2023.1 AI Kit Bug Fixes (#1365)

* Intel Python Numpy Numba_dpes kNN sample (#1292)

* *.py and *.ipynb files with implementation
* README.md and sample.json files with documentation
* License and thir party programs

* Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample (#1293)

* add IntelPytorch Quantization code samples (#1301)

* add IntelPytorch Quantization code samples

* fix the spelling error in the README file

* use john's README with grammar fix and title change

* Rename third-party-grograms.txt to third-party-programs.txt

Co-authored-by: Jimmy Wei <[email protected]>

* AMX bfloat16 mixed precision learning TensorFlow Transformer sample (#1317)

* [New Sample] Intel Extension for TensorFlow Getting Started (#1313)

* first draft

* Update README.md

* remove redunant file

* [New Sample] [oneDNN] Benchdnn tutorial  (#1315)

* New Sample: benchDNN tutorial

* Update readme: new sample

* Rename sample to benchdnn_tutorial

* Name fix

* Add files via upload (#1320)

* [New Sample] oneCCL Bindings for PyTorch Getting Started (#1316)

* Update README.md

* [New Sample] oneCCL Bindings for PyTorch Getting Started

* Update README.md

* add torch-ccl version check

* [New Sample] Intel Extension for PyTorch Getting Started (#1314)

* add new ipex GSG notebook for dGPU

* Update sample.json

for expertise field

* Update requirements.txt

Update package versions to comply with Snyk  tool

* Updated title field in sample.json in TF Transformer AMX bfloat16 Mixed Precision sample to fit within character length range (#1327)

* add arch checker class (#1332)

* change gpu.patch to convert the code samples from cpu to gpu correctly (#1334)

* Fixes for spelling in AMX bfloat16 transformer sample and printing error in python code in numpy vs numba sample (#1335)

* 2023.1 ai kit itex get started example fix (#1338)

* Fix the typo

* Update ResNet50_Inference.ipynb

* fix resnet inference demo link (#1339)

* Fix printing issue in numpy vs numba AI sample (#1356)

* Fix Invalid Kmeans parameters on oneAPI 2023 (#1345)

---------

Co-authored-by: krzeszew <[email protected]>
Co-authored-by: alexsin368 <[email protected]>
Co-authored-by: ZhaoqiongZ <[email protected]>
Co-authored-by: Louie Tsai <[email protected]>
Co-authored-by: Orel Yehuda <[email protected]>
Co-authored-by: yuning <[email protected]>
Co-authored-by: Wang, Kai Lawrence <[email protected]>
Co-authored-by: xiguiw <[email protected]>

Author: 9 people

AI-and-Analytics/Features-and-Functionality/IntelPython_Numpy_Numba_dpex_kNN/IntelPython_Numpy_Numba_dpex_kNN.py

@@ -139,7 +139,7 @@ def knn(X_train, y_train, X_test, k):
 predictions = knn(X_train, y_train, X_test, 3)
 true_values = y_test.to_numpy()
 accuracy = np.mean(predictions == true_values)
-print('Numpy accuracy:' + accuracy)
+print('Numpy accuracy:', accuracy)
 
 
 # ## Numba k-NN

AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans/IntelPython_daal4py_Distributed_Kmeans.py

@@ -97,7 +97,8 @@
 
 
 # compute the clusters/centroids
-kmeans_result = d4p.kmeans(nClusters = 3, maxIterations = 5, assignFlag = True).compute(X, init_result.centroids)
+kmeans_result = d4p.kmeans(nClusters = 3, maxIterations = 5, assignFlag = True,
+                           accuracyThreshold = 5.0e-6, gamma = 1.0).compute(X, init_result.centroids)
 
 
 # To **get Kmeans result objects** (assignments, centroids, goalFunction [deprecated], nIterations, and objectiveFunction):