Skip to content

ONSAM-2075 #2616

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
jafraustro wants to merge 1 commit into from
Closed

ONSAM-2075 #2616

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 12 additions & 12 deletions AI-and-Analytics/Getting-Started-Samples/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,17 +16,17 @@ Third party program Licenses can be found here: [third-party-programs.txt](https

|AI Tools preset | Component | Folder | Description
|--------------------------| --------- | ------------------------------------------------ | -
|Inference Optimization| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-PyTorch](INC-Quantization-Sample-for-PyTorch) | Performs INT8 quantization on a Hugging Face BERT model.
|Inference Optimization| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-Tensorflow](INC-Sample-for-Tensorflow) | Quantizes a FP32 model into INT8 by Intel® Neural Compressor (INC) and compares the performance between FP32 and INT8.
|Data Analytics <br/> Classical Machine Learning | Modin* | [Modin_GettingStarted](Modin_GettingStarted) | Run Modin*-accelerated Pandas functions and note the performance gain.
|Data Analytics <br/> Classical Machine Learning | Modin* |[Modin_Vs_Pandas](Modin_Vs_Pandas)| Compares the performance of Intel® Distribution of Modin* and the performance of Pandas.
|Classical Machine Learning| Intel® Optimization for XGBoost* | [IntelPython_XGBoost_GettingStarted](IntelPython_XGBoost_GettingStarted) | Set up and trains an XGBoost* model on datasets for prediction.
|Classical Machine Learning| daal4py | [IntelPython_daal4py_GettingStarted](IntelPython_daal4py_GettingStarted) | Batch linear regression using the Python API package daal4py from oneAPI Data Analytics Library (oneDAL).
|Deep Learning <br/> Inference Optimization| Intel® Optimization for TensorFlow* | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted) | A simple training example for TensorFlow.
|Deep Learning <br/> Inference Optimization|Intel® Extension of PyTorch | [Getting Started with Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md) | A simple training example for Intel® Extension of PyTorch.
|Classical Machine Learning| Scikit-learn (OneDAL) | [Intel_Extension_For_SKLearn_GettingStarted](Intel_Extension_For_SKLearn_GettingStarted) | Speed up a scikit-learn application using Intel oneDAL.
|Deep Learning <br/> Inference Optimization|Intel® Extension of TensorFlow | [Intel® Extension For TensorFlow GettingStarted](Intel_Extension_For_TensorFlow_GettingStarted) | Guides users how to run a TensorFlow inference workload on both GPU and CPU.
|Deep Learning Inference Optimization|oneCCL Bindings for PyTorch | [Intel oneCCL Bindings For PyTorch GettingStarted](Intel_oneCCL_Bindings_For_PyTorch_GettingStarted) | Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU. |
|Inference Optimization|JAX Getting Started Sample | [IntelJAX GettingStarted](https://github.com/oneapi-src/oneAPI-samples/tree/development/AI-and-Analytics/Getting-Started-Samples/IntelJAX_GettingStarted) | The JAX Getting Started sample demonstrates how to train a JAX model and run inference on Intel® hardware. |
|Deep Learning PyTorch\* CPU | <li>[Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), <br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [Intel® Neural Compressor (INC) Sample-for-PyTorch](INC-Quantization-Sample-for-PyTorch) | Performs INT8 quantization on a Hugging Face BERT model.
|Deep Learning TensorFlow\* CPU | <li>[Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [Intel® Neural Compressor (INC) Sample-for-Tensorflow](INC-Sample-for-Tensorflow) | Quantizes a FP32 model into INT8 by Intel® Neural Compressor (INC) and compares the performance between FP32 and INT8.
|Classical Machine Learning | <li>[Modin*](https://github.com/modin-project/modin) | [Modin_GettingStarted](Modin_GettingStarted) | Run Modin*-accelerated Pandas functions and note the performance gain.
|Classical Machine Learning | <li>[Modin*](https://github.com/modin-project/modin) |[Modin_Vs_Pandas](Modin_Vs_Pandas)| Compares the performance of Intel® Distribution of Modin* and the performance of Pandas.
|Classical Machine Learning| <li>[Intel® Optimization for XGBoost\*](https://github.com/IntelPython/xgboost_oneapi) | [IntelPython_XGBoost_GettingStarted](IntelPython_XGBoost_GettingStarted) | Set up and trains an XGBoost* model on datasets for prediction.
|Classical Machine Learning| <li>[daal4py](https://github.com/uxlfoundation/scikit-learn-intelex/tree/main/daal4py) | [IntelPython_daal4py_GettingStarted](IntelPython_daal4py_GettingStarted) | Batch linear regression using the Python API package daal4py from oneAPI Data Analytics Library (oneDAL).
|Deep Learning Tensorflow\* CPU| <li>[Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted) | A simple training example for TensorFlow.
|Deep Learning PyTorch\* CPU and Deep Learning PyTorch\* GPU|<li>[Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), <br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [Getting Started with Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md) | A simple training example for Intel® Extension of PyTorch.
|Classical Machine Learning| <li>[Scikit-learn\* (OneDAL)](https://github.com/uxlfoundation/oneDAL) | [Intel_Extension_For_SKLearn_GettingStarted](Intel_Extension_For_SKLearn_GettingStarted) | Speed up a scikit-learn application using Intel oneDAL.
|Deep Learning TensorFlow\* CPU and Deep Learning TensorFlow\* GPU|<li>[Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [Intel® Extension For TensorFlow GettingStarted](Intel_Extension_For_TensorFlow_GettingStarted) | Guides users how to run a TensorFlow inference workload on both GPU and CPU.
|Deep Learning PyTorch\* GPU |<li>[Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), <br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [Intel oneCCL Bindings For PyTorch GettingStarted](Intel_oneCCL_Bindings_For_PyTorch_GettingStarted) | Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU. |
|Deep Learning JAX\* CPU|<li>[JAX\*](https://github.com/jax-ml/jax) | [IntelJAX GettingStarted](https://github.com/oneapi-src/oneAPI-samples/tree/development/AI-and-Analytics/Getting-Started-Samples/IntelJAX_GettingStarted) | The JAX Getting Started sample demonstrates how to train a JAX model and run inference on Intel® hardware. |

*Other names and brands may be claimed as the property of others. [Trademarks](https://www.intel.com/content/www/us/en/legal/trademarks.html)