diff --git a/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/README.md b/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/README.md index e01554be45..50b7e716ca 100644 --- a/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/README.md +++ b/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/README.md @@ -1,11 +1,11 @@ -# Modin Get Started Sample +# Modin* Get Started Sample -The `Modin Getting Started` sample demonstrates how to use distributed Pandas using the Modin package. +The `Modin* Getting Started` sample demonstrates how to use distributed Pandas using the Modin package. | Area | Description | :--- | :--- | Category | Getting Started -| What you will learn | Basic Modin programming model for Intel processors +| What you will learn | Basic Modin* programming model for Intel processors | Time to complete | 5 to 8 minutes ## Purpose @@ -52,7 +52,7 @@ This get started sample code is implemented for CPU using the Python language. T conda install ipykernel python -m ipykernel install --user --name usr_modin ``` -## Run the `Modin Get Started` Sample +## Run the `Modin* Get Started` Sample You can run the Jupyter notebook with the sample code on your local server or download the sample code from the notebook as a Python file and run it locally. diff --git a/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/sample.json b/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/sample.json index b037a98ba4..270b1249f6 100644 --- a/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/sample.json +++ b/AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/sample.json @@ -1,8 +1,8 @@ { "guid": "AE280EFE-9EB1-406D-B32D-5991F707E195", - "name": "Intel® Distribution of Modin* Getting Started", + "name": "Modin* Getting Started", "categories": ["Toolkit/oneAPI AI And Analytics/Getting Started"], - "description": "This sample illustrates how to use Modin accelerated Pandas functions and notes the performance gain when compared to standard Pandas functions", + "description": "This sample illustrates how to use Modin* accelerated Pandas functions and notes the performance gain when compared to standard Pandas functions", "builder": ["cli"], "languages": [{"python":{}}], "os":["linux"], @@ -19,7 +19,7 @@ "conda activate intel-aikit-modin", "pip install -r requirements.txt # Installing notebook's dependencies", "pip install runipy # Installing 'runipy' for extended abilities to execute the notebook", - "runipy Modin_GettingStarted.ipynb # Test 'Modin is faster than pandas' case", + "runipy Modin_GettingStarted.ipynb # Test 'Modin* is faster than pandas' case", "MODIN_CPUS=1 runipy Modin_GettingStarted.ipynb # Test 'Modin is slower than pandas' case" ] } diff --git a/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/README.md b/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/README.md index 9ee6529109..14f3f0f442 100644 --- a/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/README.md +++ b/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/README.md @@ -1,11 +1,11 @@ -# Modin Vs. Pandas Performance Sample +# Modin* Vs. Pandas Performance Sample -The `Modin Vs. Pandas Performance` code illustrates how to use Modin* to replace the Pandas API. The sample compares the performance of Modin and the performance of Pandas for specific dataframe operations. +The `Modin* Vs. Pandas Performance` code illustrates how to use Modin* to replace the Pandas API. The sample compares the performance of Modin* and the performance of Pandas for specific dataframe operations. | Area | Description |:--- |:--- | Category | Concepts and Functionality -| What you will learn | How to accelerate the Pandas API using Modin. +| What you will learn | How to accelerate the Pandas API using Modin*. | Time to complete | Less than 10 minutes ## Purpose @@ -19,17 +19,17 @@ You can run the sample locally or in Google Colaboratory (Colab). |:--- |:--- | OS | Ubuntu* 20.04 (or newer) | Hardware | Intel® Core™ Gen10 Processor
Intel® Xeon® Scalable Performance processors -| Software | Intel® Distribution of Modin* +| Software | Modin* ## Key Implementation Details -This code sample is implemented for CPU using Python programming language. The sample requires NumPy, Pandas, Modin libraries, and the time module in Python. +This code sample is implemented for CPU using Python programming language. The sample requires NumPy, Pandas, Modin* libraries, and the time module in Python. ## Environment Setup If you want to run the sample on a local system using a command-line interface (CLI), you must install the Modin in a new Conda* environment first. -### Install Modin +### Install Modin* 1. Create a Conda environment. ``` @@ -65,7 +65,7 @@ If you want to run the sample on a local system using a command-line interface ( ipython Modin_Vs_Pandas.ipynb ``` -## Run the `Modin Vs Pandas Performance` Sample in Google Colaboratory +## Run the `Modin* Vs Pandas Performance` Sample in Google Colaboratory 1. Change to the directory containing the `Modin_Vs_Pandas.ipynb` notebook file on your local system. diff --git a/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/sample.json b/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/sample.json index d51b1525e8..bab9d6980f 100644 --- a/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/sample.json +++ b/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/sample.json @@ -1,8 +1,8 @@ { "guid": "FE479C5C-C7A0-4612-B8D0-F83D07155411", - "name": "Intel® Modin Vs. Pandas Performance", + "name": "Modin* Vs. Pandas Performance", "categories": ["Toolkit/oneAPI AI And Analytics/Getting Started"], - "description": "This sample code illustrates how Intel® Modin accelerates the performance of Pandas for computational operations on a dataframe.", + "description": "This sample code illustrates how Modin* accelerates the performance of Pandas for computational operations on a dataframe.", "builder": ["cli"], "languages": [{ "python": {} diff --git a/AI-and-Analytics/Getting-Started-Samples/README.md b/AI-and-Analytics/Getting-Started-Samples/README.md index c465232a0e..a8d82bd7da 100644 --- a/AI-and-Analytics/Getting-Started-Samples/README.md +++ b/AI-and-Analytics/Getting-Started-Samples/README.md @@ -18,8 +18,8 @@ Third party program Licenses can be found here: [third-party-programs.txt](https |--------------------------| --------- | ------------------------------------------------ | - |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
Classical Machine Learning | Modin | [Modin_GettingStarted](Modin_GettingStarted) | Run Modin-accelerated Pandas functions and note the performance gain. -|Data Analytics
Classical Machine Learning | Modin |[Modin_Vs_Pandas](Modin_Vs_Pandas)| Compares the performance of Intel® Distribution of Modin* and the performance of Pandas. +|Data Analytics
Classical Machine Learning | Modin* | [Modin_GettingStarted](Modin_GettingStarted) | Run Modin*-accelerated Pandas functions and note the performance gain. +|Data Analytics
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
Inference Optimization| Intel® Optimization for TensorFlow* | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted) | A simple training example for TensorFlow.