From cfc426e02c96ea944c2863cd38f8ef6be96d1322 Mon Sep 17 00:00:00 2001
From: Libin Tang <litang@habana.ai>
Date: Fri, 28 Jul 2023 12:00:11 -0700
Subject: [PATCH] revert format of AI_HW_Summit_2022.ipynb

---
 notebooks/AI_HW_Summit_2022.ipynb | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/notebooks/AI_HW_Summit_2022.ipynb b/notebooks/AI_HW_Summit_2022.ipynb
index 915f6f3cf9..912970e162 100644
--- a/notebooks/AI_HW_Summit_2022.ipynb
+++ b/notebooks/AI_HW_Summit_2022.ipynb
@@ -178,10 +178,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "training_args[\"use_habana\"] = True  # Whether to use HPUs or not\n",
-    "training_args[\"use_lazy_mode\"] = True  # Whether to use lazy or eager mode\n",
+    "training_args[\"use_habana\"] = True                  # Whether to use HPUs or not\n",
+    "training_args[\"use_lazy_mode\"] = True               # Whether to use lazy or eager mode\n",
     "training_args[\"gaudi_config_name\"] = \"Habana/gpt2\"  # Gaudi configuration to use\n",
-    "training_args[\"throughput_warmup_steps\"] = 3  # Remove the first N training iterations from throughput computation"
+    "training_args[\"throughput_warmup_steps\"] = 3        # Remove the first N training iterations from throughput computation"
    ]
   },
   {
@@ -306,8 +306,8 @@
     "# Instantiate a distributed runner\n",
     "distributed_runner = DistributedRunner(\n",
     "    command_list=[command],  # The command(s) to execute\n",
-    "    world_size=8,  # The number of HPUs\n",
-    "    use_deepspeed=True,  # Enable DeepSpeed\n",
+    "    world_size=8,            # The number of HPUs\n",
+    "    use_deepspeed=True,      # Enable DeepSpeed\n",
     ")\n",
     "\n",
     "# Launch training\n",
@@ -377,7 +377,9 @@
     "    text = text[: text.find(\".\")]\n",
     "\n",
     "    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing\n",
-    "    total_sequence = prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]\n",
+    "    total_sequence = (\n",
+    "        prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]\n",
+    "    )\n",
     "\n",
     "    generated_sequences.append(total_sequence)\n",
     "    print(total_sequence)"
@@ -403,12 +405,8 @@
     "gaudi_price_per_hour = 13.10904\n",
     "v100_price_per_hour = 12.24\n",
     "\n",
-    "print(\n",
-    "    f\"Gaudi    (dl1.24xlarge): training time = 630s, cost = {np.round(630 * gaudi_price_per_hour / 3600, 2)}$ ({gaudi_price_per_hour}$/hr)\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"4 x V100 (p3.8xlarge)  : training time = 858s, cost = {np.round(858 * v100_price_per_hour / 3600, 2)}$ ({v100_price_per_hour}$/hr)\"\n",
-    ")"
+    "print(f\"Gaudi    (dl1.24xlarge): training time = 630s, cost = {np.round(630 * gaudi_price_per_hour / 3600, 2)}$ ({gaudi_price_per_hour}$/hr)\")\n",
+    "print(f\"4 x V100 (p3.8xlarge)  : training time = 858s, cost = {np.round(858 * v100_price_per_hour / 3600, 2)}$ ({v100_price_per_hour}$/hr)\")"
    ]
   },
   {