dags/map_reproducibility/a3mega/gpt3_175b_nemo.py [31:64]:
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SCHEDULED_TIME = (
    get_scheduled_time(HYPERCOMPUTER, MODEL_ID, FRAMEWORK)
    if composer_env.is_prod_env()
    else None
)

VALUE_YAML_PATH = (
    f"training/{HYPERCOMPUTER}/{MODEL_ID}/nemo-pretraining-gke/values.yaml"
)
SOFTWARE_ID = "pytorch_nemo"
IMAGE_VERSION = "nemo_workload:24.07"
KUEUE_NAME = "multislice-kueue"
NUM_GPUS = 256

with models.DAG(
    dag_id=f"{HYPERCOMPUTER}_recipes_{MODEL_ID}_{FRAMEWORK}",
    schedule=SCHEDULED_TIME,
    tags=[
        "reproducibility",
        "experimental",
        "xlml",
        "regressiontests",
        "a3ultra",
    ],
    start_date=datetime.datetime(2024, 11, 15),
    catchup=False,
) as dag:
  run_nemo_workload(
      hypercomputer=HYPERCOMPUTER,
      model_id=MODEL_ID,
      framework=FRAMEWORK,
      precision=PRECISION,
      kueue_name=KUEUE_NAME,
      metrics_model_id=MODEL_ID,
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dags/map_reproducibility/a3mega/llama_3_70b_nemo.py [37:71]:
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SCHEDULED_TIME = (
    get_scheduled_time(HYPERCOMPUTER, MODEL_ID, FRAMEWORK)
    if composer_env.is_prod_env()
    else None
)

VALUE_YAML_PATH = (
    f"training/{HYPERCOMPUTER}/{MODEL_ID}/nemo-pretraining-gke/values.yaml"
)
SOFTWARE_ID = "pytorch_nemo"
IMAGE_VERSION = "nemo_workload:24.07"
KUEUE_NAME = "multislice-kueue"
NUM_GPUS = 256


with models.DAG(
    dag_id=f"{HYPERCOMPUTER}_recipes_{MODEL_ID}_{FRAMEWORK}",
    schedule=SCHEDULED_TIME,
    tags=[
        "reproducibility",
        "experimental",
        "xlml",
        "regressiontests",
        "a3ultra",
    ],
    start_date=datetime.datetime(2024, 11, 15),
    catchup=False,
) as dag:
  run_nemo_workload(
      hypercomputer=HYPERCOMPUTER,
      model_id=MODEL_ID,
      framework=FRAMEWORK,
      precision=PRECISION,
      kueue_name=KUEUE_NAME,
      metrics_model_id=MODEL_ID,
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