diff --git a/docs/autogen.py b/docs/autogen.py
index d736a12..6775941 100644
--- a/docs/autogen.py
+++ b/docs/autogen.py
@@ -10,6 +10,7 @@
 import pyshac
 import pyshac.config.hyperparameters as hp
 import pyshac.config.data as data
+import pyshac.config.callbacks as callbacks
 import pyshac.core.engine as optimizer
 import pyshac.core.managed.tf_engine as tf_optimizer
 import pyshac.core.managed.keras_engine as keras_optimizer
@@ -65,6 +66,17 @@
             (data.Dataset, '*'),
         ],
     },
+    {
+        'page': 'config/callbacks.md',
+        'classes': [
+            (callbacks.Callback, '*'),
+            (callbacks.History, []),
+            (callbacks.CSVLogger, []),
+        ],
+        'functions': [
+            callbacks.get_history,
+        ]
+    },
     {
         'page': 'core/engine.md',
         'classes': [
diff --git a/mkdocs.yml b/mkdocs.yml
index 46da791..1fad850 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -41,6 +41,7 @@ pages:
 - Config:
   - Hyper Parameters: config/hyperparameters.md
   - Datasets: config/data.md
+  - Callbacks: config/callbacks.md
 - Core:
   - SHAC: core/engine.md
   - PyTorch SHAC: core/torch_engine.md
diff --git a/pyshac/__init__.py b/pyshac/__init__.py
index 12ae529..83e139d 100644
--- a/pyshac/__init__.py
+++ b/pyshac/__init__.py
@@ -5,4 +5,4 @@
 from pyshac.config.data import Dataset
 from pyshac.core.engine import SHAC
 
-__version__ = '0.3.0.5'
+__version__ = '0.3.1'
diff --git a/pyshac/config/callbacks.py b/pyshac/config/callbacks.py
index 7d8a438..f571a76 100644
--- a/pyshac/config/callbacks.py
+++ b/pyshac/config/callbacks.py
@@ -61,24 +61,78 @@ def set_engine(self, engine):
         self.engine = engine
 
     def on_train_begin(self, logs=None):
+        """
+        Called at the beginning of training.
+
+        # Arguments
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
     def on_train_end(self, logs=None):
+        """
+        Called at the end of training.
+
+        # Arguments
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
     def on_epoch_begin(self, epoch, logs=None):
+        """
+        Called at the start of an epoch.
+
+        # Arguments
+            epoch (int): index of epoch.
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
     def on_epoch_end(self, epoch, logs=None):
+        """
+        Called at the end of an epoch.
+
+        # Arguments
+            epoch (int): index of epoch.
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
     def on_evaluation_begin(self, params, logs=None):
+        """
+        Called before the generated parameters are evaluated.
+
+        # Arguments:
+            params (list(OrderedDict)): A list of OrderedDicts,
+                such that each item is a dictionary of the names
+                and sampled values of a HyperParemeterList.
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
     def on_evaluation_ended(self, evaluations, logs=None):
+        """
+        Called after the generated parameters are evaluated.
+
+        # Arguments:
+            evaluations (list(float)): A list of floating point
+                values, corresponding to the provided parameter
+                settings.
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
     def on_dataset_changed(self, dataset, logs=None):
+        """
+        Called with the dataset maintained by the engine is
+        updated with new samples or data.
+
+        # Arguments:
+            dataset (Dataset): A Dataset object which contains
+                the history of sampled parameters and their
+                corresponding evaluation values.
+            logs (dict | None): dictionary of logs.
+        """
         pass
 
 
@@ -229,10 +283,23 @@ def __init__(self):
         super(History, self).__init__()
 
     def on_train_begin(self, logs=None):
+        """
+        Initializes the epoch list and history dictionary.
+
+        # Arguments:
+            logs (dict | None): dictionary of logs.
+        """
         self.epochs = []
         self.history = logs or {}
 
     def on_epoch_end(self, epoch, logs=None):
+        """
+        Adds the current epoch's log values to the history.
+
+        # Arguments:
+            epoch (int): index of epoch.
+            logs (dict | None): dictionary of logs.
+        """
         logs = logs or {}
         self.epochs.append(epoch)
 
diff --git a/pyshac/core/engine.py b/pyshac/core/engine.py
index b4cf3d6..4425f75 100644
--- a/pyshac/core/engine.py
+++ b/pyshac/core/engine.py
@@ -163,6 +163,11 @@ def fit(self, eval_fn, skip_cv_checks=False, early_stop=False, relax_checks=Fals
             callbacks (list | None): Optional list of callbacks that are executed when
                 the engine is being trained. `History` callback is automatically added
                 for all calls to `fit`.
+
+        # Returns:
+            A `History` object which tracks all the important information
+            during training, and can be accessed using `history.history`
+            as a dictionary.
         """
         num_epochs = self.total_budget // self.num_workers
 
@@ -361,6 +366,11 @@ def fit_dataset(self, dataset_path, skip_cv_checks=False, early_stop=False, pres
                 required number of samples by the engine.
             FileNotFoundError: If the dataset is not available at the
                 provided filepath.
+
+        # Returns:
+            A `History` object which tracks all the important information
+            during training, and can be accessed using `history.history`
+            as a dictionary.
         """
         if self.parameters is None:
             raise ValueError("Parameter list cannot be `None` when training "
@@ -1149,6 +1159,11 @@ def fit(self, eval_fn, skip_cv_checks=False, early_stop=False, relax_checks=Fals
             callbacks (list | None): Optional list of callbacks that are executed when
                 the engine is being trained. `History` callback is automatically added
                 for all calls to `fit`.
+
+        # Returns:
+            A `History` object which tracks all the important information
+            during training, and can be accessed using `history.history`
+            as a dictionary.
         """
         return super(SHAC, self).fit(eval_fn, skip_cv_checks=skip_cv_checks,
                                      early_stop=early_stop, relax_checks=relax_checks,
diff --git a/pyshac/core/managed/keras_engine.py b/pyshac/core/managed/keras_engine.py
index 264064d..bb47b40 100644
--- a/pyshac/core/managed/keras_engine.py
+++ b/pyshac/core/managed/keras_engine.py
@@ -175,6 +175,11 @@ def fit(self, eval_fn, skip_cv_checks=False, early_stop=False, relax_checks=Fals
             callbacks (list | None): Optional list of callbacks that are executed when
                 the engine is being trained. `History` callback is automatically added
                 for all calls to `fit`.
+
+        # Returns:
+            A `History` object which tracks all the important information
+            during training, and can be accessed using `history.history`
+            as a dictionary.
         """
         return super(KerasSHAC, self).fit(eval_fn, skip_cv_checks=skip_cv_checks,
                                           early_stop=early_stop, relax_checks=relax_checks,
diff --git a/pyshac/core/managed/tf_engine.py b/pyshac/core/managed/tf_engine.py
index 41ad1ce..b5a6d1c 100644
--- a/pyshac/core/managed/tf_engine.py
+++ b/pyshac/core/managed/tf_engine.py
@@ -156,6 +156,11 @@ def fit(self, eval_fn, skip_cv_checks=False, early_stop=False, relax_checks=Fals
             callbacks (list | None): Optional list of callbacks that are executed when
                 the engine is being trained. `History` callback is automatically added
                 for all calls to `fit`.
+
+        # Returns:
+            A `History` object which tracks all the important information
+            during training, and can be accessed using `history.history`
+            as a dictionary.
         """
         return super(TensorflowSHAC, self).fit(eval_fn, skip_cv_checks=skip_cv_checks,
                                                early_stop=early_stop, relax_checks=relax_checks,
diff --git a/pyshac/core/managed/torch_engine.py b/pyshac/core/managed/torch_engine.py
index 64dc94f..a32510c 100644
--- a/pyshac/core/managed/torch_engine.py
+++ b/pyshac/core/managed/torch_engine.py
@@ -130,6 +130,11 @@ def fit(self, eval_fn, skip_cv_checks=False, early_stop=False, relax_checks=Fals
             callbacks (list | None): Optional list of callbacks that are executed when
                 the engine is being trained. `History` callback is automatically added
                 for all calls to `fit`.
+
+        # Returns:
+            A `History` object which tracks all the important information
+            during training, and can be accessed using `history.history`
+            as a dictionary.
         """
         return super(TorchSHAC, self).fit(eval_fn, skip_cv_checks=skip_cv_checks,
                                           early_stop=early_stop, relax_checks=relax_checks,