Hey Krishna! I work for Weights & Biases and the problem you’re trying to solve with experiment tracking seems like the perfect use-case for the product. Basically, you pass one line of code wandb.watch(model, log_freq=100) (wandb is the name of the Python client) and all your training metrics/test metrics, as well, as CPU/GPU usage all get pulled into a single dashboard where you can compare them side-by-side with interactive charts. Beyond that, W&B also tracks your different models’ hyperparameters, so you don’t have to worry about keeping track of them also. Basically, it’s just a lot more convenient than using matplotlib to track training metrics.
Here are the W&B PyTorch docs you can use to get started and do let me know if I you have any questions.