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Use uv tool for isolate cicd env
1.AI-and-Analytics/End-to-end-Workloads/JobRecommendationSystem 2.AI-and-Analytics/Features-and Functionality/INC_QuantizationAwareTraining_TextClassification 3.AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult 4.AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans 5.AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedLinearRegression 6.AI-and-Analytics/Features-and-Functionality/IntelPython_GPU_dpnp_Genetic_Algorithm 7.AI-and-Analytics/Features-and-Functionality/IntelPython_Numpy_Numba_dpnp_kNN 8.AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_Performance 9.AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP 10.AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16 11.AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference 12.AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training 13.AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Enabling_Auto_Mixed_Precision_for_TransferLearning 14.AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_for_LLMs 15.AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_TextGeneration_with_LSTM 16.AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization 17.AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch 18.AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow 19.AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted 20.AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted 21.AI-and-Analytics/Getting-Started-Samples/IntelPython_daal4py_GettingStarted 22.AI-and-Analytics/Getting-Started-Samples/IntelPython_XGBoost_GettingStarted 23.AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted 24.AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas Signed-off-by: Xu, He <he.xu@intel.com>
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‎AI-and-Analytics/End-to-end-Workloads/JobRecommendationSystem/JobRecommendationSystem.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import intel_extension_for_tensorflow as itex\n",
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"\n",
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"itex.experimental_ops_override()"

‎AI-and-Analytics/End-to-end-Workloads/JobRecommendationSystem/JobRecommendationSystem.py‎

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# Now, let us import Intel Extension for TensorFlow*. We are using Python API `itex.experimental_ops_override()`. It automatically replace some TensorFlow operators by Custom Operators under `itex.ops` namespace, as well as to be compatible with existing trained parameters.
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# %%
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import numpy as np
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import intel_extension_for_tensorflow as itex
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itex.experimental_ops_override()

‎AI-and-Analytics/End-to-end-Workloads/JobRecommendationSystem/sample.json‎

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"id": "JobRecommendationSystem_py",
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"steps": [
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"source /intel/oneapi/intelpython/bin/activate",
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"conda env remove -n user_tensorflow-gpu",
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"conda create --name user_tensorflow-gpu --clone tensorflow-gpu",
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"conda activate user_tensorflow-gpu",
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"pip install -r requirements.txt",
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"python -m ipykernel install --user --name=user_tensorflow-gpu",
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"python JobRecommendationSystem.py"
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"conda activate tensorflow-gpu",
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"pip install uv",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add -r requirements.txt",
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"uv add seaborn",
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"uv add numpy==1.26.4",
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"uv run python -m spacy download en_core_web_sm",
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"uv run python JobRecommendationSystem.py"
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]
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}
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]

‎AI-and-Analytics/Features-and-Functionality/INC_QuantizationAwareTraining_TextClassification/sample.json‎

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"env": [
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"source /intel/oneapi/intelpython/bin/activate",
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"conda activate pytorch",
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"pip install -r requirements.txt"
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"pip install uv",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add -r requirements.txt",
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"uv add torch==2.5.0 torchvision==0.20.0"
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],
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"id": "inc_text_classification_quantization_py",
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"steps": [
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"python INC_QuantizationAwareTraining_TextClassification.py"
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"uv run INC_QuantizationAwareTraining_TextClassification.py"
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]
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}
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]

‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/sample.json‎

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"id": "intel pytorch gpu inference optimization with amp",
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"steps": [
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"source /intel/oneapi/intelpython/bin/activate",
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"source activate pytorch-gpu",
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"pip install -r requirements.txt",
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"pip install jupyter ipykernel",
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"python -m ipykernel install --user --name=pytorch-gpu",
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"python IntelPyTorch_GPU_InferenceOptimization_with_AMP.py"
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"conda activate pytorch-gpu",
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"pip install uv notebook",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add -r requirements.txt",
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"uv add numpy==1.26.4",
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"uv add --dev ipykernel",
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"uv run ipython kernel install --user --name=pytorch-gpu",
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"uv run IntelPyTorch_GPU_InferenceOptimization_with_AMP.py"
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]
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}
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]

‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/sample.json‎

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"steps": [
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"source /intel/oneapi/intelpython/bin/activate",
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"conda activate pytorch",
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"python -m pip install -r requirements.txt",
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"python -m ipykernel install --user --name=pytorch",
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"python pytorch_training_avx512_bf16.py",
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"python pytorch_training_amx_bf16.py",
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"jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=pytorch --to notebook IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb"
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"pip install uv",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add -r requirements.txt",
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"uv add --dev ipykernel notebook",
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"uv run ipython kernel install --user --name pytorch",
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"uv run python pytorch_training_avx512_bf16.py",
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"uv run python pytorch_training_amx_bf16.py",
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"uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=pytorch --to notebook IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb"
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]
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}
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]

‎AI-and-Analytics/Features-and-Functionality/IntelPython_GPU_dpnp_Genetic_Algorithm/sample.json‎

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"os":["linux"],
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"targetDevice": ["GPU"],
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"ciTests": {
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"linux": [
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"linux": [
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{
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"env": [
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"env": [
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"source /intel/oneapi/intelpython/bin/activate",
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"conda activate base",
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"pip install -r requirements.txt"
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"pip install uv",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add -r requirements.txt"
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],
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"id": "idp_ga_dpnp_py",
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"steps": [
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"python IntelPython_GPU_dpnp_Genetic_Algorithm.py"
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]
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}
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"id": "idp_ga_dpnp_py",
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"steps": [
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"uv run python IntelPython_GPU_dpnp_Genetic_Algorithm.py"
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]
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}
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]
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},
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"expertise": "Code Optimization"

‎AI-and-Analytics/Features-and-Functionality/IntelPython_Numpy_Numba_dpnp_kNN/sample.json‎

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"env": [
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"source /intel/oneapi/intelpython/bin/activate",
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"conda activate base",
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"pip install numba"
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"pip install uv",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add numba"
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],
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"id": "idp_numpy_numba_dpnp_gs_py",
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"steps": [
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"python IntelPython_Numpy_Numba_dpnp_kNN.py"
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]
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}
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"id": "idp_numpy_numba_dpnp_gs_py",
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"steps": [
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"uv run python IntelPython_Numpy_Numba_dpnp_kNN.py"
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]
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}
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]
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},
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"expertise": "Code Optimization"

‎AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_Performance/requirements.txt‎

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###### Requirements ######
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notebook
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matplot
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xgboost==0.81
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xgboost==0.82
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scikit-learn
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pandas

‎AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_Performance/sample.json‎

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"os":["linux"],
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"targetDevice": ["CPU"],
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"ciTests": {
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"linux": [
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"linux": [
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{
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"id": "idp_xgb_performance",
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"steps": [
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"source /intel/oneapi/intelpython/bin/activate",
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"python IntelPython_XGBoost_Performance.py",
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"conda remove xgboost --y",
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"pip install -r requirements.txt",
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"python IntelPython_XGBoost_Performance.py"
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]
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}
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"env": [
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"source /intel/oneapi/intelpython/bin/activate",
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"conda activate base"
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],
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"id": "idp_xgb_performance",
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"steps": [
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"python IntelPython_XGBoost_Performance.py",
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"pip install uv",
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"uv init",
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"uv python pin $(which python)",
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"uv venv --system-site-packages",
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"uv add -r requirements.txt",
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"uv add numpy==1.26.4",
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"uv run python IntelPython_XGBoost_Performance.py"
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]
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}
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]
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},
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"expertise": "Code Optimization"

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