wandb.save, then demonstrates how they can be re-created locally with
wandb.saveaccepts a policy argument which is set to "live" by default. Available policies are:
wandb.save. This would allow you to maintain a directory hierarchy, for example:
evalfolder instead of at the root.
model.h5is saved into the
wandb.run.dirand will be uploaded at the end of training.
model-best.h5. That's automatically saved by default by the Keras integration, but you can save a checkpoint manually and we'll store it for you in association with your run.
wandb.restore(filename)will restore a file into your local run directory. Typically
filenamerefers to a file generated by an earlier experiment run and uploaded to our cloud with
wandb.save. This call will make a local copy of the file and return a local file stream open for reading.
wandb.save('latest.pth')in your script to upload those files whenever they are written or updated.
events.out.tfevents.1581193870.gpt-tpu-finetune-8jzqk-2033426287 is a cloud storage url, can't save file to wandb.
wandb.run.dirso they're synced to our cloud.
wandb.run.nameand you'll get the run name— "blissful-waterfall-2" for example.
wandb.save("*.pt")once at the top of your script after
wandb.init, then all files that match that pattern will save immediately once they're written to
wandb sync --cleanthat you can run to remove local files that have already been synced to cloud storage. More information about usage can be found with
wandb sync --help
wandbcapture the state of the code?
wandb.initis called from your script, a link is saved to the last git commit if the code is in a git repository. A diff patch is also created in case there are uncommitted changes or changes that are out of sync with your remote.