Fix bug: Update requirements and notebooks for DL examples

examples/ML+DL-Examples/Spark-DL/dl_inference/huggingface/conditional_generation_tf.ipynb

@@ -116,7 +116,7 @@
    ],
    "source": [
     "tokenizer = AutoTokenizer.from_pretrained(\"google-t5/t5-small\")\n",
-    "model = TFT5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\")\n",
+    "model = TFT5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\", use_safetensors=False)\n",
     "\n",
     "task_prefix = \"translate English to German: \"\n",
     "\n",
@@ -581,7 +581,7 @@
     "        except RuntimeError as e:\n",
     "            print(e)\n",
     "\n",
-    "    model = TFT5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\")\n",
+    "    model = TFT5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\", use_safetensors=False)\n",
     "    tokenizer = AutoTokenizer.from_pretrained(\"google-t5/t5-small\")\n",
     "\n",
     "    def predict(inputs):\n",
@@ -1033,7 +1033,7 @@
     "            print(e)\n",
     "    \n",
     "    tokenizer = AutoTokenizer.from_pretrained(\"google-t5/t5-small\")\n",
-    "    model = TFT5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\")\n",
+    "    model = TFT5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\", use_safetensors=False)\n",
     "\n",
     "    @batch\n",
     "    def _infer_fn(**inputs):\n",

examples/ML+DL-Examples/Spark-DL/dl_inference/huggingface/pipelines_tf.ipynb

@@ -44,7 +44,9 @@
    ],
    "source": [
     "import tensorflow as tf\n",
-    "from transformers import pipeline\n",
+    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline\n",
+    "\n",
+    "HF_DEFAULT_SENTIMENT_MODEL = \"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"\n",
     "\n",
     "# Manually enable Huggingface tokenizer parallelism to avoid disabling with PySpark parallelism.\n",
     "# See (https://github.com/huggingface/transformers/issues/5486) for more info. \n",
@@ -133,7 +135,12 @@
     }
    ],
    "source": [
-    "classifier = pipeline(\"sentiment-analysis\", device=device)"
+    "tokenizer = AutoTokenizer.from_pretrained(HF_DEFAULT_SENTIMENT_MODEL)\n",
+    "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
+    "    HF_DEFAULT_SENTIMENT_MODEL,\n",
+    "    use_safetensors=False,\n",
+    ")\n",
+    "classifier = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer, dtype=None)"
    ]
   },
   {
@@ -216,7 +223,7 @@
    "source": [
     "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
     "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name)\n",
+    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name, use_safetensors=False)\n",
     "tokenizer = AutoTokenizer.from_pretrained(model_name)"
    ]
   },
@@ -238,7 +245,7 @@
     }
    ],
    "source": [
-    "classifier = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer, device=device)\n",
+    "classifier = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer, dtype=None)\n",
     "classifier(\"Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.\")"
    ]
   },
@@ -557,7 +564,7 @@
    "source": [
     "def predict_batch_fn():\n",
     "    import tensorflow as tf\n",
-    "    from transformers import pipeline\n",
+    "    from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline\n",
     "\n",
     "    # Enable GPU memory growth\n",
     "    gpus = tf.config.experimental.list_physical_devices('GPU')\n",
@@ -569,7 +576,10 @@
     "            print(e)\n",
     "    \n",
     "    device = 0 if tf.config.list_physical_devices('GPU') else -1\n",
-    "    pipe = pipeline(\"sentiment-analysis\", device=device)\n",
+    "    model_name = \"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "    model = TFAutoModelForSequenceClassification.from_pretrained(model_name, use_safetensors=False)\n",
+    "    pipe = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer, dtype=None)\n",
     "    def predict(inputs):\n",
     "        return pipe(inputs.tolist())\n",
     "    return predict"
@@ -822,7 +832,7 @@
     "    import signal\n",
     "    import numpy as np\n",
     "    import tensorflow as tf\n",
-    "    from transformers import pipeline\n",
+    "    from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline\n",
     "    from pytriton.decorators import batch\n",
     "    from pytriton.model_config import DynamicBatcher, ModelConfig, Tensor\n",
     "    from pytriton.triton import Triton, TritonConfig\n",
@@ -840,7 +850,10 @@
     "    \n",
     "    device = 0 if tf.config.list_physical_devices('GPU') else -1\n",
     "    \n",
-    "    pipe = pipeline(\"sentiment-analysis\", device=device)\n",
+    "    model_name = \"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "    model = TFAutoModelForSequenceClassification.from_pretrained(model_name, use_safetensors=False)\n",
+    "    pipe = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer, dtype=None)\n",
     "    print(f\"SERVER: Using {device} device.\")\n",
     "\n",
     "    @batch\n",

examples/ML+DL-Examples/Spark-DL/dl_inference/requirements.txt

@@ -1,4 +1,4 @@
-# Copyright (c) 2025, NVIDIA CORPORATION.
+# Copyright (c) 2025-2026, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-numpy
+numpy>=1.26.4,<2
 pandas
 matplotlib
 portalocker
@@ -23,7 +23,8 @@ scikit-learn
 jupyterlab
 pyspark>=3.4.0
 huggingface
-datasets
+datasets==3.*
+huggingface-hub<1.0
 transformers
 ipywidgets
 nvidia-pytriton

examples/ML+DL-Examples/Spark-DL/dl_inference/tensorflow/keras_preprocessing_tf.ipynb

@@ -351,7 +351,12 @@
     }
    ],
    "source": [
-    "train, val, test = np.split(dataframe.sample(frac=1), [int(0.8*len(dataframe)), int(0.9*len(dataframe))])"
+    "shuffled = dataframe.sample(frac=1)\n",
+    "train_end = int(0.8 * len(shuffled))\n",
+    "val_end = int(0.9 * len(shuffled))\n",
+    "train = shuffled.iloc[:train_end]\n",
+    "val = shuffled.iloc[train_end:val_end]\n",
+    "test = shuffled.iloc[val_end:]"
    ]
   },
   {
@@ -394,7 +399,7 @@
     "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
     "    df = dataframe.copy()\n",
     "    labels = df.pop('target')\n",
-    "    df = {key: value.to_numpy()[:,tf.newaxis] for key, value in dataframe.items()}\n",
+    "    df = {key: value.to_numpy()[:,tf.newaxis] for key, value in df.items()}\n",
     "    ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n",
     "    if shuffle:\n",
     "        ds = ds.shuffle(buffer_size=len(dataframe))\n",

examples/ML+DL-Examples/Spark-DL/dl_inference/tensorflow/text_classification_tf.ipynb

@@ -47,6 +47,7 @@
     "import re\n",
     "import shutil\n",
     "import string\n",
+    "import unicodedata\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import tensorflow as tf\n",
@@ -977,6 +978,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "def normalize_vocabulary(vocab):\n",
+    "    normalized_vocab = []\n",
+    "    seen = set()\n",
+    "    for word in vocab:\n",
+    "        word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8')\n",
+    "        if word in (\"\", \"[UNK]\") or word in seen:\n",
+    "            continue\n",
+    "        seen.add(word)\n",
+    "        normalized_vocab.append(word)\n",
+    "    return normalized_vocab\n",
+    "\n",
+    "vectorize_layer.set_vocabulary(normalize_vocabulary(vectorize_layer.get_vocabulary()))\n",
     "export_model.save('models/text_model.keras')"
    ]
   },
@@ -1624,25 +1637,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import unicodedata\n",
-    "\n",
-    "def normalize_vocabulary(vocab):\n",
-    "    # Normalize each word in the vocabulary to remove non-ASCII characters\n",
-    "    normalized_vocab = [\n",
-    "        unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8')\n",
-    "        for word in vocab\n",
-    "    ]\n",
-    "    normalized_vocab = filter(lambda x: x != '', normalized_vocab)\n",
-    "    normalized_vocab = list(set(normalized_vocab)) \n",
-    "\n",
-    "\n",
-    "    return normalized_vocab\n",
-    "\n",
-    "vocab = vectorize_layer.get_vocabulary()\n",
-    "normalized_vocab = normalize_vocabulary(vocab)\n",
-    "\n",
-    "# Reassign the cleaned vocabulary to the TextVectorization layer\n",
-    "vectorize_layer.set_vocabulary(normalized_vocab)"
+    "# Reuse the cleaned vocabulary for the Triton model as well.\n",
+    "vectorize_layer.set_vocabulary(normalize_vocabulary(vectorize_layer.get_vocabulary()))"
    ]
   },
   {

examples/ML+DL-Examples/Spark-DL/dl_inference/torch_requirements.txt

@@ -1,4 +1,4 @@
-# Copyright (c) 2025, NVIDIA CORPORATION.
+# Copyright (c) 2025-2026, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,10 +13,10 @@
 # limitations under the License.
 
 -r requirements.txt
-torch<=2.5.1
-torchvision
-torch-tensorrt
+torch==2.8.0
+torchvision==0.23.0
+torch-tensorrt==2.8.0
 tensorrt --extra-index-url https://download.pytorch.org/whl/cu121
 sentence_transformers
 sentencepiece
-nvidia-modelopt[all] --extra-index-url https://pypi.nvidia.com
+nvidia-modelopt[all] --extra-index-url https://pypi.nvidia.com

examples/ML+DL-Examples/Spark-DL/dl_inference/vllm/qwen-2.5-7b_vllm.ipynb

@@ -598,9 +598,8 @@
    "source": [
     "server_manager.start_servers(gpu_memory_utilization=0.95,\n",
     "                             max_model_len=6600,\n",
-    "                             task=\"generate\",\n",
     "                             enforce_eager=enforce_eager,\n",
-    "                             wait_retries=60)"
+    "                             wait_retries=180)"
    ]
   },
   {