docker

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Docker Sandbox

Docker沙箱

Run research code inside Docker containers while Feynman stays on the host. The container gets the project files, runs the commands, and results sync back.

在Docker容器内运行研究代码，同时Feynman留在主机上。容器会获取项目文件、运行命令，并将结果同步回主机。

When to use

使用场景

User selects "Docker Sandbox" as the execution environment in
```
/replicate
```
or
```
/autoresearch
```
Running untrusted code from a paper's repository
Experiments that install packages or modify system state
Any time the user asks to run something "safely" or "isolated"

用户在
```
/replicate
```
或
```
/autoresearch
```
中选择“Docker Sandbox”作为执行环境
运行论文仓库中的不可信代码
需要安装包或修改系统状态的实验
用户要求“安全地”或“隔离地”运行代码的任何场景

How it works

工作原理

Build or pull an appropriate base image for the research code
Mount the project directory into the container
Run experiment commands inside the container
Results write back to the mounted directory

为研究代码构建或拉取合适的基础镜像
将项目目录挂载到容器中
在容器内运行实验命令
将结果写入挂载的目录

Running commands in a container

在容器中运行命令

For Python research code (most common):

bash

docker run --rm -v "$(pwd)":/workspace -w /workspace python:3.11 bash -c "
  pip install -r requirements.txt &&
  python train.py
"

For projects with a Dockerfile:

bash

docker build -t feynman-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results feynman-experiment

For GPU workloads:

bash

docker run --rm --gpus all -v "$(pwd)":/workspace -w /workspace pytorch/pytorch:latest bash -c "
  pip install -r requirements.txt &&
  python train.py
"

对于Python研究代码（最常见）：

bash

docker run --rm -v "$(pwd)":/workspace -w /workspace python:3.11 bash -c "
  pip install -r requirements.txt &&
  python train.py
"

对于包含Dockerfile的项目：

bash

docker build -t feynman-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results feynman-experiment

对于GPU工作负载：

bash

docker run --rm --gpus all -v "$(pwd)":/workspace -w /workspace pytorch/pytorch:latest bash -c "
  pip install -r requirements.txt &&
  python train.py
"

Choosing the base image

选择基础镜像

Research type	Base image
Python ML/DL	`pytorch/pytorch:latest` or `tensorflow/tensorflow:latest-gpu`
Python general	`python:3.11`
Node.js	`node:20`
R / statistics	`rocker/r-ver:4`
Julia	`julia:1.10`
Multi-language	`ubuntu:24.04` with manual installs

研究类型	基础镜像
Python机器学习/深度学习	`pytorch/pytorch:latest` 或 `tensorflow/tensorflow:latest-gpu`
通用Python	`python:3.11`
Node.js	`node:20`
R/统计学	`rocker/r-ver:4`
Julia	`julia:1.10`
多语言	`ubuntu:24.04` （需手动安装依赖）

Persistent containers

持久化容器

For iterative experiments (like

/autoresearch

), create a named container instead of

--rm

. Choose a descriptive name based on the experiment:

bash

docker create --name <name> -v "$(pwd)":/workspace -w /workspace python:3.11 tail -f /dev/null
docker start <name>
docker exec <name> bash -c "pip install -r requirements.txt"
docker exec <name> bash -c "python train.py"

This preserves installed packages across iterations. Clean up with:

bash

docker stop <name> && docker rm <name>

对于迭代式实验（如

/autoresearch

），创建一个命名容器而非使用

--rm

参数。根据实验选择一个描述性名称：

bash

docker create --name <name> -v "$(pwd)":/workspace -w /workspace python:3.11 tail -f /dev/null
docker start <name>
docker exec <name> bash -c "pip install -r requirements.txt"
docker exec <name> bash -c "python train.py"

这样可以在多次迭代中保留已安装的包。清理命令如下：

bash

docker stop <name> && docker rm <name>

Notes

注意事项

The mounted workspace syncs results back to the host automatically
Containers are network-enabled by default — add
```
--network none
```
for full isolation
For GPU access, Docker must be configured with the NVIDIA Container Toolkit

挂载的工作区会自动将结果同步回主机
容器默认启用网络连接——添加
```
--network none
```
参数可实现完全隔离
若要使用GPU，Docker必须配置NVIDIA Container Toolkit