Labrador Synthetic Data Generation Pipeline

Introduction

Run Instructions (Automation)

Step 1: Environment Setup

1. Initialize a .env file with the following access tokens:
   GIT_ACCESS_TOKEN={ACCESS-TOKEN-TO-ACCESS-TAXONOMY-REPO} # this personal access token is used to access instruct-lab/taxonomy repo

Step 2: Execution

1. Execute the following command:
   NOTE: Depending on whether you are running on old or new vela, change this line in the orchestrator.py to use the appropriate old vela or new vela template. save_job_with_jinja_template(cfg, "templates/labrador_datagen_vela.yaml.j2", output_dir=f"jobs/{branch}")
   python orchestrator.py branch-name
   This will:
   • Create a file with a list of leaf nodes in the jobs directory.
   • Generate YAML files for each leaf node and store them in the jobs directory something like test-7984f9cae729b798bed1ba222715b880.yaml
2. To initiate the skill generation pipeline, run:
   To trigger a job, take the above yaml and
   oc create -f jobs/yaml_name.yaml
   This command will execute the pipeline and store the results in the new_data/labrador-datagen directory within the COS bucket mounted on the Vela cluster.

Run Instructions (Manual - Testing)

Step 1: Run model

  LEAF_NODE=knowledge/textbooks/ethics/qna.yaml # Path to the leaf node that you want to download
  NUM_SAMPLES=30
  NUM_GROUNDED_QUESTIONS=3
  NUM_GEN_PROC=32
  NUM_UTIL_PROC=8
  SAVE_PATH=new_data/labrador_datagen # Path where you want to download the data
  CONTEXT=0 # Set 0 for freeform and 1 for grounded
  DATA_PATH=.
  CHECKSUM=test
  BRANCH_NAME=test # Branch name to download data from
  KNOWLEDGE=1 # Set 0 for skills and 1 for knowledge
  PARENT_DIR=$(dirname "$LEAF_NODE")
  GIT_ACCESS_TOKEN= # Access token to access taxonomy repo

Skills

Knowledge