Of course! So all of torch.utils.trainer was an undocumented and since-removed portion of PyTorch.  As such, Monitor and LossMonitor are in the same category as Logger, to the best of my knowledge.  A lot of people have worked on tools to monitor training in PyTorch:

there are ways to hook up Tensorboard to PyTorch 1

or use TensorboardX

the aforementioned fast.ai metrics and callbacks (which I only mention again because it is what I use, but not necessarily best)
a Facebook tool called Visdom

and even commercial products like Weights and Biases (which seems pretty cool, I’ll have to check it out!)

There are probably a ton more options out there, and it’ll depend on what your use case is, how in-depth you need to go, etc.
As for checkpointing, I’m not sure if you’re talking about torch.utils.checkpoint, a tool that has existed (and still exists!) in PyTorch to trade more computing time for less memory usage, or simply saving the model, which is commonly called checkpointing both in other systems (like Tensorflow) and in codebases (like my own…).
If you’re talking about the former, I’ve never used it but a quick glance makes it look like checkpointing is mostly the same since 0.4.0.
If you’re talking about the latter, that is done via torch.save, which “checkpoints” your model by saving the model weights to a pickled file.
Hope that helps!