Add RandomAlphaIncremental

src/decentralizepy/sharing/RandomAlphaIncremental.py

+import random
+
+from decentralizepy.sharing.PartialModel import PartialModel
+
+
+class RandomAlphaIncremental(PartialModel):
+    """
+    This class implements the partial model sharing with a random alpha from an increasing range each iteration.
+
+    """
+
+    def __init__(
+        self,
+        rank,
+        machine_id,
+        communication,
+        mapping,
+        graph,
+        model,
+        dataset,
+        log_dir,
+        dict_ordered=True,
+        save_shared=False,
+        metadata_cap=1.0,
+        range_start=0.1,
+        range_end=0.2
+    ):
+        """
+        Constructor
+
+        Parameters
+        ----------
+        rank : int
+            Local rank
+        machine_id : int
+            Global machine id
+        communication : decentralizepy.communication.Communication
+            Communication module used to send and receive messages
+        mapping : decentralizepy.mappings.Mapping
+            Mapping (rank, machine_id) -> uid
+        graph : decentralizepy.graphs.Graph
+            Graph reprensenting neighbors
+        model : decentralizepy.models.Model
+            Model to train
+        dataset : decentralizepy.datasets.Dataset
+            Dataset for sharing data. Not implemented yet! TODO
+        log_dir : str
+            Location to write shared_params (only writing for 2 procs per machine)
+        dict_ordered : bool
+            Specifies if the python dict maintains the order of insertion
+        save_shared : bool
+            Specifies if the indices of shared parameters should be logged
+        metadata_cap : float
+            Share full model when self.alpha > metadata_cap
+
+        """
+        super().__init__(
+            rank,
+            machine_id,
+            communication,
+            mapping,
+            graph,
+            model,
+            dataset,
+            log_dir,
+            1.0,
+            dict_ordered,
+            save_shared,
+            metadata_cap,
+        )
+        random.seed(
+            self.mapping.get_uid(self.rank, self.machine_id)
+        )
+        self.range_start = range_start
+        self.range_end = range_end
+
+    def step(self):
+        """
+        Perform a sharing step. Implements D-PSGD with alpha randomly chosen from an increasing range.
+
+        """
+        self.alpha = round(random.random(self.range_start, self.range_end), 2)
+        self.range_end = min(1.0, self.range_end+random.random(0.0, 0.1))
+        super().step()