File size: 4,175 Bytes
825cff4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # 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
```
<div class="admonition tip">
<p class="admonition-title">Loading Trained Checkpoints</p>
Please see the [Using Pretrained Models](./using_pretrained_models.html) tutorial to see how to load the trained model checkpoints in the `models` directory.
</div>
### Viewing Tensorboard Results
The experiment results can be viewed using tensorboard:
```sh
$ tensorboard --logdir <experiment-log-dir> --bind_all
```
Below is a snapshot of the tensorboard dashboard:
<a href="../images/tensorboard.png" target="_blank">
<p align="center">
<img width="99.0%" src="../images/tensorboard.png">
</p>
</a>
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](https://arxiv.org/abs/2108.03298) 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](https://wandb.ai), 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.
|