--learning_rate 1e-3 \
--lr_scheduler cosine_with_restarts \
--lr_warmup_steps 200 \
--lr_num_cycles 1 \
--enable_slicing \
--enable_tiling \
--optimizer Adam \
--adam_beta1 0.9 \
--adam_beta2 0.95 \
--max_grad_norm 1.0 \
--report_to wandb
```  
To better track our training experiments, we're using the following flags in the command above:
* `--report_to wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.