Try smaller batch size to reduce CPU memory pressure on CI

Author: Lothiraldan

integrations/model-training/ray-train/notebooks/Comet_with_ray_train_huggingface_transformers.ipynb

@@ -180,7 +180,6 @@
     "        metric = evaluate.load(\"accuracy\")\n",
     "\n",
     "        def compute_metrics(eval_pred):\n",
-    "            print(\"COMPUTE METRICS CALLED!\")\n",
     "            logits, labels = eval_pred\n",
     "            predictions = np.argmax(logits, axis=-1)\n",
     "\n",
@@ -192,14 +191,16 @@
     "\n",
     "        # Hugging Face Trainer\n",
     "        training_args = TrainingArguments(\n",
-    "            seed=SEED,\n",
+    "            do_eval=True,\n",
+    "            do_train=True,\n",
+    "            eval_strategy=\"epoch\",\n",
+    "            num_train_epochs=config[\"epochs\"],\n",
     "            output_dir=\"./results\",\n",
     "            overwrite_output_dir=True,\n",
-    "            num_train_epochs=config[\"epochs\"],\n",
-    "            eval_strategy=\"epoch\",\n",
-    "            do_train=True,\n",
-    "            do_eval=True,\n",
+    "            per_device_eval_batch_size=4,\n",
+    "            per_device_train_batch_size=4,\n",
     "            report_to=[\"comet_ml\"],\n",
+    "            seed=SEED,\n",
     "        )\n",
     "        trainer = Trainer(\n",
     "            model=model,\n",