Permission Error When Executing Vertex AI Fine Tuning Job with Node.js: Service Account Roles Insufficient?

I'm using a pipeline job to fine tune a text-bison model with Vertex AI on GCP. My API is logged using a dedicated service account. It has the roles : Vertex AI Service Agent and Service Accounts User.

I'm using this Node.js code :

import aiplatform from '@google-cloud/aiplatform';

class ModelTuningController {
  initiateModelTuning = catchAsync(async (req, res) => {
    const { modelDisplayName = 'fineTunedTest' } = req.body;

    const { PipelineServiceClient } = aiplatform.v1;

    const location = 'us-central1';
    const project = 'projectID';
    const model = 'text-bison@001';

const pipelineClient = new PipelineServiceClient({
  apiEndpoint: `${location}-aiplatform.googleapis.com`,
  keyFilename: config.vector_store_key_filename,
});

const parameters = {
  source_model: { stringValue: model },
  train_dataset: {
    stringValue: 'gs://path/file.jsonl',
  },
  tuned_model_display_name: {
    stringValue: modelDisplayName,
  },
  epochs: {
    numberValue: 4,
  },
  learning_rate_multiplier: {
    numberValue: 1,
  },
};

const runtimeConfig = {
  gcsOutputDirectory: 'gs://output-path',
  parameterValues: parameters,
};

try {
  const parent = `projects/${project}/locations/${location}`;
  const pipelineJob = {
    displayName: modelDisplayName,
    runtimeConfig,
  };

  const request = {
    parent,
    pipelineJob,
  };

  const [response] = await pipelineClient.createPipelineJob(request);

  return sendResponse(res, {
    message: ModelTuningMessages.jobInitiated(response.name),
  });
} catch (err) {
  return sendError(
    err,
    new ApiError(
      httpStatus.INTERNAL_SERVER_ERROR,
      ModelTuningMessages.jobError


       )
      );
    }
  });
}

export default ModelTuningController;

I have the following error : You do not have permission to act as service_account: ${projectID}-compute@developer.gserviceaccount.com. (or it may not exist).

The problem is ${projectID}-compute@developer.gserviceaccount.com is the project default service account. My guess it that my service account should act as the default service account of the project.

Does my service account is missing a role to execute the fine tuning job on Vertex AI ?

___________

EDIT

It seems like I needed to activate the Compute Engine API to have the default compute engine service account to make it work !

However I still have the error : Error: 3 INVALID_ARGUMENT: . It's hard to know which argument is invalid. This is the request JSON I send to the pipeline job :

{
  "parent": "projects/${projectID}/locations/us-central1",
  "pipelineJob": {
    "displayName": "fineTunedTest",
    "runtimeConfig": {
      "gcsOutputDirectory": "gs://${output}",
      "parameterValues": {
        "source_model": { "stringValue": "gemini-1.0-pro-002" },
        "train_dataset": { "stringValue": "gs://${input}/${file}" },
        "tuned_model_display_name": { "stringValue": "fineTunedTest" },
        "epochs": { "numberValue": 4 },
        "learning_rate_multiplier": { "numberValue": 1 }
      }
    }
  }
}

For the first problem : (You do not have permission to act as service_account: ${projectID}-compute@developer.gserviceaccount.com. (or it may not exist).) I needed to activate the Compute Engine API to have the default compute engine service account. My service account only needed the roles Vertex AI Service Agent and Service Accounts User.

For the second problem : (Error: 3 INVALID_ARGUMENT:) I needed to add the missing parameter templateUri to my pipelineJob object. His value is https://us-kfp.pkg.dev/ml-pipeline/large-language-model-pipelines/tune-large-model/v2.0.0 for text model and https://us-kfp.pkg.dev/ml-pipeline/large-language-model-pipelines/tune-large-chat-model/v3.0.0 for chat model. See this for more informations.

My updated code is :

import aiplatform from '@google-cloud/aiplatform';

class ModelTuningController {
  initiateModelTuning = catchAsync(async (req, res) => {
    const { modelDisplayName = 'fineTunedTest' } = req.body;

    const { PipelineServiceClient } = aiplatform.v1;

    const location = 'us-central1';
    const project = 'projectID';
    const model = 'text-bison@001';

const pipelineClient = new PipelineServiceClient({
  apiEndpoint: `${location}-aiplatform.googleapis.com`,
  keyFilename: config.vector_store_key_filename,
});

const parameters = {
  source_model: { stringValue: model },
  train_dataset: {
    stringValue: 'gs://path/file.jsonl',
  },
  tuned_model_display_name: {
    stringValue: modelDisplayName,
  },
  epochs: {
    numberValue: 4,
  },
  learning_rate_multiplier: {
    numberValue: 1,
  },
};

const runtimeConfig = {
  gcsOutputDirectory: 'gs://output-path',
  parameterValues: parameters,
};

try {
  const parent = `projects/${project}/locations/${location}`;
  const pipelineJob = {
    displayName: modelDisplayName,
    templateUri: 'https://us-kfp.pkg.dev/ml-pipeline/large-language-model-pipelines/tune-large-model/v2.0.0',
    runtimeConfig,
  };

  const request = {
    parent,
    pipelineJob,
  };

  const [response] = await pipelineClient.createPipelineJob(request);

  return sendResponse(res, {
    message: ModelTuningMessages.jobInitiated(response.name),
  });
} catch (err) {
  return sendError(
    err,
    new ApiError(
      httpStatus.INTERNAL_SERVER_ERROR,
      ModelTuningMessages.jobError
       )
      );
    }
  });
    }

export default ModelTuningController;