elswork/tf-jupyterlab-py2
elswork
自动构建
DEPRECATED - BE AWARE JupyterLab over a Tensorflow base install, Python 2 flavour.
1 次收藏下载次数: 0状态：自动构建维护者：elswork仓库类型：镜像最近更新：7 年前
让 AI 帮你使用轩辕镜像？ · 展开查看说明 · 点击收起说明
如果你使用 DeepSeek、元宝 AI、Copilot、ChatGPT 等 AI 工具，协助配置轩辕镜像、编写 docker pull 命令、修改 Docker Compose 镜像地址、配置镜像加速、排查镜像拉取失败、分析报错日志等问题，请先让 AI 阅读并遵守轩辕镜像的规则文档。
只需在 AI 对话中先发送下面这句话即可：
请先完整阅读并严格遵守以下文档中的全部规则与要求：
https://xuanyuan.cloud/agents.md
在未充分阅读并理解该文档前，不要生成任何命令、配置、修改建议、故障排查方案或技术回答。后续所有输出都必须严格以该文档中的规范为最高优先级执行。查看 agents.md 用法指南与完整示范。国内用户首推 元宝 AI、DeepSeek 的深度思考模式，不推荐豆包 AI；Cursor 等编辑器可在对话 @ 该链接，或加入 User Rules。 若 AI 无法访问外链，可 打开说明文档 复制全文粘贴。文档会随站点更新，复制内容可能过期，建议定期检查。
DEPRECATED - BE AWARE
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7.
A http://docker.com file to build images for AMD & ARM devices over a base image based with a minimal installation of https://www.tensorflow.org/ an open source software library for numerical computation using data flow graphs. Over this base will be installed https://github.com/jupyterlab/jupyterlab an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Computational Narratives as the Engine of Collaborative Data Science. All this under Python 2 language. https://github.com/DeftWork/tf-jupyterlab.
Be aware! You should read carefully the usage documentation of every tool!
Details
- https://github.com/DeftWork/tf-jupyterlab-py2
- Deft.Work my personal blog
| Docker Hub | Docker Pulls | Docker Stars | Docker Build | Size/Layers |
|---|---|---|---|---|
| https://hub.docker.com/r/elswork/tf-jupyterlab-py2 "elswork/tf-jupyterlab-py2 on Docker Hub" | https://img.shields.io/docker/pulls/elswork/tf-jupyterlab-py2.svg](https://hub.docker.com/r/elswork/tf-jupyterlab-py2 "tf-jupyterlab-py2 on Docker Hub") | https://img.shields.io/docker/stars/elswork/tf-jupyterlab-py2.svg](https://hub.docker.com/r/elswork/tf-jupyterlab-py2 "tf-jupyterlab-py2 on Docker Hub") | https://img.shields.io/docker/build/elswork/tf-jupyterlab-py2.svg](https://hub.docker.com/r/elswork/tf-jupyterlab-py2 "tf-jupyterlab-py2 on Docker Hub") | https://images.microbadger.com/badges/image/elswork/tf-jupyterlab-py2.svg](https://microbadger.com/images/elswork/tf-jupyterlab-py2 "tf-jupyterlab-py2 on microbadger.com") |
This image is the base image for a set of images Data Science Docker Stacks
Build Instructions
Build Python2 flavour for amd64 or arm32v7 architecture (thanks to its https://blog.docker.com/2017/11/multi-arch-all-the-things/ base image)
shdocker build -t elswork/tf-jupyterlab-py2:latest .
My Real Usage Example
In order everyone could take full advantages of the usage of this docker container, I'll describe my own real usage setup.
shdocker run -d -p 8888:8888 elswork/tf-jupyterlab-py2:latest
A more complex sample:
shdocker run -d -p 8888:8888 -p 0.0.0.0:6006:6006 \ --restart=unless-stopped elswork/tf-jupyterlab-py2:latest
Point your browser to http://localhost:8888
First time you open it, you should provide a Token to log on you cand find it with this command:
shdocker logs container_name
With the second example you can run TensorBoard executing this command in the container:
shtensorboard --logdir=path/to/log-directory --host=0.0.0.0
And pointing your browser to http://localhost:6006
镜像拉取方式
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问 标签列表页面。
轩辕镜像加速拉取命令点我查看更多 tf-jupyterlab-py2 镜像标签
docker pull docker.xuanyuan.run/elswork/tf-jupyterlab-py2:<标签>
DockerHub 原生拉取命令
docker pull elswork/tf-jupyterlab-py2:<标签>
镜像拉取常见问题
功能
错误码
用户好评
来自真实用户的反馈，见证轩辕镜像的优质服务
oldzhang
运维工程师
Linux服务器
5
"Docker访问体验非常流畅，大镜像也能快速完成下载。"