Important

You are viewing the NeMo 2.0 documentation. This release introduces significant changes to the API and a new library, NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to the NeMo 24.07 documentation.

Experiment Manager#

The NeMo Framework Experiment Manager leverages PyTorch Lightning for model checkpointing, TensorBoard Logging, Weights and Biases, DLLogger and MLFlow logging. The Experiment Manager is included by default in all NeMo example scripts.

To use the Experiment Manager, call exp_manager and pass in the PyTorch Lightning Trainer.

exp_dir = exp_manager(trainer, cfg.get("exp_manager", None))

The Experiment Manager is configurable using YAML with Hydra.

exp_manager:
    exp_dir: /path/to/my/experiments
    name: my_experiment_name
    create_tensorboard_logger: True
    create_checkpoint_callback: True

Optionally, launch TensorBoard to view the training results in exp_dir, which by default is set to ./nemo_experiments.

tensorboard --bind_all --logdir nemo_experiments

If create_checkpoint_callback is set to True, then NeMo automatically creates checkpoints during training using PyTorch Lightning’s ModelCheckpoint. We can configure the ModelCheckpoint via YAML or CLI:

exp_manager:
    ...
    # configure the PyTorch Lightning ModelCheckpoint using checkpoint_call_back_params
    # any ModelCheckpoint argument can be set here

    # save the best checkpoints based on this metric
    checkpoint_callback_params.monitor=val_loss

    # choose how many total checkpoints to save
    checkpoint_callback_params.save_top_k=5

Resume Training#

To auto-resume training, configure the exp_manager. This feature is important for long training runs that might be interrupted or shut down before the procedure has completed. To auto-resume training, set the following parameters via YAML or CLI:

exp_manager:
    ...
    # resume training if checkpoints already exist
    resume_if_exists: True

    # to start training with no existing checkpoints
    resume_ignore_no_checkpoint: True

    # by default experiments will be versioned by datetime
    # we can set our own version with
    exp_manager.version: my_experiment_version

Experiment Loggers#

Alongside Tensorboard, NeMo also supports Weights and Biases, MLFlow, DLLogger, ClearML and NeptuneLogger. To use these loggers, set the following via YAML or ExpManagerConfig.

Weights and Biases (WandB)#

exp_manager:
    ...
    create_checkpoint_callback: True
    create_wandb_logger: True
    wandb_logger_kwargs:
        name: ${name}
        project: ${project}
        entity: ${entity}
        <Add any other arguments supported by WandB logger here>

MLFlow#

exp_manager:
    ...
    create_checkpoint_callback: True
    create_mlflow_logger: True
    mlflow_logger_kwargs:
        experiment_name: ${name}
        tags:
            <Any key:value pairs>
        save_dir: './mlruns'
        prefix: ''
        artifact_location: None
        # provide run_id if resuming a previously started run
        run_id: Optional[str] = None

DLLogger#

exp_manager:
    ...
    create_checkpoint_callback: True
    create_dllogger_logger: True
    dllogger_logger_kwargs:
        verbose: False
        stdout: False
        json_file: "./dllogger.json"

ClearML#

exp_manager:
    ...
    create_checkpoint_callback: True
    create_clearml_logger: True
    clearml_logger_kwargs:
        project: None  # name of the project
        task: None  # optional name of task
        connect_pytorch: False
        model_name: None  # optional name of model
        tags: None  # Should be a list of str
        log_model: False  # log model to clearml server
        log_cfg: False  # log config to clearml server
        log_metrics: False  # log metrics to clearml server

Neptune#

exp_manager:
    ...
    create_checkpoint_callback: True
    create_neptune_logger: false
    neptune_logger_kwargs:
        project: ${project}
        name: ${name}
        prefix: train
        log_model_checkpoints: false # set to True if checkpoints need to be pushed to Neptune
        tags: null # can specify as an array of strings in yaml array format
        description: null
        <Add any other arguments supported by Neptune logger here>

Exponential Moving Average#

NeMo supports using exponential moving average (EMA) for model parameters. This can be useful for improving model generalization and stability. To use EMA, set the following parameters via YAML or ExpManagerConfig.

exp_manager:
    ...
    # use exponential moving average for model parameters
    ema:
        enabled: True  # False by default
        decay: 0.999  # decay rate
        cpu_offload: False  # If EMA parameters should be offloaded to CPU to save GPU memory
        every_n_steps: 1  # How often to update EMA weights
        validate_original_weights: False  # Whether to use original weights for validation calculation or EMA weights

Hydra Multi-Run with NeMo#

When training neural networks, it is common to perform a hyperparameter search to improve the model’s performance on validation data. However, manually preparing a grid of experiments and managing all checkpoints and their metrics can be tedious. To simplify these tasks, NeMo integrates with Hydra Multi-Run support, providing a unified way to run a set of experiments directly from the configuration.

There are certain limitations to this framework, which we list below:

All experiments are assumed to be run on a single GPU, and multi GPU for single run (model parallel models are not supported as of now).
NeMo Multi-Run currently supports only grid search over a set of hyperparameters. Support for advanced hyperparameter search strategies will be added in the future.
NeMo Multi-Run requires one or more GPUs to function and will not work without GPU devices.

Config Setup#

In order to enable NeMo Multi-Run, we first update our YAML configs with some information to let Hydra know we expect to run multiple experiments from this one config -

# Required for Hydra launch of hyperparameter search via multirun
defaults:
  - override hydra/launcher: nemo_launcher

# Hydra arguments necessary for hyperparameter optimization
hydra:
  # Helper arguments to ensure all hyper parameter runs are from the directory that launches the script.
  sweep:
    dir: "."
    subdir: "."

  # Define all the hyper parameters here
  sweeper:
    params:
      # Place all the parameters you wish to search over here (corresponding to the rest of the config)
      # NOTE: Make sure that there are no spaces between the commas that separate the config params !
      model.optim.lr: 0.001,0.0001
      model.encoder.dim: 32,64,96,128
      model.decoder.dropout: 0.0,0.1,0.2

  # Arguments to the process launcher
  launcher:
    num_gpus: -1  # Number of gpus to use. Each run works on a single GPU.
    jobs_per_gpu: 1  # If each GPU has large memory, you can run multiple jobs on the same GPU for faster results (until OOM).

Next, we will setup the config for Experiment Manager. When we perform hyper parameter search, each run may take some time to complete. We want to therefore avoid the case where a run ends (say due to OOM or timeout on the machine) and we need to redo all experiments. We therefore setup the experiment manager config such that every experiment has a unique “key”, whose value corresponds to a single resumable experiment.

Let us see how to setup such a unique “key” via the experiment name. Simply attach all the hyper parameter arguments to the experiment name as shown below -

exp_manager:
  exp_dir: null  # Can be set by the user.

  # Add a unique name for all hyper parameter arguments to allow continued training.
  # NOTE: It is necessary to add all hyperparameter arguments to the name !
  # This ensures successful restoration of model runs in case HP search crashes.
  name: ${name}-lr-${model.optim.lr}-adim-${model.adapter.dim}-sd-${model.adapter.adapter_strategy.stochastic_depth}

  ...
  checkpoint_callback_params:
    ...
    save_top_k: 1  # Dont save too many .ckpt files during HP search
    always_save_nemo: True # saves the checkpoints as nemo files for fast checking of results later
  ...

  # We highly recommend use of any experiment tracking took to gather all the experiments in one location
  create_wandb_logger: True
  wandb_logger_kwargs:
    project: "<Add some project name here>"

  # HP Search may crash due to various reasons, best to attempt continuation in order to
  # resume from where the last failure case occurred.
  resume_if_exists: true
  resume_ignore_no_checkpoint: true

Run a NeMo Multi-Run Configuration#

Once the config has been updated, we can now run it just like any normal Hydra script, with one special flag (-m).

python script.py --config-path=ABC --config-name=XYZ -m \
    trainer.max_steps=5000 \  # Any additional arg after -m will be passed to all the runs generated from the config !
    ...

Tips and Tricks#

This section provides recommendations for using the Experiment Manager.

Preserving disk space for a large number of experiments#

Some models may have a large number of parameters, making it very expensive to save numerous checkpoints on physical storage drives. For example, if you use the Adam optimizer, each PyTorch Lightning “.ckpt” file will be three times the size of just the model parameters. This can become exorbitant if you have multiple runs.

In the above configuration, we explicitly set save_top_k: 1 and always_save_nemo: True. This limits the number of “.ckpt” files to just one and also saves a NeMo file, which contains only the model parameters without the optimizer state. This NeMo file can be restored immediately for further work.

We can further save storage space by using NeMo’s utility functions to automatically delete either “.ckpt” or NeMo files after a training run has finished. This is sufficient if you are collecting results in an experiment tracking tool and can simply rerun the best configuration after the search is completed.

# Import `clean_exp_ckpt` along with exp_manager
from nemo.utils.exp_manager import clean_exp_ckpt, exp_manager

@hydra_runner(...)
def main(cfg):
    ...

    # Keep track of the experiment directory
    exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None))

    ... add any training code here as needed ...

    # Add following line to end of the training script
    # Remove PTL ckpt file, and potentially also remove .nemo file to conserve storage space.
    clean_exp_ckpt(exp_log_dir, remove_ckpt=True, remove_nemo=False)

Debugging Multi-Run Scripts#

When running Hydra scripts, you may encounter configuration issues that crash the program. In NeMo Multi-Run, a crash in any single run will not crash the entire program. Instead, we will note the error and proceed to the next job. Once all jobs are completed, we will raise the errors in the order they occurred, crashing the program with the first error’s stack trace.

To debug NeMo Multi-Run, we recommend commenting out the entire hyperparameter configuration set inside sweep.params. Instead, run a single experiment with the configuration, which will immediately raise the error.

Experiment name cannot be parsed by Hydra#

Sometimes our hyperparameters include PyTorch Lightning trainer arguments, such as the number of steps, number of epochs, and whether to use gradient accumulation. When we attempt to add these as keys to the experiment manager’s name, Hydra may complain that trainer.xyz cannot be resolved.

A simple solution is to finalize the Hydra config before you call exp_manager() as follows:

@hydra_runner(...)
def main(cfg):
    # Make any changes as necessary to the config
    cfg.xyz.abc = uvw

    # Finalize the config
    cfg = OmegaConf.resolve(cfg)

    # Carry on as normal by calling trainer and exp_manager
    trainer = pl.Trainer(**cfg.trainer)
    exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
    ...

ExpManagerConfig#

class nemo.utils.exp_manager.ExpManagerConfig( explicit_log_dir: str | None = None, exp_dir: str | None = None, name: str | None = None, version: str | None = None, use_datetime_version: bool | None = True, resume_if_exists: bool | None = False, resume_past_end: bool | None = False, resume_ignore_no_checkpoint: bool | None = False, resume_from_checkpoint: str | None = None, create_tensorboard_logger: bool | None = True, summary_writer_kwargs: ~typing.Dict[~typing.Any, ~typing.Any] | None = None, create_wandb_logger: bool | None = False, wandb_logger_kwargs: ~typing.Dict[~typing.Any, ~typing.Any] | None = None, create_mlflow_logger: bool | None = False, mlflow_logger_kwargs: ~nemo.utils.loggers.mlflow_logger.MLFlowParams | None = <factory>, create_dllogger_logger: bool | None = False, dllogger_logger_kwargs: ~nemo.utils.loggers.dllogger.DLLoggerParams | None = <factory>, create_clearml_logger: bool | None = False, clearml_logger_kwargs: ~nemo.utils.loggers.clearml_logger.ClearMLParams | None = <factory>, create_neptune_logger: bool | None = False, neptune_logger_kwargs: ~typing.Dict[~typing.Any, ~typing.Any] | None = None, create_checkpoint_callback: bool | None = True, checkpoint_callback_params: ~nemo.utils.exp_manager.CallbackParams | None = <factory>, create_early_stopping_callback: bool | None = False, early_stopping_callback_params: ~nemo.utils.exp_manager.EarlyStoppingParams | None = <factory>, create_preemption_callback: bool | None = True, files_to_copy: ~typing.List[str] | None = None, log_step_timing: bool | None = True, log_delta_step_timing: bool | None = False, step_timing_kwargs: ~nemo.utils.exp_manager.StepTimingParams | None = <factory>, log_local_rank_0_only: bool | None = False, log_global_rank_0_only: bool | None = False, disable_validation_on_resume: bool | None = True, ema: ~nemo.utils.exp_manager.EMAParams | None = <factory>, max_time_per_run: str | None = None, seconds_to_sleep: float = 5, create_straggler_detection_callback: bool | None = False, straggler_detection_params: ~nemo.utils.exp_manager.StragglerDetectionParams | None = <factory>, create_fault_tolerance_callback: bool | None = False, fault_tolerance: ~nemo.utils.exp_manager.FaultToleranceParams | None = <factory>, log_tflops_per_sec_per_gpu: bool | None = True, )

Bases: object

Experiment Manager config for validation of passed arguments.