NeMo’s 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 simply call exp_manager and pass in the PyTorch Lightning Trainer.
exp_dir = exp_manager(trainer, cfg.get("exp_manager", None))
And is configurable via 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 ./nemo_experiments (by default).
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
We can auto-resume training as well by configuring the exp_manager. Being able to auto-resume is important when doing long training
runs that are premptible or may be shut down before the training procedure has completed. To auto-resume training, set the following
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
Alongside Tensorboard, NeMo also supports Weights and Biases, MLFlow, DLLogger, ClearML and NeptuneLogger. To use these loggers, simply 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>
NeMo supports using exponential moving average (EMA) for model parameters. This can be useful for improving model generalization
and stability. To use EMA, simply set the following 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
NeMo adds support for a callback upon preemption while running the models on clusters. The callback takes care of saving the current state of training via the .ckpt
file followed by a graceful exit from the run. The checkpoint saved upon preemption has the *last.ckpt suffix and replaces the previously saved last checkpoints.
This feature is useful to increase utilization on clusters.
The PreemptionCallback is enabled by default. To disable it simply add create_preemption_callback: False under exp_manager in the config YAML file.
When training neural networks, it is common to perform hyper parameter search in order to improve the performance of a model on some validation data. However, it can be tedious to manually prepare a grid of experiments and management of all checkpoints and their metrics. In order to simplify such tasks, NeMo integrates with Hydra Multi-Run support in order to provide a unified way to run a set of experiments all from the config.
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 supports only grid search over a set of hyper-parameters, but we will eventually add support for advanced hyper parameter search strategies.
NeMo Multi-Run only supports running on one or more GPUs and will not work if no GPU devices are present.
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: "<Addsomeprojectnamehere>"
# 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
Running a Multi-Run config
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
Preserving disk space for large number of experiments
Some models may have a large number of parameters, and it may be very expensive to save a large number of checkpoints on physical storage drives. For example, if you use Adam optimizer, each PyTorch Lightning “.ckpt” file will actually be 3x the size of just the model parameters - per ckpt file ! This can be exhorbitant if you have multiple runs.
In the above config, we explicitly set save_top_k: 1 and always_save_nemo: True - what this does is limit the number of
ckpt files to just 1, and also save a NeMo file (which will contain just the model parameters without optimizer state) and
can be restored immediately for further work.
We can further reduce the storage space by utilizing some utility functions of NeMo to automatically delete either ckpt or NeMo files after a training run has finished. This is sufficient in case you are collecting results in some experiment tracking tool and can simply rerun the best config after the search is finished.
# 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 sometimes face config issues which crash the program. In NeMo Multi-Run, a crash in any one run will not crash the entire program, we will simply take note of it and move onto the next job. Once all jobs are completed, we then raise the error in the order that it occurred (it will crash the program with the first error’s stack trace).
In order to debug Muti-Run, we suggest to comment out the full hyper parameter config set inside sweep.params
and instead run just a single experiment with the config - which would immediately raise the error.
Experiment name cannot be parsed by Hydra
Sometimes our hyper parameters include PyTorch Lightning trainer arguments - such as number of steps, number of epochs
whether to use gradient accumulation or not etc. When we attempt to add these as keys to the expriment 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))
...
- class nemo.utils.exp_manager.ExpManagerConfig(explicit_log_dir: typing.Optional[str] = None, exp_dir: typing.Optional[str] = None, name: typing.Optional[str] = None, version: typing.Optional[str] = None, use_datetime_version: typing.Optional[bool] = True, resume_if_exists: typing.Optional[bool] = False, resume_past_end: typing.Optional[bool] = False, resume_ignore_no_checkpoint: typing.Optional[bool] = False, resume_from_checkpoint: typing.Optional[str] = None, create_tensorboard_logger: typing.Optional[bool] = True, summary_writer_kwargs: typing.Optional[typing.Dict[typing.Any, typing.Any]] = None, create_wandb_logger: typing.Optional[bool] = False, wandb_logger_kwargs: typing.Optional[typing.Dict[typing.Any, typing.Any]] = None, create_mlflow_logger: typing.Optional[bool] = False, mlflow_logger_kwargs: typing.Optional[nemo.utils.loggers.mlflow_logger.MLFlowParams] = <factory>, create_dllogger_logger: typing.Optional[bool] = False, dllogger_logger_kwargs: typing.Optional[nemo.utils.loggers.dllogger.DLLoggerParams] = <factory>, create_clearml_logger: typing.Optional[bool] = False, clearml_logger_kwargs: typing.Optional[nemo.utils.loggers.clearml_logger.ClearMLParams] = <factory>, create_neptune_logger: typing.Optional[bool] = False, neptune_logger_kwargs: typing.Optional[typing.Dict[typing.Any, typing.Any]] = None, create_checkpoint_callback: typing.Optional[bool] = True, checkpoint_callback_params: typing.Optional[nemo.utils.exp_manager.CallbackParams] = <factory>, create_early_stopping_callback: typing.Optional[bool] = False, early_stopping_callback_params: typing.Optional[nemo.utils.exp_manager.EarlyStoppingParams] = <factory>, create_preemption_callback: typing.Optional[bool] = True, files_to_copy: typing.Optional[typing.List[str]] = None, log_step_timing: typing.Optional[bool] = True, step_timing_kwargs: typing.Optional[nemo.utils.exp_manager.StepTimingParams] = <factory>, log_local_rank_0_only: typing.Optional[bool] = False, log_global_rank_0_only: typing.Optional[bool] = False, disable_validation_on_resume: typing.Optional[bool] = True, ema: typing.Optional[nemo.utils.exp_manager.EMAParams] = <factory>, max_time_per_run: typing.Optional[str] = None, seconds_to_sleep: float = 5)
Bases: object
Experiment Manager config for validation of passed arguments.