Glossary

The following glossary items assume you import the SMDebug framework modules as follows:

  • TensorFlow

    import smdebug.tensorflow as smd

  • PyTorch

    import smdebug.pytorch as smd

  • MXNet

    import smdebug.mxnet as smd

  • XGBoost

    import smdebug.xgboost as smd

Terminologies

  • Step: Step means the work done for one batch by a training job (i.e. forward and backward pass). (An exception is with TensorFlow’s Session interface, where a step also includes the initialization session run calls). SageMaker Debugger is designed in terms of steps. When to save data is specified using steps. Also, invocation of Rules is on a step-by-step basis.

  • Hook: The main class to pass as a callback object or to create callback functions. It keeps track of collections and writes output files at each step. The current hook implementation does not support merging tensors from current job with tensors from previous job(s). Therefore, ensure that the out_dir does not exist prior to instantiating the ‘Hook’ object. - hook = smd.Hook(out_dir="/tmp/mnist_job")

  • Mode: One of “train”, “eval”, “predict”, or “global”. Helpful for segmenting data based on the phase you’re in. Defaults to “global”. - train_mode = smd.modes.TRAIN

  • Collection: A group of tensors. Each collection contains its configuration for what tensors are part of it, and when to save them. - collection = hook.get_collection("losses")

  • SaveConfig: A Python dict specifying how often to save losses and tensors. - save_config = smd.SaveConfig(save_interval=10)

  • ReductionConfig: Allows you to save a reduction, such as ‘mean’ or ‘l1 norm’, instead of the full tensor. Reductions are simple floats. - reduction_config = smd.ReductionConfig(reductions=['min', 'max', 'mean'], norms=['l1'])

  • Trial: The main interface to use when analyzing a completed training job. Access collections and tensors. See trials documentation. - trial = smd.create_trial(out_dir="/tmp/mnist_job")

  • Rule: A condition to monitor the saved data for. It can trigger an exception when the condition is met, for example a vanishing gradient. See rules documentation.