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Pr1 fixes (#6)
* fix Intel_Extension_For_SKLearn_GettingStarted * fix INC-Sample-for-Tensorflow * fixed INC-Quantization-Sample-for-PyTorch * fix IntelTransformers_Quantization * fix IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision * fixed IntelTensorFlow_InferenceOptimization * fixed IntelTensorFlow_AMX_BF16_Inference * fixed IntelTensorFlow_AMX_BF16_Training * fix IntelPyTorch_TrainingOptimizations_AMX_BF16 * fix IntelPyTorch_GPU_InferenceOptimization_with_AMP
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‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb

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},
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{
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"cell_type": "markdown",
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"id": "0d6d8fb4-983b-417f-88c3-dc2747de3d4a",
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"id": "415587fc",
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"metadata": {},
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"source": [
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"### Model Training on CPU(Optional, if device is CPU) "
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1ff86b79-9bc8-4e6e-8d43-b2cfa1eb500f",
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"id": "69093d44",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "87063fd1",
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"id": "7f5ef9d6",
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"metadata": {},
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"source": [
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"Model Evaluation on CPU(optional, if trained on CPU)"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8a59b600",
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"id": "c84c5b1e",
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"metadata": {},
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"outputs": [],
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"source": [
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]
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}
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],
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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},
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"vscode": {
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"interpreter": {
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"hash": "ed6ae0d06e7bec0fef5f1fb38f177ceea45508ce95c68ed2f49461dd6a888a39"
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}
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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

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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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requires-python = ">=3.11,<3.12"
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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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[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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‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/sample.json

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"steps": [
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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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"uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --to notebook IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb"
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]
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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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}

‎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/IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb

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"id": "2f7486b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"ONEDNN_MAX_CPU_ISA\"] = \"AVX512_CORE_BF16\""
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]
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"source": []
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},
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{
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"cell_type": "code",
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" training_time: the time in seconds it takes to train the model\n",
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" \"\"\"\n",
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"\n",
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" if amx:\n",
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" # Set the environment variable to enable Intel® AMX\n",
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" os.environ[\"ONEDNN_MAX_CPU_ISA\"] = \"AVX512_CORE_AMX\"\n",
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" else:\n",
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" # Set the environment variable to disable Intel® AMX\n",
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" os.environ[\"ONEDNN_MAX_CPU_ISA\"] = \"AVX512_CORE_BF16\"\n",
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"\n",
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" # Initialize the model \n",
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" model = torchvision.models.resnet50()\n",
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" model = model.to(memory_format=torch.channels_last)\n",
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"outputs": [],
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"print(\"Training model with BF16 with AVX512\")\n",
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"!python pytorch_training_avx512_bf16.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a9bd6dec",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Record the training time for BF16 using AVX512\n",
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"bf16_avx512_training_time = None #TODO: enter in training time"
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"bf16_avx512_training_time = trainModel(train_loader, amx=False, modelName=\"bf16_withAmx\", dataType=\"bf16\")"
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]
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{
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"outputs": [],
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"print(\"Training model with BF16 with Intel® AMX\")\n",
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"bf16_amx_training_time = trainModel(train_loader, modelName=\"bf16_withAmx\", dataType=\"bf16\")"
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"bf16_amx_training_time = trainModel(train_loader, amx=True, modelName=\"bf16_noAmx\", dataType=\"bf16\")"
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]
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{
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit (microsoft store)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"vscode": {
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"interpreter": {
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"hash": "ed6ae0d06e7bec0fef5f1fb38f177ceea45508ce95c68ed2f49461dd6a888a39"
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}
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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

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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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requires-python = ">=3.11,<3.12"
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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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"ipykernel>=6.29.5",
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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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"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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torch = { index = "pytorch" }
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torchvision = { index = "pytorch" }
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[[tool.uv.index]]
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name = "pytorch"
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# Required URL for the index.
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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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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url = "https://software.repos.intel.com/python/pypi"
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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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]

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

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"id": "intel amx bf16 training",
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"steps": [
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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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"uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --to notebook IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb"
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]
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}
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]

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