SageMaker HyperPod에서 Nova 2.0에 대한 지속적 사전 학습(CPT)

다음은 CPT에 대한 레시피 샘플입니다. 이 레시피 및 기타 레시피는 GitHub의 SageMaker HyperPod recipes 리포지토리에서 찾을 수 있습니다.

# Note:
# This recipe can run on p5.48xlarge
# Run config
run:
  name: "my-cpt-run"                           # A descriptive name for your training job
  model_type: "amazon.nova-2-lite-v1:0:256k"   # Model variant specification, do not change
  model_name_or_path: "nova-lite-2/prod"        # Base model path, do not change
  replicas: 8                                   # Number of compute instances for training, allowed values are 4, 8, 16, 32
  data_s3_path: ""                              # Customer data paths
  validation_data_s3_path: ""                   # Customer validation data paths
  output_s3_path: ""                            # Output artifact path, SageMaker HyperPod job-specific configuration - not compatible with standard SageMaker Training Jobs
  mlflow_tracking_uri: ""                       # Required for MLFlow
  mlflow_experiment_name: "my-cpt-experiment"   # Optional for MLFlow. Note: leave this field non-empty
  mlflow_run_name: "my-cpt-run"                 # Optional for MLFlow. Note: leave this field non-empty

## Training specific configs
training_config:
  task_type: cpt
  max_length: 8192                              # Maximum context window size (tokens)
  global_batch_size: 256                        # Global batch size, allowed values are 32, 64, 128, 256.

  trainer:
    max_steps: 10                               # The number of training steps to run total
    val_check_interval: 10                      # The number of steps between running validation. Integer count or float percentage
    limit_val_batches: 2                        # Batches of the validation set to use each trigger

  model:
    hidden_dropout: 0.0                         # Dropout for hidden states, must be between 0.0 and 1.0
    attention_dropout: 0.0                      # Dropout for attention weights, must be between 0.0 and 1.0

  optim:
    optimizer: adam
    lr: 1e-5                                    # Learning rate
    name: distributed_fused_adam                # Optimizer algorithm, do not change
    adam_w_mode: true                           # Enable AdamW mode
    eps: 1e-06                                  # Epsilon for numerical stability
    weight_decay: 0.0                           # L2 regularization strength, must be between 0.0 and 1.0
    adam_beta1: 0.9                             # Beta1 for Adam optimizer
    adam_beta2: 0.95                            # Beta2 for Adam optimizer
    sched:
      warmup_steps: 10                          # Learning rate warmup steps
      constant_steps: 0                         # Steps at constant learning rate
      min_lr: 1e-6                              # Minimum learning rate, must be lower than lr