2710 lines
59 KiB
Markdown
2710 lines
59 KiB
Markdown
---
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title: Stable Diffusion AWS extension API v2023-07-05
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language_tabs:
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- python: Python
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- javascript: Javascript
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language_clients:
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- python: ""
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- javascript: ""
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toc_footers: []
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includes: []
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headingLevel: 2
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---
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<!-- Generator: Widdershins v4.0.1 -->
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<h1 id="stable-diffusion-train-and-deploy-api">Stable Diffusion 亚马逊云科技插件 API</h1>
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# 概述
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本文档描述了Stable Diffusion 亚马逊云科技插件解决方案的所有 API。此解决方案包括两个部分，一部分是Stable Diffusion WebUI 扩展，它是基于 Gradio 的客户端，提供用户友好的界面，另一部分是称为中间件的资源部署在 AWS 云上，中间件提供了多个 API 接口，允许Stable Diffusion 亚马逊云科技插件客户端与 AWS 云上的服务进行交互，如 Sagemaker 和 S3，以执行模型更新/训练和推理操作。
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为了支持不使用Stable Diffusion WebUI的用户，我们提供了本文档，列出了所有的 API 接口，以帮助用户了解如何调用 API 方法进行训练或推理。
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在用户[部署](../deployment/deployment.md)解决方案中间件 CloudFormation 后，用户可以在主堆栈的输出部分获取 API URL 和令牌。请参考[此文档](../deployment/deployment.md)。
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**URL示例:**
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* https://{api_id}.execute-api.{region}.amazonaws.com/prod
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**认证**
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* API Key (api_key)
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- API 调用时您需要在标头的**x-api-key**参数中配置此值。
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* Authorization
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- 在 headers 中使用 Bearer Token
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# 用户场景
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## 1. 部署新的终端节点
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调用 [/endpoints](#inferencedeploy-sagemaker-endpoint) 来创建一个新的 Sagemaker 终端节点，您需要指定两个用于创建的参数，一个是 `instance_type`，可选值为 "ml.g4dn.2xlarge"、"ml.g4dn.4xlarge"、"ml.g4dn.8xlarge"、"ml.g4dn.12xlarge"，另一个是 `initial_instance_count`，可选值为 1|2|3|4|5|6。
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在调用 [/endpoints](#inferencedeploy-sagemaker-endpoint) 后，您需要调用 [/endpoints](#inferencelist-endpoint-deployment-jobs) 来列出所有终端节点的状态。通常需要超过10分钟时间才能使新的 Sagemaker 终端节点更改为 "InService" 状态。Sagemaker 终端节点只能在 "InService" 状态下用于推理。
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如果终端节点处于失败状态，您可以调用 [/inferences/{id}](#inferenceget-endpoint-deployment-job) 并传递参数 `jobID`，响应将显示终端节点部署失败的原因，通常是由于 AWS 帐户配额限制引起的。
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<details>
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<summary>sequence digram raw</summary>
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title Create a Sagemaker Endpoint
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Client->Middleware:Call /endpoints
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Middleware->Middleware: Start a workflow to configure sagemaker endpoint \n based on uer request configuration
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Client->Middleware:Call /endpoints \n to list all the endpoint creation job list
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</details>
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## 2. 上传模型
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## 3. 训练模型
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## 4. 进行推理
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在 Sagemaker 终端节点处于 "InService" 状态之后，您可以调用 [/inferences](#inference-l2-api) 来进行文本到图像或图像到图像的推理。您需要在请求的 POST 主体中的 "sagemaker_endpoint" 参数中指定终端节点的名称。其他所需参数位于 [/inferences](#inference-l2-api) 中。
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[/inferences](#inference-l2-api) 将向客户端返回以下 JSON 结构：
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```json
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{
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"inference_id": "XXXXXXX",
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"status": "inprogress | failed",
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"endpoint_name": "NAME_OF_ENDPOINT",
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"output_path": "path_of_prediction_output"
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}
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```
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然后，客户端可以调用 [/inference/{id}](#inferenceget-inference-job)，并将 `inference_id` 作为参数来查询推理作业的状态。
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<details>
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<summary>sequence diagram raw</summary>
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title Do Inference
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Client->Middleware:Call **/inferences**
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Middleware->Middleware: Start an async inference job \n on configure sagemaker endpoint \n based on uer request configuration
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Middleware->Client: return inference_id
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Client->Middleware:Call **/inference/{id}** \n to query the inference job status
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Middleware->Client: return inference_id and the job status(inprocess | succeed | failure)
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Middleware->Client: return the inference result images in presigned url format
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Middleware->Client: return the inference parameter in presigned url format
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</details>
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# API 列表
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| 序号 | HTTP 方法 | API 名称 | 描述 |
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|-----|---------|---------------------------------------------------------------------------------------------------------|-------------------------------------|
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| 1 | GET | [/ping](#ping) | 测试客户端是否可以连接到 API 并检查 API_TOKEN 是否正确 |
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| 2 | GET | [/inferences](#inferencelist-inference-jobs) | 列出所有推理作业。 |
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| 3 | GET | [/inferences/{id}](#inferenceget-inference-job) | 获取特定推理作业的详细信息。 |
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| 23 | PUT | [/inferences/{id}/start](#train-api-post) | 启动训练作业。 |
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| 6 | POST | [/inferences](#inference-l2-api) | 使用默认参数运行 SageMaker 推理 |
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| 7 | POST | [/endpoints](#inferencedeploy-sagemaker-endpoint) | 部署 SageMaker 终端节点。 |
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| 8 | DELETE | [/endpoints](#inferencedelete-sagemaker-endpoint) | 删除 SageMaker 终端节点。 |
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| 9 | GET | [/endpoints](#inferencelist-endpoint-deployment-jobs) | 列出所有终端节点部署作业。 |
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| 12 | GET | [/inference/get-texual-inversion-list](#inferenceget-texual-inversion-list) | 获取文本反演列表。 |
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| 13 | GET | [/inference/get-lora-list](#inferenceget-lora-list) | 获取 LoRa 列表。 |
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| 14 | GET | [/inference/get-hypernetwork-list](#inferenceget-hypernetwork-list) | 获取超网络列表。 |
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| 15 | GET | [/inference/get-controlnet-model-list](#inferenceget-controlnet-model-list) | 获取 ControlNet 模型列表。 |
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| 16 | POST | [/inference/run-model-merge](#inferencerun-model-merge) | 运行模型合并。 |
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| 17 | POST | [/models](#modelpost) | 创建新模型。 |
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| 18 | PUT | [/models/{id}](#modelput) | 上传模型文件。 |
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| 19 | GET | [/models](#modelsget) | 列出所有模型。 |
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| 20 | GET | [/checkpoints](#checkpoint) | 获取检查点。 |
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| 21 | PUT | [/checkpoints/{id}](#checkpointput) | 更新检查点。 |
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| 22 | GET | [/checkpoints](#checkpoints) | 列出所有检查点。 |
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| 24 | PUT | [/trainings](#trainput) | 更新训练作业。 |
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| 25 | GET | [/trainings](#trainsget) | 列出所有训练作业。 |
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| 26 | POST | [/datasets](#datasetpost) | 创建新数据集。 |
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| 27 | PUT | [/datasets/{name}](#datasetput) | 更新数据集。 |
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| 28 | GET | [/datasets](#datasetsget) | 列出所有数据集。 |
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| 29 | GET | [/datasets/{name}](#dataset_namedata) | 获取特定数据集的数据。 |
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<br/>
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# /ping
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## 测试中间件连接状态
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<a id="opIdtest_connection_get"></a>
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### **示例：**
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Python示例代码：
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```Python
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import requests
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headers = {
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'Accept': 'application/json',
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'x-api-key': 'API_TOKEN_VALUE'
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}
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r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/ping', headers = headers)
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print(r.json())
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```
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Javascript示例代码：
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```javascript
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const headers = {
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'Accept':'application/json',
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'x-api-key': 'API_TOKEN_VALUE'
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};
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fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/ping',
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{
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method: 'GET',
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headers: headers
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})
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.then(function(res) {
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return res.json();
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}).then(function(body) {
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console.log(body);
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});
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```
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`GET /ping`
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> 响应示例
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> 200 响应
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```json
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{
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"message": "Success"
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}
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```
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<h3 id="used-to-let-client-test-connection-responses">响应</h3>
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|状态码|含义|描述|结构|
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|---|---|---|---|
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|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
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<br/>
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# /inferences
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文生图，通过文字生成图片
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<a id="inference-l2-api"></a>
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### **示例：**
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Python示例代码：
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```Python
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import requests
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headers = {
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"Content-Type": "application/json",
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"Accept": "application/json",
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'x-api-key': 'API_TOKEN_VALUE'
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}
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body = {
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"task_type": "txt2img",
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"models": {
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"Stable-diffusion": [
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"v1-5-pruned-emaonly.safetensors"
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]
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},
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"sagemaker_endpoint_name": "infer-endpoint-cb821ea",
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"prompt": "a cute panda",
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"denoising_strength": 0.75
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}
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r = requests.post("https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences", headers = headers, json = body)
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print(r.json())
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```
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Javascript示例代码：
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```javascript
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const inputBody = '{
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"task_type": "txt2img",
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"models": {
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"Stable-diffusion": [
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"v1-5-pruned-emaonly.safetensors"
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]
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},
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"sagemaker_endpoint_name": "infer-endpoint-cb821ea",
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"prompt": "a cute panda",
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"denoising_strength": 0.75
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}';
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const headers = {
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"Content-Type":"application/json",
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"Accept":"application/json",
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'x-api-key': 'API_TOKEN_VALUE'
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};
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fetch("https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences",
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{
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method: "POST",
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body: inputBody,
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headers: headers
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})
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.then(function(res) {
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return res.json();
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}).then(function(body) {
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console.log(body);
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});
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```
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`POST /inferences`
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> Body
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```json
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{
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"task_type": "txt2img",
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"models": {
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"Stable-diffusion": [
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"v1-5-pruned-emaonly.safetensors"
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]
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},
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"sagemaker_endpoint_name": "infer-endpoint-cb821ea",
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"prompt": "a cute panda",
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"denoising_strength": 0.75
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}
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```
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<h3 id="inference-api">参数</h3>
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> Parameter example
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Below JSON shows the parameters with default value.
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sagemaker_endpoint_name, task_type, prompt and Stable-Diffusion is mandatory, other parameters are optional.
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```json
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{
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"sagemaker_endpoint_name": "infer-endpoint-ef3abcd",
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"task_type": "txt2img",
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"prompt": "",
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"models": {
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"Stable-diffusion": [
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"v1-5-pruned-emaonly.safetensors"
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],
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"Lora": [
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"raidenshogun1-000009.safetensors"
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]
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},
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"enable_hr": false,
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"denoising_strength": null,
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"firstphase_width": 0,
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"firstphase_height": 0,
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"hr_scale": 2.0,
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"hr_upscaler": "Latent",
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"hr_second_pass_steps": 0,
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"hr_resize_x": 0,
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"hr_resize_y": 0,
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"hr_sampler_name": null,
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"hr_prompt": "",
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"hr_negative_prompt": "",
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"prompt": "",
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"styles": [],
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"seed": -1,
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"subseed": -1,
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"subseed_strength": 0.0,
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"seed_resize_from_h": 0,
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"seed_resize_from_w": 0,
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"sampler_name": "Euler a",
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"batch_size": 1,
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"n_iter": 1,
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"steps": 28,
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"cfg_scale": 7.0,
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"width": 512,
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"height": 512,
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"restore_faces": false,
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"tiling": false,
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"do_not_save_samples": false,
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"do_not_save_grid": false,
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"negative_prompt": "",
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"eta": null,
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"s_min_uncond": 0.0,
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"s_churn": 0.0,
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"s_tmax": 1.0,
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"s_tmin": 0.0,
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"s_noise": 1.0,
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"override_settings": {},
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"override_settings_restore_afterwards": true,
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"script_args": [],
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"sampler_index": "Euler a",
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"script_name": null,
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"send_images": true,
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"save_images": false,
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"alwayson_scripts": {
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"controlnet": {
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"args": [
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{
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"enabled": false,
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"module": "none",
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"model": "None",
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"weight": 1,
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"image": null,
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"resize_mode": "Crop and Resize",
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"low_vram": false,
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"processor_res": -1,
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"threshold_a": -1,
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"threshold_b": -1,
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"guidance_start": 0,
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"guidance_end": 1,
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"pixel_perfect": false,
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"control_mode": "Balanced",
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"is_ui": true,
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"input_mode": "simple",
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"batch_images": "",
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"output_dir": "",
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"loopback": false
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}
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]
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},
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"extra options": {
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"args": []
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}
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}
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}
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```
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> 响应示例
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> 200 响应
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```json
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{
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"inference_id": "XXXXXXX",
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"status": "inprogress | failed",
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"endpoint_name": "NAME_OF_ENDPOINT",
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"output_path": "path_of_prediction_output"
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}
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```
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<h3 id="inference-api">响应</h3>
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|状态码|含义|描述|结构|
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|---|---|---|---|
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|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
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<br/>
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# /endpoints(POST)
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创建 SageMaker 节点
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<a id="opIddeploy_sagemaker_endpoint_inference_deploy_sagemaker_endpoint_post"></a>
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|
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### **示例：**
|
||
|
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Python示例代码：
|
||
|
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```Python
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import requests
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headers = {
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'Content-Type': 'application/json',
|
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'Accept': 'application/json',
|
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'x-api-key': 'API_TOKEN_VALUE'
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}
|
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inputBody = {
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"instance_type": "ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge",
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"initial_instance_count": "1|2|3|4|5|6"
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}
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r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints', headers = headers, json = inputBody)
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print(r.json())
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|
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```
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|
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Javascript示例代码：
|
||
|
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```javascript
|
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const inputBody = '{
|
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"instance_type": "ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge",
|
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"initial_instance_count": "1|2|3|4|5|6"
|
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}';
|
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const headers = {
|
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'Content-Type':'application/json',
|
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'Accept':'application/json',
|
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'x-api-key': 'API_TOKEN_VALUE'
|
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};
|
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|
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fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints',
|
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{
|
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method: 'POST',
|
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body: inputBody,
|
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headers: headers
|
||
})
|
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.then(function(res) {
|
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return res.json();
|
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}).then(function(body) {
|
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console.log(body);
|
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});
|
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|
||
```
|
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|
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`POST /endpoints`
|
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|
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> Body参数
|
||
|
||
```json
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{
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||
"instance_type": "ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge",
|
||
"initial_instance_count": "1|2|3|4|5|6"
|
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}
|
||
```
|
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|
||
<h3 id="deploy-sagemaker-endpoint-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
null
|
||
```
|
||
|
||
<h3 id="deploy-sagemaker-endpoint-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
|
||
|
||
|
||
<br/>
|
||
|
||
# /endpoints(DELETE)
|
||
|
||
删除 Endpoints
|
||
|
||
<a id="opIddelete_sagemaker_endpoint_inference_delete_sagemaker_endpoint_post"></a>
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
inputBody = {
|
||
"delete_endpoint_list": [
|
||
"infer-endpoint-XXXXXX",
|
||
"infer-endpoint-YYYYYY"
|
||
]
|
||
}
|
||
|
||
r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"delete_endpoint_list": [
|
||
"infer-endpoint-XXXXXX",
|
||
"infer-endpoint-YYYYYY"
|
||
]
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints',
|
||
{
|
||
method: 'POST',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`POST /endpoints`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"delete_endpoint_list": [
|
||
"infer-endpoint-XXXXXX",
|
||
"infer-endpoint-YYYYYY"
|
||
]
|
||
}
|
||
```
|
||
|
||
<h3 id="delete-sagemaker-endpoint-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
Endpoint deleted
|
||
```
|
||
|
||
<h3 id="delete-sagemaker-endpoint-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
|
||
|
||
|
||
<br/>
|
||
|
||
# /endpoints(GET)
|
||
|
||
列出所有终端节点部署作业
|
||
|
||
<a id="opIdlist_endpoint_deployment_jobs_inference_list_endpoint_deployment_jobs_get"></a>
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /endpoints`
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
[
|
||
{
|
||
"EndpointDeploymentJobId": "e0f9ccfd-8d14-4e77-9e75-b340e1ef23c8",
|
||
"startTime": "2023-07-04 08:00:35.171309",
|
||
"endTime": "2023-07-04 08:00:37.158519",
|
||
"error": "",
|
||
"status": "failed"
|
||
},
|
||
{
|
||
"EndpointDeploymentJobId": "1bd447d2-e561-4cb3-965d-2707b30aea81",
|
||
"startTime": "2023-07-04 08:00:22.435828",
|
||
"endTime": "2023-07-04 08:00:25.421777",
|
||
"error": "",
|
||
"status": "failed"
|
||
},
|
||
{
|
||
"EndpointDeploymentJobId": "cb821ea9-e9d1-4bae-98f8-c20ecadf11e0",
|
||
"startTime": "2023-07-04 08:00:47.736033",
|
||
"endTime": "2023-07-04 08:12:55.148070",
|
||
"endpoint_name": "infer-endpoint-cb821ea",
|
||
"endpoint_status": "InService",
|
||
"status": "success"
|
||
}
|
||
]
|
||
```
|
||
|
||
<h3 id="list-endpoint-deployment-jobs-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
|
||
|
||
|
||
<br/>
|
||
|
||
# /inferences
|
||
|
||
<a id="opIdlist_inference_jobs_inference_list_inference_jobs_get"></a>
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /inferences`
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
[
|
||
{
|
||
"inference_info_name": "/tmp/417d8bc5-f6d0-49c6-9669-c981beeb602a_param.json",
|
||
"startTime": "2023-07-04 09:14:21.170303",
|
||
"taskType": "txt2img",
|
||
"completeTime": "2023-07-04-09-14-26",
|
||
"InferenceJobId": "417d8bc5-f6d0-49c6-9669-c981beeb602a",
|
||
"status": "succeed",
|
||
"sagemakerRaw": "",
|
||
"image_names": [
|
||
"image_0.jpg"
|
||
]
|
||
},
|
||
{
|
||
"inference_info_name": "/tmp/1f9679f3-25b8-4c44-8345-0a845da30094_param.json",
|
||
"startTime": "2023-07-05 06:38:45.752740",
|
||
"taskType": "txt2img",
|
||
"completeTime": "2023-07-05-06-38-49",
|
||
"InferenceJobId": "1f9679f3-25b8-4c44-8345-0a845da30094",
|
||
"status": "succeed",
|
||
"sagemakerRaw": "{'awsRegion': 'us-west-2', 'eventTime': '2023-07-05T06:38:47.73Z', 'receivedTime': '2023-07-05T06:38:45.725Z', 'invocationStatus': 'Completed', 'requestParameters': {'accept': '*/*', 'endpointName': 'infer-endpoint-cb821ea', 'inputLocation': 's3://sagemaker-us-west-2-489670441870/async-endpoint-inputs/infer-endpoint-cb821ea-230705-0638/2023-07-05-06-38-45-445-81a1ec03-f000-4a20-9a60-032ab1558a9d'}, 'responseParameters': {'contentType': 'application/json', 'outputLocation': 's3://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3/sagemaker_output/211d2095-68bc-4404-a9e1-8a18a41f4dc7.out'}, 'inferenceId': '1f9679f3-25b8-4c44-8345-0a845da30094', 'eventVersion': '1.0', 'eventSource': 'aws:sagemaker', 'eventName': 'InferenceResult'}",
|
||
"image_names": [
|
||
"image_0.jpg"
|
||
]
|
||
}
|
||
]
|
||
```
|
||
|
||
<h3 id="list-inference-jobs-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
|
||
|
||
|
||
|
||
<br/>
|
||
|
||
# /inferences/{id}
|
||
|
||
<a id="opIdget_inference_job_inference_get_inference_job_get"></a>
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences/{id}', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences/{id}',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /inferences/{id}`
|
||
|
||
<h3 id="get-inference-job-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|jobID|query|string|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"inference_info_name": "/tmp/1f9679f3-25b8-4c44-8345-0a845da30094_param.json",
|
||
"startTime": "2023-07-05 06:38:45.752740",
|
||
"taskType": "txt2img",
|
||
"completeTime": "2023-07-05-06-38-49",
|
||
"InferenceJobId": "1f9679f3-25b8-4c44-8345-0a845da30094",
|
||
"status": "succeed",
|
||
"sagemakerRaw": "{'awsRegion': 'us-west-2', 'eventTime': '2023-07-05T06:38:47.73Z', 'receivedTime': '2023-07-05T06:38:45.725Z', 'invocationStatus': 'Completed', 'requestParameters': {'accept': '*/*', 'endpointName': 'infer-endpoint-cb821ea', 'inputLocation': 's3://sagemaker-us-west-2-489670441870/async-endpoint-inputs/infer-endpoint-cb821ea-230705-0638/2023-07-05-06-38-45-445-81a1ec03-f000-4a20-9a60-032ab1558a9d'}, 'responseParameters': {'contentType': 'application/json', 'outputLocation': 's3://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3/sagemaker_output/211d2095-68bc-4404-a9e1-8a18a41f4dc7.out'}, 'inferenceId': '1f9679f3-25b8-4c44-8345-0a845da30094', 'eventVersion': '1.0', 'eventSource': 'aws:sagemaker', 'eventName': 'InferenceResult'}",
|
||
"image_names": [
|
||
"image_0.jpg"
|
||
]
|
||
}
|
||
```
|
||
|
||
<h3 id="get-inference-job-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
|
||
|422|[Unprocessable Entity](https://tools.ietf.org/html/rfc2518#section-10.3)|Validation Error|[HTTPValidationError](#schemahttpvalidationerror)|
|
||
|
||
|
||
|
||
<br/>
|
||
|
||
|
||
|
||
|
||
|
||
# /inference/generate-s3-presigned-url-for-uploading
|
||
|
||
<a id="opIdgenerate_s3_presigned_url_for_uploading_inference_generate_s3_presigned_url_for_uploading_get"></a>
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/generate-s3-presigned-url-for-uploading', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/generate-s3-presigned-url-for-uploading',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /inference/generate-s3-presigned-url-for-uploading`
|
||
|
||
<h3 id="generate-s3-presigned-url-for-uploading-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|s3_bucket_name|query|string|false|none|
|
||
|key|query|string|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
"https://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3.s3.amazonaws.com/config/aigc.json?XXX"
|
||
```
|
||
|
||
<h3 id="generate-s3-presigned-url-for-uploading-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|string|
|
||
|422|[Unprocessable Entity](https://tools.ietf.org/html/rfc2518#section-10.3)|Validation Error|[HTTPValidationError](#schemahttpvalidationerror)|
|
||
|
||
|
||
|
||
<br/>
|
||
|
||
|
||
# /inference/run-model-merge
|
||
|
||
<a id="opIdrun_model_merge_inference_run_model_merge_post"></a>
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/run-model-merge', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/run-model-merge',
|
||
{
|
||
method: 'POST',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`POST /inference/run-model-merge`
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"primary_model_name": "primary_model_name,",
|
||
"secondary_model_name": "secondary_model_name,",
|
||
"tertiary_model_name": "teritary_model_name"
|
||
}
|
||
```
|
||
|
||
<h3 id="run-model-merge-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful Response|Inline|
|
||
|
||
|
||
|
||
|
||
|
||
<h1 id="stable-diffusion-train-and-deploy-api-default">default</h1>
|
||
|
||
<br/>
|
||
|
||
# /model(POST)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"model_type": "Stable-diffusion",
|
||
"name": "testmodelcreation01",
|
||
"filenames": [
|
||
{
|
||
"filename": "v1-5-pruned-emaonly.safetensors.tar",
|
||
"parts_number": 5
|
||
}
|
||
],
|
||
"params": {
|
||
"create_model_params": {
|
||
"new_model_name": "testmodelcreation01",
|
||
"ckpt_path": "v1-5-pruned-emaonly.safetensors",
|
||
"from_hub": false,
|
||
"new_model_url": "",
|
||
"new_model_token": "",
|
||
"extract_ema": false,
|
||
"train_unfrozen": false,
|
||
"is_512": true
|
||
}
|
||
}
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model',
|
||
{
|
||
method: 'POST',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`POST /models`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"model_type": "Stable-diffusion",
|
||
"name": "testmodelcreation01",
|
||
"filenames": [
|
||
{
|
||
"filename": "v1-5-pruned-emaonly.safetensors.tar",
|
||
"parts_number": 5
|
||
}
|
||
],
|
||
"params": {
|
||
"create_model_params": {
|
||
"new_model_name": "testmodelcreation01",
|
||
"ckpt_path": "v1-5-pruned-emaonly.safetensors",
|
||
"from_hub": false,
|
||
"new_model_url": "",
|
||
"new_model_token": "",
|
||
"extract_ema": false,
|
||
"train_unfrozen": false,
|
||
"is_512": true
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
<h3 id="create-model-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"job": {
|
||
"id": "id",
|
||
"status": "Initialed",
|
||
"s3_base": "s3://S3_Location",
|
||
"model_type": "Stable-diffusion",
|
||
"params": {},
|
||
"s3PresignUrl": [
|
||
{
|
||
"upload_id": "id",
|
||
"bucket": "bucket name",
|
||
"key": "object key"
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="create-model-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="create-model-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /models/{id} (PUT)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
inputBody = {
|
||
"model_id": "c9f59ee7-0672-4fd1-8a45-8a494de8a48d",
|
||
"status": "Creating",
|
||
"multi_parts_tags": {
|
||
"v1-5-pruned-emaonly.safetensors.tar": [
|
||
{
|
||
"ETag": "cc95c41fa28463c8e9b88d67805f24e0",
|
||
"PartNumber": 1
|
||
},
|
||
{
|
||
"ETag": "e4378bd84b0497559c55be8373cb79d0",
|
||
"PartNumber": 2
|
||
},
|
||
{
|
||
"ETag": "815b68042f6ac5e60b9cff5c697ffea6",
|
||
"PartNumber": 3
|
||
},
|
||
{
|
||
"ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
|
||
"PartNumber": 4
|
||
},
|
||
{
|
||
"ETag": "e613d37e5065b0cd63f1cad216423141",
|
||
"PartNumber": 5
|
||
}
|
||
]
|
||
}
|
||
}
|
||
|
||
r = requests.put('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"model_id": "c9f59ee7-0672-4fd1-8a45-8a494de8a48d",
|
||
"status": "Creating",
|
||
"multi_parts_tags": {
|
||
"v1-5-pruned-emaonly.safetensors.tar": [
|
||
{
|
||
"ETag": "cc95c41fa28463c8e9b88d67805f24e0",
|
||
"PartNumber": 1
|
||
},
|
||
{
|
||
"ETag": "e4378bd84b0497559c55be8373cb79d0",
|
||
"PartNumber": 2
|
||
},
|
||
{
|
||
"ETag": "815b68042f6ac5e60b9cff5c697ffea6",
|
||
"PartNumber": 3
|
||
},
|
||
{
|
||
"ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
|
||
"PartNumber": 4
|
||
},
|
||
{
|
||
"ETag": "e613d37e5065b0cd63f1cad216423141",
|
||
"PartNumber": 5
|
||
}
|
||
]
|
||
}
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model',
|
||
{
|
||
method: 'PUT',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`PUT /models/{id}`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"status": "Creating",
|
||
"multi_parts_tags": {
|
||
"v1-5-pruned-emaonly.safetensors.tar": [
|
||
{
|
||
"ETag": "cc95c41fa28463c8e9b88d67805f24e0",
|
||
"PartNumber": 1
|
||
},
|
||
{
|
||
"ETag": "e4378bd84b0497559c55be8373cb79d0",
|
||
"PartNumber": 2
|
||
},
|
||
{
|
||
"ETag": "815b68042f6ac5e60b9cff5c697ffea6",
|
||
"PartNumber": 3
|
||
},
|
||
{
|
||
"ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
|
||
"PartNumber": 4
|
||
},
|
||
{
|
||
"ETag": "e613d37e5065b0cd63f1cad216423141",
|
||
"PartNumber": 5
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
|
||
<h3 id="update-model-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"job": {
|
||
"output_path": "s3://S3_Location",
|
||
"id": "job.id",
|
||
"endpointName": "endpoint_name",
|
||
"jobStatus": "Created",
|
||
"jobType": "Stable-diffusion"
|
||
}
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="update-model-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="update-model-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /models (GET)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/models', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/models',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /models`
|
||
|
||
<h3 id="list-models-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|types|query|array[string]|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"models": {
|
||
"id": "id",
|
||
"model_name": "name",
|
||
"created": 12341234,
|
||
"params": {},
|
||
"status": "Initialed",
|
||
"output_s3_location": "s3://S3_LOCATION/"
|
||
}
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="list-models-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="list-models-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /checkpoints
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
inputBody = {
|
||
"checkpoint_type": "Stable-diffusion",
|
||
"filenames": [
|
||
{
|
||
"filename": "v1-5-pruned-emaonly.safetensors",
|
||
"parts_number": 5
|
||
}
|
||
],
|
||
"params": {
|
||
"new_model_name": "test_api",
|
||
"number": 1,
|
||
"string": "abc"
|
||
}
|
||
}
|
||
|
||
r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoint', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"checkpoint_type": "Stable-diffusion",
|
||
"filenames": [
|
||
{
|
||
"filename": "v1-5-pruned-emaonly.safetensors",
|
||
"parts_number": 5
|
||
}
|
||
],
|
||
"params": {
|
||
"new_model_name": "test_api",
|
||
"number": 1,
|
||
"string": "abc"
|
||
}
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoint',
|
||
{
|
||
method: 'POST',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`POST /checkpoints`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"checkpoint_type": "Stable-diffusion",
|
||
"filenames": [
|
||
{
|
||
"filename": "v1-5-pruned-emaonly.safetensors",
|
||
"parts_number": 5
|
||
}
|
||
],
|
||
"params": {
|
||
"new_model_name": "test_api",
|
||
"number": 1,
|
||
"string": "abc"
|
||
}
|
||
}
|
||
```
|
||
|
||
<h3 id="create-checkpoint-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"checkpoint": {
|
||
"id": "id",
|
||
"type": "Stable-diffusion",
|
||
"s3_location": "s3://S3_Location",
|
||
"status": "Initialed",
|
||
"params": {}
|
||
},
|
||
"s3PresignUrl": [
|
||
{
|
||
"upload_id": "id,",
|
||
"bucket": "bucket name,",
|
||
"key": "key,"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="create-checkpoint-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="create-checkpoint-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /checkpoints/{id} (put)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
inputBody = {
|
||
"checkpoint_id": "906a5a1f-6833-45aa-8a10-fb0e983e0eae",
|
||
"status": "Active",
|
||
"multi_parts_tags": {
|
||
"v1-5-pruned-emaonly.safetensors.tar": [
|
||
{
|
||
"ETag": "cc95c41fa28463c8e9b88d67805f24e0",
|
||
"PartNumber": 1
|
||
},
|
||
{
|
||
"ETag": "e4378bd84b0497559c55be8373cb79d0",
|
||
"PartNumber": 2
|
||
},
|
||
{
|
||
"ETag": "815b68042f6ac5e60b9cff5c697ffea6",
|
||
"PartNumber": 3
|
||
},
|
||
{
|
||
"ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
|
||
"PartNumber": 4
|
||
},
|
||
{
|
||
"ETag": "e613d37e5065b0cd63f1cad216423141",
|
||
"PartNumber": 5
|
||
}
|
||
]
|
||
}
|
||
}
|
||
|
||
r = requests.put('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoint', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"checkpoint_id": "906a5a1f-6833-45aa-8a10-fb0e983e0eae",
|
||
"status": "Active",
|
||
"multi_parts_tags": {
|
||
"v1-5-pruned-emaonly.safetensors.tar": [
|
||
{
|
||
"ETag": "cc95c41fa28463c8e9b88d67805f24e0",
|
||
"PartNumber": 1
|
||
},
|
||
{
|
||
"ETag": "e4378bd84b0497559c55be8373cb79d0",
|
||
"PartNumber": 2
|
||
},
|
||
{
|
||
"ETag": "815b68042f6ac5e60b9cff5c697ffea6",
|
||
"PartNumber": 3
|
||
},
|
||
{
|
||
"ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
|
||
"PartNumber": 4
|
||
},
|
||
{
|
||
"ETag": "e613d37e5065b0cd63f1cad216423141",
|
||
"PartNumber": 5
|
||
}
|
||
]
|
||
}
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoint',
|
||
{
|
||
method: 'PUT',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`PUT /checkpoints/{id}`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"checkpoint_id": "906a5a1f-6833-45aa-8a10-fb0e983e0eae",
|
||
"status": "Active",
|
||
"multi_parts_tags": {
|
||
"v1-5-pruned-emaonly.safetensors.tar": [
|
||
{
|
||
"ETag": "cc95c41fa28463c8e9b88d67805f24e0",
|
||
"PartNumber": 1
|
||
},
|
||
{
|
||
"ETag": "e4378bd84b0497559c55be8373cb79d0",
|
||
"PartNumber": 2
|
||
},
|
||
{
|
||
"ETag": "815b68042f6ac5e60b9cff5c697ffea6",
|
||
"PartNumber": 3
|
||
},
|
||
{
|
||
"ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
|
||
"PartNumber": 4
|
||
},
|
||
{
|
||
"ETag": "e613d37e5065b0cd63f1cad216423141",
|
||
"PartNumber": 5
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
|
||
<h3 id="update-checkpoints-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"checkpoint": {
|
||
"id": "id",
|
||
"type": "Stable-diffusion",
|
||
"s3_location": "s3://S3_Location",
|
||
"status": "Active",
|
||
"params": {}
|
||
}
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="update-checkpoints-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="update-checkpoints-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /checkpoints
|
||
|
||
列出所有检查点
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoints', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoints',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /checkpoints`
|
||
|
||
<h3 id="list-checkpoints-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|status|query|array[string]|false|none|
|
||
|types|query|array[string]|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"checkpoints": [
|
||
{
|
||
"id": "id",
|
||
"s3Location": "s3://S3_Location",
|
||
"type": "Stable-diffusion",
|
||
"status": "Active",
|
||
"name": [
|
||
"object_1",
|
||
"object_2"
|
||
],
|
||
"created": 12341234
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="list-checkpoints-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="list-checkpoints-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /inferences/{id}/start
|
||
|
||
<a id="train-api-post"></a>
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
const inputBody = {
|
||
"train_type": "Stable-diffusion",
|
||
"model_id": "7ec754d6-1f68-46ea-9cfe-efeeed0c986c",
|
||
"params": {
|
||
"training_params": {
|
||
"data_tar_list": [
|
||
"s3://<your_s3_bucket>/dataset/<your_dataset_name>"
|
||
],
|
||
"class_data_tar_list": [
|
||
""
|
||
],
|
||
"training_instance_type": "ml.g5.2xlarge"
|
||
},
|
||
"config_params": {
|
||
"concepts_list": [{
|
||
"class_data_dir": "",
|
||
"class_guidance_scale": 7.5,
|
||
"class_infer_steps": 40,
|
||
"class_negative_prompt": "",
|
||
"class_prompt": "",
|
||
"class_token": "",
|
||
"instance_prompt": "hanportraittest123",
|
||
"num_class_images_per": 0,
|
||
"instance_data_dir": "s3://<your_s3_bucket>/dataset/<your_dataset_name>",
|
||
"instance_token": "",
|
||
"is_valid": true,
|
||
"n_save_sample": 1,
|
||
"sample_seed": -1,
|
||
"save_guidance_scale": 7.5,
|
||
"save_infer_steps": 20,
|
||
"save_sample_negative_prompt": "",
|
||
"save_sample_prompt": "",
|
||
"save_sample_template": ""
|
||
}],
|
||
"model_dir": "models/dreambooth/<your_model_name>",
|
||
"model_name": "your_model_name",
|
||
"pretrained_model_name_or_path": "models/dreambooth/<your_model_name>/working",
|
||
"num_train_epochs": 100,
|
||
"use_lora": true,
|
||
"revision": ""
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
r = requests.put('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences/{id}/start', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"train_type": "Stable-diffusion",
|
||
"model_id": "7ec754d6-1f68-46ea-9cfe-efeeed0c986c",
|
||
"params": {
|
||
"training_params": {
|
||
"data_tar_list": [
|
||
"s3://<your_s3_bucket>/dataset/<your_dataset_name>"
|
||
],
|
||
"class_data_tar_list": [
|
||
""
|
||
],
|
||
"training_instance_type": "ml.g5.2xlarge"
|
||
},
|
||
"config_params": {
|
||
"concepts_list": [{
|
||
"class_data_dir": "",
|
||
"class_guidance_scale": 7.5,
|
||
"class_infer_steps": 40,
|
||
"class_negative_prompt": "",
|
||
"class_prompt": "",
|
||
"class_token": "",
|
||
"instance_prompt": "hanportraittest123",
|
||
"num_class_images_per": 0,
|
||
"instance_data_dir": "s3://<your_s3_bucket>/dataset/<your_dataset_name>",
|
||
"instance_token": "",
|
||
"is_valid": true,
|
||
"n_save_sample": 1,
|
||
"sample_seed": -1,
|
||
"save_guidance_scale": 7.5,
|
||
"save_infer_steps": 20,
|
||
"save_sample_negative_prompt": "",
|
||
"save_sample_prompt": "",
|
||
"save_sample_template": ""
|
||
}],
|
||
"model_dir": "models/dreambooth/<your_model_name>",
|
||
"model_name": "your_model_name",
|
||
"pretrained_model_name_or_path": "models/dreambooth/<your_model_name>/working",
|
||
"num_train_epochs": 100,
|
||
"use_lora": true,
|
||
"revision": ""
|
||
}
|
||
}
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inferences/{id}/start',
|
||
{
|
||
method: 'POST',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`PUT /inferences/{id}/start`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"train_type": "Stable-diffusion",
|
||
"model_id": "7ec754d6-1f68-46ea-9cfe-efeeed0c986c",
|
||
"params": {
|
||
"training_params": {
|
||
"data_tar_list": [
|
||
"s3://<your_s3_bucket>/dataset/<your_dataset_name>"
|
||
],
|
||
"class_data_tar_list": [
|
||
""
|
||
],
|
||
"training_instance_type": "ml.g5.2xlarge"
|
||
},
|
||
"config_params": {
|
||
"concepts_list": [{
|
||
"class_data_dir": "",
|
||
"class_guidance_scale": 7.5,
|
||
"class_infer_steps": 40,
|
||
"class_negative_prompt": "",
|
||
"class_prompt": "",
|
||
"class_token": "",
|
||
"instance_prompt": "hanportraittest123",
|
||
"num_class_images_per": 0,
|
||
"instance_data_dir": "s3://<your_s3_bucket>/dataset/<your_dataset_name>",
|
||
"instance_token": "",
|
||
"is_valid": true,
|
||
"n_save_sample": 1,
|
||
"sample_seed": -1,
|
||
"save_guidance_scale": 7.5,
|
||
"save_infer_steps": 20,
|
||
"save_sample_negative_prompt": "",
|
||
"save_sample_prompt": "",
|
||
"save_sample_template": ""
|
||
}],
|
||
"model_dir": "models/dreambooth/<your_model_name>",
|
||
"model_name": "your_model_name",
|
||
"pretrained_model_name_or_path": "models/dreambooth/<your_model_name>/working",
|
||
"num_train_epochs": 100,
|
||
"use_lora": true,
|
||
"revision": ""
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
<h3 id="create-train-job-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"job": {
|
||
"id": "81f00711-7cc3-4cac-90f3-13934f29524a",
|
||
"status": "Initial",
|
||
"trainType": "Stable-diffusion",
|
||
"params": {
|
||
"training_params": {
|
||
...
|
||
},
|
||
"config_params": {
|
||
...
|
||
}
|
||
},
|
||
"input_location": "s3://<your_s3_bucket>/train/<your_model_name>/81f00711-7cc3-4cac-90f3-13934f29524a/input"
|
||
},
|
||
"s3PresignUrl": null
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="create-train-job-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="create-train-job-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /trainings/{id} (PUT)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
inputBody = {
|
||
"train_job_id": "b5183dd3-0279-46ff-b64e-6cd687c0fe71",
|
||
"status": "Training"
|
||
}
|
||
|
||
r = requests.put('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/train', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"train_job_id": "b5183dd3-0279-46ff-b64e-6cd687c0fe71",
|
||
"status": "Training"
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/train',
|
||
{
|
||
method: 'PUT',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`PUT /trainings/{id}`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"train_job_id": "b5183dd3-0279-46ff-b64e-6cd687c0fe71",
|
||
"status": "Training"
|
||
}
|
||
```
|
||
|
||
<h3 id="update-train-job-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"job": {
|
||
"id": "id",
|
||
"status": "Training",
|
||
"created": 12341234,
|
||
"trainType": "Stable-diffusion",
|
||
"params": {},
|
||
"input_location": "s3://S3_Location"
|
||
}
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="update-train-job-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="update-train-job-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /trainings (GET)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/trains', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/trains',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /trainings`
|
||
|
||
<h3 id="list-train-jobs-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|types|query|array[string]|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": "200,",
|
||
"trainJobs": [
|
||
{
|
||
"id": "id",
|
||
"modelName": "model_name",
|
||
"status": "Complete",
|
||
"trainType": "Stable-diffusion",
|
||
"created": 1234124,
|
||
"sagemakerTrainName": "sagemaker_train_name"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="list-train-jobs-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="list-train-jobs-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /datasets (POST)
|
||
|
||
创建新数据集
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
const inputBody = {
|
||
"dataset_name": "test_dataset",
|
||
"content": [
|
||
{
|
||
"filename": "/path/to/a/file.png",
|
||
"name": "another_name",
|
||
"type": "png"
|
||
}
|
||
],
|
||
"params": {}
|
||
}
|
||
|
||
r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/dataset', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"dataset_name": "test_dataset",
|
||
"content": [
|
||
{
|
||
"filename": "/path/to/a/file.png",
|
||
"name": "another_name",
|
||
"type": "png"
|
||
}
|
||
],
|
||
"params": {}
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/dataset',
|
||
{
|
||
method: 'POST',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`POST /dataset`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"dataset_name": "test_dataset",
|
||
"content": [
|
||
{
|
||
"filename": "/path/to/a/file.png",
|
||
"name": "another_name",
|
||
"type": "png"
|
||
}
|
||
],
|
||
"params": {}
|
||
}
|
||
```
|
||
|
||
<h3 id="create-dataset-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"datasetName": "dataset_name",
|
||
"s3PresignUrl": [
|
||
{
|
||
"filename": "s3://S3_Location"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="create-dataset-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="create-dataset-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /datasets/{name} (PUT)
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
inputBody = {
|
||
"dataset_name": "test_dataset",
|
||
"status": "Enabled"
|
||
}
|
||
|
||
r = requests.put('https://<Your API Gateway ID>.execute-api.{region}.amazonaws.com/{basePath}/dataset', headers = headers, json = inputBody)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
const inputBody = '{
|
||
"dataset_name": "test_dataset",
|
||
"status": "Enabled"
|
||
}';
|
||
const headers = {
|
||
'Content-Type':'application/json',
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://<Your API Gateway ID>.execute-api.{region}.amazonaws.com/{basePath}/dataset',
|
||
{
|
||
method: 'PUT',
|
||
body: inputBody,
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`PUT /dataset`
|
||
|
||
> Body 参数
|
||
|
||
```json
|
||
{
|
||
"dataset_name": "test_dataset",
|
||
"status": "Enabled"
|
||
}
|
||
```
|
||
|
||
<h3 id="update-dataset-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|body|body|object|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"datasetName": "dataset_name",
|
||
"status": "Enabled"
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="update-dataset-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="update-dataset-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /datasets (GET)
|
||
|
||
列出所有数据集
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://<Your API Gateway ID>.execute-api.{region}.amazonaws.com/{basePath}/datasets', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://<Your API Gateway ID>.execute-api.{region}.amazonaws.com/{basePath}/prod/datasets',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /prod/datasets`
|
||
|
||
<h3 id="list-datasets-by-dataset-status-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|dataset_status|query|array[string]|false|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"datasets": [
|
||
{
|
||
"datasetName": "dataset_name",
|
||
"s3": "s3://S3_Location",
|
||
"status": "Enabled",
|
||
"timestamp": 1234124
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="list-datasets-by-dataset-status-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="list-datasets-by-dataset-status-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|
||
# /datasets/{name}
|
||
|
||
### **示例：**
|
||
|
||
Python示例代码：
|
||
|
||
```Python
|
||
import requests
|
||
headers = {
|
||
'Accept': 'application/json',
|
||
'x-api-key': 'API_TOKEN_VALUE'
|
||
}
|
||
|
||
r = requests.get('https://<Your API Gateway ID>.execute-api.{region}.amazonaws.com/{basePath}/{dataset_name}/data', headers = headers)
|
||
|
||
print(r.json())
|
||
|
||
```
|
||
|
||
Javascript示例代码：
|
||
|
||
```javascript
|
||
|
||
const headers = {
|
||
'Accept':'application/json',
|
||
'x-api-key':'API_TOKEN_VALUE'
|
||
};
|
||
|
||
fetch('https://<Your API Gateway ID>.execute-api.{region}.amazonaws.com/{basePath}/{dataset_name}/data',
|
||
{
|
||
method: 'GET',
|
||
|
||
headers: headers
|
||
})
|
||
.then(function(res) {
|
||
return res.json();
|
||
}).then(function(body) {
|
||
console.log(body);
|
||
});
|
||
|
||
```
|
||
|
||
`GET /datasets/{name}`
|
||
|
||
<h3 id="list-dataset-items-by-dataset-name-parameters">参数</h3>
|
||
|
||
|名称|位置|数据类型|是否需要填写|描述|
|
||
|---|---|---|---|---|
|
||
|dataset_name|path|string|true|none|
|
||
|
||
> 响应示例
|
||
|
||
> 200 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 200,
|
||
"dataset_name": "dataset_name",
|
||
"data": [
|
||
{
|
||
"key": "key",
|
||
"name": "file name",
|
||
"type": "image",
|
||
"preview_url": "https://presigned_s3_url",
|
||
"dataStatus": "Enabled"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
> 500 响应
|
||
|
||
```json
|
||
{
|
||
"statusCode": 500,
|
||
"error": "error_message"
|
||
}
|
||
```
|
||
|
||
<h3 id="list-dataset-items-by-dataset-name-responses">响应</h3>
|
||
|
||
|状态码|含义|描述|结构|
|
||
|---|---|---|---|
|
||
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|Successful response|Inline|
|
||
|500|[Internal Server Error](https://tools.ietf.org/html/rfc7231#section-6.6.1)|Error response|Inline|
|
||
|
||
<h3 id="list-dataset-items-by-dataset-name-responseschema">响应结构</h3>
|
||
|
||
<aside class="warning">
|
||
要执行此操作，需要在请求时添加api_key进行身份验证
|
||
</aside>
|
||
|
||
<br/>
|
||
|