docker.io/pytorch/torchserve:0.11.0-cpu linux/amd64

docker.io/pytorch/torchserve:0.11.0-cpu - 国内下载镜像源 浏览次数:7 安全受验证的发布者-Pytorch
Torcheserve是PyTorch推出的一个用于模型部署和服务的框架。它提供了一个易于使用的API,使得开发者能够轻松地将模型部署到各种环境中。
  1. 快速部署:Torcheserve使得开发者能够快速部署模型,并且可以在短时间内进行迭代和更新。
  2. 易于使用:Torcheserve提供了一个简单易用的API,使得开发者能够轻松地将模型部署到各种环境中。
  3. 支持多种模型:Torcheserve支持多种类型的模型,包括PyTorch、TensorFlow和Keras等。

Torcheserve是一个强大且易于使用的框架,使得开发者能够快速部署模型并将其推广到各种环境中。它是 PyTorch 生态系统中的一个重要组成部分,旨在使模型部署和服务更加高效和容易。

源镜像 docker.io/pytorch/torchserve:0.11.0-cpu
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu
镜像ID sha256:8391271abb7a75ece2ed9528ff9ed1565e970ac5a901ef5fc15d8ae7974e2e7b
镜像TAG 0.11.0-cpu
大小 2.00GB
镜像源 docker.io
项目信息 Docker-Hub主页 🚀项目TAG 🚀
CMD serve
启动入口 /usr/local/bin/dockerd-entrypoint.sh
工作目录 /home/model-server
OS/平台 linux/amd64
浏览量 7 次
贡献者
镜像创建 2024-05-16T18:30:39.706557492Z
同步时间 2025-06-26 16:49
更新时间 2025-06-27 01:05
开放端口
7070/tcp 7071/tcp 8080/tcp 8081/tcp 8082/tcp
环境变量
PATH=/home/venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin PYTHONUNBUFFERED=TRUE TEMP=/home/model-server/tmp
镜像标签
ubuntu: org.opencontainers.image.ref.name 20.04: org.opencontainers.image.version

Docker拉取命令

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu  docker.io/pytorch/torchserve:0.11.0-cpu

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu  docker.io/pytorch/torchserve:0.11.0-cpu

Shell快速替换命令

sed -i 's#pytorch/torchserve:0.11.0-cpu#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu#' deployment.yaml

Ansible快速分发-Docker

#ansible k8s -m shell -a 'docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu  docker.io/pytorch/torchserve:0.11.0-cpu'

Ansible快速分发-Containerd

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/torchserve:0.11.0-cpu  docker.io/pytorch/torchserve:0.11.0-cpu'

镜像构建历史


# 2024-05-17 02:30:39  0.00B 设置默认要执行的命令
CMD ["serve"]
                        
# 2024-05-17 02:30:39  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/usr/local/bin/dockerd-entrypoint.sh"]
                        
# 2024-05-17 02:30:39  0.00B 设置环境变量 TEMP
ENV TEMP=/home/model-server/tmp
                        
# 2024-05-17 02:30:39  0.00B 设置工作目录为/home/model-server
WORKDIR /home/model-server
                        
# 2024-05-17 02:30:39  0.00B 指定运行容器时使用的用户
USER model-server
                        
# 2024-05-17 02:30:39  0.00B 声明容器运行时监听的端口
EXPOSE map[7070/tcp:{} 7071/tcp:{} 8080/tcp:{} 8081/tcp:{} 8082/tcp:{}]
                        
# 2024-05-17 02:30:39  0.00B 执行命令并创建新的镜像层
RUN |1 PYTHON_VERSION=3.9 /bin/sh -c mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store # buildkit
                        
# 2024-05-17 02:30:39  309.00B 复制新文件或目录到容器中
COPY docker/config.properties /home/model-server/config.properties # buildkit
                        
# 2024-05-17 02:30:39  5.00KB 执行命令并创建新的镜像层
RUN |1 PYTHON_VERSION=3.9 /bin/sh -c chmod +x /usr/local/bin/dockerd-entrypoint.sh     && chown -R model-server /home/model-server # buildkit
                        
# 2024-05-17 02:30:39  197.00B 复制新文件或目录到容器中
COPY docker/dockerd-entrypoint.sh /usr/local/bin/dockerd-entrypoint.sh # buildkit
                        
# 2024-05-17 02:30:39  0.00B 设置环境变量 PATH
ENV PATH=/home/venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2024-05-17 02:30:39  1.02GB 复制新文件或目录到容器中
COPY /home/venv /home/venv # buildkit
                        
# 2024-05-17 02:29:27  335.11KB 执行命令并创建新的镜像层
RUN |1 PYTHON_VERSION=3.9 /bin/sh -c useradd -m model-server     && mkdir -p /home/model-server/tmp # buildkit
                        
# 2024-05-17 02:29:27  903.26MB 执行命令并创建新的镜像层
RUN |1 PYTHON_VERSION=3.9 /bin/sh -c apt-get update &&     apt-get upgrade -y &&     apt-get install software-properties-common -y &&     add-apt-repository -y ppa:deadsnakes/ppa &&     apt remove python-pip  python3-pip &&     DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y     python$PYTHON_VERSION     python3-distutils     python$PYTHON_VERSION-dev     python$PYTHON_VERSION-venv     openjdk-17-jdk     build-essential     && rm -rf /var/lib/apt/lists/*     && cd /tmp # buildkit
                        
# 2024-05-17 02:29:27  0.00B 设置环境变量 PYTHONUNBUFFERED
ENV PYTHONUNBUFFERED=TRUE
                        
# 2024-05-17 02:29:27  0.00B 定义构建参数
ARG PYTHON_VERSION
                        
# 2024-04-27 22:03:41  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2024-04-27 22:03:41  72.81MB 
/bin/sh -c #(nop) ADD file:e5742fae181dc02a419e48d202fdd6a561b79ccbe7d3415e15e3d2c12e444a2a in / 
                        
# 2024-04-27 22:03:39  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=20.04
                        
# 2024-04-27 22:03:39  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2024-04-27 22:03:39  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2024-04-27 22:03:39  0.00B 
/bin/sh -c #(nop)  ARG RELEASE
                        
                    

镜像信息

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更多版本

docker.io/pytorch/torchserve:0.11.0-gpu

linux/amd64 docker.io6.59GB2024-08-30 15:55
434

docker.io/pytorch/torchserve-kfs:0.9.0

linux/amd64 docker.io2.92GB2025-04-28 16:07
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docker.io/pytorch/torchserve:0.11.0-cpu

linux/amd64 docker.io2.00GB2025-06-26 16:49
6