Gaudi Doc Updates (#467)

* initial commit for gaudi doc updates

* created README for anomaly detection

* addressed review comments

* Update README.md

* added comment

Author: okhleif-IL

notebooks/README.md

@@ -11,15 +11,15 @@ and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-p
 
 ## Intel® Transfer Learning Tool Tutorial Notebooks
 
-| Notebook | Domain: Use Case | Framework| Description |
-| ---------| ---------|----------|-------------|
-| [BERT Text Classification with TensorFlow using the Intel® Transfer Learning Tool](/notebooks/text_classification/tlt_api_tf_text_classification) | NLP: Text Classification | TensorFlow and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to fine tune a BERT model from Hugging Face using text classification datasets. |
-| [BERT Text Classification with PyTorch using the Intel® Transfer Learning Tool](/notebooks/text_classification/tlt_api_pyt_text_classification) | NLP: Text Classification | PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to fine tune a BERT model from Hugging Face using text classification datasets. |
-| [Image Classification with TensorFlow using Intel® Transfer Learning Tool](/notebooks/image_classification/tlt_api_tf_image_classification) | CV: Image Classification | TensorFlow and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning for image classification using a TensorFlow model. |
-| [Image Classification with TensorFlow using Graph Optimization and Intel® Transfer Learning Tool](/notebooks/image_classification/tlt_api_tf_image_classification) | CV: Image Classification | TensorFlow and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning with graph optimization that increases throughput for image classification using a TensorFlow model. |
-| [Image Classification with PyTorch using Intel® Transfer Learning Tool](/notebooks/image_classification/tlt_api_pyt_image_classification) | CV: Image Classification | PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning for image classification using a PyTorch model. |
-| [Image Anomaly Detection with PyTorch using Intel® Transfer Learning Tool](/notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection) | CV: Image Anomaly Detection| PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do feature extraction and pca analysis using a single function for image anomaly detection using a Torchvision model. |
-| [Text Generation with PyTorch using Intel® Transfer Learning Tool](/notebooks/text_generation/tlt_api_pyt_text_generation) | NLP: Text Generation| PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do instruction fine-tuning for text generation with a large language model (LLM) from Hugging Face. |
+| Notebook | Domain: Use Case | Framework| Description | Devices |
+| ---------| ---------|----------|-------------|-------|
+| [BERT Text Classification with TensorFlow using the Intel® Transfer Learning Tool](/notebooks/text_classification/tlt_api_tf_text_classification) | NLP: Text Classification | TensorFlow and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to fine tune a BERT model from Hugging Face using text classification datasets. | cpu
+| [BERT Text Classification with PyTorch using the Intel® Transfer Learning Tool](/notebooks/text_classification/tlt_api_pyt_text_classification) | NLP: Text Classification | PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to fine tune a BERT model from Hugging Face using text classification datasets. | cpu
+| [Image Classification with TensorFlow using Intel® Transfer Learning Tool](/notebooks/image_classification/tlt_api_tf_image_classification) | CV: Image Classification | TensorFlow and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning for image classification using a TensorFlow model. | cpu
+| [Image Classification with TensorFlow using Graph Optimization and Intel® Transfer Learning Tool](/notebooks/image_classification/tlt_api_tf_image_classification) | CV: Image Classification | TensorFlow and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning with graph optimization that increases throughput for image classification using a TensorFlow model. | cpu
+| [Image Classification with PyTorch using Intel® Transfer Learning Tool](/notebooks/image_classification/tlt_api_pyt_image_classification) | CV: Image Classification | PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning for image classification using a PyTorch model. | cpu, Intel® Gaudi®
+| [Image Anomaly Detection with PyTorch using Intel® Transfer Learning Tool](/notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection) | CV: Image Anomaly Detection| PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do feature extraction and pca analysis using a single function for image anomaly detection using a Torchvision model. | cpu, Intel Gaudi
+| [Text Generation with PyTorch using Intel® Transfer Learning Tool](/notebooks/text_generation/tlt_api_pyt_text_generation) | NLP: Text Generation| PyTorch and the Intel Transfer Learning Tool API | Demonstrates how to use the Intel Transfer Learning Tool API to do instruction fine-tuning for text generation with a large language model (LLM) from Hugging Face. | cpu
 
 ## Native Framework Transfer Learning Notebooks
 

notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection/Anomaly_Detection.ipynb

@@ -16,7 +16,7 @@
    "metadata": {},
    "source": [
     "###  Intel® Gaudi® AI accelerator\n",
-    "To use HPU training and inference with Gaudi, follow these steps to install required HPU drivers and software from the official [Habana Docs](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html)"
+    "To use HPU training and inference with Gaudi, follow these steps to install required HPU drivers and software from [README](/notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection/README.md) or the official [Habana Docs](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html)"
    ]
   },
   {

notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection/README.md

@@ -0,0 +1,54 @@
+# Transfer Learning for PyTorch Image Anomaly Classification using the Intel® Transfer Learning Tool API
+
+This notebook demonstrates how to use the Intel Transfer Learning Tool API to do transfer learning for
+image anomaly detection using PyTorch.
+
+The notebook performs the following steps:
+1. Import dependencies and setup parameters
+1. Get the model
+1. Get the dataset
+1. Prepare the dataset
+1. Predict using the original model
+1. Finetuning / Feature Extraction
+1. Predict
+1. Export
+1. Post-Training Quantization
+
+## Running the notebook
+
+To run the notebook, follow the instructions to setup the [PyTorch notebook environment](/notebooks/setup.md).
+
+To use Gaudi for training and inference, install required software for Intel Gaudi: 
+1.  Temporarily uninstall torch
+```
+# Torch will later be re-installed below
+pip uninstall torch
+```
+2.  Install the Gaudi Intel SW Stack
+```
+wget -nv https://vault.habana.ai/artifactory/gaudi-installer/1.15.0/habanalabs-installer.sh
+chmod +x habanalabs-installer.sh
+sudo apt-get update
+```
+```
+# Note: This may not be required depending on what is already installed on your machine
+./habanalabs-installer.sh install --type base
+```
+3.	Install the Gaudi Intel Pytorch environment
+```
+# Note: This step may not be required depending on what is already installed on your machine
+./habanalabs-installer.sh install -t dependencies
+```
+```
+./habanalabs-installer.sh install --type pytorch –venv
+```
+
+See [Habana Docs](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html) for detailed installation instructions
+
+## Dataset Citations
+```
+Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger: The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: International Journal of Computer Vision 129(4):1038-1059, 2021, DOI: 10.1007/s11263-020-01400-4.
+```
+```
+Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger: MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9584-9592, 2019, DOI: 10.1109/CVPR.2019.00982.
+```

notebooks/image_classification/tlt_api_pyt_image_classification/README.md

@@ -17,6 +17,33 @@ The notebook performs the following steps:
 
 To run the notebook, follow the instructions to setup the [PyTorch notebook environment](/notebooks/setup.md).
 
+To use Gaudi for training and inference, install required software for Intel Gaudi: 
+1.  Temporarily uninstall torch
+```
+# Torch will later be re-installed below
+pip uninstall torch
+```
+2.  Install the Gaudi Intel SW Stack
+```
+wget -nv https://vault.habana.ai/artifactory/gaudi-installer/1.15.0/habanalabs-installer.sh
+chmod +x habanalabs-installer.sh
+sudo apt-get update
+```
+```
+# Note: This may not be required depending on what is already installed on your machine
+./habanalabs-installer.sh install --type base
+```
+3.	Install the Gaudi Intel Pytorch environment
+```
+# Note: This step may not be required depending on what is already installed on your machine
+./habanalabs-installer.sh install -t dependencies
+```
+```
+./habanalabs-installer.sh install --type pytorch –venv
+```
+
+See [Habana Docs](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html) for detailed installation instructions
+
 ## References
 
 Dataset citations