Resource Config Guide

GiGL Resource Config is a yaml file that is passed in at runtime and specifies the resource and environment configurations for each component in the GiGL. The proto definition for GiGL Resource Config can be seen here

Prerequisites

If you don’t have cloud assets already setup i.e. a GCP project. See guide

Resource Config Breakdown

Example: Full Template for Resource Config

See Full Template Resource Config

shared_resource_config:
  resource_labels:
    cost_resource_group_tag: ""
    cost_resource_group: ""
  common_compute_config:
    project: "project_id"
    region: "gcp_region_here"
    temp_assets_bucket: "gs://"
    temp_regional_assets_bucket: "gs://"
    perm_assets_bucket: "gs://"
    temp_assets_bq_dataset_name: "bq_dataset_name_here"
    embedding_bq_dataset_name: "bq_dataset_name_here"
    gcp_service_account_email: "service_account_email_here"
    k8_service_account: "service_account_name_here"
    dataflow_worker_harness_image: "gcr.io/..."
    dataflow_runner: "" # DataflowRunner or DirectRunner
preprocessor_config:
  edge_preprocessor_config:
    num_workers: 1
    max_num_workers: 2
    machine_type: "" # e.g. n1-highmem-32
    disk_size_gb: 100
  node_preprocessor_config:
    num_workers: 1
    max_num_workers: 2
    machine_type: "" # e.g. n1-highmem-64
    disk_size_gb: 100
subgraph_sampler_config:
  machine_type: "" # e.g. n1-highmem-32
  num_local_ssds: 1
  num_replicas: 1
split_generator_config:
  machine_type: "" # e.g. n1-highmem-32
  num_local_ssds: 1
  num_replicas: 1
trainer_config:
  vertex_ai_trainer_config:
    machine_type: "" # e.g. n1-highmem-16
    gpu_type: "" # e.g. nvidia-tesla-p100
    gpu_limit: 1 
    num_replicas: 1
inferencer_config:
  num_workers: 1
  max_num_workers: 2
  machine_type: "" # e.g. n1-highmem-16
  disk_size_gb: 100
        

Shared Resource Config

The shared_resource_config field includes settings that apply across all GiGL components. You need to customize this section according to your GCP project specifics.

• Resource Labels: Resource labels help you manage costs and organzie resources. Modify the resource_labels section to fit your project’s labeling scheme.

• Common Compute Config: This section includes important project specifications. Fill out the fields with your project ID, region, asset buckets, and service account email.

common_compute_config:
  project: "your-gcp-project-id"
  region: "your-region"
  temp_assets_bucket: "gs://your-temp-bucket"
  perm_assets_bucket: "gs://your-permanent-bucket"
  gcp_service_account_email: "your-service-account-email"

Preprocessor Config

The preprocessor_config specifies settings for the Dataflow preprocessor component, includes number of workers, machine type, and disk size. You must specify both the node_preprocessor_config and edge_preprocessor_config. See example:

preprocessor_config:
  edge_preprocessor_config:
    num_workers: 1
    max_num_workers: 2
    machine_type: "n1-highmem-32"
    disk_size_gb: 100
  node_preprocessor_config:
    num_workers: 1
    max_num_workers: 2
    machine_type: "n1-highmem-32"
    disk_size_gb: 100

Subgraph Sampler Config

The subgraph_sampler_config specifies settings for the Spark subgraph sampler component, includes machine type, local SSDs, and number of replicas. See example:

subgraph_sampler_config:
  machine_type: "n1-standard-4"
  num_local_ssds: 1
  num_replicas: 2

Split Generator Config

The split_generator_config specifies settings for the Spark split generator component, includes machine type, local SSDs, and number of replicas

split_generator_config:
  machine_type: "n1-standard-4"
  num_local_ssds: 1
  num_replicas: 2

Trainer Config

The trainer_config specifies settings for the trainer config, currently supporting Vertex AI training or Local Training.

• Vertex AI Trainer Config: The vertex_ai_trainer_config field of the trainer config requires a machine type, GPU type, GPU limit, and number of replicas. See example:

  trainer_config:
    vertex_ai_trainer_config:
      machine_type: "n1-standard-8"
      gpu_type: "nvidia-tesla-t4"
      gpu_limit: 1
      num_replicas: 1
  

• Local Trainer Config: The local_trainer_config field of the trainer config just requires num_workers which can be used for data loaders.

Inferencer Config

The inferencer_config specifies settings for the Dataflow preprocessor component, includes number of workers, machine type, and disk size. See example:

inferencer_config:
  num_workers: 1
  max_num_workers: 256
  machine_type: "c2-standard-16"
  disk_size_gb: 100