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‎AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/README.md

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# Modin* Vs. Pandas Performance Sample
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# Modin Vs. Pandas Performance Sample
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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.
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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.
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| Area | Description
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|:--- |:---
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| Category | Concepts and Functionality
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| What you will learn | How to accelerate the Pandas API using Modin*.
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| What you will learn | How to accelerate the Pandas API using Modin.
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| Time to complete | Less than 10 minutes
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## Purpose
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|:--- |:---
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| OS | Ubuntu* 20.04 (or newer)
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| Hardware | Intel® Core™ Gen10 Processor <br> Intel® Xeon® Scalable Performance processors
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| Software | Modin*
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| Software | Intel® Distribution of Modin*
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> **Note**: AI and Analytics samples are validated on AI Tools Offline Installer. For the full list of validated platforms refer to [Platform Validation](https://github.com/oneapi-src/oneAPI-samples/tree/master?tab=readme-ov-file#platform-validation).
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<!-- for migrated samples - modify the note above to provide information on samples validation and preferred installation option -->
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## Key Implementation Details
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This code sample is implemented for CPU using Python programming language. The sample requires NumPy, Pandas, Modin* libraries, and the time module in Python.
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This code sample is implemented for CPU using Python programming language. The sample requires NumPy, Pandas, Modin libraries, and the time module in Python.
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## Environment Setup
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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.
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You will need to download and install the following toolkits, tools, and components to use the sample.
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<!-- Use numbered steps instead of subheadings -->
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### Install Modin*
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**1. Get AI Tools**
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1. Create a Conda environment.
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```
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conda create --name modin
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```
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2. Activate the Conda environment.
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```
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source activate modin
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```
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3. Remove existing versions of Modin* (if any exist).
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```
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conda remove modin --y
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```
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4. Install Modin (v0.12.1 or newer).
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```
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pip install modin[all]==0.12.1
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```
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5. Install the NumPy and Pandas libraries.
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```
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pip install numpy
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pip install pandas
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```
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6. Install ipython to run the notebook on your system.
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```
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pip install ipython
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```
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### Run the Sample
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Required AI Tools: Modin
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1. Change to the directory containing the `Modin_Vs_Pandas.ipynb` notebook file on your local system.
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If you have not already, select and install these Tools via [AI Tools Selector](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html). AI and Analytics samples are validated on AI Tools Offline Installer. It is recommended to select Offline Installer option in AI Tools Selector.
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2. Run the sample notebook.
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```
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ipython Modin_Vs_Pandas.ipynb
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```
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>**Note**: If Docker option is chosen in AI Tools Selector, refer to [Working with Preset Containers](https://github.com/intel/ai-containers/tree/main/preset) to learn how to run the docker and samples.
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## Run the `Modin* Vs Pandas Performance` Sample in Google Colaboratory
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**2. (Offline Installer) Activate the AI Tools bundle base environment**
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<!-- this step is from AI Tools GSG, please don't modify unless GSG is updated -->
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If the default path is used during the installation of AI Tools:
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```
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source $HOME/intel/oneapi/intelpython/bin/activate
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```
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If a non-default path is used:
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```
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source <custom_path>/bin/activate
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```
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1. Change to the directory containing the `Modin_Vs_Pandas.ipynb` notebook file on your local system.
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**3. (Offline Installer) Activate relevant Conda environment**
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<!-- specify relevant conda environment name in Offline Installer for this sample -->
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```
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conda activate modin
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```
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**4. Clone the GitHub repository**
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<!-- for oneapi-samples: git clone https://github.com/oneapi-src/oneAPI-samples.git
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cd oneAPI-samples/AI-and-Analytics/<samples-folder>/<individual-sample-folder> -->
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<!-- for migrated samples - provide git clone command for individual repo and cd to sample dir -->
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```
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git clone https://github.com/oneapi-src/oneAPI-samples.git
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cd oneAPI-samples/AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas
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```
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2. Open the notebook file, and remove the prepended number sign (#) symbol from the following lines:
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```
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#!pip install modin[all]==0.12.1
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#!pip install numpy
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#!pip install pandas
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```
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These changes will install the Modin and the NumPy and Pandas libraries when run in the Colab notebook.
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**5. Install dependencies**
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<!-- It is required to have requirement.txt file in sample dir. It should list additional libraries, such as matplotlib, ipykernel etc. -->
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>**Note**: Before running the following commands, make sure your Conda/Python environment with AI Tools installed is activated
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3. Save your changes.
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```
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pip install -r requirements.txt
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pip install notebook
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```
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For Jupyter Notebook, refer to [Installing Jupyter](https://jupyter.org/install) for detailed installation instructions.
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4. Open [Google Colaboratory](https://colab.research.google.com/?utm_source=scs-index).
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## Run the Sample
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>**Note**: Before running the sample, make sure [Environment Setup](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch#environment-setup) is completed.
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5. Sign in to Colab using your Google account.
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Go to the section which corresponds to the installation method chosen in [AI Tools Selector](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html) to see relevant instructions:
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* [AI Tools Offline Installer (Validated)](#ai-tools-offline-installer-validated)
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* [Conda/PIP](#condapip)
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* [Docker](#docker)
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<!-- for migrated samples - it's acceptable to change the order of the sections based on the validated/preferred installation options. However, all 3 sections (Offline, Conda/PIP, Docker) should be present in the doc -->
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### AI Tools Offline Installer (Validated)
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6. Select **File** > **Upload notebook**.
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**1. Register Conda kernel to Jupyter Notebook kernel**
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7. Upload the modified notebook file.
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If the default path is used during the installation of AI Tools:
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```
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$HOME/intel/oneapi/intelpython/envs/modin/bin/python -m ipykernel install --user --name=modin
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```
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If a non-default path is used:
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```
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<custom_path>/bin/python -m ipykernel install --user --name=modin
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```
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8. Change to the notebook, and click **Open**.
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**2. Launch Jupyter Notebook**
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<!-- add other flags to jupyter notebook command if needed, such as port 8888 or allow-root -->
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```
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jupyter notebook --ip=0.0.0.0
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```
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**3. Follow the instructions to open the URL with the token in your browser**
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9. Select **Runtime** > **Run all**.
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**4. Select the Notebook**
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<!-- add sample file name -->
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```
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Modin_Vs_Pandas.ipynb
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```
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**5. Change the kernel to `modin`**
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<!-- specify relevant kernel name(s), for example `pytorch` -->
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**6. Run every cell in the Notebook in sequence**
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### Conda/PIP
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> **Note**: Before running the instructions below, make sure your Conda/Python environment with AI Tools installed is activated
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**1. Register Conda/Python kernel to Jupyter Notebook kernel**
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<!-- keep placeholders in this step, user could use any name for Conda/PIP env -->
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For Conda:
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```
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<CONDA_PATH_TO_ENV>/bin/python -m ipykernel install --user --name=<your-env-name>
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```
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To know <CONDA_PATH_TO_ENV>, run `conda env list` and find your Conda environment path.
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For PIP:
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```
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python -m ipykernel install --user --name=<your-env-name>
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```
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**2. Launch Jupyter Notebook**
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<!-- add other flags to jupyter notebook command if needed, such as port 8888 or allow-root -->
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```
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jupyter notebook --ip=0.0.0.0
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```
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**3. Follow the instructions to open the URL with the token in your browser**
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**4. Select the Notebook**
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```
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Modin_Vs_Pandas.ipynb
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```
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**5. Change the kernel to `<your-env-name>`**
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<!-- leave <your-env-name> as a placeholder as user could choose any name for the env -->
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**6. Run every cell in the Notebook in sequence**
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### Docker
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AI Tools Docker images already have Get Started samples pre-installed. Refer to [Working with Preset Containers](https://github.com/intel/ai-containers/tree/main/preset) to learn how to run the docker and samples.
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<!-- Remove Intel® DevCloud section or other outdated sections -->
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## Example Output
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## License
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt)
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for details.
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Third party program licenses are at [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt).
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Third party program Licenses can be found here:
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[third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt)
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*Other names and brands may be claimed as the property of others. [Trademarks](https://www.intel.com/content/www/us/en/legal/trademarks.html)
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