Logging and Viewing Training Results

In this section, we describe how to configure the logging and evaluations that occur during your training run, and how to view the results of a training run.

Configuring Logging

Saving Experiment Logs

Configured under experiment.logging:

"logging": {
    # save terminal outputs under `logs/log.txt` in experiment folder
    "terminal_output_to_txt": true,
    
    # save tensorboard logs under `logs/tb` in experiment folder
    "log_tb": true

    # save wandb logs under `logs/wandb` in experiment folder
    "log_wandb": true
},

Saving Model Checkpoints

Configured under experiment.save:

"save": {
    # enable saving model checkpoints
    "enabled": true,
    
    # controlling frequency of checkpoints
    "every_n_seconds": null,
    "every_n_epochs": 50,
    "epochs": [],
    
    # saving the best checkpoints
    "on_best_validation": false,
    "on_best_rollout_return": false,
    "on_best_rollout_success_rate": true
},

Evaluating Rollouts and Saving Videos

Evaluating Rollouts

Configured under experiment.rollout:

"rollout": {
    "enabled": true,              # enable evaluation rollouts
    "n": 50,                      # number of rollouts per evaluation
    "horizon": 400,               # number of timesteps per rollout
    "rate": 50,                   # frequency of evaluation (in epochs)
    "terminate_on_success": true  # terminating rollouts upon task success
}

Saving Videos

To save videos of the rollouts, set experiment.render_video to true.

Viewing Training Results

Contents of Training Outputs

After the script finishes, you can check the training outputs in the <train.output_dir>/<experiment.name>/<date> experiment directory:

config.json               # config used for this experiment
logs/                     # experiment log files
  log.txt                    # terminal output
  tb/                        # tensorboard logs
  wandb/                     # wandb logs
videos/                   # videos of robot rollouts during training
models/                   # saved model checkpoints

Loading Trained Checkpoints

Please see the Using Pretrained Models tutorial to see how to load the trained model checkpoints in the models directory.

Viewing Tensorboard Results

The experiment results can be viewed using tensorboard:

$ tensorboard --logdir <experiment-log-dir> --bind_all

Below is a snapshot of the tensorboard dashboard:

Experiment results (y-axis) are logged across epochs (x-axis). You may find the following logging metrics useful:

Rollout/: evaluation rollout metrics, eg. success rate, rewards, etc.
- Rollout/Success_Rate/{envname}-max: maximum success rate over time (this is the metric the study paper uses to evaluate baselines)
Timing_Stats/: time spent by the algorithm loading data, training, performing rollouts, etc.
Timing_Stats/: time spent by the algorithm loading data, training, performing rollouts, etc.
Train/: training stats
Validation/: validation stats
System/RAM Usage (MB): system RAM used by algorithm

Viewing wandb Results

You can also view results in wandb, similarly to tensorboard. To do so, ensure that you have set experiment.logging.log_wandb to True in the experiment config.

When first logging to wandb, you will need to specify a wandb entity name, ie. the wandb account under which results will be logged. You can do so by setting WANDB_ENTITY to the desired wandb account name in robomimic/macros_private.py. Note: if this file does not exist, run python robomimic/scripts/setup_macros.py to setup the private macros file.

By default all results will be logged under a wandb project labled default, however you can set the project name by setting experiment.logging.wandb_proj_name in the configs.