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UV conversion Features-and-Functionality dir (#2)
* Migration to uv for intel_pytorch_gpu_inference_optimization_with_amp * uv for genetic_algorithms_on_gpu_using_intel_distribution_of_python_dpnp * uv for quantizing_transformer_model_using_intel_extension_for_transformers_(itrex) Signed-off-by: Mora Jimenez, Kevin <[email protected]> * working tests with uv: tensorflow-amx-bf16-inference and training * uv for IntelPyTorch_TrainingOptimization_AMX_BF16 * uv set up for IntelPython_Numpy_Numba_dpnp_kNN * uv for tensorflow_transformer_with_advanced_matrix_extensions_bfloat16_mixed_precision_learning. Signed-off-by: Mora Jimenez, Kevin <[email protected]> * add uv to IntelPython_XGBoost_Performance * Fix: add pip install uv to samples.json AMX_BF16 and Numpy_Numba_dpnp Signed-off-by: Mora Jimenez, Kevin <[email protected]> Co-authored-by: Mora Brenes, Allan <[email protected]> Co-authored-by: Mora Jimenez, Kevin <[email protected]> Co-authored-by: Edgar Parra <[email protected]>
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‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/pyproject.toml

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[project]
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name = "intel_pytorch_gpu_inference_optimization_with_amp"
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version = "0.1.0"
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description = "This sample illustrates how to use AMP BFLOAT16 in PyTorch on Intel dGPU"
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authors = [
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{name = "Copyright © 2023 Intel Corporation"}
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]
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license = {text = "MIT"}
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"deepspeed==0.15.4",
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"intel-extension-for-pytorch==2.5.10",
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"matplotlib>=3.10.1",
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"neural-compressor==3.1.1",
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"numpy==1.26.4",
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"oneccl-bind-pt==2.5.0",
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"requests>=2.32.3",
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"torch==2.5.1",
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"torchaudio==2.5.1",
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"torchvision==0.20.1",
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"tqdm>=4.67.1",
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]
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[dependency-groups]
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dev = [
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"ipykernel>=6.29.5",
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"jupyter>=1.1.1",
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]
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[[tool.uv.index]]
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url = "https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/"

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

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{
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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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"pip install uv==0.6.3",
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"uv sync",
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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_GPU_InferenceOptimization_with_AMP/uv.lock

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‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/pyproject.toml

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[project]
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name = "intelpytorch-trainingoptimizations-amx-bf16"
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version = "0.1.0"
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description = "This sample illustrates how to train a PyTorch model using Advanced Matrix Extensions Bfloat16"
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authors = [
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{name = "Copyright © 2020 Intel Corporation"}
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]
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license = {text = "MIT"}
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"deepspeed==0.15.4",
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"intel-extension-for-pytorch==2.5.0",
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"ipywidgets>=8.1.5",
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"jupyter>=1.1.1",
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"matplotlib>=3.10.1",
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"neural-compressor==3.1.1",
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"numpy==1.26.4",
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"oneccl-bind-pt==2.5.0",
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"onnxruntime==1.19.2",
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"py-cpuinfo>=9.0.0",
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"setuptools>=75.8.2",
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"torch==2.5.0",
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"torchaudio==2.5.0",
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"torchvision==0.20.0",
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"transformers>=4.49.0",
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]
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[tool.uv.sources]
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oneccl-bind-pt = { index = "intel" }
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[[tool.uv.index]]
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name = "intel"
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url = "https://pytorch-extension.intel.com/release-whl/stable/cpu/cn/"
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[dependency-groups]
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dev = [
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"ipykernel>=6.29.5",
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]

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

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{
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"id": "intel amx bf16 training",
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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==0.6.3",
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"uv venv",
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"uv sync",
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"uv run python -m ipykernel install --user --name=torch_trainOpt_AMX_BF16",
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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=torch_trainOpt_AMX_BF16 --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/IntelPyTorch_TrainingOptimizations_AMX_BF16/uv.lock

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‎AI-and-Analytics/Features-and-Functionality/IntelPython_GPU_dpnp_Genetic_Algorithm/pyproject.toml

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[project]
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name = "genetic_algorithms_on_gpu_using_intel_distribution_of_python_dpnp"
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version = "0.1.0"
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description = "This sample shows how to implement general genetic algorithm (GA) and offload computation to GPU using dpnp."
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authors = [
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{name = "Copyright © 2023 Intel Corporation"}
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]
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license = {text = "MIT"}
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"dpnp>=0.16.3",
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"matplotlib>=3.10.1",
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]
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[dependency-groups]
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dev = [
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"ipykernel>=6.29.5",
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"jupyter>=1.1.1",
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]
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[[tool.uv.index]]
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url = "https://software.repos.intel.com/python/pypi"

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

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"linux": [
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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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"pip install -r requirements.txt"
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"pip install uv==0.6.3",
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"uv sync"
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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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"uv run IntelPython_GPU_dpnp_Genetic_Algorithm.py"
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]
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}
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]

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