Jiao hongbao's Trash

gitget ssh key: 1234567ssh-keygen -t rsa -C "[xxx@xxx.com](mailto:xxx@xxx.com)"<enter until ...>cd ~/.sshcat id_rsa.pub git initialize: 123git config --global user.name "username"git config --global user.email "<email>" for proxy: 123456789git config --global http.proxy http://127.0.0.1:7897git config --global https.proxy http://127.0.0.1:7897# and we can test with:git config --global --get http.proxygit config --global --get https.proxy firewall...

liststo export your requirements as a txt. 12345678910pacman -Qqe > pacman_installed.txtyay -Qqe > yay_installed.txt//reusme// sudo pacman -S --needed - < pacman_installed.txt// yay -S --needed - < yay_installed.txtpip3 freeze > pip_installed.txt// resume : pip3 install -r pip_installed.txt command lines homepageclick here to come back to command line homepage.

xmakeprotal:https://xmake.io/#/zh-cn/ command lines homepageclick here to come back to command line homepage.

FPGA on archall the tool-chain we should have is : 1234567chisel # for generate verilog code , optionaliverilog # for compile the verilog filegtkwave # for observe the chartsyosys # for synthsissymbiflow # for synthsis, placement and routing, generate bitstreamsF4PGA # new version of symbiflowopenocd # for uploading to board during these tools, we can pull the docker iamge of symbiflow: 1docker pull daverichmond/fpga-symbiflow:latest and we can use vivado: after download for AMD website...

go on labeling togetherCVAT is a tool that can support many people to collaborate together to prepare the dataset of computer vision. how to install CVAT or you can use roboflow… side occupation homepageclick here to come back to side occupationhomepage.

ollamait is a way super easy to deploy AI to local machine. 1234567yay -S ollama# modify .bashrcif ! pgrep -f "ollama serve" > /dev/null; then ollama serve & disownfi 12345#useollama pull deepseek-coder:6.7bollama pull deepseek-coder(latest) side occupation homepageclick here to come back to side occupationhomepage.

NVIDIA DRIVERon archlinux if we want to use NVIDIA-GPUS in container of docker, we should install some NVIDIA packages. at first we should install nvidia and nvidia-utils, which can be downloaded by: 123sudo pacman -S nvidia nvidia-utilssudo pacman -S nvidia-dkms (to automatic build based on the core type) when run command of : 1nvidia-smi; it will show you the information of GPUS of your computer, it done. and then we should install nvidia-container-runtime: 12345paru -S...

modify savingif I make some modifications and I want to save these modifications to continue use them next time, we should save the modifications in the containers. for example, I install the jupyer inside a container, like this: 1pip install jupyter we should commit the container to an image, 1docker commit <container_id_or_name> <new_image_name>:<tag> for example: 12docker commit my_running_container jupyter_notebook_image:v1 when next time, run: 12docker run -it -p...

normally usecontainers images build&save images network volumes&storages compose docker command line homepageclick here to come back to docker command line homepage.

how to install CVATCVAT uses Docker Compose to manage multiple containers. Install it using the following commands: 12sudo pacman -S docker-composedocker-compose --version then we should clone the repository: 12git clone https://github.com/openvinotoolkit/cvat.gitcd cvat export parameter in .bashrc: 12export CVAT_HOST=FQDN_or_YOUR-IP-ADDRESS set up CVAT with docker-compose: 1docker-compose up -d (maybe should try much more times) run follow command to create a superuser: 1docker exec -it...