Export a model

  using 
  
  Google.Cloud.BigQuery.V2 
 
 ; 
 using 
  
 System 
 ; 
 public 
  
 class 
  
 BigQueryExtractModel 
 { 
  
 public 
  
 void 
  
 ExtractModel 
 ( 
 string 
  
 projectId 
 , 
  
 string 
  
 datasetId 
 , 
  
 string 
  
 modelId 
 , 
  
 string 
  
 destinationUri 
 ) 
  
 { 
  
  BigQueryClient 
 
  
 client 
  
 = 
  
  BigQueryClient 
 
 . 
  Create 
 
 ( 
 projectId 
 ); 
  
  BigQueryJob 
 
  
 job 
  
 = 
  
 client 
 . 
  CreateModelExtractJob 
 
 ( 
  
 projectId 
 : 
  
 projectId 
 , 
  
 datasetId 
 : 
  
 datasetId 
 , 
  
 modelId 
 : 
  
 modelId 
 , 
  
 destinationUri 
 : 
  
 destinationUri 
  
 ); 
  
 job 
  
 = 
  
 job 
 . 
  PollUntilCompleted 
 
 (). 
 ThrowOnAnyError 
 (); 
  
 // Waits for the job to complete. 
  
 System 
 . 
 IO 
 . 
 File 
 . 
 AppendAllText 
 ( 
 "log.txt" 
 , 
  
 $"Exported model to {destinationUri}" 
 ); 
  
 Console 
 . 
 Write 
 ( 
 $"Exported model to {destinationUri}" 
 ); 
  
 } 
 } 
 

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "cloud.google.com/go/bigquery" 
 ) 
 // exportModel demonstrates how to export an existing 
 // BigQuery ML Model to Google Cloud Storage. 
 func 
  
 exportModel 
 ( 
 projectID 
 , 
  
 datasetID 
 , 
  
 modelID 
 , 
  
 gcsURI 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // datasetID := "dataset-id" 
  
 // modelID := "model-id" 
  
 // gcsURI := "gs://mybucket/path/to/model" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 bigquery 
 . 
 NewClient 
 ( 
 ctx 
 , 
  
 projectID 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "bigquery.NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 gcsRef 
  
 := 
  
 bigquery 
 . 
  NewGCSReference 
 
 ( 
 gcsURI 
 ) 
  
 extractor 
  
 := 
  
 client 
 . 
  DatasetInProject 
 
 ( 
 projectID 
 , 
  
 datasetID 
 ). 
 Model 
 ( 
 modelID 
 ). 
 ExtractorTo 
 ( 
 gcsRef 
 ) 
  
 // You can choose to run the job in a specific location for more complex data locality scenarios. 
  
 // Ex: In this example, source dataset and GCS bucket are in the US. 
  
 extractor 
 . 
  Location 
 
  
 = 
  
 "US" 
  
 job 
 , 
  
 err 
  
 := 
  
 extractor 
 . 
 Run 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 status 
 , 
  
 err 
  
 := 
  
 job 
 . 
 Wait 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 if 
  
 err 
  
 := 
  
 status 
 . 
  Err 
 
 (); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 return 
  
 nil 
 } 
 

  import 
  
 com.google.cloud.bigquery. BigQuery 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryException 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryOptions 
 
 ; 
 import 
  
 com.google.cloud.bigquery. ExtractJobConfiguration 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Job 
 
 ; 
 import 
  
 com.google.cloud.bigquery. JobInfo 
 
 ; 
 import 
  
 com.google.cloud.bigquery. ModelId 
 
 ; 
 // Sample to extract model to GCS bucket 
 public 
  
 class 
 ExtractModel 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 InterruptedException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectName 
  
 = 
  
 "bigquery-public-data" 
 ; 
  
 String 
  
 datasetName 
  
 = 
  
 "samples" 
 ; 
  
 String 
  
 modelName 
  
 = 
  
 "model" 
 ; 
  
 String 
  
 bucketName 
  
 = 
  
 "MY-BUCKET-NAME" 
 ; 
  
 String 
  
 destinationUri 
  
 = 
  
 "gs://" 
  
 + 
  
 bucketName 
  
 + 
  
 "/path/to/file" 
 ; 
  
 extractModel 
 ( 
 projectName 
 , 
  
 datasetName 
 , 
  
 modelName 
 , 
  
 destinationUri 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 extractModel 
 ( 
  
 String 
  
 projectName 
 , 
  
 String 
  
 datasetName 
 , 
  
 String 
  
 modelName 
 , 
  
 String 
  
 destinationUri 
 ) 
  
 throws 
  
 InterruptedException 
  
 { 
  
 try 
  
 { 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. 
  
  BigQuery 
 
  
 bigquery 
  
 = 
  
  BigQueryOptions 
 
 . 
 getDefaultInstance 
 (). 
 getService 
 (); 
  
  ModelId 
 
  
 modelId 
  
 = 
  
  ModelId 
 
 . 
 of 
 ( 
 projectName 
 , 
  
 datasetName 
 , 
  
 modelName 
 ); 
  
  ExtractJobConfiguration 
 
  
 extractConfig 
  
 = 
  
  ExtractJobConfiguration 
 
 . 
 newBuilder 
 ( 
 modelId 
 , 
  
 destinationUri 
 ). 
 build 
 (); 
  
  Job 
 
  
 job 
  
 = 
  
 bigquery 
 . 
  create 
 
 ( 
 JobInfo 
 . 
 of 
 ( 
 extractConfig 
 )); 
  
 // Blocks until this job completes its execution, either failing or succeeding. 
  
  Job 
 
  
 completedJob 
  
 = 
  
 job 
 . 
  waitFor 
 
 (); 
  
 if 
  
 ( 
 completedJob 
  
 == 
  
 null 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Job not executed since it no longer exists." 
 ); 
  
 return 
 ; 
  
 } 
  
 else 
  
 if 
  
 ( 
 completedJob 
 . 
 getStatus 
 (). 
 getError 
 () 
  
 != 
  
 null 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
  
 "BigQuery was unable to extract due to an error: \n" 
  
 + 
  
 job 
 . 
 getStatus 
 (). 
 getError 
 ()); 
  
 return 
 ; 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model extract successful" 
 ); 
  
 } 
  
 catch 
  
 ( 
  BigQueryException 
 
  
 ex 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model extraction job was interrupted. \n" 
  
 + 
  
 ex 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 } 
 

  require 
  
 "google/cloud/bigquery" 
 ## 
 # Exports a model to a Google Cloud Storage bucket. 
 # 
 # @param dataset_id [String] The ID of the dataset that contains the model. 
 # @param model_id   [String] The ID of the model to export. 
 # @param destination_uri [String] The Google Cloud Storage bucket to export the model to. 
 def 
  
 export_model 
  
 dataset_id 
 , 
  
 model_id 
 , 
  
 destination_uri 
  
 bigquery 
  
 = 
  
 Google 
 :: 
 Cloud 
 :: 
  Bigquery 
 
 . 
  new 
 
  
 dataset 
  
 = 
  
 bigquery 
 . 
 dataset 
  
 dataset_id 
  
 model 
  
 = 
  
 dataset 
 . 
 model 
  
 model_id 
  
 puts 
  
 "Extracting model 
 #{ 
 model 
 . 
  model_id 
 
 } 
 to 
 #{ 
 destination_uri 
 } 
 " 
  
 job 
  
 = 
  
 model 
 . 
 extract_job 
  
 destination_uri 
  
 job 
 . 
 wait_until_done! 
  
 if 
  
 job 
 . 
  failed? 
 
  
 puts 
  
 "Error extracting model: 
 #{ 
 job 
 . 
 error 
 } 
 " 
  
 else 
  
 puts 
  
 "Model extracted successfully" 
  
 end 
 end