AI prompts
base on 很多镜像都在国外。比如 gcr 。国内下载很慢，需要加速。致力于提供连接全世界的稳定可靠安全的容器镜像服务。 # public-image-mirror
源仓库 [Github](https://github.com/DaoCloud/public-image-mirror)
Mirror 仓库 [Gitee](https://gitee.com/daocloud/public-image-mirror)
- 白名单 & 限流 的公开信息 [#2328](https://github.com/DaoCloud/public-image-mirror/issues/2328)
- 如有疑问请咨询 [#4183](https://github.com/DaoCloud/public-image-mirror/issues/4183)
- 建议将拉取任务放在闲时 凌晨(北京时间 01-07 点), 其他时间段非常拥挤
- 建议使用明确版本号的 tag, 对于 latest 这种变更后响应旧数据, 并且后台重新同步
- [本服务后端代码](https://github.com/OpenCIDN/ocimirror)
- [同步队列](https://queue.m.daocloud.io/status/) 仅保留一小时的同步记录
- [服务状态监控](https://status.daocloud.io/status/docker)
## 背景 & 目标
很多镜像都在国外。比如 gcr 。国内下载很慢，需要加速。
* 一个简洁有效的方法能够加速这些包。简洁的名称映射
* 易于添加，添加新的包，不需要去修改代码。
* 稳定可靠，更新实时。每天检查同步情况。
* 此项目仅是源镜像仓库 (Registry) 的 Mirror
* 所有 hash(sha256) 均和源保持一致 (懒加载机制)。
* 缓存的内容只保留 90 天, 过期后会需要重新同步。
* Manifest 内存缓存 1 小时, 所以 tag 被更新了 1 小时之后才会同步新的。
* Blob 内存缓存 1 分钟, 在期间如果 blob 到达 90 天期限被删除会报 404。
## 快速开始
```
docker run -d -P m.daocloud.io/docker.io/library/nginx
```
## 使用方法
**增加前缀** (推荐方式)。比如：
``` log
docker.io/library/busybox
|
V
m.daocloud.io/docker.io/library/busybox
```
或者 支持的镜像仓库 的 *前缀替换* 就可以使用。比如：
``` log
docker.io/library/busybox
|
V
docker.m.daocloud.io/library/busybox
```
## 支持前缀替换的 Registry (不推荐)
推荐使用添加前缀的方式.
前缀替换的 Registry 的规则, 这是人工配置的, 有需求提 Issue.
除了这里每一个源站, 内容都是不同的, 不要把 docker.io 之外的站点配置给 registry-mirrors
| 源站 | 替换为 | 备注 |
| ------------------ | --------------------- | ---------------------------------------------- |
| docker.elastic.co | elastic.m.daocloud.io | |
| docker.io | docker.m.daocloud.io | |
| dhi.io | dhi.m.daocloud.io | |
| gcr.io | gcr.m.daocloud.io | |
| ghcr.io | ghcr.m.daocloud.io | |
| k8s.gcr.io | k8s-gcr.m.daocloud.io | k8s.gcr.io 已被迁移到 registry.k8s.io |
| registry.k8s.io | k8s.m.daocloud.io | |
| mcr.microsoft.com | mcr.m.daocloud.io | |
| nvcr.io | nvcr.m.daocloud.io | |
| quay.io | quay.m.daocloud.io | |
| registry.ollama.ai | ollama.m.daocloud.io | 实验内测中，[使用方法](#加速-ollama--deepseek) |
## 最佳实践
### 部署内网缓存
本地缓存部署用于在内网环境中加速镜像拉取，减少对外网的依赖。通过设置一个本地镜像仓库，您可以缓存常用的镜像。
[请参考文档](https://github.com/DaoCloud/public-image-mirror/tree/main/docs/local-cache)
### 加速 Kubneretes
#### 加速安装 kubeadm
``` yaml
apiVersion: kubeadm.k8s.io/v1beta3
kind: ClusterConfiguration
dns:
imageRepository: k8s.m.daocloud.io/coredns
imageRepository: k8s.m.daocloud.io
...
```
#### 加速安装 kind
``` bash
kind create cluster --name kind --image m.daocloud.io/docker.io/kindest/node:v1.22.1
```
#### 加速所有 Pod
https://github.com/wzshiming/repimage
不修改 yaml, helm 等, 仅使用 Webhook, 自动修改所有新建 Pod 的 image 使用本 mirror
``` bash
kubectl create -f https://files.m.daocloud.io/github.com/wzshiming/repimage/releases/download/latest/repimage.yaml
kubectl rollout status deployment/repimage -n kube-system
```
#### 加速 Containerd
* 参考 Containerd 官方文档: [hosts.md](https://github.com/containerd/containerd/blob/main/docs/hosts.md#registry-host-namespace)
* 如果您使用 kubespray 安装 containerd, 可以配置 [`containerd_registries_mirrors`](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/CRI/containerd.md#containerd-config)
### 加速 Docker
添加到 `/etc/docker/daemon.json`
``` json
{
"registry-mirrors": [
"https://docker.m.daocloud.io"
]
}
```
### 加速 Ollama & DeepSeek
#### 加速安装 Ollama
CPU:
```bash
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama docker.m.daocloud.io/ollama/ollama
```
GPU 版本:
1. 首先安装 Nvidia Container Toolkit
2. 运行以下命令启动 Ollama 容器：
```bash
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama docker.m.daocloud.io/ollama/ollama
```
更多信息请参考：
* [Ollama Docker 官方文档](https://ollama.com/blog/ollama-is-now-available-as-an-official-docker-image)
#### 加速使用 Deepseek-R1 模型（实验内测中）
如上述步骤，在启动了ollama容器的前提下，还可以通过加速源，加速启动DeepSeek相关的模型服务
注：目前 Ollama 官方源的下载速度已经很快，您也可以直接使用[官方源](https://ollama.com/library/deepseek-r1:1.5b)。
```bash
# 使用加速源
docker exec -it ollama ollama run ollama.m.daocloud.io/library/deepseek-r1:1.5b
# 或直接使用官方源下载模型
# docker exec -it ollama ollama run deepseek-r1:1.5b
```
## [友情链接]加速二剑客
* 镜像加速：https://github.com/DaoCloud/public-image-mirror
* 二进制文件加速：https://github.com/DaoCloud/public-binary-files-mirror
## 贡献者
<a href="https://github.com/DaoCloud/public-image-mirror/graphs/contributors">
<img src="https://contrib.rocks/image?repo=DaoCloud/public-image-mirror" />
</a>
Made with [contrib.rocks](https://contrib.rocks).
", Assign "at most 3 tags" to the expected json: {"id":"10695","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"