Batch predictions

Batch predictions lets you efficiently send multiple text-only prompts that aren't latency sensitive to a Llama model. Compared to online predictions, where you send one input prompt for each request, you can batch a large number of input prompts in a single request.

There are no charges for batch predictions during the Preview period.

Supported Llama models

Vertex AI supports batch predictions for the following Llama models:

  • Llama 3.1 405B (llama-3.1-405b-instruct-maas)
  • Llama 3.1 70B (llama-3.1-70b-instruct-maas)
  • Llama 3.1 8B (llama-3.1-8b-instruct-maas)

Prepare input

Before you begin, prepare your inputs in a BigQuery table or as a JSONL file in Cloud Storage. The input for both sources must follow the OpenAI API schema JSON format, as shown in the following example:

{"custom_id": "test-request-0", "method": "POST", "url": "/https/cloud.google.com/v1/chat/completions", "body": {"model": "meta/llama-3.1-405b-instruct-maas", "messages": [{"role": "system", "content": "You are a chef."}, {"role": "user", "content": "Give me a recipe for banana bread"}], "max_tokens": 1000}}

BigQuery

Your BigQuery input table must adhere to the following schema:

Column name Description
custom_id An ID for each request to match the input with the output.
method The request method.
url The request endpoint.
body(JSON) Your input prompt.
  • Your input table can have other columns, which are ignored by the batch job and passed directly to the output table.
  • Batch prediction jobs reserve two column names for the batch prediction output: response(JSON) and id. Don't use these columns in the input table.
  • The method and url columns are dropped and not included in the output table.

Cloud Storage

For Cloud Storage, the input file must be a JSONL file that is located in a Cloud Storage bucket.

Request a batch prediction

Make a batch prediction against a Llama model by using input from BigQuery or Cloud Storage. You can independently choose to output predictions to either a BigQuery table or a JSONL file in a Cloud Storage bucket.

BigQuery

Specify your BigQuery input table, model, and output location. The batch prediction job and your table must be in the same region.

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

  • LOCATION: A region that supports Llama models.
  • PROJECT_ID: Your project ID.
  • MODEL: The name of the model to tune.
  • INPUT_URI: The BigQuery table where your batch prediction input is located such as myproject.mydataset.input_table.
  • OUTPUT_FORMAT: To output to a BigQuery table, specify bigquery. To output to a Cloud Storage bucket, specify jsonl.
  • DESTINATION: For BigQuery, specify bigqueryDestination. For Cloud Storage, specify gcsDestination.
  • OUTPUT_URI_FIELD_NAME: For BigQuery, specify outputUri. For Cloud Storage, specify outputUriPrefix.
  • OUTPUT_URI: For BigQuery, specify the table location such as myproject.mydataset.output_result. For Cloud Storage, specify the bucket and folder location such as gs://mybucket/path/to/outputfile.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs

Request JSON body: 

'{
  "displayName": "JOB_NAME",
  "model": "publishers/meta/models/MODEL",
  "inputConfig": {
    "instancesFormat":"bigquery",
    "bigquerySource":{
      "inputUri" : "INPUT_URI"
    }
  },
  "outputConfig": {
    "predictionsFormat":"OUTPUT_FORMAT",
    "DESTINATION":{
      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"
    }
  }
}'

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

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_ID/locations/LOCATION/batchPredictionJobs"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$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_ID/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Cloud Storage

Specify your JSONL file's Cloud Storage location, model, and output location.

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

  • LOCATION: A region that supports Llama models.
  • PROJECT_ID: Your project ID.
  • MODEL: The name of the model to tune.
  • INPUT_URI: The Cloud Storage location of your JSONL batch prediction input such as gs://bucketname/path/to/jsonl.
  • OUTPUT_FORMAT: To output to a BigQuery table, specify bigquery. To output to a Cloud Storage bucket, specify jsonl.
  • DESTINATION: For BigQuery, specify bigqueryDestination. For Cloud Storage, specify gcsDestination.
  • OUTPUT_URI_FIELD_NAME: For BigQuery, specify outputUri. For Cloud Storage, specify outputUriPrefix.
  • OUTPUT_URI: For BigQuery, specify the table location such as myproject.mydataset.output_result. For Cloud Storage, specify the bucket and folder location such as gs://mybucket/path/to/outputfile.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs

Request JSON body: 

'{
  "displayName": "JOB_NAME",
  "model": "publishers/meta/models/MODEL",
  "inputConfig": {
    "instancesFormat":"jsonl",
    "gcsDestination":{
      "uris" : "INPUT_URI"
    }
  },
  "outputConfig": {
    "predictionsFormat":"OUTPUT_FORMAT",
    "DESTINATION":{
      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"
    }
  }
}'

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

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_ID/locations/LOCATION/batchPredictionJobs"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$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_ID/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Get the status of a batch prediction job

Get the state of your batch prediction job to check whether it has completed successfully. The job length depends on the number input items that you submitted.

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: Your project ID.
  • LOCATION: The region where your batch job is located.
  • JOB_ID: The batch job ID that was returned when you created the job.

HTTP method and URL: 

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/JOB_ID

To send your request, choose one of these options:

curl

Execute the following command: 

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

PowerShell

Execute the following command: 

$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_ID/locations/LOCATION/batchPredictionJobs/JOB_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Retrieve output

When a batch prediction job completes, retrieve the output from the location that you specified. For BigQuery, the output is in the response(JSON) column of your destination BigQuery table. For Cloud Storage, the output is saved as a JSONL file in the output Cloud Storage location.