GSP972

概览
Vertex AI 将用于构建机器学习 (ML) 模型的 Google Cloud 服务整合到了统一的界面和 API 中。在 Vertex AI 中,您现在可以使用 AutoML 或自定义代码训练功能轻松训练和比较模型,并且所有模型都存储在一个中央模型库中。这些模型现在可以部署到 Vertex AI 上的相同端点。
AutoML Vision 可帮助机器学习专业知识有限的人员训练高质量的图片分类模型。在本实操实验中,您将学习如何创建一个自定义机器学习模型,用来自动识别受损汽车零部件。由于训练模型所需的时间会超过实验的时间限制,因此,您将与另一个项目中已使用同一数据集完成训练的托管模型交互,并请求该模型提供预测结果。然后,您将调整预测请求的数据值,并分析数据值如何影响模型预测结果。
目标
在本实验中,您将学习如何执行以下任务:
- 使用 CSV 文件将带标签的数据集上传到 Cloud Storage,并将其作为托管式数据集连接到 Vertex AI。
- 检查上传的图片,确保数据集没有错误。
- 启动 AutoML Vision 模型训练作业。
- 请求基于同一数据集进行训练的托管模型提供预测结果。
设置和要求
点击“开始实验”按钮前的注意事项
请阅读以下说明。实验是计时的,并且您无法暂停实验。计时器在您点击开始实验后即开始计时,显示 Google Cloud 资源可供您使用多长时间。
此实操实验可让您在真实的云环境中开展实验活动,免受模拟或演示环境的局限。为此,我们会向您提供新的临时凭据,您可以在该实验的规定时间内通过此凭据登录和访问 Google Cloud。
为完成此实验,您需要:
- 能够使用标准的互联网浏览器(建议使用 Chrome 浏览器)。
注意:请使用无痕模式(推荐)或无痕浏览器窗口运行此实验。这可以避免您的个人账号与学生账号之间发生冲突,这种冲突可能导致您的个人账号产生额外费用。
注意:请仅使用学生账号完成本实验。如果您使用其他 Google Cloud 账号,则可能会向该账号收取费用。
如何开始实验并登录 Google Cloud 控制台
-
点击开始实验按钮。如果该实验需要付费,系统会打开一个对话框供您选择支付方式。左侧是“实验详细信息”窗格,其中包含以下各项:
- “打开 Google Cloud 控制台”按钮
- 剩余时间
- 进行该实验时必须使用的临时凭据
- 帮助您逐步完成本实验所需的其他信息(如果需要)
-
点击打开 Google Cloud 控制台(如果您使用的是 Chrome 浏览器,请右键点击并选择在无痕式窗口中打开链接)。
该实验会启动资源并打开另一个标签页,显示“登录”页面。
提示:将这些标签页安排在不同的窗口中,并排显示。
注意:如果您看见选择账号对话框,请点击使用其他账号。
-
如有必要,请复制下方的用户名,然后将其粘贴到登录对话框中。
{{{user_0.username | "<用户名>"}}}
您也可以在“实验详细信息”窗格中找到“用户名”。
-
点击下一步。
-
复制下面的密码,然后将其粘贴到欢迎对话框中。
{{{user_0.password | "<密码>"}}}
您也可以在“实验详细信息”窗格中找到“密码”。
-
点击下一步。
重要提示:您必须使用实验提供的凭据。请勿使用您的 Google Cloud 账号凭据。
注意:在本实验中使用您自己的 Google Cloud 账号可能会产生额外费用。
-
继续在后续页面中点击以完成相应操作:
- 接受条款及条件。
- 由于这是临时账号,请勿添加账号恢复选项或双重验证。
- 请勿注册免费试用。
片刻之后,系统会在此标签页中打开 Google Cloud 控制台。
注意:如需访问 Google Cloud 产品和服务,请点击导航菜单,或在搜索字段中输入服务或产品的名称。
激活 Cloud Shell
Cloud Shell 是一种装有开发者工具的虚拟机。它提供了一个永久性的 5GB 主目录,并且在 Google Cloud 上运行。Cloud Shell 提供可用于访问您的 Google Cloud 资源的命令行工具。
-
点击 Google Cloud 控制台顶部的激活 Cloud Shell
。
-
在弹出的窗口中执行以下操作:
- 继续完成 Cloud Shell 信息窗口中的设置。
- 授权 Cloud Shell 使用您的凭据进行 Google Cloud API 调用。
如果您连接成功,即表示您已通过身份验证,且项目 ID 会被设为您的 Project_ID 。输出内容中有一行说明了此会话的 Project_ID:
Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}
gcloud
是 Google Cloud 的命令行工具。它已预先安装在 Cloud Shell 上,且支持 Tab 自动补全功能。
- (可选)您可以通过此命令列出活跃账号名称:
gcloud auth list
- 点击授权。
输出:
ACTIVE: *
ACCOUNT: {{{user_0.username | "ACCOUNT"}}}
To set the active account, run:
$ gcloud config set account `ACCOUNT`
- (可选)您可以通过此命令列出项目 ID:
gcloud config list project
输出:
[core]
project = {{{project_0.project_id | "PROJECT_ID"}}}
注意:如需查看在 Google Cloud 中使用 gcloud
的完整文档,请参阅 gcloud CLI 概览指南。
任务 1. 将训练图片上传到 Cloud Storage
在此任务中,将要使用的训练图片上传到 Cloud Storage。这样更便于稍后将相关数据导入 Vertex AI。
要训练模型来对受损汽车零部件图片进行分类,您需要向机器提供带标签的训练数据。模型将使用这些数据来理解每张图片,区分完好的和受损的汽车零部件。
注意:在本实验中,您不需要给图片加标签,因为 CSV 文件中已经提供了带标签的数据集(也就是说,该文件包含图片以及对应的标签)。下一部分将介绍使用 CSV 文件的步骤。
在本示例中,您的模型将学习对五个不同的受损汽车零部件进行分类:保险杠、发动机舱、引擎盖、车身侧面和挡风玻璃。
创建 Cloud Storage 存储桶
- 打开 Cloud Shell 窗口并执行以下命令,以创建 Cloud Storage 存储桶:
gsutil mb -p {{{project_0.project_id | PROJECT_ID}}} \
-c standard \
-l "{{{project_0.default_region | REGION}}}" \
gs://{{{project_0.project_id | BUCKET}}}
将汽车图片上传到 Storage 存储桶中
训练图片存储在公开的 Cloud Storage 存储桶中。再次将后面的脚本模板复制并粘贴到 Cloud Shell 中,以便将图片复制到您自己的存储桶中。
- 要将图片复制到您的 Cloud Storage 存储桶中,请执行以下命令:
gsutil -m cp -r gs://car_damage_lab_images/* gs://{{{project_0.project_id | BUCKET}}}
-
在导航窗格中,依次点击 Cloud Storage > 存储桶。
-
点击 Cloud Storage 浏览器顶部的刷新按钮。
-
点击您的存储桶名称。您应该会看到五个包含待分类图片的文件夹,每个文件夹对应一个受损汽车零部件。

- 您可以点击其中一个文件夹,查看里面的图片。
太棒了!汽车图片现在已经整理好了,可用来进行训练了。
点击检查我的进度,以验证是否完成了以下目标:
将汽车图片上传到您的存储桶中
任务 2. 创建数据集
在此任务中,您将创建一个新的数据集,并将数据集与训练图片相关联,以便 Vertex AI 访问这些图片。
通常,您会创建一个 CSV 文件,其中每一行包含一张训练图片的网址,以及与该图片关联的标签。本实验已为您创建好 CSV 文件,您只需更新该文件,在其中插入存储桶名称,然后将 CSV 文件上传到 Cloud Storage 存储桶中。
更新 CSV 文件
将后面的脚本模板复制并粘贴到 Cloud Shell 中,然后按 Enter 键更新并上传 CSV 文件。
- 要创建该文件的副本,请执行以下命令:
gsutil cp gs://car_damage_lab_metadata/data.csv .
- 要更新 CSV 文件中的图片存储路径,请执行以下命令:
sed -i -e "s/car_damage_lab_images/{{{project_0.project_id | BUCKET}}}/g" ./data.csv
- 运行以下命令,以验证存储桶名称是否已正确插入到 CSV 文件中:
cat ./data.csv
- 要将 CSV 文件上传到 Cloud Storage 存储桶中,请执行以下命令:
gsutil cp ./data.csv gs://{{{project_0.project_id | BUCKET}}}
-
命令执行完毕后,点击 Cloud Storage 浏览器顶部的刷新按钮,然后打开存储桶。
-
确认 data.csv
文件已在存储桶中列出。

在 Cloud Shell IDE 中启用 Gemini Code Assist
您可以在集成开发环境 (IDE)(例如 Cloud Shell)中使用 Gemini Code Assist,以获取代码方面的指导或解决代码问题。您需要先启用 Gemini Code Assist,然后才能使用该功能。
- 在 Cloud Shell 中,使用以下命令启用 Gemini for Google Cloud API:
gcloud services enable cloudaicompanion.googleapis.com
- 点击 Cloud Shell 工具栏上的打开编辑器。
注意:如需打开 Cloud Shell 编辑器,请点击 Cloud Shell 工具栏上的打开编辑器。您可以根据需要点击打开编辑器或打开终端,在 Cloud Shell 和代码编辑器之间切换。
-
在左侧窗格中,点击设置图标,然后在设置视图中搜索 Gemini Code Assist。
-
找到Geminicodeassist: Enable(Geminicodeassist:启用)并确保选中了该复选框,然后关闭设置。
-
点击屏幕底部状态栏中的 Cloud Code - 无项目。
-
按照说明对插件进行授权。如果系统未自动选择项目,请点击选择 Google Cloud 项目,然后选择 。
-
检查您的 Google Cloud 项目 () 是否显示在状态栏的 Cloud Code 状态消息中。
分析 CSV 文件
为了在最大程度减少上下文切换的同时提高效率,Gemini Code Assist 直接在代码编辑器中提供依托 AI 技术的智能操作。在本部分中,您可以让 Gemini Code Assist 帮助您向团队成员解释 CSV 文件的结构和用途。
-
在 Cloud Shell 编辑器的文件探索器中,打开 data.csv
文件。此操作会启用 Gemini Code Assist,启用后,编辑器右上角会显示
图标。
-
点击 Gemini Code Assist:智能操作
图标,然后选择 Explain this(解释此内容)。
-
Gemini Code Assist 会打开一个聊天窗格,其中预填充了 Explain this
提示。在 Code Assist 聊天窗格的内嵌文本框中,将预填充的提示替换为以下内容,然后点击发送:
You are a Machine Learning Engineer at Cymbal AI. Provide a formal analysis of the CSV data data.csv, which is a training manifest for a Vertex AI single-label image classification model. Explain the file's structure and purpose to assist a new team member in understanding this dataset, which is designed to recognize damaged car parts.
Based on this data, provide the following insights in a structured and professional format:
* Dataset Overview: Describe the purpose and structure of the three columns (dataset split, image URI, and label).
* Dataset Splits: Provide a count of the total number of images in the TRAINING, VALIDATION, and TEST splits.
* Class Distribution: For each class (bumper, engine_compartment, hood, lateral, windshield), provide a count of the number of images assigned to it.
* Data Imbalance Analysis: Based on the class and split counts, assess if the dataset is balanced or imbalanced. Explain how this might impact model performance during training.
For the suggested improvements, don't make any changes to the file's content.
关于 CSV 文件 data.csv
文件结构和用途的详细说明会显示在 Gemini Code Assist 聊天中。
创建托管式数据集
-
在 Google Cloud 控制台的导航菜单 (
) 中,依次点击 Vertex AI > 信息中心。
-
点击启用所有推荐的 API(如果未启用的话)。
-
在左侧的 Vertex AI 导航菜单中,点击数据集。
-
在控制台顶部,点击 + 创建。
-
关于数据集名称,请输入 damaged_car_parts
。
-
选择单标签分类。(注意:在您自己的项目中,如果您在进行多类别分类,建议勾选“多标签分类”复选框。)
-
选择 作为区域。
-
点击创建。
将数据集与训练图片相关联
在本部分中,您将选择在上一步上传的训练图片的位置。
-
在选择导入方法部分,点击从 Cloud Storage 中选择导入文件。
-
在从 Cloud Storage 中选择导入文件部分,点击浏览。
-
根据提示导航至您的存储桶,然后点击 data.csv
文件。点击选择。
-
在正确选择文件后,文件路径左侧将显示绿色复选框。点击继续进行下一步。
注意:大约需要 9 到 12 分钟才能完成图片导入并将图片与相应的类别相匹配。您需要等到该步骤完成后才能查看进度。
- 导入完成后,点击浏览标签页,准备进入下一个部分。(提示:您可能需要刷新页面才能确认。)
点击检查我的进度,以验证是否完成了以下目标:
创建数据集
任务 3. 检查图片
在本任务中,您将检查图片以确保数据集没有错误。

检查图片标签
-
如果您的浏览页面已刷新,请点击数据集,选择您的图片名称,然后点击浏览。
-
在过滤标签下,点击任何一个标签,查看具体的训练图片。(示例:engine_compartment。)
注意:如果您在构建生产模型,则每个标签至少需要 100 张图片才能确保高准确率。这里仅仅是演示,为快速完成模型训练,每个类型仅包含 20 张图片。
- 如果图片所带的标签不正确,可点击该图片以选择正确的标签,或者从训练集中删除该图片:

- 接下来,点击分析标签页,查看每个标签的图片数量。您的浏览器中会显示标签统计信息窗口。
任务 4. 训练模型
现在可以开始训练模型了!Vertex AI 可自动为您处理这一步,无需编写任何模型代码。
-
在右侧点击训练新模型。
-
在训练方法窗口中,保留默认配置,选择 AutoML 作为训练方法。点击继续。
-
在模型详情窗口中,为您的模型输入名称,例如:damaged_car_parts_model
。点击继续。
-
在训练选项窗口中,选择启用增量训练功能,然后点击继续。
-
在计算和价格窗口中,将预算设置为节点时上限 8。
-
点击开始训练。
注意:模型训练需要的时间可能超过为本实验设定的时间限制。该模型不需要完成训练,您可以继续学习下一部分。
点击检查我的进度,以验证是否完成了以下目标:
训练模型
任务 5. 请求托管模型提供预测结果
为了达到本次实验的目的,另一个项目中为您准备好了一个使用完全相同的数据集完成训练的模型。这样,在本地模型还在训练时,您可以向这个托管模型请求预测结果。这是因为本地模型的训练可能会超出本实验的时间限制。
我们为您设置了预训练模型的代理,这样,您无需额外执行任何步骤,即可让其在您的实验环境中运行。
要请求该模型提供预测结果,您需要将预测请求发送到项目中的一个端点,该端点会将您的请求转发给托管模型并返回输出结果。您刚刚在创建模型时,与模型进行了各种交互,向 AutoML 代理发送预测请求的过程几乎是一样的,您可以把它当做一次练习。
获取 AutoML 代理端点的名称
-
在 Google Cloud 控制台的导航菜单 (≡) 中,点击 Cloud Run。
-
点击 automl-proxy。

- 将网址复制到端点。它应该与以下网址类似:
https://automl-proxy-xfpm6c62ta-uc.a.run.app
。

在下一部分创建预测请求时,您将使用此端点。
创建预测请求
-
打开新的 Cloud Shell 窗口。
-
在 Cloud Shell 工具栏上,点击打开编辑器。如果出现提示,点击在新窗口中打开。
-
依次点击文件 > 新建文件。
-
在新文件窗口中,输入文件名 payload.json
,然后从下拉菜单中选择路径 (/home/student_xx_xxxxx)。
-
点击确定。
-
将以下内容粘贴到您刚刚创建的新文件中:
{
"instances": [{
"content": 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"
}],
"parameters": {
"confidenceThreshold": 0.5,
"maxPredictions": 5
}
}
- 依次点击文件 -> 保存。
作为参考,您提供的内容是对下图进行 Base64 编码而得到的字符串。

现在,您可以让 Gemini Code Assist 帮助您向团队成员解释 JSON 载荷的结构和用途。
-
打开 payload.json
文件,点击工具栏上的 Gemini Code Assist:智能操作
图标,然后选择 Explain this(解释此内容)。
-
Gemini Code Assist 会打开一个聊天窗格,其中预填充了 Explain this
提示。在 Code Assist 聊天窗格的内嵌文本框中,将预填充的提示替换为以下内容,然后点击发送:
You are a Machine Learning Engineer at Cymbal AI. A new team member needs help understanding the structure and purpose of the payload.json file. This payload is used to request a prediction from a deployed Vertex AI image classification model. Your explanation should cover:
* The overall purpose of the payload in the context of a Vertex AI prediction workflow.
* The role of the instances key, explaining the base64-encoded image data within it.
* The function of the parameters key, including the purpose of confidenceThreshold and maxPredictions.
* How this payload would be sent to the model's endpoint.
For the suggested improvements, don't make any changes to the file's content.
JSON 载荷文件 payload.json
的详细说明会显示在 Gemini Code Assist 聊天中。
- 接下来,设置以下环境变量。复制您之前检索到的 AutoML 代理地址。
AUTOML_PROXY=<automl-proxy url>
INPUT_DATA_FILE=payload.json
- 运行以下命令,向 AutoML 代理端点发起 API 请求,让托管模型提供预测结果:
curl -X POST -H "Content-Type: application/json" $AUTOML_PROXY/v1 -d "@${INPUT_DATA_FILE}"
如果预测成功,输出应类似于以下内容:
输出:
{"predictions":[{"confidences":[0.951557755],"displayNames":["bumper"],"ids":["1960986684719890432"]}],"deployedModelId":"4271461936421404672","model":"projects/1030115194620/locations/"{{{project_0.default_region | REGION}}}"/models/2143634257791156224","modelDisplayName":"damaged_car_parts_vertex","modelVersionId":"1"}
对于本模型,预测结果一目了然。displayNames
字段应该能正确预测出 bumper
的值(即保险杠),并且置信度阈值很高。现在,您可以修改之前创建的 JSON 文件中图片的 Base64 编码值。
点击检查我的进度,以验证是否完成了以下目标:
创建预测请求
-
右键点击以下每张图片,然后选择图片另存为…。
-
按照提示保存每张图片,并指定唯一名称。(提示:为了便于上传,请指定简单的名称,如“Image1”“Image2”等)。

-
打开 Base64 图片编码器,按照说明上传图片,并将图片编码为 Base64 字符串。
-
替换 JSON 载荷文件中 content
字段包含的 Base64 编码字符串值,然后再次运行预测。对于其他图片,请重复上述操作。
您模型的表现如何?对三张图片的预测结果都正确吗?您应该会分别看到以下输出内容:
{"predictions":[{"ids":["5419751198540431360"],"confidences":[0.985487759],"displayNames":["engine_compartment"]}],"deployedModelId":"4271461936421404672","model":"projects/1030115194620/locations/"{{{project_0.default_region | REGION}}}"/models/2143634257791156224","modelDisplayName":"damaged_car_parts_vertex","modelVersionId":"1"}
{"predictions":[{"displayNames":["hood"],"ids":["3113908189326737408"],"confidences":[0.962432086]}],"deployedModelId":"4271461936421404672","model":"projects/1030115194620/locations/"{{{project_0.default_region | REGION}}}"/models/2143634257791156224","modelDisplayName":"damaged_car_parts_vertex","modelVersionId":"1"}
恭喜!
在本实验中,您学习了如何在 Gemini Code Assist 的帮助下训练自己的自定义机器学习模型,并通过 API 请求在托管模型上生成了预测。您将训练图片上传到了 Cloud Storage,并使用 CSV 文件来让 Vertex AI 找到了这些图片。在最后评估经训练的模型之前,您检查了带标签的图片是否存在差异。现在,您已经掌握了基于自己的图片数据集训练模型的方法!
后续步骤/了解详情
Google Cloud 培训和认证
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