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2023.1 AI Kit Bug Fixes #1365

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Merged
merged 21 commits into from
Feb 16, 2023
Merged

2023.1 AI Kit Bug Fixes #1365

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d0b3299
Intel Python Numpy Numba_dpes kNN sample (#1292)
krzeszew Jan 12, 2023
303ab84
Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample (#1…
alexsin368 Jan 13, 2023
d5aea45
add IntelPytorch Quantization code samples (#1301)
ZhaoqiongZ Jan 19, 2023
48264e5
AMX bfloat16 mixed precision learning TensorFlow Transformer sample (…
krzeszew Jan 26, 2023
b4c3231
[New Sample] Intel Extension for TensorFlow Getting Started (#1313)
louie-tsai Jan 26, 2023
799b80c
[New Sample] [oneDNN] Benchdnn tutorial (#1315)
yehudaorel Jan 26, 2023
80823d7
Add files via upload (#1320)
YuningQiu Jan 26, 2023
23f50dc
[New Sample] oneCCL Bindings for PyTorch Getting Started (#1316)
louie-tsai Jan 26, 2023
5bca91c
[New Sample] Intel Extension for PyTorch Getting Started (#1314)
louie-tsai Jan 26, 2023
0d582e8
Update requirements.txt
jimmytwei Jan 27, 2023
5546046
Updated title field in sample.json in TF Transformer AMX bfloat16 Mix…
jimmytwei Jan 27, 2023
cadee2e
Merge branch 'master' into 2023.1_AIKit
jimmytwei Jan 27, 2023
0240431
Merge branch 'master' into 2023.1_AIKit
jimmytwei Feb 6, 2023
0c9027b
add arch checker class (#1332)
louie-tsai Feb 6, 2023
2bbf2f0
change gpu.patch to convert the code samples from cpu to gpu correctl…
ZhaoqiongZ Feb 6, 2023
f661725
Fixes for spelling in AMX bfloat16 transformer sample and printing er…
krzeszew Feb 6, 2023
f8136d7
2023.1 ai kit itex get started example fix (#1338)
wangkl2 Feb 7, 2023
fe90d1e
fix resnet inference demo link (#1339)
ZhaoqiongZ Feb 7, 2023
ce40db3
Merge branch 'master' into 2023.1_AIKit
jimmytwei Feb 13, 2023
4356c8d
Fix printing issue in numpy vs numba AI sample (#1356)
krzeszew Feb 15, 2023
219b406
Fix Invalid Kmeans parameters on oneAPI 2023 (#1345)
xiguiw Feb 16, 2023
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2 changes: 1 addition & 1 deletion ...es-and-Functionality/IntelPython_Numpy_Numba_dpex_kNN/IntelPython_Numpy_Numba_dpex_kNN.py
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Expand Up @@ -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
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3 changes: 2 additions & 1 deletion ...tionality/IntelPython_daal4py_DistributedKMeans/IntelPython_daal4py_Distributed_Kmeans.py
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Expand Up @@ -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):
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