diff --git a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb index bd779e9f51..2a14ccc0e1 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb @@ -296,7 +296,7 @@ }, { "cell_type": "markdown", - "id": "0d6d8fb4-983b-417f-88c3-dc2747de3d4a", + "id": "415587fc", "metadata": {}, "source": [ "### Model Training on CPU(Optional, if device is CPU) " @@ -305,7 +305,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1ff86b79-9bc8-4e6e-8d43-b2cfa1eb500f", + "id": "69093d44", "metadata": {}, "outputs": [], "source": [ @@ -518,7 +518,7 @@ }, { "cell_type": "markdown", - "id": "87063fd1", + "id": "7f5ef9d6", "metadata": {}, "source": [ "Model Evaluation on CPU(optional, if trained on CPU)" @@ -527,7 +527,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8a59b600", + "id": "c84c5b1e", "metadata": {}, "outputs": [], "source": [ @@ -616,25 +616,7 @@ ] } ], - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "vscode": { - "interpreter": { - "hash": "ed6ae0d06e7bec0fef5f1fb38f177ceea45508ce95c68ed2f49461dd6a888a39" - } - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/pyproject.toml b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/pyproject.toml new file mode 100644 index 0000000000..a1cd26ea30 --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/pyproject.toml @@ -0,0 +1,32 @@ +[project] +name = "intel_pytorch_gpu_inference_optimization_with_amp" +version = "0.1.0" +description = "This sample illustrates how to use AMP BFLOAT16 in PyTorch on Intel dGPU" +authors = [ + {name = "Copyright © 2023 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.12" +dependencies = [ + "deepspeed==0.15.4", + "intel-extension-for-pytorch==2.5.10", + "matplotlib>=3.10.1", + "neural-compressor==3.1.1", + "numpy==1.26.4", + "oneccl-bind-pt==2.5.0", + "requests>=2.32.3", + "torch==2.5.1", + "torchaudio==2.5.1", + "torchvision==0.20.1", + "tqdm>=4.67.1", +] + + +[dependency-groups] +dev = [ + "jupyter>=1.1.1", +] + +[[tool.uv.index]] +url = "https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/" diff --git a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/sample.json b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/sample.json index e01073f174..1f663a9f5e 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/sample.json +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/sample.json @@ -13,15 +13,12 @@ { "id": "intel pytorch gpu inference optimization with amp", "steps": [ - "source /intel/oneapi/intelpython/bin/activate", - "source activate pytorch-gpu", - "pip install -r requirements.txt", - "pip install jupyter ipykernel", - "python -m ipykernel install --user --name=pytorch-gpu", - "python IntelPyTorch_GPU_InferenceOptimization_with_AMP.py" - ] + "pip install uv==0.6.3", + "uv sync", + "uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --to notebook IntelPyTorch_GPU_InferenceOptimization_with_AMP.ipynb" + ] } - ] + ] }, "expertise": "Code Optimization" } diff --git a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/uv.lock b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/uv.lock new file mode 100644 index 0000000000..0049cced5a --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_GPU_InferenceOptimization_with_AMP/uv.lock @@ -0,0 +1,2285 @@ +version = 1 +revision = 1 +requires-python = "==3.11.*" +resolution-markers = [ + "sys_platform == 'darwin'", + "platform_machine == 'aarch64' and sys_platform == 'linux'", + "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')", +] + +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb index ae9fdb76a5..2a077088ff 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb @@ -127,9 +127,7 @@ "id": "2f7486b6", "metadata": {}, "outputs": [], - "source": [ - "os.environ[\"ONEDNN_MAX_CPU_ISA\"] = \"AVX512_CORE_BF16\"" - ] + "source": [] }, { "cell_type": "code", @@ -152,6 +150,13 @@ " training_time: the time in seconds it takes to train the model\n", " \"\"\"\n", "\n", + " if amx:\n", + " # Set the environment variable to enable Intel® AMX\n", + " os.environ[\"ONEDNN_MAX_CPU_ISA\"] = \"AVX512_CORE_AMX\"\n", + " else:\n", + " # Set the environment variable to disable Intel® AMX\n", + " os.environ[\"ONEDNN_MAX_CPU_ISA\"] = \"AVX512_CORE_BF16\"\n", + "\n", " # Initialize the model \n", " model = torchvision.models.resnet50()\n", " model = model.to(memory_format=torch.channels_last)\n", @@ -265,18 +270,7 @@ "outputs": [], "source": [ "print(\"Training model with BF16 with AVX512\")\n", - "!python pytorch_training_avx512_bf16.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9bd6dec", - "metadata": {}, - "outputs": [], - "source": [ - "# Record the training time for BF16 using AVX512\n", - "bf16_avx512_training_time = None #TODO: enter in training time" + "bf16_avx512_training_time = trainModel(train_loader, amx=False, modelName=\"bf16_withAmx\", dataType=\"bf16\")" ] }, { @@ -287,7 +281,7 @@ "outputs": [], "source": [ "print(\"Training model with BF16 with Intel® AMX\")\n", - "bf16_amx_training_time = trainModel(train_loader, modelName=\"bf16_withAmx\", dataType=\"bf16\")" + "bf16_amx_training_time = trainModel(train_loader, amx=True, modelName=\"bf16_noAmx\", dataType=\"bf16\")" ] }, { @@ -389,30 +383,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 64-bit (microsoft store)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "ed6ae0d06e7bec0fef5f1fb38f177ceea45508ce95c68ed2f49461dd6a888a39" - } - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/pyproject.toml b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/pyproject.toml new file mode 100644 index 0000000000..ce5eb0c196 --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/pyproject.toml @@ -0,0 +1,45 @@ +[project] +name = "intelpytorch-trainingoptimizations-amx-bf16" +version = "0.1.0" +description = "This sample illustrates how to train a PyTorch model using Advanced Matrix Extensions Bfloat16" +authors = [ + {name = "Copyright © 2020 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.12" +dependencies = [ + "deepspeed==0.15.4", + "intel-extension-for-pytorch==2.5.0", + "ipykernel>=6.29.5", + "ipywidgets>=8.1.5", + "jupyter>=1.1.1", + "matplotlib>=3.10.1", + "neural-compressor==3.1.1", + "numpy==1.26.4", + "onnxruntime==1.19.2", + "py-cpuinfo>=9.0.0", + "setuptools>=75.8.2", + "torch==2.5.0", + "torchaudio==2.5.0", + "torchvision>=0.20.0", + "transformers>=4.49.0", +] + +[tool.uv.sources] +torch = { index = "pytorch" } +torchvision = { index = "pytorch" } + +[[tool.uv.index]] +name = "pytorch" +# Required URL for the index. +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[[tool.uv.index]] +url = "https://software.repos.intel.com/python/pypi" + +[dependency-groups] +dev = [ + "jupyter>=1.1.1", +] diff --git a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/sample.json b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/sample.json index bebcc021cf..cb1154d101 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/sample.json +++ b/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/sample.json @@ -13,13 +13,9 @@ { "id": "intel amx bf16 training", "steps": [ - "source /intel/oneapi/intelpython/bin/activate", - "conda activate pytorch", - "python -m pip install -r requirements.txt", - "python -m ipykernel install --user --name=pytorch", - "python pytorch_training_avx512_bf16.py", - "python pytorch_training_amx_bf16.py", - 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b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/IntelTensorFlow_AMX_BF16_Inference.ipynb index 2857ee7b5a..482ee33518 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/IntelTensorFlow_AMX_BF16_Inference.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/IntelTensorFlow_AMX_BF16_Inference.ipynb @@ -432,25 +432,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/pyproject.toml 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a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/sample.json b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/sample.json index 5b232dabf0..ce89fb3e73 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/sample.json +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/sample.json @@ -11,15 +11,13 @@ "ciTests": { "linux": [ { + "env": [ + "pip install uv==0.6.3", + "uv sync" + ], "id": "intel amx bf16 inference", "steps": [ - "source /intel/oneapi/intelpython/bin/activate", - "conda activate tensorflow", - "pip install -r requirements.txt --no-deps", - "pip install ipykernel jupyter", - "python Intel_TensorFlow_AMX_BF16_Inference.py", - "python -m ipykernel install --user --name=tensorflow", - "jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=tensorflow --to notebook IntelTensorFlow_AMX_BF16_Inference.ipynb" + "uv 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"sha256:26046cd03936ae745a502abf44dac702a5e6880b2b01c29aea8ddf3353b68224", size = 35616 }, +] diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/IntelTensorFlow_AMX_BF16_Training.ipynb b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/IntelTensorFlow_AMX_BF16_Training.ipynb index 7e28e181ff..7057eb112e 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/IntelTensorFlow_AMX_BF16_Training.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/IntelTensorFlow_AMX_BF16_Training.ipynb @@ -59,7 +59,10 @@ "is_tune_model = True # or False\n", "log_dir = \"logs\"\n", "profiling_needed = False\n", + "execution_mode_param = os.getenv('execution_mode')\n", "execution_mode = \"graph\"\n", + "if execution_mode_param == \"eager\":\n", + " execution_mode = \"eager\"\n", "load_weights_dir = \"weights\"\n", "save_weights_dir = \"weights\"" ] @@ -165,9 +168,7 @@ "cell_type": "code", "execution_count": null, "id": "de61f744-d2af-403f-b948-154431b4624e", - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ "train = pd.read_csv(\"data/train.csv\")\n", @@ -182,9 +183,7 @@ { "cell_type": "markdown", "id": "2f1a474d-7070-46f1-8d3e-569f656d3695", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ "# Training with FP32 and BF16, including Intel® AMX\n", "Train the DistilBERT model in three different cases:\n", @@ -373,25 +372,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "tf_amx_bf16_2", - "language": "python", - "name": "tf_amx_bf16_2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/pyproject.toml b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/pyproject.toml new file mode 100644 index 0000000000..00de32ec59 --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/pyproject.toml @@ -0,0 +1,37 @@ +[project] +name = "inteltensorflow-amx-bf16-training" +version = "0.1.0" +description = "This sample illustrates how to train a TensorFlow model using Advanced Matrix Extensions Bfloat16" +authors = [ + {name = "Copyright © 2020 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.12" +dependencies = [ + "absl-py>=1.4.0", + "astunparse>=1.6.3", + "h5py>=3.13.0", + "huggingface-hub>=0.29.1", + "intel-extension-for-tensorflow>=2.15.0", + "ipykernel>=6.29.5", + "keras>=2.15.0", + "keras-preprocessing>=1.1.2", + "matplotlib>=3.10.1", + "numpy>=1.26.4", + "pandas>=2.2.3", + "pillow>=11.1.0", + "py-cpuinfo>=9.0.0", + "tensorboard>=2.15.2", + "tensorboard-data-server>=0.7.2", + "tensorboard-plugin-wit>=1.8.1", + "tf-estimator-nightly>=2.16.0.dev2024012409", + "tokenizers>=0.21.0", + "transformers>=4.49.0", +] +[dependency-groups] +dev = [ + "jupyter>=1.1.1", +] +[[tool.uv.index]] +url = "https://software.repos.intel.com/python/pypi" diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/sample.json b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/sample.json index dbcdcb77ad..dc6843cab8 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/sample.json +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/sample.json @@ -10,15 +10,14 @@ "cpuInstructionSets": ["AVX512", "AMX"], "ciTests": { "linux": [{ + "env": [ + "pip install uv==0.6.3", + "uv sync" + ], "id": "intel amx bf16 training", "steps": [ - "source 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"neural-compressor==2.4.1", + "pandas==2.2.3", +] +[dependency-groups] +dev = [ + "jupyter>=1.1.1", +] +[[tool.uv.index]] +url = "https://software.repos.intel.com/python/pypi" diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/sample.json b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/sample.json index 5e43dba12e..79fbebb88f 100755 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/sample.json +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/sample.json @@ -11,14 +11,13 @@ "targetDevice": ["CPU"], "ciTests": { "linux": [{ + "env": [ + "pip install uv==0.6.3", + "uv sync" + ], "id": "tensorflow_inference_optimization", "steps": [ - "source /intel/oneapi/intelpython/bin/activate", - "conda activate tensorflow", - "pip install tensorflow==2.15.0.post1", - "pip install ipykernel jupyter", - "python -m ipykernel install --user --name=tensorflow", - "jupyter nbconvert --to notebook --execute tutorial_optimize_TensorFlow_pretrained_model.ipynb" + "uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --to notebook tutorial_optimize_TensorFlow_pretrained_model.ipynb" ] }] }, diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/tutorial_optimize_TensorFlow_pretrained_model.ipynb b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/tutorial_optimize_TensorFlow_pretrained_model.ipynb index 967e68e86a..c4305cd587 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/tutorial_optimize_TensorFlow_pretrained_model.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_InferenceOptimization/tutorial_optimize_TensorFlow_pretrained_model.ipynb @@ -442,20 +442,7 @@ ] } ], - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - 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b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision.ipynb index be5a5e83df..bf73f98493 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision.ipynb @@ -72,6 +72,41 @@ "!wget https://raw.githubusercontent.com/keras-team/keras-io/master/examples/nlp/text_classification_with_transformer.py" ] }, + { + "cell_type": "markdown", + "id": "e738ccdb", + "metadata": {}, + "source": [ + "**Run this cell only if the 'text_classification_with_transformer.py' downloaded file doesn't have import time**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72b9a774", + "metadata": {}, + "outputs": [], + "source": [ + "# Read the downloaded file\n", + "with open('text_classification_with_transformer.py', 'r') as file:\n", + " lines = file.readlines()\n", + "\n", + "# Find the end of the initial docstring\n", + "end_of_docstring_index = 0\n", + "for i, line in enumerate(lines):\n", + " if line.strip() == '\"\"\"' and i != 0: # Find the closing triple quotes of the docstring\n", + " end_of_docstring_index = i\n", + " break\n", + "\n", + "# Insert missing import statements after the docstring\n", + "if 'import time' not in lines[end_of_docstring_index + 1:]:\n", + " lines.insert(end_of_docstring_index + 1, 'from time import time\\nimport tensorflow as tf\\n')\n", + "\n", + "# Write the modified content back to the file\n", + "with open('text_classification_with_transformer.py', 'w') as file:\n", + " file.writelines(lines)" + ] + }, { "cell_type": "markdown", "id": "22554015-3cd4-4654-af42-2afcc801ff75", @@ -163,7 +198,14 @@ "echo \"########## Executing the run\"\n", "\n", "source /opt/intel/oneapi/setvars.sh\n", - "source activate tensorflow\n", + "# Check if the \"tensorflow\" environment exists in Conda\n", + "if conda info --envs 2>/dev/null | grep -q \"tensorflow\"; then\n", + " # Activate the Conda environment\n", + " source activate tensorflow\n", + "else\n", + " # Activate the virtual environment\n", + " source tensorflow/bin/activate\n", + "fi\n", "\n", "ONEDNN_VERBOSE_TIMESTAMP=1 ONEDNN_VERBOSE=1 python ./text_classification_with_transformer.py > ./logs/dnn_logs.txt\n", "\n", @@ -191,7 +233,7 @@ "metadata": {}, "outputs": [], "source": [ - "!export property=spr; chmod 755 q; chmod 755 job.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q job.sh; else ./job.sh; fi" + "!export property=spr; chmod 755 ../../q; chmod 755 job.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q job.sh; else ./job.sh; fi" ] }, { @@ -261,7 +303,14 @@ "echo \"########## Executing the run\"\n", "\n", "source /opt/intel/oneapi/setvars.sh\n", - "source activate tensorflow\n", + "# Check if the \"tensorflow\" environment exists in Conda\n", + "if conda info --envs 2>/dev/null | grep -q \"tensorflow\"; then\n", + " # Activate the Conda environment\n", + " source activate tensorflow\n", + "else\n", + " # Activate the virtual environment\n", + " source tensorflow/bin/activate\n", + "fi\n", "\n", "ONEDNN_VERBOSE_TIMESTAMP=1 ONEDNN_VERBOSE=1 python ./text_classification_with_transformer.py > ./logs/dnn_logs_mixed.txt\n", "\n", @@ -275,7 +324,7 @@ "metadata": {}, "outputs": [], "source": [ - "!export property=spr; chmod 755 q; chmod 755 job_mixed.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q job_mixed.sh; else ./job_mixed.sh; fi" + "!export property=spr; chmod 755 ../../q; chmod 755 job_mixed.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q job_mixed.sh; else ./job_mixed.sh; fi" ] }, { @@ -341,7 +390,14 @@ "\n", "echo \"########## Executing the run\"\n", "\n", - "source activate tensorflow\n", + "# Check if the \"tensorflow\" environment exists in Conda\n", + "if conda info --envs 2>/dev/null | grep -q \"tensorflow\"; then\n", + " # Activate the Conda environment\n", + " source activate tensorflow\n", + "else\n", + " # Activate the virtual environment\n", + " source tensorflow/bin/activate\n", + "fi\n", "\n", "# enable verbose log\n", "export DNNL_VERBOSE=2 \n", @@ -360,7 +416,7 @@ "metadata": {}, "outputs": [], "source": [ - "!export property=spr; chmod 755 q; chmod 755 run_amx.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q run_amx.sh; else ./run_amx.sh; fi" + "!export property=spr; chmod 755 ../../q; chmod 755 run_amx.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q run_amx.sh; else ./run_amx.sh; fi" ] }, { @@ -385,7 +441,14 @@ "\n", "echo \"########## Executing the run\"\n", "\n", - "source activate tensorflow\n", + "# Check if the \"tensorflow\" environment exists in Conda\n", + "if conda info --envs 2>/dev/null | grep -q \"tensorflow\"; then\n", + " # Activate the Conda environment\n", + " source activate tensorflow\n", + "else\n", + " # Activate the virtual environment\n", + " source tensorflow/bin/activate\n", + "fi\n", "\n", "# enable verbose log\n", "export DNNL_VERBOSE=2 \n", @@ -404,7 +467,7 @@ "metadata": {}, "outputs": [], "source": [ - "!export property=spr; chmod 755 q; chmod 755 run.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q run.sh; else ./run.sh; fi" + "!export property=spr; chmod 755 ../../q; chmod 755 run.sh; if [ -x \"$(command -v qsub)\" ]; then ../../q run.sh; else ./run.sh; fi" ] }, { @@ -506,30 +569,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.11 ('idp')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "vscode": { - "interpreter": { - "hash": "f954598d05c49c7b2a3e840295d971716fdeffd17843be95d2639682acd709ff" - } - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/pyproject.toml b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/pyproject.toml new file mode 100644 index 0000000000..aeb71d26e5 --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "tensorflow_transformer_with_advanced_matrix_extensions_bfloat16_mixed_precision_learning" +version = "0.1.0" +description = "This sample demonstrates optimizing a TensorFlow* model with Intel® Advanced Matrix Extensions (Intel® AMX) using bfloat16 (Brain Floating Point) on 4th Gen Intel® Xeon® processors (formerly Sapphire Rapids)." +authors = [ + {name = "Copyright © 2023 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.12" +dependencies = [ + "intel-extension-for-tensorflow[cpu]>=2.15.0", + "keras==3.0", + "neural-compressor>=3.3", + "onnxruntime>=1.20.1", +] + +[tool.uv] +override-dependencies = ["keras>=3.0"] + +[dependency-groups] +dev = [ + "jupyter>=1.1.1", +] diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/sample.json b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/sample.json index 884c000945..268168e0e4 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/sample.json +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/sample.json @@ -11,14 +11,13 @@ "ciTests": { "linux": [ { - "env": [], + "env": [ + "pip install uv==0.6.3", + "uv sync" + ], "id": "Transformer_AMX_bfloat16_Mixed_Precision_Learning", "steps": [ - "source /intel/oneapi/intelpython/bin/activate", - "conda activate tensorflow", - "pip install jupyter ipykernel", - "python -m ipykernel install --user --name=tensorflow", - "jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=tensorflow --to notebook IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision.ipynb" + "uv run --active jupyter nbconvert --ExecutePreprocessor.enabled=True --to notebook IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision.ipynb" ] } ] diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/uv.lock b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/uv.lock new file mode 100644 index 0000000000..459444818a --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/uv.lock @@ -0,0 +1,2229 @@ 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This model, a fine-tuned iteration of *Mistral-7B*, undergoes quantization utilizing Weight Only Quantization (WOQ) techniques provided by Intel® Extension for Transformers (ITREX)." +authors = [ + {name = "Copyright © 2023 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.12" +dependencies = [ + "accelerate==0.29.3", + "datasets==2.9.0", + "deepspeed==0.15.4", + "intel-extension-for-pytorch==2.5.0", + "intel-extension-for-transformers==1.4.1", + "neural-compressor==2.6", + "neural-speed==1.0", + "numpy==1.26.4", + "onnxruntime==1.19.2", + "peft==0.10.0", + "sentencepiece>=0.2.0", + "setuptools>=75.8.2", + "torch==2.5.0", + "torchaudio==2.5.0", + "torchvision==0.20.0", + "transformers==4.38.0", +] + +[dependency-groups] +dev = [ + "ipykernel>=6.29.5", + "jupyter>=1.1.1", +] diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/quantize_transformer_models_with_itrex.ipynb b/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/quantize_transformer_models_with_itrex.ipynb index 5bab949ccc..9587484dbd 100644 --- a/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/quantize_transformer_models_with_itrex.ipynb +++ b/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/quantize_transformer_models_with_itrex.ipynb @@ -57,27 +57,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/localdisk1/ankur/intel/oneapi/intelpython/envs/user_pytorch/lib/python3.9/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n", - " _torch_pytree._register_pytree_node(\n", - "/mnt/localdisk1/ankur/intel/oneapi/intelpython/envs/user_pytorch/lib/python3.9/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n", - " _torch_pytree._register_pytree_node(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-05-08 11:02:44,757] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cpu (auto detect)\n" - ] - } - ], + "outputs": [], "source": [ "from transformers import AutoTokenizer\n", "from intel_extension_for_transformers.transformers import AutoModelForCausalLM\n", @@ -96,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -118,24 +100,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7989a962351a41f2b80334c9d3e0e56e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Loading checkpoint shards: 0%| | 0/2 [00:00>,\n", - "2024-05-08 11:04:40 [INFO] 'relative': 0.01\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'approach': 'post_training_weight_only',\n", - "2024-05-08 11:04:40 [INFO] 'backend': 'default',\n", - "2024-05-08 11:04:40 [INFO] 'calibration_sampling_size': [\n", - "2024-05-08 11:04:40 [INFO] 100\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'device': 'cpu',\n", - "2024-05-08 11:04:40 [INFO] 'diagnosis': False,\n", - "2024-05-08 11:04:40 [INFO] 'domain': 'auto',\n", - "2024-05-08 11:04:40 [INFO] 'example_inputs': 'Not printed here due to large size tensors...',\n", - "2024-05-08 11:04:40 [INFO] 'excluded_precisions': [\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'framework': 'pytorch_fx',\n", - "2024-05-08 11:04:40 [INFO] 'inputs': [\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'model_name': '',\n", - "2024-05-08 11:04:40 [INFO] 'ni_workload_name': 'quantization',\n", - "2024-05-08 11:04:40 [INFO] 'op_name_dict': {\n", - "2024-05-08 11:04:40 [INFO] '.*lm_head': {\n", - "2024-05-08 11:04:40 [INFO] 'weight': {\n", - "2024-05-08 11:04:40 [INFO] 'dtype': [\n", - "2024-05-08 11:04:40 [INFO] 'fp32'\n", - "2024-05-08 11:04:40 [INFO] ]\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] '.*output_layer': {\n", - "2024-05-08 11:04:40 [INFO] 'weight': {\n", - "2024-05-08 11:04:40 [INFO] 'dtype': [\n", - "2024-05-08 11:04:40 [INFO] 'fp32'\n", - "2024-05-08 11:04:40 [INFO] ]\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] '.*embed_out': {\n", - "2024-05-08 11:04:40 [INFO] 'weight': {\n", - "2024-05-08 11:04:40 [INFO] 'dtype': [\n", - "2024-05-08 11:04:40 [INFO] 'fp32'\n", - "2024-05-08 11:04:40 [INFO] ]\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'op_type_dict': {\n", - "2024-05-08 11:04:40 [INFO] '.*': {\n", - "2024-05-08 11:04:40 [INFO] 'weight': {\n", - "2024-05-08 11:04:40 [INFO] 'bits': [\n", - "2024-05-08 11:04:40 [INFO] 4\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'dtype': [\n", - "2024-05-08 11:04:40 [INFO] 'nf4'\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'group_size': [\n", - "2024-05-08 11:04:40 [INFO] 32\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'scheme': [\n", - "2024-05-08 11:04:40 [INFO] 'sym'\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'algorithm': [\n", - "2024-05-08 11:04:40 [INFO] 'RTN'\n", - "2024-05-08 11:04:40 [INFO] ]\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'outputs': [\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'quant_format': 'default',\n", - "2024-05-08 11:04:40 [INFO] 'quant_level': 'auto',\n", - "2024-05-08 11:04:40 [INFO] 'recipes': {\n", - "2024-05-08 11:04:40 [INFO] 'smooth_quant': False,\n", - "2024-05-08 11:04:40 [INFO] 'smooth_quant_args': {\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'layer_wise_quant': False,\n", - "2024-05-08 11:04:40 [INFO] 'layer_wise_quant_args': {\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'fast_bias_correction': False,\n", - "2024-05-08 11:04:40 [INFO] 'weight_correction': False,\n", - "2024-05-08 11:04:40 [INFO] 'gemm_to_matmul': True,\n", - "2024-05-08 11:04:40 [INFO] 'graph_optimization_level': None,\n", - "2024-05-08 11:04:40 [INFO] 'first_conv_or_matmul_quantization': True,\n", - "2024-05-08 11:04:40 [INFO] 'last_conv_or_matmul_quantization': True,\n", - "2024-05-08 11:04:40 [INFO] 'pre_post_process_quantization': True,\n", - "2024-05-08 11:04:40 [INFO] 'add_qdq_pair_to_weight': False,\n", - "2024-05-08 11:04:40 [INFO] 'optypes_to_exclude_output_quant': [\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'dedicated_qdq_pair': False,\n", - "2024-05-08 11:04:40 [INFO] 'rtn_args': {\n", - "2024-05-08 11:04:40 [INFO] 'enable_full_range': False,\n", - "2024-05-08 11:04:40 [INFO] 'enable_mse_search': False\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'awq_args': {\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'gptq_args': {\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'teq_args': {\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'reduce_range': None,\n", - "2024-05-08 11:04:40 [INFO] 'TuningCriterion': {\n", - "2024-05-08 11:04:40 [INFO] 'max_trials': 100,\n", - "2024-05-08 11:04:40 [INFO] 'objective': [\n", - "2024-05-08 11:04:40 [INFO] 'performance'\n", - "2024-05-08 11:04:40 [INFO] ],\n", - "2024-05-08 11:04:40 [INFO] 'strategy': 'basic',\n", - "2024-05-08 11:04:40 [INFO] 'strategy_kwargs': None,\n", - "2024-05-08 11:04:40 [INFO] 'timeout': 0\n", - "2024-05-08 11:04:40 [INFO] },\n", - "2024-05-08 11:04:40 [INFO] 'use_bf16': True\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [INFO] }\n", - "2024-05-08 11:04:40 [WARNING] [Strategy] Please install `mpi4py` correctly if using distributed tuning; otherwise, ignore this warning.\n", - "2024-05-08 11:04:40 [INFO] Pass query framework capability elapsed time: 17.85 ms\n", - "2024-05-08 11:04:40 [INFO] Do not evaluate the baseline and quantize the model with default configuration.\n", - "2024-05-08 11:04:40 [INFO] Quantize the model with default config.\n", - "2024-05-08 11:04:40 [INFO] All algorithms to do: {'RTN'}\n", - "2024-05-08 11:04:40 [INFO] quantizing with the round-to-nearest algorithm\n", - "2024-05-08 11:06:20 [INFO] |******Mixed Precision Statistics******|\n", - "2024-05-08 11:06:20 [INFO] +---------+-------+-----------+--------+\n", - "2024-05-08 11:06:20 [INFO] | Op Type | Total | A32W4G32 | FP32 |\n", - "2024-05-08 11:06:20 [INFO] +---------+-------+-----------+--------+\n", - "2024-05-08 11:06:20 [INFO] | Linear | 225 | 224 | 1 |\n", - "2024-05-08 11:06:20 [INFO] +---------+-------+-----------+--------+\n", - "2024-05-08 11:06:20 [INFO] Pass quantize model elapsed time: 100265.61 ms\n", - "2024-05-08 11:06:20 [INFO] Save tuning history to /mnt/localdisk1/ankur/oneAPI-samples/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/nc_workspace/2024-05-08_11-02-39/./history.snapshot.\n", - "2024-05-08 11:06:20 [INFO] [Strategy] Found the model meets accuracy requirements, ending the tuning process.\n", - "2024-05-08 11:06:20 [INFO] Specified timeout or max trials is reached! Found a quantized model which meet accuracy goal. Exit.\n", - "2024-05-08 11:06:20 [INFO] Save deploy yaml to /mnt/localdisk1/ankur/oneAPI-samples/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/nc_workspace/2024-05-08_11-02-39/deploy.yaml\n", - "2024-05-08 11:06:40 [INFO] WeightOnlyQuant done.\n", - "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", - "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" - ] - }, - { - "data": { - "text/plain": [ - "tensor([[ 1, 5713, 3714, 264, 727, 28725, 264, 1628, 2746, 5160,\n", - " 14003, 403, 7312, 1059, 272, 16748, 28723, 985, 403, 356,\n", - " 559, 1069, 298, 3251, 559, 19023, 28725, 693, 6262, 297,\n", - " 264, 1741, 2134, 438, 272, 5358, 302, 272, 8613, 28723,\n", - " 1136, 14003, 5610, 28725, 630, 8295, 264, 8708, 28725, 1272,\n", - " 15675, 2439, 5374, 778, 272, 7099, 28723, 6119, 925]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "q4_model = AutoModelForCausalLM.from_pretrained(\n", " model_name, load_in_4bit=True, use_neural_speed=False\n", @@ -373,196 +147,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-05-08 11:06:46 [INFO] cpu device is used.\n", - "2024-05-08 11:06:46 [INFO] Applying Weight Only Quantization.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6060da2237dc4c14a23bd9943f4691b5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Loading checkpoint shards: 0%| | 0/2 [00:00>,\n", - "2024-05-08 11:06:56 [INFO] 'relative': 0.01\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'approach': 'post_training_weight_only',\n", - "2024-05-08 11:06:56 [INFO] 'backend': 'default',\n", - "2024-05-08 11:06:56 [INFO] 'calibration_sampling_size': [\n", - "2024-05-08 11:06:56 [INFO] 100\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'device': 'cpu',\n", - "2024-05-08 11:06:56 [INFO] 'diagnosis': False,\n", - "2024-05-08 11:06:56 [INFO] 'domain': 'auto',\n", - "2024-05-08 11:06:56 [INFO] 'example_inputs': 'Not printed here due to large size tensors...',\n", - "2024-05-08 11:06:56 [INFO] 'excluded_precisions': [\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'framework': 'pytorch_fx',\n", - "2024-05-08 11:06:56 [INFO] 'inputs': [\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'model_name': '',\n", - "2024-05-08 11:06:56 [INFO] 'ni_workload_name': 'quantization',\n", - "2024-05-08 11:06:56 [INFO] 'op_name_dict': {\n", - "2024-05-08 11:06:56 [INFO] '.*lm_head': {\n", - "2024-05-08 11:06:56 [INFO] 'weight': {\n", - "2024-05-08 11:06:56 [INFO] 'dtype': [\n", - "2024-05-08 11:06:56 [INFO] 'fp32'\n", - "2024-05-08 11:06:56 [INFO] ]\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] '.*output_layer': {\n", - "2024-05-08 11:06:56 [INFO] 'weight': {\n", - "2024-05-08 11:06:56 [INFO] 'dtype': [\n", - "2024-05-08 11:06:56 [INFO] 'fp32'\n", - "2024-05-08 11:06:56 [INFO] ]\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] '.*embed_out': {\n", - "2024-05-08 11:06:56 [INFO] 'weight': {\n", - "2024-05-08 11:06:56 [INFO] 'dtype': [\n", - "2024-05-08 11:06:56 [INFO] 'fp32'\n", - "2024-05-08 11:06:56 [INFO] ]\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'op_type_dict': {\n", - "2024-05-08 11:06:56 [INFO] '.*': {\n", - "2024-05-08 11:06:56 [INFO] 'weight': {\n", - "2024-05-08 11:06:56 [INFO] 'bits': [\n", - "2024-05-08 11:06:56 [INFO] 8\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'dtype': [\n", - "2024-05-08 11:06:56 [INFO] 'int8'\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'group_size': [\n", - "2024-05-08 11:06:56 [INFO] 32\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'scheme': [\n", - "2024-05-08 11:06:56 [INFO] 'sym'\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'algorithm': [\n", - "2024-05-08 11:06:56 [INFO] 'RTN'\n", - "2024-05-08 11:06:56 [INFO] ]\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'outputs': [\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'quant_format': 'default',\n", - "2024-05-08 11:06:56 [INFO] 'quant_level': 'auto',\n", - "2024-05-08 11:06:56 [INFO] 'recipes': {\n", - "2024-05-08 11:06:56 [INFO] 'smooth_quant': False,\n", - "2024-05-08 11:06:56 [INFO] 'smooth_quant_args': {\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'layer_wise_quant': False,\n", - "2024-05-08 11:06:56 [INFO] 'layer_wise_quant_args': {\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'fast_bias_correction': False,\n", - "2024-05-08 11:06:56 [INFO] 'weight_correction': False,\n", - "2024-05-08 11:06:56 [INFO] 'gemm_to_matmul': True,\n", - "2024-05-08 11:06:56 [INFO] 'graph_optimization_level': None,\n", - "2024-05-08 11:06:56 [INFO] 'first_conv_or_matmul_quantization': True,\n", - "2024-05-08 11:06:56 [INFO] 'last_conv_or_matmul_quantization': True,\n", - "2024-05-08 11:06:56 [INFO] 'pre_post_process_quantization': True,\n", - "2024-05-08 11:06:56 [INFO] 'add_qdq_pair_to_weight': False,\n", - "2024-05-08 11:06:56 [INFO] 'optypes_to_exclude_output_quant': [\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'dedicated_qdq_pair': False,\n", - "2024-05-08 11:06:56 [INFO] 'rtn_args': {\n", - "2024-05-08 11:06:56 [INFO] 'enable_full_range': False,\n", - "2024-05-08 11:06:56 [INFO] 'enable_mse_search': False\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'awq_args': {\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'gptq_args': {\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'teq_args': {\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'reduce_range': None,\n", - "2024-05-08 11:06:56 [INFO] 'TuningCriterion': {\n", - "2024-05-08 11:06:56 [INFO] 'max_trials': 100,\n", - "2024-05-08 11:06:56 [INFO] 'objective': [\n", - "2024-05-08 11:06:56 [INFO] 'performance'\n", - "2024-05-08 11:06:56 [INFO] ],\n", - "2024-05-08 11:06:56 [INFO] 'strategy': 'basic',\n", - "2024-05-08 11:06:56 [INFO] 'strategy_kwargs': None,\n", - "2024-05-08 11:06:56 [INFO] 'timeout': 0\n", - "2024-05-08 11:06:56 [INFO] },\n", - "2024-05-08 11:06:56 [INFO] 'use_bf16': True\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [INFO] }\n", - "2024-05-08 11:06:56 [WARNING] [Strategy] Please install `mpi4py` correctly if using distributed tuning; otherwise, ignore this warning.\n", - "2024-05-08 11:06:56 [INFO] Pass query framework capability elapsed time: 19.44 ms\n", - "2024-05-08 11:06:56 [INFO] Do not evaluate the baseline and quantize the model with default configuration.\n", - "2024-05-08 11:06:56 [INFO] Quantize the model with default config.\n", - "2024-05-08 11:06:56 [INFO] All algorithms to do: {'RTN'}\n", - "2024-05-08 11:06:56 [INFO] quantizing with the round-to-nearest algorithm\n", - "2024-05-08 11:07:11 [INFO] |******Mixed Precision Statistics******|\n", - "2024-05-08 11:07:11 [INFO] +---------+-------+-----------+--------+\n", - "2024-05-08 11:07:11 [INFO] | Op Type | Total | A32W8G32 | FP32 |\n", - "2024-05-08 11:07:11 [INFO] +---------+-------+-----------+--------+\n", - "2024-05-08 11:07:11 [INFO] | Linear | 225 | 224 | 1 |\n", - "2024-05-08 11:07:11 [INFO] +---------+-------+-----------+--------+\n", - "2024-05-08 11:07:11 [INFO] Pass quantize model elapsed time: 14749.45 ms\n", - "2024-05-08 11:07:11 [INFO] Save tuning history to /mnt/localdisk1/ankur/oneAPI-samples/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/nc_workspace/2024-05-08_11-02-39/./history.snapshot.\n", - "2024-05-08 11:07:11 [INFO] [Strategy] Found the model meets accuracy requirements, ending the tuning process.\n", - "2024-05-08 11:07:11 [INFO] Specified timeout or max trials is reached! Found a quantized model which meet accuracy goal. Exit.\n", - "2024-05-08 11:07:11 [INFO] Save deploy yaml to /mnt/localdisk1/ankur/oneAPI-samples/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/nc_workspace/2024-05-08_11-02-39/deploy.yaml\n", - "2024-05-08 11:12:46 [INFO] WeightOnlyQuant done.\n", - "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", - "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" - ] - }, - { - "data": { - "text/plain": [ - "tensor([[ 1, 5713, 3714, 264, 727, 28725, 264, 1628, 2746, 5160,\n", - " 14003, 403, 7312, 1059, 272, 16748, 28723, 985, 403, 356,\n", - " 559, 1069, 298, 3251, 559, 19023, 28725, 693, 6262, 297,\n", - " 264, 1741, 27270, 3534, 2373, 272, 8613, 28723, 1136, 14003,\n", - " 5610, 28725, 630, 8295, 264, 8708, 7739, 28747, 264, 8887,\n", - " 2251, 28725, 708, 9942, 821, 264, 1502, 28809, 28713]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "q8_model = AutoModelForCausalLM.from_pretrained(\n", " model_name, load_in_8bit=True, use_neural_speed=False\n", @@ -579,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -595,29 +182,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "full_model memory usage: 28649.023 MB\n", - "q8_model memory usage: 9556.578 MB\n", - "q4_model memory usage: 6206.328 MB\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_names = [\"full_model\", \"q8_model\", \"q4_model\"]\n", "ram_sizes = [\n", @@ -650,29 +217,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "full_model disk usage: 28967.044 MB\n", - "q8_model disk usage: 8947.219 MB\n", - "q4_model disk usage: 5434.158 MB\n" - ] - }, - { - "data": { - "image/png": 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b/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/sample.json @@ -12,13 +12,12 @@ "ciTests": { "linux": [{ "env": [ - "source /intel/oneapi/intelpython/bin/activate", - "conda activate pytorch", - "pip install -r requirements.txt" + "pip install uv==0.6.3", + "uv sync" ], "id": "itrex_quantize_transformer_models", "steps": [ - "python quantize_transformer_models_with_itrex.py --model_name \"Intel/neural-chat-7b-v3-1\" --quantize \"int4\" --max_new_tokens 50" + "uv run quantize_transformer_models_with_itrex.py --model_name \"Intel/neural-chat-7b-v3-1\" --quantize \"int4\" --max_new_tokens 50" ] }] }, diff --git a/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/uv.lock b/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/uv.lock new file mode 100644 index 0000000000..235bfc725a --- /dev/null +++ b/AI-and-Analytics/Features-and-Functionality/IntelTransformers_Quantization/uv.lock @@ -0,0 +1,2624 @@ +version = 1 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This allows us to achieve performance boosts on Intel hardware." +authors = [ + {name = "Copyright © 2023 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.MD" +requires-python = ">=3.11.11,<3.12" +dependencies = [ + "datasets>=3.3.2", + "intel-extension-for-pytorch==2.5.0", + "neural-compressor==2.1", + "torch==2.5.0", + "transformers>=4.49.0", +] + +[tool.uv.sources] +torch = { index = "pytorch" } + +[dependency-groups] +dev = [ + "ipykernel>=6.29.5", + "jupyter>=1.1.1", +] + +[[tool.uv.index]] +url = "https://software.repos.intel.com/python/pypi" + +[[tool.uv.index]] +name = "pytorch" +# Required URL for the index. +url = "https://download.pytorch.org/whl/cpu" +explicit = true + diff --git a/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/quantize_with_inc.ipynb b/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/quantize_with_inc.ipynb index b394341dea..eea598d366 100644 --- a/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/quantize_with_inc.ipynb +++ b/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/quantize_with_inc.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "5405d69f-e912-4f89-9dec-6a110099fe8b", "metadata": {}, "outputs": [], @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "73b282ca-adc1-4793-beb4-e4827e9b83c3", "metadata": {}, "outputs": [], @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "7e0b8c21-93f5-43f0-bde0-6ce6e00909a7", "metadata": {}, "outputs": [], @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "0ffd0588-b4e8-4423-a730-fb39d30b5558", "metadata": {}, "outputs": [], @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "835b4070-349d-4a13-b5b0-71fa32172585", "metadata": {}, "outputs": [], @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "e524b269-f5d6-43c9-8f75-f5c044e4740e", "metadata": {}, "outputs": [], @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a6a7c2e6-ab96-4d49-8e39-4eb495ff2efc", "metadata": {}, "outputs": [], @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b8a6ae8c-0307-4157-a1d8-e0c6f52fba58", "metadata": {}, "outputs": [], @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "e7813321-8d8f-4098-a6c6-fe2a17e4d465", "metadata": {}, "outputs": [], @@ -334,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "0ef1dbc2-2167-4fbe-9cff-066088c1fbe1", "metadata": {}, "outputs": [], @@ -499,25 +499,7 @@ ] } ], - "metadata": { - 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libgl1-mesa-glx libglib2.0-0 libsm6 libxrender1 libxext6", - "source /intel/oneapi/intelpython/bin/activate", - "conda activate pytorch", - "pip install -r requirements.txt", - "pip install ipykernel jupyter", - "python -m ipykernel install --user --name=pytorch" + "env": [ + "apt-get update && apt-get install -y libgl1 libgl1-mesa-glx libglib2.0-0 libsm6 libxrender1 libxext6", + "pip install uv==0.6.3", + "uv sync" ], "id": "quantize with inc", "steps": [ - "jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=pytorch --to notebook quantize_with_inc.ipynb" + "uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --to notebook quantize_with_inc.ipynb" ] } ] diff --git a/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/uv.lock b/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/uv.lock new file mode 100644 index 0000000000..d7fd653cc1 --- /dev/null +++ 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b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/inc_sample_tensorflow.ipynb index 1cf0d4fc1e..e175003ce9 100644 --- a/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/inc_sample_tensorflow.ipynb +++ b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/inc_sample_tensorflow.ipynb @@ -275,9 +275,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "def auto_tune(input_graph_path, batch_size):\n", @@ -321,9 +319,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "display.Code('inc_quantize_model.py')" @@ -339,9 +335,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "!python inc_quantize_model.py" @@ -386,9 +380,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "!python profiling_inc.py --input-graph=./fp32_frozen.pb --omp-num-threads=4 --num-inter-threads=1 --num-intra-threads=4 --index=32" @@ -404,9 +396,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "!python profiling_inc.py --input-graph=./alexnet_int8_model.pb --omp-num-threads=4 --num-inter-threads=1 --num-intra-threads=4 --index=8" @@ -415,9 +405,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "display.Code('32.json')\n", @@ -543,25 +531,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Tensorflow", - "language": "python", - "name": "tensorflow" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/pyproject.toml b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/pyproject.toml new file mode 100644 index 0000000000..b5111ef1f7 --- /dev/null +++ b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "intel_neural_compressor_accelerate_inference_with_intel_optimization_for_tensorflow" +version = "0.1.0" +description = "This sample illustrates how to run Intel® Neural Compressor to quantize the FP32 model trained by Keras on Tensorflow to INT8 model to speed up the inference." +authors = [ + {name = "Copyright © 2023 Intel Corporation"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.12" +dependencies = [ + "intel-extension-for-tensorflow[cpu]==2.15", + "matplotlib>=3.10.1", + "neural-compressor==3.1.1", +] + +[dependency-groups] +dev = [ + "jupyter>=1.1.1", +] diff --git a/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/sample.json b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/sample.json index ccbab7fde7..8b15b9247a 100644 --- a/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/sample.json +++ b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/sample.json @@ -11,18 +11,14 @@ "ciTests": { "linux": [ { - "env": ["source /intel/oneapi/intelpython/bin/activate", - "conda activate tensorflow", - "conda install -n tensorflow -c conda-forge python-flatbuffers -y", - "conda install -n tensorflow -c https://software.repos.intel.com/python/conda/ -c conda-forge neural-compressor -y", - "conda install -n tensorflow -y", - "pip install jupyter ipykernel", - "python -m ipykernel install --user --name=tensorflow" + "env": [ 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a/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/ResNet50_Inference.ipynb b/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/ResNet50_Inference.ipynb index 3e116fa11d..7abd26d8a7 100755 --- a/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/ResNet50_Inference.ipynb +++ b/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/ResNet50_Inference.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -79,22 +79,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TensorTlow version: 2.10.0\n", - "MKL enabled : False\n", - "itex_version : 1.1.0\n", - "scikit learn Version: 1.1.1\n", - "neural_compressor version 1.14.2\n", - "Arch : ICX|CLX\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "run ../../version_check.py" ] @@ -124,7 +111,7 @@ "%%writefile run.sh\n", "#!/bin/bash\n", "source $ONEAPI_INSTALL/setvars.sh --force > /dev/null 2>&1\n", - "source activate user-tensorflow-gpu\n", + "source tensorflow-gpu/bin/activate\n", "echo \"########## Executing the run\"\n", "DNNL_VERBOSE=1 python infer_resnet50.py > infer_rn50_gpu.csv\n", "echo \"########## Done with the run\"" @@ -168,7 +155,7 @@ "%%writefile run.sh\n", "#!/bin/bash\n", "source $ONEAPI_INSTALL/setvars.sh --force > /dev/null 2>&1\n", - "source activate user-tensorflow\n", + "source tensorflow/bin/activate\n", "echo \"########## Executing the run\"\n", "DNNL_VERBOSE=1 python infer_resnet50.py > infer_rn50_cpu.csv\n", "echo \"########## Done with the run\"" @@ -388,9 +375,9 @@ ], "metadata": { "kernelspec": { - "display_name": "tensorflow-gpu", + "display_name": "tensorflow", "language": "python", - "name": "tensorflow-gpu" + "name": 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= ">=3.11" +dependencies = [ + "matplotlib>=3.10.1", + "neural-compressor==3.1.1", + "tensorflow-hub>=0.16.1", +] + +[dependency-groups] +dev = [ + "ipykernel>=6.29.5", + "jupyter>=1.1.1", +] + +tensorflow = [ + "intel-extension-for-tensorflow[cpu]==2.15", +] + +tensorflow-gpu = [ + "intel-extension-for-tensorflow[xpu]==2.15.0.2", +] + +[tool.uv] +conflicts = [ + [ + { group = "tensorflow" }, + { group = "tensorflow-gpu" }, + ], +] \ No newline at end of file diff --git a/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/sample.json b/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/sample.json index 8dbc14142d..657d144455 100755 --- a/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/sample.json +++ b/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_TensorFlow_GettingStarted/sample.json @@ -13,24 +13,25 @@ "ciTests": { "linux": [{ "env": [ - "source 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