(Prototype) Tracker
TorchX Tracker is a new prototype library that provides a flexible and customizable experiment and artifact tracking interface. This allows you to track inputs and outputs for jobs across multiple steps to make it easier to use TorchX with pipelines and other external systems.
from torchx import tracker

app_run = tracker.app_run_from_env()
app_run.add_metadata(lr=lr, gamma=gamma) # hyper parameters
app_run.add_artifact("model", "storage://path/mnist_cnn.pt") # logs / checkpoints
app_run.add_source(parent_run_id, "model") # lineage

Example:

https://github.com/pytorch/torchx/tree/main/torchx/examples/apps/tracker
https://pytorch.org/torchx/main/tracker.html

(Prototype) Elastic Training and Autoscaling