debug triton install outetts (#2716)

Author: eaidova

.ci/ignore_treon_docker.txt

@@ -83,4 +83,5 @@ notebooks/multilora-image-generation/multilora-image-generation.ipynb
 notebooks/llm-agent-react/llm-agent-react-langchain.ipynb
 notebooks/multimodal-rag/multimodal-rag-llamaindex.ipynb
 notebooks/llm-rag-langchain/llm-rag-langchain-genai.ipynb
-notebooks/ltx-video/ltx-video.ipynb
+notebooks/ltx-video/ltx-video.ipynb
+notebooks/outetts-text-to-speech/outetts-text-to-speech.ipynb

notebooks/deepseek-r1/README.md

@@ -11,7 +11,7 @@ The tutorial supports different models, you can select one from the provided opt
 * **DeepSeek-R1-Distill-Llama-8B** is a distilled model based on [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), that prioritizes high performance and advanced reasoning capabilities, particularly excelling in tasks requiring mathematical and factual precision. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) for more info.
 * **DeepSeek-R1-Distill-Qwen-1.5B** is the smallest DeepSeek-R1 distilled model based on [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B). Despite its compact size, the model demonstrates strong capabilities in solving basic mathematical tasks, at the same time its programming capabilities are limited. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for more info.
 * **DeepSeek-R1-Distill-Qwen-7B** is a distilled model based on [Qwen-2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). The model demonstrates a good balance between mathematical and factual reasoning and can be less suited for complex coding tasks. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) for more info.
-* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-15B) for more info.
+* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more info.
 
 ## Notebook Contents
 

notebooks/deepseek-r1/deepseek-r1.ipynb

@@ -109,7 +109,7 @@
     "* **DeepSeek-R1-Distill-Llama-8B** is a distilled model based on [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), that prioritizes high performance and advanced reasoning capabilities, particularly excelling in tasks requiring mathematical and factual precision. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) for more info.\n",
     "* **DeepSeek-R1-Distill-Qwen-1.5B** is the smallest DeepSeek-R1 distilled model based on [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B). Despite its compact size, the model demonstrates strong capabilities in solving basic mathematical tasks, at the same time its programming capabilities are limited. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for more info.\n",
     "* **DeepSeek-R1-Distill-Qwen-7B** is a distilled model based on [Qwen-2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). The model demonstrates a good balance between mathematical and factual reasoning and can be less suited for complex coding tasks. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) for more info.\n",
-    "* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-15B) for more info.\n",
+    "* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more info.\n",
     "\n",
     "[Weight compression](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html) is a technique for enhancing the efficiency of models, especially those with large memory requirements. This method reduces the model’s memory footprint, a crucial factor for Large Language Models (LLMs). We provide several options for model weight compression:\n",
     "\n",

notebooks/hugging-face-hub/README.md

@@ -5,7 +5,7 @@
 The Hugging Face (HF) Model Hub is a central repository for pre-trained deep learning models. It allows exploration and provides access to thousands of models for a wide range of tasks, including text classification, question answering, and image classification.
 Hugging Face provides Python packages that serve as APIs and tools to easily download and fine tune state-of-the-art pretrained models, namely [transformers] and [diffusers] packages.
 
-![](https://github.com/huggingface/optimum-intel/raw/main/readme_logo.png)
+![](https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/logo/hf_intel_logo.png)
 
 ## Contents: 
 Throughout this notebook we will learn:

notebooks/hugging-face-hub/hugging-face-hub.ipynb

@@ -10,7 +10,7 @@
     "The Hugging Face (HF) [Model Hub](https://huggingface.co/models) is a central repository for pre-trained deep learning models. It allows exploration and provides access to thousands of models for a wide range of tasks, including text classification, question answering, and image classification.\n",
     "Hugging Face provides Python packages that serve as APIs and tools to easily download and fine tune state-of-the-art pretrained models, namely [transformers](https://github.com/huggingface/transformers) and [diffusers](https://github.com/huggingface/diffusers) packages.\n",
     "\n",
-    "![](https://github.com/huggingface/optimum-intel/raw/main/readme_logo.png)\n",
+    "![](https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/logo/hf_intel_logo.png)\n",
     "\n",
     "Throughout this notebook we will learn:\n",
     "1. How to load a HF pipeline using the `transformers` package and then convert it to OpenVINO.\n",

notebooks/outetts-text-to-speech/outetts-text-to-speech.ipynb

@@ -51,13 +51,43 @@
    "outputs": [],
    "source": [
     "import platform\n",
+    "import requests\n",
+    "from pathlib import Path\n",
+    "\n",
+    "utility_files = [\"cmd_helper.py\", \"notebook_utils.py\", \"pip_helper.py\"]\n",
+    "base_utility_url = \"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/\"\n",
+    "\n",
+    "for utility_file in utility_files:\n",
+    "    if not Path(utility_file).exists():\n",
+    "        r = requests.get(base_utility_url + utility_file)\n",
+    "        with Path(utility_file).open(\"w\") as f:\n",
+    "            f.write(r.text)\n",
     "\n",
-    "%pip install -q \"torch>=2.1\" \"torchaudio\" \"einops\" \"transformers>=4.46.1\" \"loguru\" \"inflect\" \"pesq\" \"torchcrepe\" \"natsort\" \"polars\" uroman mecab-python3 unidic-lite --extra-index-url https://download.pytorch.org/whl/cpu\n",
-    "%pip install -q \"gradio>=4.19\" \"openvino>=2024.4.0\" \"tqdm\" \"pyyaml\" \"librosa\" \"soundfile\"\n",
-    "%pip install -q \"git+https://github.com/huggingface/optimum-intel.git\"  --extra-index-url https://download.pytorch.org/whl/cpu\n",
+    "\n",
+    "from pip_helper import pip_install\n",
+    "\n",
+    "pip_install(\n",
+    "    \"torch>=2.1\",\n",
+    "    \"torchaudio\",\n",
+    "    \"einops\",\n",
+    "    \"transformers>=4.46.1\",\n",
+    "    \"loguru\",\n",
+    "    \"inflect\",\n",
+    "    \"pesq\",\n",
+    "    \"torchcrepe\",\n",
+    "    \"natsort\",\n",
+    "    \"polars\",\n",
+    "    \"uroman\",\n",
+    "    \"mecab-python3\",\n",
+    "    \"unidic-lite\",\n",
+    "    \"--extra-index-url\",\n",
+    "    \"https://download.pytorch.org/whl/cpu\",\n",
+    ")\n",
+    "pip_install(\"gradio>=4.19\", \"openvino>=2024.4.0\", \"tqdm\", \"pyyaml\", \"librosa\", \"soundfile\")\n",
+    "pip_install(\"git+https://github.com/huggingface/optimum-intel.git\", \"--extra-index-url\", \"https://download.pytorch.org/whl/cpu\")\n",
     "\n",
     "if platform.system() == \"Darwin\":\n",
-    "    %pip install -q \"numpy<2.0.0\""
+    "    pip_install(\"numpy<2.0.0\")"
    ]
   },
   {
@@ -69,15 +99,6 @@
     "import requests\n",
     "from pathlib import Path\n",
     "\n",
-    "utility_files = [\"cmd_helper.py\", \"notebook_utils.py\"]\n",
-    "base_utility_url = \"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/\"\n",
-    "\n",
-    "for utility_file in utility_files:\n",
-    "    if not Path(utility_file).exists():\n",
-    "        r = requests.get(base_utility_url + utility_file)\n",
-    "        with Path(utility_file).open(\"w\") as f:\n",
-    "            f.write(r.text)\n",
-    "\n",
     "\n",
     "helper_files = [\"gradio_helper.py\", \"ov_outetts_helper.py\"]\n",
     "base_helper_url = \"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/outetts-text-to-speech\"\n",