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Mengidentifikasi Komponen Mobil yang Rusak dengan Vertex AutoML Vision

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Mengidentifikasi Komponen Mobil yang Rusak dengan Vertex AutoML Vision

Lab 1 jam 30 menit universal_currency_alt 5 Kredit show_chart Menengah
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GSP972

Lab Mandiri Google Cloud

Ringkasan

Vertex AI menyatukan layanan Google Cloud untuk membangun ML dalam satu UI dan API terpadu. Di Vertex AI, kini Anda dapat dengan mudah melatih dan membandingkan model menggunakan AutoML atau pelatihan kode kustom, dan semua model Anda disimpan dalam satu repositori model terpusat. Model-model ini sekarang dapat di-deploy ke endpoint yang sama di Vertex AI.

AutoML Vision membantu agar orang-orang yang kurang menguasai Machine Learning (ML) dapat melatih model klasifikasi gambar berkualitas tinggi. Dalam lab praktik ini, Anda akan mempelajari cara membuat model ML kustom yang dapat mengidentifikasi komponen mobil yang rusak secara otomatis. Karena waktu yang diperlukan untuk melatih model melampaui batas waktu lab, Anda akan melakukan interaksi dan meminta prediksi dari model yang dihosting di project lain yang dilatih pada set data yang sama. Selanjutnya, Anda akan menyempurnakan nilai data untuk permintaan prediksi dan mempelajari bagaimana hal tersebut dapat mengubah prediksi yang dihasilkan dari model.

Tujuan

Di lab ini, Anda akan mempelajari cara:

  • Mengupload set data berlabel ke Cloud Storage menggunakan file CSV dan menghubungkannya ke Vertex AI sebagai Set Data Terkelola
  • Memeriksa gambar yang diupload untuk memastikan tidak ada error dalam set data Anda
  • Memulai tugas pelatihan model AutoML Vision
  • Meminta prediksi dari model yang dihosting dan dilatih dalam set data yang sama

Penyiapan dan persyaratan

Sebelum mengklik tombol Mulai Lab

Baca petunjuk ini. Lab memiliki timer dan Anda tidak dapat menjedanya. Timer, yang dimulai saat Anda mengklik Start Lab, akan menampilkan durasi ketersediaan resource Google Cloud untuk Anda.

Lab praktik ini dapat Anda gunakan untuk melakukan sendiri aktivitas lab di lingkungan cloud sungguhan, bukan di lingkungan demo atau simulasi. Untuk mengakses lab ini, Anda akan diberi kredensial baru yang bersifat sementara dan dapat digunakan untuk login serta mengakses Google Cloud selama durasi lab.

Untuk menyelesaikan lab ini, Anda memerlukan:

  • Akses ke browser internet standar (disarankan browser Chrome).
Catatan: Gunakan jendela Samaran atau browser pribadi untuk menjalankan lab ini. Hal ini akan mencegah konflik antara akun pribadi Anda dan akun Siswa yang dapat menyebabkan tagihan ekstra pada akun pribadi Anda.
  • Waktu untuk menyelesaikan lab. Ingat, setelah dimulai, lab tidak dapat dijeda.
Catatan: Jika Anda sudah memiliki project atau akun pribadi Google Cloud, jangan menggunakannya untuk lab ini agar terhindar dari tagihan ekstra pada akun Anda.

Cara memulai lab dan login ke Google Cloud Console

  1. Klik tombol Start Lab. Jika Anda perlu membayar lab, jendela pop-up akan terbuka untuk memilih metode pembayaran. Di sebelah kiri adalah panel Lab Details dengan info berikut:

    • Tombol Open Google Cloud console
    • Waktu tersisa
    • Kredensial sementara yang harus Anda gunakan untuk lab ini
    • Informasi lain, jika diperlukan, untuk menyelesaikan lab ini

  2. Klik Open Google Cloud console (atau klik kanan dan pilih Open Link in Incognito Window jika Anda menjalankan browser Chrome).

    Lab akan menjalankan resource, lalu membuka tab lain yang menampilkan halaman Sign in.

    Tips: Atur tab di jendela terpisah secara berdampingan.

    Catatan: Jika Anda melihat dialog Choose an account, klik Use Another Account.

  3. Jika perlu, salin Username di bawah dan tempel ke dialog Sign in.

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

    Anda juga dapat menemukan Username di panel Lab Details.

  4. Klik Next.

  5. Salin Password di bawah dan tempel ke dialog Welcome.

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

    Anda juga dapat menemukan Password di panel Lab Details.

  6. Klik Next.

    Penting: Anda harus menggunakan kredensial yang diberikan lab. Jangan menggunakan kredensial akun Google Cloud Anda. Catatan: Menggunakan akun Google Cloud sendiri untuk lab ini dapat dikenai biaya tambahan.

  7. Klik halaman berikutnya:

    • Setujui persyaratan dan ketentuan.
    • Jangan tambahkan opsi pemulihan atau autentikasi 2 langkah (karena ini akun sementara).
    • Jangan mendaftar uji coba gratis.

Setelah beberapa saat, Konsol Google Cloud akan terbuka di tab ini.

Catatan: Untuk melihat menu dengan daftar produk dan layanan Google Cloud, klik Navigation menu di kiri atas. Ikon Navigation menu

Mengaktifkan Cloud Shell

Cloud Shell adalah mesin virtual yang dilengkapi dengan berbagai alat pengembangan. Mesin virtual ini menawarkan direktori beranda persisten berkapasitas 5 GB dan berjalan di Google Cloud. Cloud Shell menyediakan akses command-line untuk resource Google Cloud Anda.

  1. Klik Activate Cloud Shell Ikon Activate Cloud Shell di bagian atas konsol Google Cloud.

Setelah terhubung, Anda sudah diautentikasi, dan project ditetapkan ke PROJECT_ID Anda. Output berisi baris yang mendeklarasikan PROJECT_ID untuk sesi ini:

Project Cloud Platform Anda dalam sesi ini disetel ke YOUR_PROJECT_ID

gcloud adalah alat command line untuk Google Cloud. Alat ini sudah terinstal di Cloud Shell dan mendukung pelengkapan command line.

  1. (Opsional) Anda dapat menampilkan daftar nama akun yang aktif dengan perintah ini:
gcloud auth list

  1. Klik Authorize.

  2. Output Anda sekarang akan terlihat seperti ini:

Output:

ACTIVE: * ACCOUNT: student-01-xxxxxxxxxxxx@qwiklabs.net Untuk menyetel akun aktif, jalankan: $ gcloud config set account `ACCOUNT`
  1. (Opsional) Anda dapat menampilkan daftar project ID dengan perintah ini:
gcloud config list project

Output:

[core] project = <project_ID>

Contoh output:

[core] project = qwiklabs-gcp-44776a13dea667a6 Catatan: Untuk mendapatkan dokumentasi gcloud yang lengkap di Google Cloud, baca panduan ringkasan gcloud CLI.

Tugas 1. Mengupload gambar pelatihan ke Cloud Storage

Dalam tugas ini, Anda akan mengupload gambar pelatihan yang ingin Anda gunakan pada Cloud Storage. Hal ini akan memudahkan Anda untuk mengimpor data ke Vertex AI nanti.

Untuk melatih model guna mengklasifikasi gambar komponen mobil yang rusak, Anda perlu memberikan data pelatihan berlabel ke mesin. Model kemudian akan menggunakan data tersebut untuk meningkatkan pemahaman dari setiap gambar, membedakan antara komponen mobil yang rusak dan yang tidak rusak.

Catatan: Untuk keperluan lab ini, Anda tidak perlu melabeli gambar karena kami telah menyediakan set data berlabel (yakni gambar serta labelnya) dalam file CSV. Bagian berikutnya menguraikan langkah-langkah untuk menggunakan file CSV.

Dalam contoh ini, model Anda akan mempelajari cara mengklasifikasi lima komponen mobil yang rusak: bumper (bumper), engine compartment (ruang mesin), hood (kap mesin), lateral (suspensi), dan windshield (kaca depan).

Membuat bucket Cloud Storage

  1. Untuk memulai, buka jendela Cloud Shell baru dan jalankan perintah berikut untuk menetapkan variabel lingkungan:
export PROJECT_ID=$DEVSHELL_PROJECT_ID export BUCKET=$PROJECT_ID
  1. Kemudian, jalankan perintah berikut untuk membuat bucket Cloud Storage:
gsutil mb -p $PROJECT_ID \ -c standard \ -l "{{{project_0.default_region | REGION}}}" \ gs://${BUCKET}

Mengupload gambar mobil ke Bucket Cloud Storage

Gambar pelatihan tersedia secara publik di bucket Cloud Storage. Sekali lagi, salin dan tempel template skrip di bawah ke Cloud Shell untuk menyalin gambar ke bucket Anda sendiri.

  1. Untuk menyalin gambar ke bucket Cloud Storage, jalankan perintah berikut:
gsutil -m cp -r gs://car_damage_lab_images/* gs://${BUCKET}

  1. Di panel navigasi, klik Cloud Storage > Buckets.

  2. Klik tombol Refresh di bagian atas browser Cloud Storage.

  3. Klik nama bucket Anda. Anda akan melihat lima folder foto untuk kelima komponen mobil yang rusak yang akan diklasifikasikan:

Bucket dengan folder berjudul: bumper, engine compartment, hood, lateral, dan windshield.

  1. Anda juga dapat mengklik salah satu folder untuk melihat gambar yang ada di dalamnya.

Bagus! Gambar mobil Anda telah tertata rapi dan siap untuk pelatihan.

Klik Check my progress untuk memverifikasi tujuan. Mengupload gambar mobil ke Bucket Cloud Storage

Tugas 2. Membuat set data

Dalam tugas ini, Anda akan membuat set data baru dan menghubungkannya ke gambar pelatihan agar Vertex AI dapat mengaksesnya.

Biasanya, Anda akan membuat file CSV yang setiap barisnya berisi URL ke gambar pelatihan dan label terkait untuk gambar tersebut. Dalam kasus ini, file CSV telah dibuatkan untuk Anda, jadi Anda hanya perlu mengupdatenya dengan nama bucket dan mengupload file CSV tersebut ke bucket Cloud Storage Anda.

Mengupdate file CSV

Salin dan tempel template skrip di bawah ke Cloud Shell, lalu tekan enter untuk mengupdate, dan upload file CSV.

  1. Untuk membuat salinan file, jalankan perintah berikut:
gsutil cp gs://car_damage_lab_metadata/data.csv .
  1. Untuk mengupdate CSV dengan jalur ke penyimpanan Anda, jalankan perintah berikut:
sed -i -e "s/car_damage_lab_images/${BUCKET}/g" ./data.csv
  1. Pastikan nama bucket Anda telah disisipkan ke dalam CSV dengan benar:
cat ./data.csv
  1. Untuk mengupload file CSV ke bucket Cloud Storage Anda, jalankan perintah berikut:
gsutil cp ./data.csv gs://${BUCKET}

  1. Setelah perintah selesai, klik tombol Refresh di bagian atas browser Cloud Storage dan buka bucket Anda.

  2. Pastikan bahwa file data.csv telah tercantum dalam bucket Anda.

File data-csv

Membuat set data terkelola

  1. Di Konsol Google Cloud, pada Navigation menu (Ikon Navigation menu), klik Vertex AI > Dashboard.

  2. Klik Enable All Recommended APIs jika belum diaktifkan.

  3. Dari menu navigasi Vertex AI di sebelah kiri, klik Datasets.

  4. Di bagian atas konsol, klik + Create.

  5. Untuk nama Set Data, ketik damaged_car_parts.

  6. Pilih Image classification (Single label). (Catatan: di project Anda sendiri, sebaiknya centang kotak "Multi-label Classification" jika Anda melakukan klasifikasi kelas jamak).

  7. Pilih Region sebagai .

  8. Klik Create.

Menghubungkan set data ke gambar pelatihan

Di bagian ini, Anda akan memilih lokasi gambar pelatihan yang telah Anda upload di langkah sebelumnya.

  1. Di bagian Select an import method, klik Select import files from Cloud Storage.

  2. Di bagian Select import files from Cloud Storage, klik Browse.

  3. Ikuti petunjuk untuk membuka bucket penyimpanan, lalu klik file data.csv. Klik Select.

  4. Setelah memilih file dengan benar, tanda centang hijau akan muncul di sebelah kiri jalur file. Klik Continue untuk melanjutkan.

Catatan: Diperlukan waktu sekitar 9 sampai 12 menit untuk mengimpor gambar dan menyesuaikannya dengan kategori. Anda harus menunggu langkah ini hingga selesai sebelum dapat memeriksa progres Anda.
  1. Setelah proses impor selesai, lanjutkan ke bagian berikutnya dengan mengklik tab Browse. (Petunjuk: Anda mungkin perlu memuat ulang halaman untuk mengonfirmasi).

Klik Check my progress untuk memverifikasi tujuan. Membuat set data

Tugas 3. Memeriksa gambar

Di tugas ini, Anda akan memeriksa gambar untuk memastikan tidak ada error dalam set data Anda.

Petak gambar di halaman tab Browse

Memeriksa label gambar

  1. Jika halaman browser Anda telah dimuat ulang, klik Datasets, pilih nama gambar Anda, lalu klik Browse.

  2. Di bagian Filter labels, klik salah satu label untuk melihat gambar pelatihan tertentu. (Misalnya: engine_compartment.)

Catatan: Jika Anda membangun model produksi, siapkan setidaknya 100 gambar per label untuk memastikan akurasi yang tinggi. Karena ini adalah demo, hanya 20 gambar yang digunakan untuk setiap jenis sehingga modelnya dapat dilatih dengan cepat.
  1. Jika gambar diberi label yang keliru, Anda dapat mengkliknya untuk memilih label yang benar atau menghapus gambar tersebut dari set pelatihan Anda:

Detail gambar

  1. Berikutnya, klik tab Analyze untuk melihat jumlah gambar per label. Jendela Label Stats akan muncul di browser Anda.
Catatan: Jika Anda perlu bantuan untuk melabeli set data, Vertex AI Labeling Services memungkinkan Anda bekerja dengan pemberi label manusia untuk menghasilkan label yang berakurasi tinggi.

Tugas 4. Melatih model Anda

Sekarang, Anda siap untuk melatih model Anda. Vertex AI menangani proses ini secara otomatis dan Anda tidak perlu menulis kode model apa pun.

  1. Dari sisi kanan, klik Train New Model.

  2. Dari jendela Training method, jangan ubah konfigurasi default dan pilih AutoML sebagai metode pelatihan. Klik Continue.

  3. Dari jendela Model details, masukkan nama model Anda, gunakan: damaged_car_parts_model. Klik Continue.

  4. Dari jendela Training options, pilih Higher accuracy (new) dan klik Continue.

  5. Dari jendela Compute and pricing, tetapkan anggaran Anda ke jam kerja node maksimum, yaitu 8.

  6. Klik Start Training.

Catatan: Pelatihan model dapat memerlukan waktu lebih lama dari yang dialokasikan untuk menyelesaikan lab. Model ini tidak perlu menyelesaikan pelatihan untuk melanjutkan ke bagian berikutnya.

Klik Check my progress untuk memverifikasi tujuan. Melatih model Anda

Tugas 5. Meminta prediksi dari model yang dihosting

Untuk tujuan lab ini, model yang dilatih di set data yang sama persis akan dihosting di project lain sehingga Anda dapat meminta prediksi darinya ketika model lokal Anda menyelesaikan pelatihan. Ini dikarenakan pelatihan model lokal tersebut cenderung akan melebihi batasan lab ini.

Proxy ke model terlatih akan disiapkan untuk Anda sehingga Anda tidak perlu menjalankan langkah-langkah ekstra untuk membuatnya berfungsi dalam lingkungan lab Anda.

Untuk meminta prediksi dari model tersebut, Anda perlu mengirimkan prediksi ke endpoint di dalam project Anda. Selanjutnya, permintaan tersebut akan diteruskan ke model yang dihosting dan menampilkan kembali output. Cara mengirimkan prediksi ke Proxy AutoML sangat mirip dengan cara berinteraksi dengan model yang baru saja dibuat, jadi Anda dapat menggunakannya untuk berlatih.

Mendapatkan nama endpoint proxy AutoML

  1. Di Konsol Google Cloud, pada Navigation menu (≡), klik Cloud Run.

  2. Klik automl-proxy.

endpoint proxy automl

  1. Salin URL ke endpoint. Tampilannya akan terlihat sebagai berikut: https://automl-proxy-xfpm6c62ta-uc.a.run.app.

url endpoint

Anda akan menggunakan endpoint ini untuk permintaan prediksi di bagian berikutnya.

Membuat permintaan prediksi

  1. Buka jendela Cloud Shell baru.

  2. Di toolbar Cloud Shell, klik Open Editor. Jika diminta, klik Open in New Window.

  3. Klik File > New File.

  4. Salin konten berikut ke dalam file baru yang baru saja Anda buat:

{ "instances": [{ "content": 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" }], "parameters": { "confidenceThreshold": 0.5, "maxPredictions": 5 } }

  1. Klik File > Save, lalu pilih jalur dari dropdown (/home/student_xx_xxxxx).

  2. Beri nama file Anda sebagai payload.json, lalu klik Save.

Sebagai referensi, konten yang Anda sediakan adalah string Base64 dari gambar berikut.

kap mesin

  1. Berikutnya, tetapkan variabel lingkungan berikut. Salin di URL Proxy AutoML Anda yang diambil sebelumnya.
AUTOML_PROXY=<automl-proxy url> INPUT_DATA_FILE=payload.json
  1. Jalankan permintaan API ke endpoint Proxy AutoML untuk meminta prediksi dari model yang dihosting:
curl -X POST -H "Content-Type: application/json" $AUTOML_PROXY/v1 -d "@${INPUT_DATA_FILE}"

Jika Anda menjalankan prediksi yang berhasil, output Anda akan terlihat seperti berikut:

{"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"}

Untuk model ini, hasil prediksinya cukup mudah dipahami. Kolom displayNames seharusnya memprediksi secara benar bumper dengan nilai minimum keyakinan yang tinggi. Sekarang, Anda dapat mengubah nilai gambar yang dienkode Base64 dalam file JSON yang Anda buat.

Klik Check my progress untuk memverifikasi tujuan. Membuat permintaan prediksi

  1. Klik kanan di setiap gambar di bawah, lalu pilih Save image As….

  2. Ikuti prompt untuk menyimpan setiap gambar dengan nama unik. (Petunjuk: Tetapkan nama yang sederhana seperti 'Image1' dan 'Image2' untuk mempermudah proses upload).

gambar 2 gambar 3

  1. Buka Base64 Image Encoder, lalu ikuti petunjuk untuk mengupload dan mengenkode gambar ke string Base64.

  2. Ganti nilai string yang dienkode Base64 di kolom content di file payload JSON Anda, lalu jalankan prediksinya kembali. Ulangi untuk gambar lain.

Bagaimana performa model Anda? Apakah ketiga gambar diprediksi dengan benar? Anda akan melihat output berikut untuk setiap gambar:

{"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"}

Selamat!

Di lab ini, Anda telah belajar cara melatih model machine learning kustom dan menghasilkan prediksi di model yang dihosting melalui permintaan API. Anda telah mengupload gambar pelatihan ke Cloud Storage dan menggunakan file CSV untuk Vertex AI guna menemukan gambar tersebut. Anda telah memeriksa semua perbedaan pada gambar berlabel sebelum akhirnya mengevaluasi model terlatih. Sekarang, Anda telah mempelajari semua yang diperlukan untuk melatih model set data gambar milik Anda sendiri.

Langkah berikutnya/pelajari lebih lanjut

Sertifikasi dan pelatihan Google Cloud

...membantu Anda mengoptimalkan teknologi Google Cloud. Kelas kami mencakup keterampilan teknis dan praktik terbaik untuk membantu Anda memahami dengan cepat dan melanjutkan proses pembelajaran. Kami menawarkan pelatihan tingkat dasar hingga lanjutan dengan opsi on demand, live, dan virtual untuk menyesuaikan dengan jadwal Anda yang sibuk. Sertifikasi membantu Anda memvalidasi dan membuktikan keterampilan serta keahlian Anda dalam teknologi Google Cloud.

Manual Terakhir Diperbarui pada 17 Januari 2024

Lab Terakhir Diuji pada 17 Januari 2024

Hak cipta 2024 Google LLC Semua hak dilindungi undang-undang. Google dan logo Google adalah merek dagang dari Google LLC. Semua nama perusahaan dan produk lain mungkin adalah merek dagang masing-masing perusahaan yang bersangkutan.

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