{
  "buckets": {
    "Run basics": {
      "description": "High level run configuration and local persistence behavior: run variables, data type, run device, and testing flags.",
      "settings": {
        "testing_dendrite_capacity": {
          "description": "testing_dendrite_capacity toggles to just add 3 over the first few epochs to test memory capacity and confirm settings don't have errors which will crash the program.",
          "label": "supporting"
        },
        "save_name": {
          "description": "save_name defines the run folder under the current working directory where saved models, configuration files, and output graphs and csvs are stored.",
          "label": "supporting"
        },
        "output_dimensions": {
          "description": "output_dimensions defines the expected output tensor shape for perforated modules and affects dendrite routing assumptions.  The setting here should match the shape of the most frequent output of perforated modules.  When the output shape varies, individual modules will need to be set either one by one in the UI by opening Overrides on a perforated module, or with calls to set_this_output_dimensions programmatically after perforate_model in your training script.",
          "label": "supporting"
        },
        "device": {
          "description": "device controls where tensors and modules are expected to run, such as cpu or cuda.",
          "label": "supporting"
        },
        "use_cuda": {
          "description": "use_cuda indicates whether CUDA is available and selected for this runtime.",
          "label": "supporting"
        },
        "d_type": {
          "description": "d_type defines the numeric dtype used for perforated weights and related tensors.  Should be set to a valid PyTorch dtype such as torch.float32 or torch.float64.",
          "label": "supporting"
        }
      }
    },
    "Switch Strategy": {
      "description": "These settings control when the training loop decides to add or switch dendrite structure.  On the backend an epoch is defined by a call to add_validation_score.",
      "settings": {
        "switch_mode": {
          "description": "switch_mode chooses which switching policy is active during training. DOING_HISTORY switches when validation has not improved over a patience window, DOING_FIXED_SWITCH switches on a fixed epoch interval, DOING_SWITCH_EVERY_TIME switches every epoch (implementation debugging), and DOING_NO_SWITCH never adds dendrites.",
          "label": "impactful"
        },
        "n_epochs_to_switch": {
          "description": "In history-based mode, this value acts like a patience window before triggering a switch consideration.",
          "label": "impactful"
        },
        "history_lookback": {
          "description": "This defines the averaging window used by history-based switching logic.  When set to 1, only the most recent epoch is considered for switching decisions, otherwise a running average over the specified number of epochs is used.",
          "label": "impactful"
        },
        "fixed_switch_num": {
          "description": "In fixed-switch mode, this sets the interval in epochs between switches.",
          "label": "impactful"
        },
        "first_fixed_switch_num": {
          "description": "Delay threshold for the first fixed switch before regular fixed intervals apply. This number is ignored unless it is > fixed_switch_num, its primary use is to ensure the pre-dendrite model has sufficient training before the first structural change.",
          "label": "impactful"
        },
        "initial_history_after_switches": {
          "description": "Amount of epochs to run after adding dendrites before history checks resume.  After dendrites are added there is often an initial drop in scores as the model adjusts, this setting ensures that history-based switching logic does not react prematurely to a score not being better quickly.",
          "label": "supporting"
        }
      }
    },
    "Dendrite acceptance": {
      "description": "These settings govern score thresholds, retries, and search behavior after structural updates.",
      "settings": {
        "maximizing_score": {
          "description": "Whether higher validation scores are better or lower values are better.  I.e. True for accuracy or other metrics where higher is better, False for loss or other metrics where lower is better",
          "label": "supporting"
        },
        "improvement_threshold": {
          "description": "Relative improvement thresholds used by tracker acceptance logic.  For a new best score to be defined it must beat the previous best score by at least this much over total number of epochs set by the history settings.  E.g. if this is set to 0.01 and the history window is 10 epochs, the new score must be at least 1% better than the previous best over those 10 epochs.  Consider your metric when choosing this, loss vs accuracy should have different settings.",
          "label": "impactful"
        },
        "improvement_threshold_raw": {
          "description": "Absolute improvement floor to avoid treating tiny metric deltas as meaningful.",
          "label": "impactful"
        },
        "find_best_lr": {
          "description": "Enables learning-rate search behavior after structural changes.  This can only be used when calling setup_optimizer and not set_optimizer_instance.  When True it will iterate over a range of learning rates to find the most effective one based on what learning rates were seen at the last neuron training cycle.  This should only be used with step based schedulers or else it will add large timing addition to the run as it iterates over every candidate learning rate.",
          "label": "impactful"
        },
        "dont_give_up_unless_learning_rate_lowered": {
          "description": "Requires at least one LR-lowered path before ending search attempts.  This is a defensive setting to ensure that the history switch does not get triggered before the learning rate is stepped at least once.  Should not be set to True without a scheduler",
          "label": "impactful"
        },
        "max_dendrite_tries": {
          "description": "Retry cap for candidate dendrite initializations before concluding no improvement.  If this is >= 2 then when an added set of dendrites does not improve validation scores it will be deleted, and a new dendrite cycle will begin with a different random initialization.",
          "label": "impactful"
        },
        "max_dendrites": {
          "description": "Hard upper bound on dendrites added.",
          "label": "impactful"
        },
        "candidate_weight_initialization_multiplier": {
          "description": "Multiplier controlling random candidate weight initialization magnitude.  Can be adjusted if dendrites have too high or too small of an impact when added to network.",
          "label": "supporting"
        },
        "candidate_weight_init_by_main": {
          "description": "Scales initialization weights by magnitude of parent weights during candidate initialization.",
          "label": "supporting"
        },
        "retain_all_dendrites": {
          "description": "Keep dendrites even when they do not improve score.  Useful for debugging to view all calculated metrics over multiple cycles of dendrite addition even when the main validation metric is not improving.",
          "label": "supporting"
        }
      }
    },
    "Dendrite Settings": {
      "description": "Settings for the dendrites themselves.",
      "settings": {
        "pai_forward_function": {
          "description": "Activation function used by dendrites where applicable. Options are relu, tanh, and sigmoid.",
          "label": "impactful"
        }
      }
    },
    "Output & logging": {
      "description": "These settings control console verbosity, graph/csv/model artifact output, and debugging flags.",
      "settings": {
        "verbose": {
          "description": "verbose enables broader informational logging across the pipeline.",
          "label": "supporting"
        },
        "extra_verbose": {
          "description": "extra_verbose expands on verbose with additional low-level details.",
          "label": "supporting"
        },
        "silent": {
          "description": "silent suppresses most normal informational output.",
          "label": "supporting"
        },
        "confirm_correct_sizes": {
          "description": "Enables additional dimension checks for tensor shape assumptions.",
          "label": "supporting"
        },
        "debugging_output_dimensions": {
          "description": "When set to True, all output dimension problems can be printed at once instead of running the program multiple times to find each one.  Useful for identifying which output dimension setting should be used as the main one and which should be set individually.",
          "label": "supporting"
        },
        "drawing_pai": {
          "description": "Toggles standard graph generation associated with PAI training progress.",
          "label": "supporting"
        },
        "drawing_extra_graphs": {
          "description": "Enables additional graph outputs beyond the core set.",
          "label": "supporting"
        },
        "save_old_graph_scores": {
          "description": "Keep historical graph score artifacts when writing outputs to see what actually happened during the run before early stopping triggers an earlier model to be loaded before adding dendrites.",
          "label": "supporting"
        },
        "library_validation_score": {
          "description": "When using a library, such as huggingface transformers, and add_validation_score is integrated into that library rather than your code this string setting specifies which metric of the library you are optimizing.  This only works if perforated has been integrated into the library so be sure to check our repository of integrated libraries.",
          "label": "supporting"
        },
        "library_extra_scores": {
          "description": "When using a library, such as huggingface transformers, and add_extra_score is integrated into that library rather than your code this string setting specifies which metric of the library you are optimizing.  This only works if perforated has been integrated into the library so be sure to check our repository of integrated libraries.",
          "label": "supporting"
        },
        "library_extra_scores_without_graphing": {
          "description": "When using a library, such as huggingface transformers, and library_extra_score_without_graphing is integrated into that library rather than your code this string setting specifies which metric of the library you are optimizing.  This only works if perforated has been integrated into the library so be sure to check our repository of integrated libraries..",
          "label": "supporting"
        }
      }
    },
    "Saving & loading": {
      "description": "Settings related to saving, loading, and checkpoint restore behavior.",
      "settings": {
        "checked_skipped_modules": {
          "description": "Can be set to True to shut off the warning that you have not perforated or tracked all parameters.  Reccomended to not use this setting.",
          "label": "supporting"
        },
        "using_safe_tensors": {
          "description": "Use safe tensors format for serialization where applicable.  Can be swapped if there are saving issues with your model and the perforated tools reccomend trying this approach.",
          "label": "supporting"
        },
        "strict_loading": {
          "description": "Use strict state-dict loading behavior for checkpoint restore.  Must be used when loaded model and current model have different architectures.  Useful when changing network heads, or other significant modifications to the model structure from the loaded state-dict.",
          "label": "supporting"
        },
        "test_saves": {
          "description": "Enable intermediary test saves for debugging and experimentation.  Setting to False will save on memory usage.",
          "label": "supporting"
        },
        "pai_saves": {
          "description": "Enable PAI-specific save behavior for streamlined artifacts.  These models have removed dendritic scaffolding for optimized inference model footprint.  Useful when building a deployment model, not needed when running initial experiments.",
          "label": "supporting"
        }
      }
    },
    "Dashboard": {
      "description": "These settings configure event emission and dashboard integration endpoints.",
      "settings": {
        "dashboard_events_enabled": {
          "description": "Toggles emission of dashboard events during run lifecycle and updates.",
          "label": "supporting"
        },
        "dashboard_url": {
          "description": "Dashboard endpoint used when event emission is enabled.",
          "label": "supporting"
        },
        "dashboard_debug": {
          "description": "Enables additional debugging output for dashboard event emission paths.",
          "label": "supporting"
        }
      }
    },
    "Perforated Backprop": {
      "description": "Perforated Backprop settings provided by the external perforatedbp package when available. This adds additional tools, settings, and optimizations for Perforated Backpropagation. Contact Perforated for access and guidance.",
      "settings": {
        "perforated_backpropagation": {
          "description": "This flag enables integration with perforated backpropagation when the external package is available.",
          "label": "supporting"
        }
      }
    }
  }
}