Uv conversion additional samples GettingStarted, Features, End-to-End

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

@@ -1012,6 +1012,8 @@
    },
    "outputs": [],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "X_final = np.array(embedded_docs)\n",
     "y_final = np.array(y)"
    ]
@@ -1258,7 +1260,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import seaborn as sns\n",
     "\n",
     "def plot_similarity(labels, features, rotation):\n",

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

@@ -476,6 +476,7 @@ def lemmatize_text(text):
 print(model_itex.summary())
 
 # %%
+import numpy as np
 X_final = np.array(embedded_docs)
 y_final = np.array(y)
 
@@ -578,7 +579,6 @@ def lemmatize_text(text):
 # Now, we are preparing functions to show similarities between given sentences in the for of heat map. 
 
 # %%
-import numpy as np
 import seaborn as sns
 
 def plot_similarity(labels, features, rotation):

AI-and-Analytics/End-to-end-Workloads/JobRecommendationSystem/pyproject.toml

@@ -0,0 +1,35 @@
+[project]
+name = "jobrecommendationsystem"
+version = "0.1.0"
+description = "This sample illustrates the use of Intel® Extension for TensorFlow* to build and run an end-to-end AI workload on the example of the job recommendation system"
+authors = [
+    {name = "Copyright © 2020 Intel Corporation"}
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "accelerate>=1.4.0",
+    "datasets>=3.3.2",
+    "intel-extension-for-tensorflow>=2.15.0.2",
+    "intel-extension-for-tensorflow-lib>=2.15.0.2.2",
+    "ipykernel>=6.29.5",
+    "jinja2>=3.1.6",
+    "matplotlib>=3.10.1",
+    "nltk>=3.9.1",
+    "pip>=25.0.1",
+    "seaborn>=0.13.2",
+    "sentence-transformers>=3.4.1",
+    "spacy>=3.8.4",
+    "transformers>=4.49.0",
+    "wordcloud>=1.9.4",
+]
+[dependency-groups]
+dev = [
+    "ipykernel>=6.29.5",
+    "jupyter>=1.1.1",
+]
+[[tool.uv.index]]
+url = "https://software.repos.intel.com/python/pypi"
+
+

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

@@ -11,16 +11,16 @@
   "ciTests": {
   	"linux": [
       {
-        "env": [],
+        "env": [
+          "pip install uv==0.6.3",
+          "uv sync",
+          "uv run python -m ipykernel install --user --name=JobRecommendationSystem_py"
+        ],
         "id": "JobRecommendationSystem_py",
         "steps": [
-          "source /intel/oneapi/intelpython/bin/activate",
-          "conda env remove -n user_tensorflow-gpu",
-          "conda create --name user_tensorflow-gpu --clone tensorflow-gpu",
-          "conda activate user_tensorflow-gpu",
-          "pip install -r requirements.txt",
-          "python -m ipykernel install --user --name=user_tensorflow-gpu",
-          "python JobRecommendationSystem.py"
+          "uv run spacy download en_core_web_sm",
+          "uv run JobRecommendationSystem.py",
+          "uv run jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=JobRecommendationSystem_py --to notebook JobRecommendationSystem.ipynb"
         ]
       }
     ]

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

@@ -0,0 +1,31 @@
+[project]
+name = "fine_tuning_text_classification_model_with_intel_neural_compressor"
+version = "0.1.0"
+description = "This sample demonstrates how to fine-tune text model for emotion classification task using Intel® Neural Compressor Quantization Aware Training (QAT)."
+authors = [
+    {name = "Copyright © 2023 Intel Corporation"}
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "transformers (>=4.36,<4.46)",  # Adjusted to match the compatible range
+    "evaluate (>=0.4.3,<0.5.0)",
+    "accelerate (>=1.4.0,<2.0.0)",
+    "datasets (>=3.3.2,<4.0.0)",
+    "neural-compressor (>=2.2.0,<3.0)",  # Adjusted to match the compatible range
+    "optimum-intel[neural-compressor] (>=1.18.0,<1.22.0)"  # Added to ensure compatibility
+]
+
+
+[project.scripts]
+python-validate = "INC_QuantizationAwareTraining_TextClassification:main"
+
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[dependency-groups]
+dev = [
+    "ipykernel>=6.29.5",
+]

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

@@ -12,13 +12,12 @@
         "linux": [
       {
             "env": [
-                "source /intel/oneapi/intelpython/bin/activate", 
-                "conda activate pytorch",
-                "pip install -r requirements.txt"
+                "pip install uv",
+                "uv sync"
             ],
             "id": "inc_text_classification_quantization_py",
             "steps": [
-              "python INC_QuantizationAwareTraining_TextClassification.py"
+              "uv run INC_QuantizationAwareTraining_TextClassification.py"
              ]
         }
       ]

AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_daal4pyPrediction/pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "intelpython-xgboost-daal4pyprediction"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "daal4py>=2024.7.0",
+    "ipykernel>=6.29.5",
+    "jupyter>=1.1.1",
+    "matplotlib>=3.10.1",
+    "pandas>=2.2.3",
+    "scikit-learn>=1.6.1",
+    "xgboost>=2.1.4",
+]

AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_daal4pyPrediction/sample.json

@@ -13,10 +13,11 @@
       {
         "id": "idp_xgb_d4p_prediction",
         "steps": [
-          "source /intel/oneapi/intelpython/bin/activate",
-          "pip install ipykernel jupyter",
-          "python -m ipykernel install --user --name=xgboost",
-          "jupyter nbconvert --to notebook --execute IntelPython_XGBoost_daal4pyPrediction.ipynb"
+          "pip install uv",
+          "uv venv",
+          "uv sync",
+          "uv run python -m ipykernel install --user --name=xgboost",
+          "uv run jupyter nbconvert --to notebook --execute IntelPython_XGBoost_daal4pyPrediction.ipynb"
         ]
       }
     ]