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description=Create a Gemini Enterprise Agent Platform dataset from your tabular data so you can start training forecast models.;

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Text of the page (random words):
e cloud console in the google cloud console in the agent platform section go to the datasets page go to the datasets page click create to open the create dataset details page modify the dataset name field to create a descriptive dataset display name select the tabular tab select the forecasting objective select a region from the region drop down list click create to create your empty dataset and advance to the source tab choose one of the following options based on your data source csv files on your computer click radio_button_checked upload csv files from your computer click select files and choose all the local files to upload to a cloud storage bucket in the select a cloud storage path section enter the path to the cloud storage bucket or click browse to choose a bucket location csv files in cloud storage click radio_button_checked select csv files from cloud storage in the select csv files from cloud storage section enter the path to the cloud storage bucket or click browse to choose the location of your csv files a table or view in bigquery click radio_button_checked select a table or view from bigquery enter the project dataset and table ids for your input file click continue your data source is associated with your dataset on the analyze tab specify the timestamp column and the series identifier column for this dataset you can also specify these columns when you train your model but generally a forecasting dataset has specific time and time series identifier columns so specifying them in the dataset is a best practice api csv rest you use the datasets create method to create a dataset before using any of the request data make the following replacements location region where the dataset will be stored this must be a region that supports dataset resources for example us central1 project your project id dataset_name display name for the dataset metadata_schema_uri the uri to the schema file for your objective gs google cloud aiplatform schema dataset metadata time_series_1 0 0 yaml uri paths uris to the cloud storage buckets containing the training data there can be more than one each uri has the form gs gcsprojectid bucketname filename project_number your project s automatically generated project number http method and url post https location aiplatform googleapis com v1 projects project locations location datasets request json body display_name dataset_name metadata_schema_uri metadata_schema_uri metadata input_config gcs_source uri uri1 uri2 to send your request choose one of these options curl note the following command assumes that you have logged in to the gcloud cli with your user account by running gcloud init or gcloud auth login or by using cloud shell which automatically logs you into the gcloud cli you can check the currently active account by running gcloud auth list 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 locations location datasets powershell note the following command assumes that you have logged in to the gcloud cli with your user account by running gcloud init or gcloud auth login you can check the currently active account by running gcloud auth list 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 locations location datasets select object expand content you should receive a json response similar to the following name projects project_number locations location datasets dataset_id operations operation_id metadata type type googleapis com google cloud aiplatform v1 createdatasetoperationmetadata genericmetadata createtime 2020 07 07t21 27 35 964882z updatetime 2020 07 07t21 27 35 964882z java before trying this sample follow the java setup instructions in the agent platform quickstart using client libraries for more information see the agent platform java api reference documentation to authenticate to agent platform set up application default credentials for more information see set up authentication for a local development environment import com google api gax longrunning operationfuture import com google cloud aiplatform v1 createdatasetoperationmetadata import com google cloud aiplatform v1 dataset import com google cloud aiplatform v1 datasetserviceclient import com google cloud aiplatform v1 datasetservicesettings import com google cloud aiplatform v1 locationname import com google protobuf value import com google protobuf util jsonformat import java io ioexception import java util concurrent executionexception import java util concurrent timeunit import java util concurrent timeoutexception public class createdatasettabulargcssample public static void main string args throws interruptedexception executionexception timeoutexception ioexception todo developer replace these variables before running the sample string project your_project_id string datasetdisplayname your_dataset_display_name string gcssourceuri gs your_gcs_source_bucket path_to_your_gcs_table file csv createdatasettablegcs project datasetdisplayname gcssourceuri static void createdatasettablegcs string project string datasetdisplayname string gcssourceuri throws ioexception executionexception interruptedexception timeoutexception datasetservicesettings settings datasetservicesettings 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 datasetserviceclient datasetserviceclient datasetserviceclient create settings string location us central1 string metadataschemauri gs google cloud aiplatform schema dataset metadata tables_1 0 0 yaml locationname locationname locationname of project location string jsonstring input_config gcs_source uri gcssourceuri value builder metadata value newbuilder jsonformat parser merge jsonstring metadata dataset dataset dataset newbuilder setdisplayname datasetdisplayname setmetadataschemauri metadataschemauri setmetadata metadata build operationfuture dataset createdatasetoperationmetadata datasetfuture datasetserviceclient createdatasetasync locationname dataset system out format operation name s n datasetfuture getinitialfuture get getname system out println waiting for operation to finish dataset datasetresponse datasetfuture get 300 timeunit seconds system out println create dataset table gcs sample system out format name s n datasetresponse getname system out format display name s n datasetresponse getdisplayname system out format metadata schema uri s n datasetresponse getmetadataschemauri system out format metadata s n datasetresponse getmetadata node js before trying this sample follow the node js setup instructions in the agent platform quickstart using client libraries for more information see the agent platform node js api reference documentation to authenticate to agent platform set up application default credentials for more information see set up authentication for a local development environment todo developer uncomment these variables before running the sample not necessary if passing values as arguments const datasetdisplayname your_dataset_display_name const gcssourceuri your_gcs_source_uri const project your_project_id const location your_project_location imports the google cloud dataset service client library const datasetserviceclient require google cloud aiplatform specifies the location of the api endpoint const clientoptions apiendpoint us central1 aiplatform googleapis com instantiates a client const datasetserviceclient new datasetserviceclient clientoptions async function createdatasettabulargcs configure the parent resource const parent projects project locations location const metadata structvalue fields inputconfig structvalue fields gcssource structvalue fields uri listvalue values stringvalue gcssourceuri configure the dataset resource const dataset displayname datasetdisplayname metadataschemauri gs google cloud aiplatform schema dataset metadata tabular_1 0 0 yaml metadata metadata const request parent dataset create dataset request const response await datasetserviceclient createdataset request console log long running operation response name wait for operation to complete await response promise const result response result console log create dataset tabular gcs response console log tname result name console log tdisplay name result displayname console log tmetadata schema uri result metadataschemauri console log tmetadata json stringify result metadata createdatasettabulargcs python to learn how to install or update the vertex ai sdk for python see install the vertex ai sdk for python for more information see the python api reference documentation def create_and_import_dataset_time_series_gcs_sample display_name str project str location str gcs_source union str list str aiplatform init project project location location dataset aiplatform timeseriesdataset create display_name display_name gcs_source gcs_source dataset wait print f tdataset dataset display_name print f tname dataset resource_name api bigquery rest you use the datasets create method to create a dataset before using any of the request data make the following replacements location region where the dataset will be stored this must be a region that supports dataset resources for example us central1 project dataset_name display name for the dataset metadata_schema_uri the uri to the schema file for your objective gs google cloud aiplatform schema dataset metadata time_series_1 0 0 yaml uri path to the bigquery table containing the training data in the form bq bqprojectid bqdatasetid bqtableid project_number your project s automatically generated project number http method and url post https location aiplatform googleapis com v1 projects project locations location datasets request json body display_name dataset_name metadata_schema_uri metadata_schema_uri metadata input_config bigquery_source uri uri to send your request choose one of these options curl note the following command assumes that you have logged in to the gcloud cli with your user account by running gcloud init or gcloud auth login or by using cloud shell which automatically logs you into the gcloud cli you can check the currently active account by running gcloud auth list 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 locations location datasets powershell note the following command assumes that you have logged in to the gcloud cli with your user account by running gcloud init or gcloud auth login you can check the currently active account by running gcloud auth list 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 locations location datasets select object expand content you should receive a json response similar to the following name projects project_number locations location datasets dataset_id operations operation_id metadata type type googleapis com google cloud aiplatform v1 createdatasetoperationmetadata genericmetadata createtime 2020 07 07t21 27 35 964882z updatetime 2020 07 07t21 27 35 964882z java before trying this sample follow the java setup instructions in the agent platform quickstart using client libraries for more information see the agent platform java api reference documentation to authenticate to agent platform set up application default credentials for more information see set up authentication for a local development environment import com google api gax longrunning operationfuture import com google cloud aiplatform v1 createdatasetoperationmetadata import com google cloud aiplatform v1 dataset import com google cloud aiplatform v1 datasetserviceclient import com google cloud aiplatform v1 datasetservicesettings import com google cloud aiplatform v1 locationname import com google protobuf value import com google protobuf util jsonformat import java io ioexception import java util concurrent executionexception import java util concurrent timeunit import java util concurrent timeoutexception public class createdatasettabularbigquerysample public static void main string args throws interruptedexception executionexception timeoutexception ioexception todo developer replace these variables before running the sample string project your_project_id string bigquerydisplayname your_dataset_display_name string bigqueryuri bq your_google_cloud_project_id bigquery_dataset_id bigquery_table_or_view_id createdatasettablebigquery project bigquerydisplayname bigqueryuri static void createdatasettablebigquery string project string bigquerydisplayname string bigqueryuri throws ioexception executionexception interruptedexception timeoutexception datasetservicesettings settings datasetservicesettings 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 datasetserviceclient datasetserviceclient datasetserviceclient create settings string location us central1 string metadataschemauri gs google cloud aiplatform schema dataset metadata tables_1 0 0 yaml locationname locationname locationname of project location string jsonstring input_config bigquery_source uri bigqueryuri value builder metadata value newbuilder jsonformat parser merge jsonstring metadata dataset dataset dataset newbuilder setdisplayname bigquerydisplayname setmetadataschemauri metadataschemauri setmetadata metadata build operationfuture dataset createdatasetoperationmetadata datasetfuture datasetserviceclient createdatasetasync locationname dataset system out format operation name s n datasetfuture getinitialfuture get getname system out println waiting for operation to finish dataset datasetresponse datasetfuture get 300 timeunit seconds system out println create dataset table bigquery sample system out format name...