Logging¶

NetsPresso Trainer provides training results in a variety of multiple formats. As a following example, users can determine most of output formats through boolean flags, and can adjust the intervals of evaluations and checkpoint saves with a simple configuration.

logging:
  project_id: ~
  output_dir: ./outputs
  tensorboard: true
  csv: true
  image: true
  stdout: true
  save_optimizer_state: true
  validation_epoch: &validation_epoch 5
  save_checkpoint_epoch: *validation_epoch  # Multiplier of `validation_epoch`.

Tensorboard¶

We provide basic tensorboard to track your training status. Run the tensorboard with the following command:

tensorboard --logdir ./outputs --port 50001 --bind_all

Note that the default directory of saving result will be ./outputs directory. The port number 50001 is same with the port forwarded in example docker setup. You can change with any port number available in your environment.

Field list¶

Field	Description
`logging.project_id`	(str) Project name to save the experiment. If `None`, it is set as `{task}_{model}` (e.g. `segmentation_segformer`).
`logging.output_dir`	(str) Root directory for saving the experiment. Default location is `./outputs`.
`logging.tensorboard`	(bool) Whether to use the tensorboard.
`logging.csv`	(bool) Whether to save the result in csv format.
`logging.image`	(bool) Whether to save the validation results. It is ignored if the task is `classification`.
`logging.stdout`	(bool) Whether to log the standard output.
`logging.save_optimizer_state`	(bool) Whether to save optimizer state with model checkpoint to resume training.
`logging.validation_epoch`	(int) Validation frequency in total training process.
`logging.save_checkpoint_epoch`	(int) Checkpoint saving frequency in total training process.