Docker Image: CUDA+CodeSever+Miniconda 这个CodeServer+CUDA+Conda的搭配,借助IPv6,让我拥有了一致性非常好的开发环境,随时随地打开浏览器就进行编码和调试。Docker使得我的开发环境与host环境完全隔离,同时,要更改环境(比如CUDA版本)只需一条docker build命令,完全可以放心大胆地折腾了。有同样需求的可以参考我的githu...
docker run -i -t -p 8080:8080 --name code codercom/code-server /bin/bash 5. 查看容器 docker ps -a 6. 启动容器 docker start code 7. 进入容器并查看密码 docker exec -it code /bin/bash cat ~/.config/code-server/config.yaml 8. 在浏览器输入ip加端口,再输入密码即可...
retries: 3 # starts 4 docker containers running minio server instances. # using nginx reverse proxy, load balancing, you can access # it through port 9000. services: minio1: <<: *minio-common hostname: minio1 ports: - "9001:9001" volumes: - .data/data1:/data minio2: <<: *minio-c...
先执行第一个conda install命令安装PyTorch,如果安装不成功则执行第二个pip install命令。其余Python package都采用pip install命令来安装,例如安装OpenCV的命令是 pip install opencv-python 3. 检查PyTorch安装 进入Python环境,输入如下指令查看PyTorch安装是否成功,如下图所示: 五、配置VS Code Python是深度学习最流行的...
conda env create -f environment.yml # Make RUN commands use the new environment: SHELL ["conda", "run", "-n", "myenv", "/bin/bash", "-c"] EXPOSE 5003 # The code to run when container is started: ENTRYPOINT ["conda", "run", "-n", "myenv", "python3", "src/server.py"...
在新的xterm终端中激活conda环境 Python位于Conda环境中,但尚未在Windows虚拟环境中激活 创建python 2.7 conda环境设置python 3.5 无法使用python 3.10设置conda环境 在Visual Studio code Linux上激活conda环境 在运行的Docker容器中激活conda env? 在Python cronjob中设置Docker环境变量 ...
自己做了个工作上要用的 docker镜像,集成 rstudio-server, jupyterlab, code-server在linux下一直使用良好(好吧,其实一开始也出过问题,很快解决了,后来居然没想起来当初是怎么解决的),但是在windows下rstudio-server启动不了。把对应的rserver目录的权限进行了修改,没
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//镜像由于预装好了pytorch2.0,体积比较大,约9.35GB docker pull mortals/codeenv:pytorch2.0-cuda12.0 //预装miniconda,基于cuda12.0,无pytorch //镜像体积约4.85GB docker pull mortals/codeenv:conda-cuda12.0 //同时约有基于cuda11.8的镜像 //镜像体积约为3.78GB docker pull mortals/codeenv:conda-cuda11.8 ...
Inside the container a nginx Webserver is running on port 80 and that port can be bound to a local port on your host computer. With this parameter you can access your Galaxy instance viahttp://localhost:8080immediately after executing the command above. If you work with theDocker Toolboxon...