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運用 Vertex AutoML 識別受損的汽車零件

实验 50 分钟 universal_currency_alt 5 积分 show_chart 中级
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GSP972

Google Cloud 自學實驗室標誌

總覽

Vertex AI 將建構機器學習 (ML) 所需的 Google Cloud 服務,整合為統一的介面和 API。在 Vertex AI,您現在可以使用 AutoML 或自訂程式碼訓練功能來輕鬆訓練及比較模型。所有模型都儲存在同一個中央模型存放區,現在也能部署到 Vertex AI 上的相同端點。

就算使用者的機器學習專業知識有限,也能透過 Vertex AI AutoML 訓練出優質的圖像分類模型。在本實作實驗室中,您將瞭解如何建立可自動辨識受損汽車零件的自訂機器學習模型。由於模型訓練作業會超出實驗室的時間限制,我們提供了以相同資料集訓練而成、託管於其他專案的模型,您將與這個託管模型互動並要求預測結果。接著,您將調整預測要求中的資料值,並檢查對模型的預測結果造成什麼影響。

目標

在本實驗室中,您將瞭解如何執行下列工作:

  • 使用 CSV 檔案將加上標籤的資料集上傳至 Cloud Storage,並以代管資料集的形式連結至 Vertex AI。
  • 檢查已上傳的圖片,確認資料集內容正確無誤。
  • 執行 AutoML 模型訓練工作。
  • 向以相同資料集訓練而成的託管模型要求預測結果。

設定和需求

瞭解以下事項後,再點選「Start Lab」按鈕

請詳閱以下操作說明。實驗室活動會計時,且中途無法暫停。點選「Start Lab」後就會開始計時,顯示可使用 Google Cloud 資源的時間。

您將在真正的雲端環境完成實作實驗室活動,而不是模擬或示範環境。為此,我們會提供新的暫時憑證,供您在實驗室活動期間登入及存取 Google Cloud。

為了順利完成這個實驗室,請先確認:

  • 可以使用標準的網際網路瀏覽器 (Chrome 瀏覽器為佳)。
注意事項:請使用無痕模式 (建議選項) 或私密瀏覽視窗執行此實驗室,這可以防止個人帳戶和學員帳戶之間的衝突,避免個人帳戶產生額外費用。
  • 是時候完成實驗室活動了!別忘了,活動一旦開始將無法暫停。
注意事項:務必使用實驗室專用的學員帳戶。如果使用其他 Google Cloud 帳戶,可能會產生額外費用。

如何開始研究室及登入 Google Cloud 控制台

  1. 點選「Start Lab」按鈕。如果實驗室會產生費用,畫面上會出現選擇付款方式的對話方塊。左側的「Lab Details」窗格會顯示下列項目:

    • 「Open Google Cloud console」按鈕
    • 剩餘時間
    • 必須在這個研究室中使用的臨時憑證
    • 完成這個實驗室所需的其他資訊 (如有)
  2. 點選「Open Google Cloud console」;如果使用 Chrome 瀏覽器,也能按一下滑鼠右鍵,選取「在無痕視窗中開啟連結」

    接著,實驗室會啟動相關資源,並開啟另一個分頁,顯示「登入」頁面。

    提示:您可以在不同的視窗中並排開啟分頁。

    注意:如果頁面中顯示「選擇帳戶」對話方塊,請點選「使用其他帳戶」
  3. 如有必要,請將下方的 Username 貼到「登入」對話方塊。

    {{{user_0.username | "Username"}}}

    您也可以在「Lab Details」窗格找到 Username。

  4. 點選「下一步」

  5. 複製下方的 Password,並貼到「歡迎使用」對話方塊。

    {{{user_0.password | "Password"}}}

    您也可以在「Lab Details」窗格找到 Password。

  6. 點選「下一步」

    重要事項:請務必使用實驗室提供的憑證,而非自己的 Google Cloud 帳戶憑證。 注意:如果使用自己的 Google Cloud 帳戶來進行這個實驗室,可能會產生額外費用。
  7. 按過後續的所有頁面:

    • 接受條款及細則。
    • 由於這是臨時帳戶,請勿新增救援選項或雙重驗證機制。
    • 請勿申請免費試用。

Google Cloud 控制台稍後會在這個分頁開啟。

注意:如要使用 Google Cloud 產品和服務,請點選「導覽選單」,或在「搜尋」欄位輸入服務或產品名稱。「導覽選單」圖示和搜尋欄位

啟動 Cloud Shell

Cloud Shell 是搭載多項開發工具的虛擬機器,提供永久的 5 GB 主目錄,而且在 Google Cloud 中運作。Cloud Shell 提供指令列存取權,方便您使用 Google Cloud 資源。

  1. 點按 Google Cloud 控制台頂端的「啟用 Cloud Shell」圖示 「啟動 Cloud Shell」圖示

  2. 系統顯示視窗時,請按照下列步驟操作:

    • 繼續操作 Cloud Shell 視窗。
    • 授權 Cloud Shell 使用您的憑證發出 Google Cloud API 呼叫。

連線建立完成即代表已通過驗證,而且專案已設為您的 Project_ID。輸出內容中有一行文字,宣告本工作階段的 Project_ID

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud 是 Google Cloud 的指令列工具,已預先安裝於 Cloud Shell,並支援 Tab 鍵自動完成功能。

  1. (選用) 您可以執行下列指令來列出使用中的帳戶:
gcloud auth list
  1. 點按「授權」

輸出內容:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (選用) 您可以使用下列指令來列出專案 ID:
gcloud config list project

輸出內容:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} 注意:如需 gcloud 的完整說明,請前往 Google Cloud 參閱 gcloud CLI 總覽指南

工作 1:將訓練圖片上傳至 Cloud Storage

在這項工作中,您會將用於訓練的圖片上傳至 Cloud Storage,這樣之後匯入資料至 Vertex AI 會更輕鬆。

為了訓練模型分類受損汽車零件的圖片,您必須向機器提供加上標籤的訓練資料。模型會利用這類資料來理解圖片,學會分辨受損及未受損的汽車零件。

注意:為方便您進行實驗室活動,我們隨附的 CSV 檔案已包含加上標籤的資料集 (圖片已有標籤),您不必自行為圖片加上標籤。下一節將概述 CSV 檔案的使用步驟。

在這個示例中,模型會學習分類五種不同的受損汽車零件:bumper (保險桿)、engine compartment (引擎室)、hood (引擎蓋)、lateral (車身側邊) 和 windshield (擋風玻璃)。

建立 Cloud Storage bucket

  • 開啟 Cloud Shell 視窗,執行下列指令來建立 Cloud Storage bucket:
gsutil mb -p {{{project_0.project_id | PROJECT_ID}}} \ -c standard \ -l "{{{project_0.default_region | REGION}}}" \ gs://{{{project_0.project_id | BUCKET}}}

將汽車零件圖片上傳至 Storage bucket

Cloud Storage bucket 中的訓練圖片屬於公開資源。請再次複製底下的指令碼範本並貼到 Cloud Shell,將圖片複製到您自己的 bucket。

  1. 執行下列指令,即可將圖片複製到 Cloud Storage bucket:
gsutil -m cp -r gs://car_damage_lab_images/* gs://{{{project_0.project_id | BUCKET}}}
  1. 在導覽窗格中,依序點按「Cloud Storage」>「Bucket」

  2. 點按 Cloud Storage 瀏覽器頂端的「重新整理」按鈕。

  3. 點按 bucket 名稱,您會看見五個資料夾,分別收錄五種要分類的受損汽車零件圖片:

含有 bumper、engine compartment、hood、lateral 和 windshield 資料夾的 bucket。

  1. 您可以點開資料夾,查看裡面的圖片。

太好了!汽車零件圖片現已分類完畢,可以用於訓練。

點選「Check my progress」,確認目標已達成。 將汽車零件圖片上傳至 Storage bucket

工作 2:建立資料集

在這項工作中,您將建立新的資料集並將其連結至訓練圖片,以便 Vertex AI 存取。

一般情況下,您需要自行建立 CSV 檔案,當中每一列都包含訓練圖片的網址及該圖片的相關標籤。由於本實驗室已預先建立 CSV 檔案,您只需要將該檔案更新成自己的 bucket 名稱,並上傳至 Cloud Storage bucket 即可。

更新 CSV 檔案

複製下方的指令碼範本並貼到 Cloud Shell,然後按下 Enter 鍵更新 CSV 檔案,接著再上傳檔案。

  1. 執行下列指令來建立檔案副本:
gsutil cp gs://car_damage_lab_metadata/data.csv .
  1. 執行下列指令,將 CSV 檔案路徑改成指向您的儲存空間:
sed -i -e "s/car_damage_lab_images/{{{project_0.project_id | BUCKET}}}/g" ./data.csv
  1. 執行下列指令,確認 bucket 名稱正確插入 CSV 檔案中:
cat ./data.csv
  1. 執行下列指令,將 CSV 檔案上傳至 Cloud Storage bucket:
gsutil cp ./data.csv gs://{{{project_0.project_id | BUCKET}}}
  1. 指令執行完畢後,請點按 Cloud Storage 瀏覽器頂端的「重新整理」按鈕,並開啟 bucket。

  2. 確認 bucket 中列有 data.csv 檔案。

data-csv 檔案

在 Cloud Shell IDE 啟用 Gemini Code Assist

您可以在 Cloud Shell 等整合式開發環境 (IDE) 中使用 Gemini Code Assist,取得程式碼相關指引或解決程式碼問題。Gemini Code Assist 必須先啟用,才能開始使用。

  1. 在 Cloud Shell 執行下列指令,啟用 Gemini for Google Cloud API:
gcloud services enable cloudaicompanion.googleapis.com
  1. 點按 Cloud Shell 工具列中的「開啟編輯器」
注意:如要開啟 Cloud Shell 編輯器,請點選 Cloud Shell 工具列中的「開啟編輯器」。如要在 Cloud Shell 與程式碼編輯器之間切換,請視需要點選「開啟編輯器」或「開啟終端機」
  1. 點選畫面底部狀態列中的「Cloud Code - No Project」

  2. 依指示授權外掛程式。如果系統未自動選取專案,請點按「選取 Google Cloud 專案」,然後選擇

  3. 確認狀態列的 Cloud Code 狀態訊息中已顯示 Google Cloud 專案 ()。

分析 CSV 檔案

為協助您提高工作效率,同時減少環境切換次數,Gemini Code Assist 會直接在程式碼編輯器中提供 AI 輔助智慧動作。在本節中,您決定請 Gemini Code Assist 向團隊成員說明 CSV 檔案的結構和用途。

  1. 在 Cloud Shell 編輯器的檔案總管中,開啟 data.csv 檔案。這項動作會啟用 Gemini Code Assist,編輯器右上角出現 「Gemini Code Assist: Smart Actions」圖示 圖示時,就代表該工具已啟用。

  2. 點選「Gemini Code Assist: Smart Actions」圖示 「Gemini Code Assist: Smart Actions」圖示,然後選取「Explain this」

  3. Gemini Code Assist 會開啟對話窗格,並預先填入提示詞 Explain this。在 Code Assist 對話的內嵌文字方塊中,將預先填入的提示詞改成下列內容,然後點選「Send」

您是 Cymbal AI 的機器學習工程師,需要提供 CSV 資料 data.csv 的正式分析,這個檔案是 Vertex AI 單一標籤圖像分類模型的訓練資訊清單。請說明檔案的結構和用途,協助新進團隊成員瞭解這個資料集,該資料集主要用於辨識損壞的汽車零件。 請根據這項資料,以專業的結構化格式提供下列洞察資訊: * 資料集總覽:說明三個資料欄 (dataset split、image URI 和 label) 的用途和結構。 * 資料集分割:提供訓練、驗證和測試分割中圖像總數的計數。 * 類別分布:提供每個類別 (bumper、engine_compartment、hood、lateral、windshield) 的圖片數量。 * 資料不平衡分析:根據類別和分割計數,評估資料集是否平衡。說明這可能會對訓練期間的模型效能造成什麼影響。 如有建議改善的地方,請勿直接修改檔案內容。

Gemini Code Assist 對話視窗中會顯示 CSV 檔案 data.csv 程式碼的結構和用途詳細說明。

建立代管資料集

  1. 前往 Google Cloud 控制台,依序點按「導覽選單」圖示 「導覽選單」圖示 >「Vertex AI」>「資訊主頁」

  2. 點按「啟用所有建議的 API」(如果尚未啟用)。

  3. 點按左側 Vertex AI 導覽選單中的「資料集」

  4. 點按控制台頂端的「+ 建立」

  5. 在「資料集名稱」欄位輸入 damaged_car_parts

  6. 選取「單一標籤分類」 (請注意,在您自己的專案執行多元分類時,建議勾選「多標籤分類」核取方塊)。

  7. 在「區域」部分選取

  8. 點按「建立」

將資料集連結至訓練圖片

在本節中,您將選擇上一個步驟上傳訓練圖片的位置。

  1. 在「選取匯入方法」專區,點按「從 Cloud Storage 選取匯入檔案」

  2. 在「從 Cloud Storage 選取匯入檔案」專區,點按「瀏覽」

  3. 按提示前往您的 Storage bucket,依序點按 data.csv 檔案和「選取」

  4. 確實選取檔案後,檔案路徑左側會顯示綠色的核取方塊。請點按「繼續」來繼續操作。

注意:圖片通常需要 9 至 12 分鐘才能匯入完畢並各自歸類。您必須先等待這個步驟完成,再確認進度已達成。
  1. 匯入完畢後,請點按「瀏覽」分頁標籤,為下一節做好準備 (提示:建議重新整理頁面,確認匯入作業是否完成)。

點選「Check my progress」,確認目標已達成。 建立資料集

工作 3:檢查圖片

在這項工作中,您將檢查圖片,確認資料集內容正確無誤。

「瀏覽」分頁中的圖片方塊

檢查圖片標籤

  1. 如果瀏覽器頁面已重新整理,請點按「資料集」並選取圖片名稱,然後點按「瀏覽」

  2. 在「篩選標籤」底下,點按任一標籤來查看特定訓練圖片 (例如 engine_compartment)

注意:建構用於正式環境的模型時,建議每個標籤「至少」要有 100 張圖片,確保模型準確率夠高。為快速完成模型訓練步驟,這個示例中的每個零件類型只有 20 張圖片。
  1. 如果圖片的標籤有誤,您可以點按標籤並選取正確的標籤,或是從訓練集中刪除圖片:

圖片詳細資料

  1. 接著點按「分析」分頁標籤,查看各個標籤的圖片數量。瀏覽器中會顯示「標籤統計資料」視窗。

工作 4:訓練模型

您已準備好開始訓練模型了!Vertex AI 會自動完成這項工作,您不必編寫任何模型程式碼。

  1. 點按右側的「訓練新模型」

  2. 在「訓練方法」視窗中,選取「AutoML」做為訓練方法,其餘設定則保留預設值,然後點按「繼續」

  3. 在「模型詳細資料」視窗中,為模型取名 damaged_car_parts_model,然後點按「繼續」

  4. 在「訓練選項」視窗中,選取「啟用增量訓練」並點按「繼續」

  5. 在「運算與定價」視窗中,將節點時數上限設為 8 小時。

  6. 點按「開始訓練」

注意:模型訓練作業所需時間,可能會超出完成實驗室的限制時間。您可以直接前往下一節,執行後續作業,不必等待模型訓練完畢

點選「Check my progress」,確認目標已達成。 訓練模型

工作 5:向託管模型要求預測結果

由於本機模型訓練作業可能會超出本實驗室的時間限制,為方便您完成本實驗室活動,我們提供了以相同資料集訓練而成、託管於其他專案的模型,讓您直接向託管模型要求預測結果,不需等待本機模型訓練完成。

本實驗室已替您設定預先訓練模型的 Proxy,您不必進行任何額外操作,就能在實驗室環境中使用該模型。

如要向模型要求預測結果,您必須將預測要求傳送到專案中的端點,該端點會將要求轉送至託管模型,並傳回輸出內容。將預測要求傳送到 AutoML Proxy 的方式,跟剛剛您建立模型後的互動方式很類似,您可以利用這個步驟先行練習。

取得 AutoML Proxy 端點名稱

  1. 前往 Google Cloud 控制台,依序點按「導覽選單」圖示 (≡) >「Cloud Run」

  2. 點按 automl-proxy

AutoML Proxy 端點

  1. 複製「網址」後方的端點網址,格式應類似:https://automl-proxy-xfpm6c62ta-uc.a.run.app

端點網址

在下一節中,您將使用這個端點來要求預測結果。

建立預測要求

  1. 開啟新的 Cloud Shell 視窗。

  2. 在 Cloud Shell 工具列中,按一下「開啟編輯器」。如果出現提示訊息,請點按「在新視窗中開啟」

  3. 依序點按「File」>「New File」

  4. 在新的檔案視窗中輸入檔案名稱 payload.json,然後在下拉式選單中選取路徑 (/home/student_xx_xxxxx)

  5. 按一下「OK」

  6. 將以下內容貼到您剛才建立的新檔案中:

{ "instances": [{ "content": 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" }], "parameters": { "confidenceThreshold": 0.5, "maxPredictions": 5 } }
  1. 依序點選「File」>「Save」

您剛才提供的內容是一張圖片的 Base64 編碼字串,原始圖片如下:

引擎蓋

現在,您決定請 Gemini Code Assist 向團隊成員說明 JSON 酬載的結構和用途。

  1. 開啟 payload.json 檔案後,點選工具列上的「Gemini Code Assist: Smart Actions」 圖示 「Gemini Code Assist: Smart Actions」圖示,然後選取「Explain this」

  2. Gemini Code Assist 會開啟對話窗格,並預先填入提示詞 Explain this。在 Code Assist 對話的內嵌文字方塊中,將預先填入的提示詞改成下列內容,然後點選「Send」

您是 Cymbal AI 的機器學習工程師,新進團隊成員需要協助,才能瞭解 payload.json 檔案的結構和用途。這個酬載用於向已部署的 Vertex AI 圖像分類模型要求預測。說明應涵蓋下列內容: * 在 Vertex AI 預測工作流程中,酬載的整體用途。 * 說明 instances 鍵的角色,以及其中的 base64 編碼圖像資料。 * parameters 鍵的功能,包括 confidenceThreshold 和 maxPredictions 的用途。 * 如何將這個酬載傳送至模型的端點。 如有建議改善的地方,請勿直接修改檔案內容。

Gemini Code Assist 對話視窗中會顯示 JSON 酬載檔案 payload.json 的詳細說明。

  1. 接著設定下列環境變數,請加入您稍早複製的 AutoML Proxy 網址。
AUTOML_PROXY=<automl-proxy url> INPUT_DATA_FILE=payload.json
  1. 執行下列指令,對 AutoML Proxy 端點執行 API 要求,向託管模型要求預測結果:
curl -X POST -H "Content-Type: application/json" $AUTOML_PROXY/v1 -d "@${INPUT_DATA_FILE}"

如果預測作業順利完成,輸出內容應如下所示。

輸出內容:

{"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"}

這個模型提供的預測結果簡明易懂。displayNames 欄位應該會顯示正確的預測結果 bumper,並具有很高的可信度門檻。現在您可以在剛才建立的 JSON 檔案中,變更以 Base64 編碼的圖片值。

點選「Check my progress」,確認目標已達成。 建立預測要求

  1. 在下方所有圖片上按一下滑鼠右鍵,然後選取「另存圖片…」

  2. 按照提示,為每張圖片取不重複的名稱並儲存 (提示:建議取「Image1」、「Image2」這類簡單的名稱,上傳時才不容易弄混)

圖片 2 圖片 3

  1. 開啟 Base64 圖片編碼工具,然後按照指示上傳圖片並轉換為 Base64 編碼字串。

  2. 替換 JSON 酬載檔案中 content 欄位內的 Base64 編碼字串值,然後再次執行預測作業,並對其他圖片重複相同的操作。

模型的表現如何?三張圖片的預測結果都正確嗎?輸出結果應分別如下所示:

{"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!

在本實驗室中,您學會如何使用 Gemini Code Assist 訓練自己的自訂機器學習模型,以及如何對託管模型送出 API 要求並取得預測結果。您使用 CSV 檔案將訓練圖片上傳到 Cloud Storage,並讓 Vertex AI 找到這些圖片。您還檢查了加上標籤的圖片,確認是否有任何差異,最後對訓練好的模型進行評估。現在您已經掌握所有技巧,可以用自己的圖片資料集訓練模型了!

後續步驟/瞭解詳情

Google Cloud 教育訓練與認證

協助您瞭解如何充分運用 Google Cloud 的技術。我們的課程會介紹專業技能和最佳做法,讓您可以快速掌握要領並持續進修。我們提供從基本到進階等級的訓練課程,並有隨選、線上和虛擬課程等選項,方便您抽空參加。認證可協助您驗證及證明自己在 Google Cloud 技術方面的技能和專業知識。

使用手冊上次更新日期:2025 年 10 月 15 日

實驗室上次測試日期:2025 年 9 月 10 日

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