Make style

examples/summarization/run_summarization.py

@@ -28,7 +28,6 @@
 import datasets
 import evaluate
 import nltk  # Here to have a nice missing dependency error message early on
-nltk.download('punkt_tab')  # Needed for ver 3.8.2
 import numpy as np
 import torch
 import transformers
@@ -81,6 +80,8 @@ def check_optimum_habana_min_version(*a, **b):
     with FileLock(".lock") as lock:
         nltk.download("punkt", quiet=True)
 
+nltk.download("punkt_tab")  # Needed for version 3.8.2
+
 # A list of all multilingual tokenizer which require lang attribute.
 MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast]
 

notebooks/AI_HW_Summit_2022.ipynb

@@ -214,6 +214,7 @@
    "source": [
     "from optimum.habana.distributed import DistributedRunner\n",
     "\n",
+    "\n",
     "# Build the command to execute\n",
     "training_args_command_line = \" \".join(f\"--{key} {value}\" for key, value in training_args.items())\n",
     "command = f\"../examples/language-modeling/run_clm.py {training_args_command_line}\"\n",
@@ -327,15 +328,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# The sequence to complete\n",
-    "prompt_text = \"This live AI webinar is organized by Habana Labs and Hugging Face and\"\n",
-    "\n",
     "import torch\n",
-    "import habana_frameworks.torch.hpu\n",
-    "\n",
     "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
     "\n",
     "\n",
+    "# The sequence to complete\n",
+    "prompt_text = \"This live AI webinar is organized by Habana Labs and Hugging Face and\"\n",
+    "\n",
     "path_to_model = training_args[\"output_dir\"]  # the folder where everything related to our run was saved\n",
     "\n",
     "device = torch.device(\"hpu\")\n",
@@ -397,6 +396,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
+    "\n",
     "gaudi_price_per_hour = 13.10904\n",
     "v100_price_per_hour = 12.24\n",
     "\n",