The Intel® Python: NumPy vs Numba vs DPNP sample shows how to achieve the same accuracy of the k-NN model classification while using NumPy*, Numba*, and Data Parallel Extension for NumPy* (dpnp).
| Property | Description |
|---|---|
| Category | Code Optimization |
| What you will learn | How to program using the Data Parallel Extension for NumPy* (dpnp) |
| Time to complete | 5 minutes |
Note: This sample is validated on Intel® Distribution for Python* Offline Installer and AI Tools Offline Installer. For the full list of validated platforms refer to Platform Validation.
In this sample, you will run a k-nearest neighbors algorithm using 3 different Intel® Distribution for Python* libraries: NumPy, Numba, and dpnp. You will learn how to use k-NN model and how to optimize it by dpnp operations without sacrificing accuracy.
| Optimized for | Description |
|---|---|
| OS | Ubuntu* 20.04 |
| Hardware | CPU |
| Software | Intel® Distribution for Python* |
This sample code is implemented for the CPU using Python. The sample assumes you have dpnp installed inside a Conda environment, similar to what is installed with the Intel® Distribution for Python*.
The sample tutorial contains one Jupyter Notebook and one Python script. You can use either.
You will need to download and install the following toolkits to use the sample.
1. Get Intel® Distribution for Python*
If you have not already, install Intel® Distribution for Python* via Installer.
2. Activate the Intel® Distribution for Python* base environment
If the default path is used during the installation of Intel® Distribution for Python*:
source $HOME/intelpython3/bin/activate
If a non-default path is used:
source <custom_path>/bin/activate
3. Clone the GitHub repository
git clone https://github.com/oneapi-src/oneAPI-samples.git
cd oneAPI-samples/AI-and-Analytics/Features-and-Functionality/IntelPython_Numpy_Numba_dpex_kNN
4. Install dependencies
Note: Before running the following commands, make sure your Conda environment is activated
pip install ipykernel
pip install notebook
For Jupyter Notebook, refer to Installing Jupyter for detailed installation instructions.
Note: Before running the sample, make sure Environment Setup is completed.
1. Register Conda kernel to Jupyter Notebook kernel
If the default path is used during the installation of Intel® Distribution for Python*:
$HOME/intelpython3/bin/python -m ipykernel install --user --name=base
If a non-default path is used:
<custom_path>/bin/python -m ipykernel install --user --name=base
2. Launch Jupyter Notebook
jupyter notebook --ip=0.0.0.0
3. Follow the instructions to open the URL with the token in your browser
4. Select the Notebook
IntelPython_Numpy_Numba_dpex_kNN.ipynb
5. Change the kernel to base
6. Run every cell in the Notebook in sequence
Numpy accuracy: 0.7222222222222222
Numba accuracy: 0.7222222222222222
Numba_dpex accuracy 0.7222222222222222
[CODE_SAMPLE_COMPLETED_SUCCESSFULLY]
- Get Started with the Intel® Distribution for Python*
Genetic Algorithms on GPU using Intel® Distribution for Python* dpnpSample
Code samples are licensed under the MIT license. See License.txt for details.
Third party program Licenses can be found here: third-party-programs.txt
*Other names and brands may be claimed as the property of others. Trademarks