Halaman ini diterjemahkan oleh Cloud Translation API.

Melatih model AutoML Edge menggunakan Vertex AI API

Anda dapat membuat model AutoML langsung di Konsol Google Cloud, atau dengan membuat pipeline pelatihan secara terprogram, menggunakan API atau salah satu library klien Vertex AI.

Model ini dibuat menggunakan set data yang telah Anda siapkan menggunakan konsol atau Vertex AI API. Vertex AI API menggunakan item dari set data untuk melatih, mengujinya, dan mengevaluasi performa model. Tinjau hasil evaluasi, sesuaikan set data pelatihan sesuai kebutuhan, dan buat pipeline pelatihan baru menggunakan set data yang ditingkatkan.

Pelatihan model dapat memerlukan waktu beberapa jam untuk diselesaikan. Dengan Vertex AI API, Anda bisa mendapatkan status tugas pelatihan.

Membuat pipeline pelatihan AutoML Edge

Setelah memiliki set data dengan kumpulan item pelatihan yang representatif, Anda siap untuk membuat pipeline pelatihan AutoML Edge.

Pilih jenis data.

Gambar

Pilih tab di bawah ini untuk tujuan Anda:

Klasifikasi

Pada waktu pelatihan, Anda dapat memilih jenis model AutoML Edge yang diinginkan, bergantung pada kasus penggunaan tertentu Anda:

latensi rendah (MOBILE_TF_LOW_LATENCY_1)
penggunaan tujuan umum (MOBILE_TF_VERSATILE_1)
kualitas prediksi lebih tinggi (MOBILE_TF_HIGH_ACCURACY_1)

Pilih tab di bawah untuk bahasa atau lingkungan Anda:

REST

Sebelum menggunakan permintaan data mana pun, buat pengganti berikut:

LOCATION: Region tempat set data berada dan Model dibuat. Misalnya, us-central1.
PROJECT: Project ID Anda.
TRAININGPIPELINE_DISPLAYNAME: Wajib diisi. Nama tampilan untuk trainingPipeline.
DATASET_ID: Nomor ID set data yang akan digunakan untuk pelatihan.
fractionSplit: Opsional. Salah satu dari beberapa kemungkinan ML menggunakan opsi terpisah untuk data Anda. Untuk fractionSplit, nilai harus berjumlah 1. Misalnya:
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*: Nama tampilan untuk model yang diupload (dibuat) oleh TrainingPipeline.
MODEL_DESCRIPTION^*: Deskripsi model.
modelToUpload.labels^*: Kumpulan key-value pair apa pun untuk mengatur model Anda. Misalnya:
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†: Jenis model Edge yang akan dilatih. Opsinya adalah:
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†: Biaya pelatihan yang sebenarnya akan sama atau kurang dari nilai ini. Untuk model Edge, anggarannya harus: 1.000 - 100.000 jam kerja node (inklusif).
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

Isi JSON permintaan:

{
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "DECIMAL",
      "validationFraction": "DECIMAL",
      "testFraction": "DECIMAL"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "multiLabel": "false",
    "modelType": ["EDGE_MODELTYPE"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Catatan: Perintah berikut mengasumsikan bahwa Anda telah login ke gcloud CLI dengan akun pengguna Anda dengan menjalankan gcloud init atau gcloud auth login , atau dengan menggunakan Cloud Shell, yang secara otomatis membuat Anda login ke gcloud CLI . Anda dapat memeriksa akun yang saat ini aktif dengan menjalankan gcloud auth list.

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

Catatan: Perintah berikut mengasumsikan bahwa Anda telah login ke gcloud CLI dengan akun pengguna Anda dengan menjalankan gcloud init atau gcloud auth login . Anda dapat memeriksa akun yang saat ini aktif dengan menjalankan gcloud auth list.

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

Respons ini berisi informasi tentang spesifikasi serta TRAININGPIPELINE_ID.

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "EDGE_MODELTYPE",
    "budgetMilliNodeHours": "NODE_HOUR_BUDGET"
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "updateTime": "2020-07-14T17:16:55.098953Z"
}

Anda bisa mendapatkan status tugas trainingPipeline menggunakan TRAININGPIPELINE_ID.

Klasifikasi

Pada waktu pelatihan, Anda dapat memilih jenis model AutoML Edge yang diinginkan, bergantung pada kasus penggunaan tertentu Anda:

latensi rendah (MOBILE_TF_LOW_LATENCY_1)
penggunaan tujuan umum (MOBILE_TF_VERSATILE_1)
kualitas prediksi lebih tinggi (MOBILE_TF_HIGH_ACCURACY_1)

Pilih tab di bawah untuk bahasa atau lingkungan Anda:

REST

Sebelum menggunakan permintaan data mana pun, buat pengganti berikut:

LOCATION: Region tempat set data berada dan Model dibuat. Misalnya, us-central1.
PROJECT: Project ID Anda.
TRAININGPIPELINE_DISPLAYNAME: Wajib diisi. Nama tampilan untuk trainingPipeline.
DATASET_ID: Nomor ID set data yang akan digunakan untuk pelatihan.
fractionSplit: Opsional. Salah satu dari beberapa kemungkinan ML menggunakan opsi terpisah untuk data Anda. Untuk fractionSplit, nilai harus berjumlah 1. Misalnya:
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*: Nama tampilan untuk model yang diupload (dibuat) oleh TrainingPipeline.
MODEL_DESCRIPTION^*: Deskripsi model.
modelToUpload.labels^*: Kumpulan key-value pair apa pun untuk mengatur model Anda. Misalnya:
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†: Jenis model Edge yang akan dilatih. Opsinya adalah:
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†: Biaya pelatihan yang sebenarnya akan sama atau kurang dari nilai ini. Untuk model Edge, anggarannya harus: 1.000 - 100.000 jam kerja node (inklusif).
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

Isi JSON permintaan:

{
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "DECIMAL",
      "validationFraction": "DECIMAL",
      "testFraction": "DECIMAL"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "multiLabel": "true",
    "modelType": ["EDGE_MODELTYPE"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

Respons ini berisi informasi tentang spesifikasi serta TRAININGPIPELINE_ID.

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "EDGE_MODELTYPE",
    "budgetMilliNodeHours": "NODE_HOUR_BUDGET"
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "updateTime": "2020-07-14T17:16:55.098953Z"
}

Anda bisa mendapatkan status tugas trainingPipeline menggunakan TRAININGPIPELINE_ID.

Deteksi objek

Pada waktu pelatihan, Anda dapat memilih jenis model AutoML Edge yang diinginkan, bergantung pada kasus penggunaan tertentu Anda:

latensi rendah (MOBILE_TF_LOW_LATENCY_1)
penggunaan tujuan umum (MOBILE_TF_VERSATILE_1)
kualitas prediksi lebih tinggi (MOBILE_TF_HIGH_ACCURACY_1)

Pilih tab di bawah untuk bahasa atau lingkungan Anda:

REST

Sebelum menggunakan permintaan data mana pun, buat pengganti berikut:

LOCATION: Region tempat set data berada dan Model dibuat. Misalnya, us-central1.
PROJECT: Project ID Anda.
TRAININGPIPELINE_DISPLAYNAME: Wajib diisi. Nama tampilan untuk trainingPipeline.
DATASET_ID: Nomor ID set data yang akan digunakan untuk pelatihan.
fractionSplit: Opsional. Salah satu dari beberapa kemungkinan ML menggunakan opsi terpisah untuk data Anda. Untuk fractionSplit, nilai harus berjumlah 1. Misalnya:
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*: Nama tampilan untuk model yang diupload (dibuat) oleh TrainingPipeline.
MODEL_DESCRIPTION^*: Deskripsi model.
modelToUpload.labels^*: Kumpulan key-value pair apa pun untuk mengatur model Anda. Misalnya:
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†: Jenis model Edge yang akan dilatih. Opsinya adalah:
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†: Biaya pelatihan yang sebenarnya akan sama atau kurang dari nilai ini. Untuk model Cloud, anggarannya harus: 20.000 - 900.000 mili jam kerja node (inklusif). Nilai defaultnya adalah 216.000 yang mewakili satu hari dalam waktu proses, dengan asumsi 9 node digunakan.
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

Isi JSON permintaan:

{
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "DECIMAL",
      "validationFraction": "DECIMAL",
      "testFraction": "DECIMAL"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODELTYPE"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

Respons ini berisi informasi tentang spesifikasi serta TRAININGPIPELINE_ID.

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "EDGE_MODELTYPE",
    "budgetMilliNodeHours": "NODE_HOUR_BUDGET"
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "updateTime": "2020-07-14T17:16:55.098953Z"
}

Anda bisa mendapatkan status tugas trainingPipeline menggunakan TRAININGPIPELINE_ID.

Video

Pilih tab di bawah ini untuk tujuan Anda:

Pengenalan tindakan

Saat pelatihan, pilih jenis AutoML edge berikut:

MOBILE_VERSATILE_1: penggunaan tujuan umum

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

PROJECT: Project ID Anda.
LOCATION: Region tempat set data berada dan Model dibuat. Misalnya, us-central1.
TRAINING_PIPELINE_DISPLAY_NAME: Harus ada. Nama tampilan untuk TrainingPipeline.
DATASET_ID: ID untuk Set data pelatihan.
TRAINING_FRACTION, TEST_FRACTION: Objek fractionSplit bersifat opsional; Anda menggunakannya untuk mengontrol pemisahan data. Untuk mengetahui informasi selengkapnya cara mengontrol pemisahan data, lihat Tentang pemisahan data untuk model AutoML. Misalnya:
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME: Nama tampilan Model yang dilatih.
MODEL_DESCRIPTION: Deskripsi untuk Model.
MODEL_LABELS: Kumpulan key-value pair apa pun untuk mengatur model Anda. Contoh:
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE:
- MOBILE_VERSATILE_1: penggunaan tujuan umum
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

POST https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines

Isi JSON permintaan:

{
  "displayName": "TRAINING_PIPELINE_DISPLAY_NAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "TRAINING_FRACTION",
      "validationFraction": "0",
      "testFraction": "TEST_FRACTION"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_action_recognition_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODEL_TYPE"],
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

Respons ini berisi informasi tentang spesifikasi serta TRAININGPIPELINE_ID.

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.8,
      "validationFraction": 0,
      "testFraction": 0.2
    }
  },
  "trainingTaskDefinition": OBJECTIVE,
  "trainingTaskInputs": {
    "modelType": "EDGE_MODEL_TYPE",
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-11-14T17:16:55.098953Z",
  "updateTime": "2020-11-14T17:16:55.098953Z"
}

Anda dapat mendapatkan status progres trainingPipeline untuk melihat kapan prosesnya selesai.

Klasifikasi

Saat pelatihan, pilih jenis AutoML edge berikut:

MOBILE_VERSATILE_1: penggunaan tujuan umum

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

PROJECT: Project ID Anda.
LOCATION: Region tempat set data berada dan Model dibuat. Misalnya, us-central1.
TRAINING_PIPELINE_DISPLAY_NAME: Harus ada. Nama tampilan untuk TrainingPipeline.
DATASET_ID: ID untuk Set data pelatihan.
TRAINING_FRACTION, TEST_FRACTION: Objek fractionSplit bersifat opsional; Anda menggunakannya untuk mengontrol pemisahan data. Untuk mengetahui informasi selengkapnya cara mengontrol pemisahan data, lihat Tentang pemisahan data untuk model AutoML. Misalnya:
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME: Nama tampilan Model yang dilatih.
MODEL_DESCRIPTION: Deskripsi untuk Model.
MODEL_LABELS: Kumpulan key-value pair apa pun untuk mengatur model Anda. Contoh:
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE:
- MOBILE_VERSATILE_1: penggunaan tujuan umum
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

POST https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines

Isi JSON permintaan:

{
  "displayName": "TRAINING_PIPELINE_DISPLAY_NAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "TRAINING_FRACTION",
      "validationFraction": "0",
      "testFraction": "TEST_FRACTION"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODEL_TYPE"],
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

Respons ini berisi informasi tentang spesifikasi serta TRAININGPIPELINE_ID.

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.8,
      "validationFraction": 0,
      "testFraction": 0.2
    }
  },
  "trainingTaskDefinition": OBJECTIVE,
  "trainingTaskInputs": {
    "modelType": "EDGE_MODEL_TYPE",
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-10-14T17:16:55.098953Z",
  "updateTime": "2020-10-14T17:16:55.098953Z"
}

Anda dapat mendapatkan status progres trainingPipeline untuk melihat kapan prosesnya selesai.

Pelacakan objek

Saat pelatihan, pilih jenis AutoML edge:

MOBILE_VERSATILE_1: penggunaan tujuan umum
MOBILE_CORAL_VERSATILE_1: kualitas prediksi lebih tinggi untuk Google Coral
MOBILE_CORAL_LOW_LATENCY_1: latensi lebih rendah untuk Google Coral
MOBILE_JETSON_VERSATILE_1: kualitas prediksi lebih tinggi untuk NVIDIA Jetson
MOBILE_JETSON_LOW_LATENCY_1: latensi lebih rendah untuk NVIDIA Jetson

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

PROJECT: Project ID Anda.
LOCATION: Region tempat set data berada dan Model dibuat. Misalnya, us-central1.
TRAINING_PIPELINE_DISPLAY_NAME: Harus ada. Nama tampilan untuk TrainingPipeline.
DATASET_ID: ID untuk Set data pelatihan.
TRAINING_FRACTION, TEST_FRACTION: Objek fractionSplit bersifat opsional; Anda menggunakannya untuk mengontrol pemisahan data. Untuk mengetahui informasi selengkapnya cara mengontrol pemisahan data, lihat Tentang pemisahan data untuk model AutoML. Misalnya:
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME: Nama tampilan Model yang dilatih.
MODEL_DESCRIPTION: Deskripsi untuk Model.
MODEL_LABELS: Kumpulan key-value pair apa pun untuk mengatur model Anda. Contoh:
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE: Salah satu dari hal berikut:
- MOBILE_VERSATILE_1: penggunaan tujuan umum
- MOBILE_CORAL_VERSATILE_1: kualitas prediksi lebih tinggi untuk Google Coral
- MOBILE_CORAL_LOW_LATENCY_1: latensi lebih rendah untuk Google Coral
- MOBILE_JETSON_VERSATILE_1: kualitas prediksi lebih tinggi untuk NVIDIA Jetson
- MOBILE_JETSON_LOW_LATENCY_1: latensi lebih rendah untuk NVIDIA Jetson
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

POST https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines

Isi JSON permintaan:

{
  "displayName": "TRAINING_PIPELINE_DISPLAY_NAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "TRAINING_FRACTION",
      "validationFraction": "0",
      "testFraction": "TEST_FRACTION"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_object_tracking_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODEL_TYPE"],
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

Respons ini berisi informasi tentang spesifikasi serta TRAININGPIPELINE_ID.

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.8,
      "validationFraction": 0,
      "testFraction": 0.2
    }
  },
  "trainingTaskDefinition": OBJECTIVE,
  "trainingTaskInputs": {
    "modelType": "EDGE_MODEL_TYPE",
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-10-14T17:16:55.098953Z",
  "updateTime": "2020-10-14T17:16:55.098953Z"
}

Anda dapat mendapatkan status progres trainingPipeline untuk melihat kapan prosesnya selesai.

Mendapatkan status trainingPipeline

Gunakan kode berikut untuk mendapatkan status pembuatan trainingPipeline secara terprogram.

REST

Sebelum menggunakan data permintaan mana pun, lakukan penggantian berikut:

LOCATION: Region tempat TrainingPipeline berada.
PROJECT: Project ID Anda.
TRAININGPIPELINE_ID: ID TrainingPipeline tertentu.
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Jalankan perintah berikut:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID"

PowerShell

Jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID" | Select-Object -Expand Content

Kolom "state" menampilkan status operasi saat ini. TrainingPipeline yang telah selesai akan menunjukkan

Anda akan melihat output yang mirip dengan berikut ini untuk operasi pembuatan trainingPipeline yang telah selesai:

Respons

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "PIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "MOBILE_TF_VERSATILE_1",
    "budgetMilliNodeHours": "1000"
  },
  "modelToUpload": {
    "name": "projects/PROJECT_NUMBER/locations/LOCATION/models/4030440191519883264",
    "displayName": "image_classification_edge_model1",
    "labels": {
      "env": "prod",
      "tier": "backend"
    }
  },
  "state": "PIPELINE_STATE_SUCCEEDED",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "startTime": "2020-07-14T17:16:55.360691Z",
  "endTime": "2020-07-14T18:46:08.169170Z",
  "updateTime": "2020-07-14T18:46:08.169170Z"
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.


import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.TrainingPipelineName;
import com.google.rpc.Status;
import java.io.IOException;

public class GetTrainingPipelineSample {
  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String trainingPipelineId = "YOUR_TRAINING_PIPELINE_ID";
    getTrainingPipeline(project, trainingPipelineId);
  }

  static void getTrainingPipeline(String project, String trainingPipelineId) throws IOException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      TrainingPipelineName trainingPipelineName =
          TrainingPipelineName.of(project, location, trainingPipelineId);

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.getTrainingPipeline(trainingPipelineName);

      System.out.println("Get Training Pipeline Response");
      System.out.format("\tName: %s\n", trainingPipelineResponse.getName());
      System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());
      System.out.format(
          "\tTraining Task Definition: %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
      System.out.format("\tState: %s\n", trainingPipelineResponse.getState());
      System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("\tStart Time: %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());
      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();

      System.out.println("\tInput Data Config");
      System.out.format("\t\tDataset Id: %s\n", inputDataConfig.getDatasetId());
      System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());
      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();

      System.out.println("\t\tFraction Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplit.getTrainingFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplit.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", fractionSplit.getTestFraction());
      FilterSplit filterSplit = inputDataConfig.getFilterSplit();

      System.out.println("\t\tFilter Split");
      System.out.format("\t\t\tTraining Filter: %s\n", filterSplit.getTrainingFilter());
      System.out.format("\t\t\tValidation Filter: %s\n", filterSplit.getValidationFilter());
      System.out.format("\t\t\tTest Filter: %s\n", filterSplit.getTestFilter());
      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();

      System.out.println("\t\tPredefined Split");
      System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();

      System.out.println("\t\tTimestamp Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());
      Model modelResponse = trainingPipelineResponse.getModelToUpload();

      System.out.println("\t\tModel to upload");
      System.out.format("\t\tName: %s\n", modelResponse.getName());
      System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());
      System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\t\tMeta Data: %s\n", modelResponse.getMetadata());
      System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());
      System.out.format(
          "\t\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList().toString());
      System.out.format(
          "\t\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList().toString());
      System.out.format(
          "\t\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList().toString());
      System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\t\tLabels: %s\n", modelResponse.getLabelsMap());
      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();

      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (Model.ExportFormat supportedExportFormat :
          modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
      }
      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();

      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }

      Status status = trainingPipelineResponse.getError();
      System.out.println("\tError");
      System.out.format("\t\tCode: %s\n", status.getCode());
      System.out.format("\t\tMessage: %s\n", status.getMessage());
    }
  }
}

Python

Untuk mempelajari cara menginstal atau mengupdate Vertex AI SDK untuk Python, lihat Menginstal Vertex AI SDK untuk Python. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi Python API.

from google.cloud import aiplatform


def get_training_pipeline_sample(
    project: str,
    training_pipeline_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
    name = client.training_pipeline_path(
        project=project, location=location, training_pipeline=training_pipeline_id
    )
    response = client.get_training_pipeline(name=name)
    print("response:", response)

Mendapatkan informasi model

Setelah pembuatan trainingPipeline selesai, Anda dapat menggunakan nama tampilan model untuk mendapatkan informasi model yang lebih mendetail.

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

LOCATION: Region tempat Model berada. Misalnya, us-central1
PROJECT: Project ID Anda.
MODEL_DISPLAYNAME: Nama tampilan model yang Anda tentukan saat membuat tugas trainingPipeline.
PROJECT_NUMBER: Nomor project yang dibuat secara otomatis untuk project Anda

Metode HTTP dan URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models?filter=display_name=MODEL_DISPLAYNAME

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Jalankan perintah berikut:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models?filter=display_name=MODEL_DISPLAYNAME"

PowerShell

Jalankan perintah berikut:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models?filter=display_name=MODEL_DISPLAYNAME" | Select-Object -Expand Content

Anda akan melihat output yang mirip dengan berikut ini untuk model AutoML Edge terlatih. Contoh output berikut adalah untuk model AutoML Edge gambar:

Respons

{
  "models": [
    {
      "name": "projects/PROJECT/locations/LOCATION/models/MODEL_ID",
      "displayName": "MODEL_DISPLAYNAME",
      "predictSchemata": {
        "instanceSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/predict/instance/image_classification_1.0.0.yaml",
        "parametersSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/predict/params/image_classification_1.0.0.yaml",
        "predictionSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/predict/prediction/classification_1.0.0.yaml"
      },
      "metadataSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/model/metadata/automl_image_classification_1.0.0.yaml",
      "metadata": {
        "modelType": "MOBILE_TF_VERSATILE_1",
        "trainingDataItemsCount": "2174",
        "validationDataItemsCount": "467",
        "trainingAnnotationsCount": "2174",
        "validationAnnotationsCount": "467",
        "eligibleAsBaseModel": true
      },
      "trainingPipeline": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
      "supportedDeploymentResourcesTypes": [
        "AUTOMATIC_RESOURCES"
      ],
      "supportedInputStorageFormats": [
        "jsonl"
      ],
      "supportedOutputStorageFormats": [
        "jsonl"
      ],
      "createTime": "2020-07-14T17:16:55.098953Z",
      "updateTime": "2020-07-14T18:46:08.169225Z",
      "etag": "AMEw9yNjhttIyiGUgec8vGNeidfX8mGcFT1owDOq1iDT6mmQ7HLHwoomie30pLCTtJ4=",
      "labels": {
        "env": "prod",
        "tier": "backend"
      },
      "supportedExportFormats": [
        {
          "id": "tflite"
        },
        {
          "id": "edgetpu-tflite"
        },
        {
          "id": "tf-saved-model"
        },
        {
          "id": "core-ml"
        },
        {
          "id": "tf-js"
        }
      ]
    }
  ]
}

Java

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.


import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.Model.ExportFormat;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.ModelName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import java.io.IOException;

public class GetModelSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    getModelSample(project, modelId);
  }

  static void getModelSample(String project, String modelId) throws IOException {
    ModelServiceSettings modelServiceSettings =
        ModelServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
      String location = "us-central1";
      ModelName modelName = ModelName.of(project, location, modelId);

      Model modelResponse = modelServiceClient.getModel(modelName);
      System.out.println("Get Model response");
      System.out.format("\tName: %s\n", modelResponse.getName());
      System.out.format("\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\tDescription: %s\n", modelResponse.getDescription());

      System.out.format("\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\tMetadata: %s\n", modelResponse.getMetadata());
      System.out.format("\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\tArtifact Uri: %s\n", modelResponse.getArtifactUri());

      System.out.format(
          "\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList());
      System.out.format(
          "\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList());
      System.out.format(
          "\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList());

      System.out.format("\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", modelResponse.getLabelsMap());

      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", exportFormat.getId());
      }

      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();
      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Node.js di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Node.js Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const modelId = 'YOUR_MODEL_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);

async function getModel() {
  // Configure the parent resource
  const name = `projects/${project}/locations/${location}/models/${modelId}`;
  const request = {
    name,
  };
  // Get and print out a list of all the endpoints for this resource
  const [response] = await modelServiceClient.getModel(request);

  console.log('Get model response');
  console.log(`\tName : ${response.name}`);
  console.log(`\tDisplayName : ${response.displayName}`);
  console.log(`\tDescription : ${response.description}`);
  console.log(`\tMetadata schema uri : ${response.metadataSchemaUri}`);
  console.log(`\tMetadata : ${JSON.stringify(response.metadata)}`);
  console.log(`\tTraining pipeline : ${response.trainingPipeline}`);
  console.log(`\tArtifact uri : ${response.artifactUri}`);
  console.log(
    `\tSupported deployment resource types : \
      ${response.supportedDeploymentResourceTypes}`
  );
  console.log(
    `\tSupported input storage formats : \
      ${response.supportedInputStorageFormats}`
  );
  console.log(
    `\tSupported output storage formats : \
      ${response.supportedOutputStoragFormats}`
  );
  console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
  console.log(`\tUpdate time : ${JSON.stringify(response.updateTime)}`);
  console.log(`\tLabels : ${JSON.stringify(response.labels)}`);

  const predictSchemata = response.predictSchemata;
  console.log('\tPredict schemata');
  console.log(`\tInstance schema uri : ${predictSchemata.instanceSchemaUri}`);
  console.log(
    `\tParameters schema uri : ${predictSchemata.prametersSchemaUri}`
  );
  console.log(
    `\tPrediction schema uri : ${predictSchemata.predictionSchemaUri}`
  );

  const [supportedExportFormats] = response.supportedExportFormats;
  console.log('\tSupported export formats');
  console.log(`\t${supportedExportFormats}`);

  const containerSpec = response.containerSpec;
  console.log('\tContainer Spec');
  if (!containerSpec) {
    console.log(`\t\t${JSON.stringify(containerSpec)}`);
    console.log('\t\tImage uri : {}');
    console.log('\t\tCommand : {}');
    console.log('\t\tArgs : {}');
    console.log('\t\tPredict route : {}');
    console.log('\t\tHealth route : {}');
    console.log('\t\tEnv');
    console.log('\t\t\t{}');
    console.log('\t\tPort');
    console.log('\t\t{}');
  } else {
    console.log(`\t\t${JSON.stringify(containerSpec)}`);
    console.log(`\t\tImage uri : ${containerSpec.imageUri}`);
    console.log(`\t\tCommand : ${containerSpec.command}`);
    console.log(`\t\tArgs : ${containerSpec.args}`);
    console.log(`\t\tPredict route : ${containerSpec.predictRoute}`);
    console.log(`\t\tHealth route : ${containerSpec.healthRoute}`);
    const env = containerSpec.env;
    console.log('\t\tEnv');
    console.log(`\t\t\t${JSON.stringify(env)}`);
    const ports = containerSpec.ports;
    console.log('\t\tPort');
    console.log(`\t\t\t${JSON.stringify(ports)}`);
  }

  const [deployedModels] = response.deployedModels;
  console.log('\tDeployed models');
  console.log('\t\t', deployedModels);
}
getModel();

Python

def get_model_sample(project: str, location: str, model_name: str):

    aiplatform.init(project=project, location=location)

    model = aiplatform.Model(model_name=model_name)

    print(model.display_name)
    print(model.resource_name)
    return model

*	Deskripsi file skema yang Anda tentukan di `trainingTaskDefinition` menjelaskan penggunaan kolom ini.
†	File skema yang Anda tentukan dalam `trainingTaskDefinition` mendeklarasikan dan mendeskripsikan kolom ini.

Melatih model AutoML Edge menggunakan Vertex AI API

Membuat pipeline pelatihan AutoML Edge

Gambar

Klasifikasi

REST

curl

PowerShell

Respons

Klasifikasi

REST

curl

PowerShell

Respons

Deteksi objek

REST

curl

PowerShell

Respons

Video

Pengenalan tindakan

REST

curl

PowerShell

Respons

Klasifikasi

REST

curl

PowerShell

Respons

Pelacakan objek

REST

curl

PowerShell

Respons

Mendapatkan status trainingPipeline

REST

curl

PowerShell

Respons

Java

Python

Mendapatkan informasi model

REST

curl

PowerShell

Respons

Java

Node.js

Python

Langkah selanjutnya