change variables

openfl-tutorials/experimental/Phi3/Workflow_Interface_Phi3.ipynb

@@ -307,8 +307,8 @@
     "    test_size=val_set_size, shuffle=True, seed=42\n",
     ")\n",
     "\n",
-    "processed_train_dataset = train_val[\"train\"].shuffle().map(generate_and_tokenize_prompt)\n",
-    "processed_test_dataset = train_val[\"test\"].shuffle().map(generate_and_tokenize_prompt)\n"
+    "processed_train_dataset = train_val[\"train\"].shuffle().map(generate_and_tokenize_prompt).select(range(3))\n",
+    "processed_test_dataset = train_val[\"test\"].shuffle().map(generate_and_tokenize_prompt).select(range(3))\n"
    ]
   },
   {
@@ -482,6 +482,7 @@
     "        print(f\"Average local model validation values = {self.local_model_accuracy}\")\n",
     "\n",
     "        self.model = FedAvg([input.peft_params for input in inputs], self.model)\n",
+    "        self.peft_params = get_peft_model_state_dict(self.model)\n",
     "\n",
     "        self.model.save_pretrained(\"./aggregated/model\")\n",
     "        tokenizer.save_pretrained(\"./aggregated/tokenizer\")\n",
@@ -490,7 +491,7 @@
     "            self.next(\n",
     "                self.aggregated_model_validation,\n",
     "                foreach=\"collaborators\",\n",
-    "                exclude=[\"private\"],\n",
+    "                exclude=[\"model\"],\n",
     "            )\n",
     "        else:\n",
     "            self.next(self.end)\n",
@@ -518,29 +519,29 @@
    "outputs": [],
    "source": [
     "# Setup participants\n",
-    "aggregator = Aggregator()\n",
-    "aggregator.private_attributes = {}\n",
+    "_aggregator = Aggregator()\n",
+    "_aggregator.private_attributes = {}\n",
     "\n",
     "# Setup collaborators with private attributes\n",
     "collaborator_names = [\n",
     "    \"Portland\",\n",
     "    \"Seattle\",\n",
     "]\n",
-    "collaborators = [Collaborator(name=name) for name in collaborator_names]\n",
+    "_collaborators = [Collaborator(name=name) for name in collaborator_names]\n",
     "\n",
-    "for idx, current_collaborator in enumerate(collaborators):\n",
+    "for idx, current_collaborator in enumerate(_collaborators):\n",
     "    # Set the private attributes of the Collaborator to include their specific training and testing data loaders\n",
     "    current_collaborator.private_attributes = {\n",
     "        \"train_dataset\": processed_train_dataset.shard(\n",
-    "            num_shards=len(collaborators), index=idx\n",
+    "            num_shards=len(_collaborators), index=idx\n",
     "        ),\n",
     "        \"eval_dataset\": processed_test_dataset.shard(\n",
-    "            num_shards=len(collaborators), index=idx\n",
+    "            num_shards=len(_collaborators), index=idx\n",
     "        ),\n",
     "    }\n",
     "\n",
     "local_runtime = LocalRuntime(\n",
-    "    aggregator=aggregator, collaborators=collaborators, backend=\"single_process\"\n",
+    "    aggregator=_aggregator, collaborators=_collaborators, backend=\"single_process\"\n",
     ")\n",
     "print(f\"Local runtime collaborators = {local_runtime.collaborators}\")"
    ]