Common Configuration Files

This section provides a detailed overview of the NeMo configuration file setup specific to models within the NeMo vision models collection . For foundational knowledge about setting up and executing experiments common to all NeMo models, such as the Experiment Manager and PyTorch Lightning trainer parameters, refer to the NeMo Models section.

Within the configuration files of the NeMo vision models, details concerning dataset(s), augmentation, optimization parameters, and model architectural specifications are central. This page explores each of these aspects.

Discover exemplary configuration files for all NeMo vision models scripts in the config directory of the examples.

Dataset Configuration

The configuration file delineates parameters for dataset path.

All initialization parameters supported by the Dataset class utilized in the experiment can be defined in the config file. .. For a comprehensive list of Datasets and their associated parameters, consult the Datasets section of the API.

A representative training configuration appears as:

data:
  data_path:
    - ${data_dir}/imagenet_1k/train
    - ${data_dir}/imagenet_1k/val
  num_workers: 8
  dataloader_type: cyclic
  validation_drop_last: True
  data_sharding: False

Trainer Configuration

This section outlines arguments for the Pytorch Lightning Trainer Object.

trainer:
  devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1]
  num_nodes: 1
  max_epochs: -1
  max_steps: 2500000 # precedence over max_epochs
  logger: False  # Provided by exp_manager
  precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP.
  accelerator: gpu
  log_every_n_steps: 5  # Interval of logging.
  resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
  num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
  enable_checkpointing: False # Provided by exp_manager
  accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models
  gradient_clip_val: 1.0
  benchmark: False
  enable_model_summary: True

For a detailed list of arguments, refer to the Pytorch Lightning Trainer API section.

Experiment Manager Configurations

The NeMo Experiment Manager provides a streamlined approach to manage various tasks such as logging, saving, and resuming.

exp_manager:
  exp_dir: null  # exp_dir for your experiment, if None, defaults to "./nemo_experiments"
  name: ${name}
  create_wandb_logger: True
  wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger
    name: training-session
    project: text2img
    group: nemo
    resume: True
  create_tensorboard_logger: True  # Whether you want exp_manger to create a tb logger
  create_checkpoint_callback: True  # Whether you want exp_manager to create a model checkpoint callback
  checkpoint_callback_params:
    monitor: reduced_train_loss
    save_top_k: 5
    every_n_epochs: 0 # Save checkpoint frequency.
    every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset.
    filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}'
  resume_if_exists: True
  resume_ignore_no_checkpoint: True
  resume_from_checkpoint: ${model.resume_from_checkpoint}
  ema:
    enable: True
    decay: 0.9999
    validate_original_weights: False
    every_n_steps: 1
    cpu_offload: False

Optimizer Configurations

optim:
  name: fused_adam
  lr: 0.0001
  eps: 1e-8
  betas: [ 0.9, 0.999 ]
  weight_decay: 0.01
  sched:
    name: WarmupPolicy
    warmup_steps: 10000
    warmup_ratio: null

The default optimizer used is fused_adam. For details on all supported optimizers, refer to the NeMo user guide. The learning rate scheduler can be specified in the optim.sched section.

Model Configurations

Each configuration file should detail the model architecture used for the experiment.

The parameters commonly shared across most vision collection models include:

Parameter	Datatype	Description
`micro_batch_size`	int	Micro batch size that fits on each GPU
`global_batch_size`	int	Global batch size considering gradient accumulation and data parallelism
`tensor_model_parallel_size`	int	Intra-layer model parallelism
`pipeline_model_parallel_size`	int	Inter-layer model parallelism
`seed`	int	Seed used in training

ViT

For model-specific configurations, refer to vit.