.sh

4

一些版本问题

1 2	pip install gradio==3.37.0 pip install matplotlib==3.7.0

Demo 🐱

Generate multi-granularity Mask on CLICK

1	python demo.py --ckpt ./weights/swinl_only_sam_many2many.pth

Comment: 效果相较于SAM更多体现了语义的一致性，而不是基于texture进行分割。

Automatically Generate Mask on Different Granularity

1	python demo_auto_generation.py --ckpt ./weights/swinl_only_sam_many2many.pth

需要解决的问题

同level下mask重合

Solved by `utils.psg_utils.mask.discard_submask` in `psg_data.segment_pipeline`

Part-seg-dataset Generation

效果

原图：

.png)

Instance identification:

Part segmentation

Posted 2024-12-14Updated 2025-05-23Note3 minutes read (About 485 words)

Cuda+Torch

nvidia-smi返回的是driver所能支持的最新的cuda版本
系统安装的cuda版本可以随意，torch会优先使用虚拟环境中安装的cuda版本

Conda管理Cuda

安装指定版本cuda-toolkit

1	conda install nvidia/label/cuda-12.4.0::cuda-toolkit -c nvidia/label/cuda-12.4.0

安装最新版本

1	conda install cuda-toolkit

某些仓库需要指定cuda路径才能编译包

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conda env config vars set LD_LIBRARY_PATH="/home/cyl/miniconda3/envs/gsam/lib/python3.10/site-packages/nvidia/cuda_runtime/lib/:$LD_LIBRARY_PATH"
conda env config vars set CPATH="/home/cyl/miniconda3/envs/gsam/lib/python3.10/site-packages/nvidia/cuda_runtime/include/:$CPATH"
conda env config vars set CUDA_HOME="/home/cyl/miniconda3/envs/gsam/"

Note: 注意改变了库路径之后nvim中的lsp会报错，建议之后改回去

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conda env config vars set LD_LIBRARY_PATH=""
conda env config vars set CPATH=""
conda env config vars set CUDA_HOME=""

Note: To find the correct path for CUDA_HOME use which nvcc. In my case, output of the command was:

1 2	>>> which nvcc /home/user/miniconda3/envs/py12/bin/nvcc

Therefore, I set the CUDA_HOME as /home/user/miniconda3/envs/py12/.

Note: To find the correct path for LD_LIBRARY_PATH use find ~ -name cuda_runtime_api.h. In my case, output of the command was:

>>> find ~ -name cuda_runtime_api.h
...
/home/user/miniconda3/envs/py12/targets/x86_64-linux/include/cuda_runtime_api.h
...

So I set the LD_LIBRARY_PATH as /home/user/miniconda3/envs/py12/targets/x86_64-linux/lib/ and CPATH as /home/user/miniconda3/envs/py12/targets/x86_64-linux/include/. If you have multiple CUDA installations, the output of find ~ -name cuda_runtime_api.h will display multiple paths. Make sure to choose the path that corresponds to the environment you have created.

ref:https://github.com/IDEA-Research/GroundingDINO/issues/355

Note: Always reboot the computer after the cuda is upgraded

Note: 在更改LD_LIBRARY_PATH后可能会导致neovim的pyright无法运行，所以建议在编译完成后设回该变量

1	conda env config vars set LD_LIBRARY_PATH=""

`cudatoolkit`和`cuda-toolkit`

这两个可以同时安装
如果不安装cudatoolkit可能会在编译时出现ld: cannot find -lcudart: No such file or directory collect2: error: ld returned 1 exit status 报错

使用以下指令获取版本信息

1	python -c 'import torch;print(torch.__version__);print(torch.version.cuda)'

1 2	2.0.0+cu117 11.7

Posted 2024-12-10Updated 2025-05-23Notea minute read (About 174 words)

Use SSH to Connect Jupyter-lab

使用ssh作为命令行远程工具，启动远程的jupyter lab并且在本地的浏览器中打开。

远程运行：

1	jupyter lab --no-browser --port=8080

--no-broswer is very important

output:

...
[I 2024-12-10 14:30:24.585 ServerApp]     http://127.0.0.1:8080/lab?token=0061d1eb31396b1bc3cd77a7161b2084da1dedcdeca0600c
[I 2024-12-10 14:30:24.586 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 2024-12-10 14:30:24.603 ServerApp]

    To access the server, open this file in a browser:
        file:///home/bohanfeng/.local/share/jupyter/runtime/jpserver-11659-open.html
    Or copy and paste one of these URLs:
        http://localhost:8080/lab?token=0061d1eb31396b1bc3cd77a7161b2084da1dedcdeca0600c
        http://127.0.0.1:8080/lab?token=0061d1eb31396b1bc3cd77a7161b2084da1dedcdeca0600c

本地运行：

1	ssh -L 8080:localhost:8080 bohanfeng@192.168.2.102

本地浏览器访问：
http://127.0.0.1:8080/lab?token=0061d1eb31396b1bc3cd77a7161b2084da1dedcdeca0600c

Posted 2024-10-17Updated 2025-05-23Note2 minutes read (About 321 words)

Hyprland Scroll Mode + Waybar

Objective

I use a hyprland plugin called hyprscroller to emulate the scroll functionality of PaperWM.
However, I can’t get whether I am in row mode or column mode, so I want to show the mode status on my waybar.

Implementation

I wrote two shell script under ~/.config/waybar/

scroller_mode_listener.sh, responsible for listening the IPC message of hyprscroller and store that information in a tmp file. This script can be executed in hyprland config file using exec-once

#!/bin/bash

MODE_FILE="/tmp/hyprland_scroller_mode"

function handle {
  if [[ ${1:0:8} == "scroller" ]]; then
    if [[ ${1:10:9} == "mode, row" ]]; then
        echo "Row" > "$MODE_FILE"
    elif [[ ${1:10:12} == "mode, column" ]]; then
        echo "Column" > "$MODE_FILE"
    #else
        #echo "" > "$MODE_FILE"
    fi
    hyprctl dispatch submap reset && pkill -SIGRTMIN+8 waybar # update the widget on waybar
  fi
}

# Initialize the mode file
echo "" > "$MODE_FILE"

socat - "UNIX-CONNECT:$XDG_RUNTIME_DIR/hypr/$HYPRLAND_INSTANCE_SIGNATURE/.socket2.sock" | while read -r line; do handle "$line"; done

scroller_mode_reader.sh, responsible for reading the tmp file and return the json value to waybar to change the shown icon.

#!/bin/bash

MODE_FILE="/tmp/hyprland_scroller_mode"

current_mode=$(cat "$MODE_FILE")

if [[ "$current_mode" == "Row" ]]; then
    icon="Row"
    percent=0
    class="mode-row"
elif [[ "$current_mode" == "Column" ]]; then
    icon="Column"
    percent=100
    class="mode-column"
else
    icon="Row"
    percent=0
    class=""
fi

echo "{\"text\":\"$icon\", \"tooltip\":\"Scroller Mode: $current_mode\", \"class\":\"$class\",\"percentage\": $percent}"

Then add the custom module to waybar in config file (we use signal to update the module):

"custom/scroller-mode": {
    "exec": "~/.config/waybar/scroller_mode_reader.sh",
    "return-type": "json",
    "interval": "once",
    "signal": 8,
    "on-click": "hyprctl dispatch submap reset && pkill -SIGRTMIN+8 waybar",
    "format": "{icon}",
    "format-icons": ["", ""],
}

Also remember to add the module to the bar:

1	"modules-right": ["custom/scroller-mode", "cpu", "memory", "temperature","battery","clock","custom/power"],

Then everything is done

`LMOD`软件管理

`oh-my-zsh`更好的shell

`nvim` 更好的编辑器

Tricks

交互式计算资源

文件传输

本地向云端传输

云端之间互相传输

Installation

Installation

Demo 🐱

Train

Installation

一些绊脚石 ^ ^

1

2

3

4

Demo 🐱

Generate multi-granularity Mask on CLICK

Automatically Generate Mask on Different Granularity

需要解决的问题

同level下mask重合

Part-seg-dataset Generation

效果

Conda管理Cuda

`cudatoolkit`和`cuda-toolkit`

Objective

Implementation

Archives

Recents

Tags

LMOD软件管理

oh-my-zsh更好的shell

nvim 更好的编辑器

Tricks

交互式计算资源

文件传输

本地向云端传输

云端之间互相传输

Installation

Installation

Demo 🐱

Train

Installation

一些绊脚石 ^ ^

1

2

3

4

Demo 🐱

Generate multi-granularity Mask on CLICK

Automatically Generate Mask on Different Granularity

需要解决的问题

同level下mask重合

Part-seg-dataset Generation

效果

Conda管理Cuda

cudatoolkit和cuda-toolkit

Objective

Implementation

Archives

Recents

Tags

`LMOD`软件管理

`oh-my-zsh`更好的shell

`nvim` 更好的编辑器

`cudatoolkit`和`cuda-toolkit`