Fix the bug of compiler wrong set in sample cmakelists

.github/workflows/github-pages.yml

@@ -3,15 +3,15 @@ name: github-samples-app
 on:
  push:
   branches:
-  - master
+  - main
 
- workflow_dispatch: 
+ workflow_dispatch:
 
 #  schedule:
 #    - cron: '55 13 * * *'
 
-# Top-level default; empty/no permissions 
-permissions: {}  
+# Top-level default; empty/no permissions
+permissions: {} 
 
 jobs:
   pages:
@@ -33,36 +33,36 @@ jobs:
         ref: 'refs/heads/app/dev'
 
     - uses: actions/checkout@v3
-      name: Check out master # checks out master in subdirectory
+      name: Check out main # checks out main in subdirectory
       with:
-        ref: 'refs/heads/master'
-        path: master
-        
+        ref: 'refs/heads/main'
+        path: main
+
     - name: Build JSON DB
       run: |
         python3 -m pip install -r src/requirements.txt
-        echo master
-        python3 src/db.py master
+        echo main
+        python3 src/db.py main
 
-    - name: Remove JSON pre-prod  
-      run: |       
+    - name: Remove JSON pre-prod
+      run: |
         rm -rf src/docs/sample_db_pre.json
 
     - name: Build Sphinx
-      run: |    
+      run: |
         python3 -m sphinx -W -b html src/docs/ src/docs/_build/
         echo $PWD
         echo ${{ github.ref }}
 
     - name: Add GPU-Occupancy-Calculator
-      env: 
+      env:
         GPU_OCC_CALC: src/docs/_build/Tools/GPU-Occupancy-Calculator/
       run: |
         mkdir -p ${GPU_OCC_CALC}
-        cp -v ${{ github.workspace }}/master/Tools/GPU-Occupancy-Calculator/index.html ${GPU_OCC_CALC}/index.html
+        cp -v ${{ github.workspace }}/main/Tools/GPU-Occupancy-Calculator/index.html ${GPU_OCC_CALC}/index.html
 
     - name: Push docs
-      if: ${{ github.ref == 'refs/heads/master' }} # only if this workflow is run from the master branch, push docs
+      if: ${{ github.ref == 'refs/heads/main' }} # only if this workflow is run from the main branch, push docs
       env:
         GITHUB_USER: ${{ github.actor }}
         GITHUB_TOKEN: ${{ github.token }}

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

@@ -14,14 +14,11 @@
         "env": [],
         "id": "Intel_Modin_E2E_py",
         "steps": [
-          "set -e # Terminate the script on first error",
-          "source /root/intel/oneapi/intelpython/bin/activate",
-          "conda create -n modin-hdk python=3.9 -y",
-          "conda activate modin-hdk",
+          "source /intel/oneapi/intelpython/bin/activate",
           "conda install modin-hdk -c conda-forge -y",
-          "pip install scikit-learn scikit-learn-intelex matplotlib jupyter ipykernel",
-          "python -m ipykernel install --name modin-hdk",
-          "jupyter nbconvert --to notebook --execute census_modin.ipynb"
+          "pip install scikit-learn scikit-learn-intelex matplotlib",
+          "pip install jupyter",
+          "jupyter nbconvert --to notebook census_modin.ipynb"
         ]
       }
     ]

AI-and-Analytics/End-to-end-Workloads/LanguageIdentification/README.md

@@ -111,7 +111,7 @@ This section explains how to train a model for language identification using the
 
 1. Install Jupyter Notebook.
    ```
-   conda install jupyter nb_conda_kernels
+   pip install notebook
    ```
 2. Launch Jupyter Notebook.
    ```
@@ -241,7 +241,7 @@ To run inference, you must have already run all of the training scripts, generat
 
 1. If you have not already done so, install Jupyter Notebook.
    ```
-   conda install jupyter nb_conda_kernels
+   pip install notebook
    ```
 2. Launch Jupyter Notebook.
    ```

AI-and-Analytics/Features-and-Functionality/INC_QuantizationAwareTraining_TextClassification/INC_QuantizationAwareTraining_TextClassification.ipynb

@@ -75,7 +75,7 @@
       "source": [
         "from datasets import load_dataset\n",
         "\n",
-        "dataset = load_dataset(\"emotion\", name=\"split\")\n",
+        "dataset = load_dataset(\"emotion\", name=\"split\", trust_remote_code=True)\n",
         "dataset['train'][:10]"
       ]
     },

AI-and-Analytics/Features-and-Functionality/INC_QuantizationAwareTraining_TextClassification/INC_QuantizationAwareTraining_TextClassification.py

@@ -49,7 +49,7 @@
 
 from datasets import load_dataset
 
-dataset = load_dataset("emotion", name="split")
+dataset = load_dataset("emotion", name="split", trust_remote_code=True)
 dataset['train'][:10]
 
 

AI-and-Analytics/Features-and-Functionality/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8/sample.json

@@ -12,13 +12,13 @@
     "linux": [{
       "id": "intel amx bf16 int8 inference",
       "steps": [
-        "source /root/intel/oneapi/intelpython/bin/activate",
+        "source /intel/oneapi/intelpython/bin/activate",
         "conda activate pytorch",
         "python -m pip install -r requirements.txt",
-        "python -m ipykernel install --user --name=user_pytorch",
+        "python -m ipykernel install --user --name=pytorch",
         "python pytorch_inference_vnni.py",
         "python pytorch_inference_amx.py",
-        "jupyter nbconvert --ExecutePreprocessor.kernel_name=pytorch --to notebook IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.ipynb"
+        "jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=pytorch --to notebook IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.ipynb"
       ]
     }]
   },

AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/requirements.txt

@@ -1,4 +1,3 @@
 matplotlib
 py-cpuinfo
-notebook
-ipykernel
+transformers

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

@@ -13,13 +13,13 @@
       {
           "id": "intel amx bf16 training",
           "steps": [
-              "source /root/intel/oneapi/intelpython/bin/activate",
+              "source /intel/oneapi/intelpython/bin/activate",
               "conda activate pytorch",
               "python -m pip install -r requirements.txt",
-              "python -m ipykernel install --user --name=user_pytorch",
+              "python -m ipykernel install --user --name=pytorch",
               "python pytorch_training_avx512_bf16.py",
               "python pytorch_training_amx_bf16.py",
-              "jupyter nbconvert --ExecutePreprocessor.kernel_name=pytorch --to notebook IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb"
+              "jupyter nbconvert --ExecutePreprocessor.enabled=True --ExecutePreprocessor.kernel_name=pytorch --to notebook IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb"
            ]
       }
        ]

AI-and-Analytics/Features-and-Functionality/IntelPython_GPU_numba-dpex_Genetic_Algorithm/IntelPython_GPU_numba-dpex_Genetic_Algorithm.ipynb

@@ -348,7 +348,7 @@
     "\n",
     "The only par that differs form the standard implementation is the evaluation function.\n",
     "\n",
-    "The most important part is to specify the global index of the computation. This is the current index of the computed chromosomes. This serves as a loop function across all chromosomes."
+    "The most important part is to specify the index of the computation. This is the current index of the computed chromosomes. This serves as a loop function across all chromosomes."
    ]
   },
   {
@@ -365,10 +365,11 @@
    "outputs": [],
    "source": [
     "import numba_dpex\n",
+    "from numba_dpex import kernel_api\n",
     "\n",
     "@numba_dpex.kernel\n",
-    "def eval_genomes_sycl_kernel(chromosomes, fitnesses, chrom_length):\n",
-    "  pos = numba_dpex.get_global_id(0)\n",
+    "def eval_genomes_sycl_kernel(item: kernel_api.Item, chromosomes, fitnesses, chrom_length):\n",
+    "  pos = item.get_id(0)\n",
     "  num_loops = 3000\n",
     "  for i in range(num_loops):\n",
     "    fitnesses[pos] += chromosomes[pos*chrom_length + 1]\n",
@@ -409,8 +410,9 @@
     "  chromosomes_flat = chromosomes.flatten()\n",
     "  chromosomes_flat_dpctl = dpnp.asarray(chromosomes_flat, device=\"gpu\")\n",
     "  fitnesses_dpctl = dpnp.asarray(fitnesses, device=\"gpu\")\n",
-    "\n",
-    "  eval_genomes_sycl_kernel[numba_dpex.Range(pop_size)](chromosomes_flat_dpctl, fitnesses_dpctl, chrom_size)\n",
+    "  \n",
+    "  exec_range = kernel_api.Range(pop_size)\n",
+    "  numba_dpex.call_kernel(eval_genomes_sycl_kernel, exec_range, chromosomes_flat_dpctl, fitnesses_dpctl, chrom_size)\n",
     "  fitnesses = dpnp.asnumpy(fitnesses_dpctl)\n",
     "  chromosomes = next_generation(chromosomes, fitnesses)\n",
     "  fitnesses = np.zeros(pop_size, dtype=np.float32)\n",
@@ -544,7 +546,7 @@
     "\n",
     "The evaluate created generation we are calculating the full distance of the given path (chromosome). In this example, the lower the fitness value is, the better the chromosome. That's different from the general GA that we implemented.\n",
     "\n",
-    "As in this example we are also using numba-dpex, we are using a global index like before."
+    "As in this example we are also using numba-dpex, we are using an index like before."
    ]
   },
   {
@@ -554,8 +556,8 @@
    "outputs": [],
    "source": [
     "@numba_dpex.kernel\n",
-    "def eval_genomes_plain_TSP_SYCL(chromosomes, fitnesses, distances, pop_length):\n",
-    "  pos = numba_dpex.get_global_id(0)\n",
+    "def eval_genomes_plain_TSP_SYCL(item: kernel_api.Item, chromosomes, fitnesses, distances, pop_length):\n",
+    "  pos = item.get_id(1)\n",
     "  for j in range(pop_length-1):\n",
     "    fitnesses[pos] += distances[int(chromosomes[pos, j]), int(chromosomes[pos, j+1])]\n"
    ]
@@ -708,7 +710,8 @@
     "  chromosomes_flat_dpctl = dpnp.asarray(chromosomes, device=\"gpu\")\n",
     "  fitnesses_dpctl = dpnp.asarray(fitnesses.copy(), device=\"gpu\")\n",
     "\n",
-    "  eval_genomes_plain_TSP_SYCL[numba_dpex.Range(pop_size)](chromosomes_flat_dpctl, fitnesses_dpctl, distances_dpctl, pop_size)\n",
+    "  exec_range = kernel_api.Range(pop_size)\n",
+    "  numba_dpex.call_kernel(eval_genomes_plain_TSP_SYCL, exec_range, chromosomes_flat_dpctl, fitnesses_dpctl, distances_dpctl, pop_size)\n",
     "  fitnesses = dpnp.asnumpy(fitnesses_dpctl)\n",
     "  chromosomes = next_generation_TSP(chromosomes, fitnesses)\n",
     "  fitnesses = np.zeros(pop_size, dtype=np.float32)\n",

AI-and-Analytics/Features-and-Functionality/IntelPython_GPU_numba-dpex_Genetic_Algorithm/IntelPython_GPU_numba-dpex_Genetic_Algorithm.py

@@ -260,16 +260,17 @@ def next_generation(chromosomes, fitnesses):
 # 
 # The only par that differs form the standard implementation is the evaluation function.
 # 
-# The most important part is to specify the global index of the computation. This is the current index of the computed chromosomes. This serves as a loop function across all chromosomes.
+# The most important part is to specify the index of the computation. This is the current index of the computed chromosomes. This serves as a loop function across all chromosomes.
 
 # In[ ]:
 
 
 import numba_dpex
+from numba_dpex import kernel_api
 
 @numba_dpex.kernel
-def eval_genomes_sycl_kernel(chromosomes, fitnesses, chrom_length):
-  pos = numba_dpex.get_global_id(0)
+def eval_genomes_sycl_kernel(item: kernel_api.Item, chromosomes, fitnesses, chrom_length):
+  pos = item.get_id(0)
   num_loops = 3000
   for i in range(num_loops):
     fitnesses[pos] += chromosomes[pos*chrom_length + 1]
@@ -300,7 +301,8 @@ def eval_genomes_sycl_kernel(chromosomes, fitnesses, chrom_length):
   chromosomes_flat_dpctl = dpnp.asarray(chromosomes_flat, device="gpu")
   fitnesses_dpctl = dpnp.asarray(fitnesses, device="gpu")
 
-  eval_genomes_sycl_kernel[numba_dpex.Range(pop_size)](chromosomes_flat_dpctl, fitnesses_dpctl, chrom_size)
+  exec_range = kernel_api.Range(pop_size)
+  numba_dpex.call_kernel(eval_genomes_sycl_kernel, exec_range, chromosomes_flat_dpctl, fitnesses_dpctl, chrom_size)
   fitnesses = dpnp.asnumpy(fitnesses_dpctl)
   chromosomes = next_generation(chromosomes, fitnesses)
   fitnesses = np.zeros(pop_size, dtype=np.float32)
@@ -398,14 +400,14 @@ def eval_genomes_sycl_kernel(chromosomes, fitnesses, chrom_length):
 # 
 # The evaluate created generation we are calculating the full distance of the given path (chromosome). In this example, the lower the fitness value is, the better the chromosome. That's different from the general GA that we implemented.
 # 
-# As in this example we are also using numba-dpex, we are using a global index like before.
+# As in this example we are also using numba-dpex, we are using an index like before.
 
 # In[ ]:
 
 
 @numba_dpex.kernel
-def eval_genomes_plain_TSP_SYCL(chromosomes, fitnesses, distances, pop_length):
-  pos = numba_dpex.get_global_id(0)
+def eval_genomes_plain_TSP_SYCL(item: kernel_api.Item, chromosomes, fitnesses, distances, pop_length):
+  pos = item.get_id(1)
   for j in range(pop_length-1):
     fitnesses[pos] += distances[int(chromosomes[pos, j]), int(chromosomes[pos, j+1])]
 
@@ -526,7 +528,8 @@ def next_generation_TSP(chromosomes, fitnesses):
   chromosomes_flat_dpctl = dpnp.asarray(chromosomes, device="gpu")
   fitnesses_dpctl = dpnp.asarray(fitnesses.copy(), device="gpu")
 
-  eval_genomes_plain_TSP_SYCL[numba_dpex.Range(pop_size)](chromosomes_flat_dpctl, fitnesses_dpctl, distances_dpctl, pop_size)
+  exec_range = kernel_api.Range(pop_size)
+  numba_dpex.call_kernel(eval_genomes_plain_TSP_SYCL, exec_range, chromosomes_flat_dpctl, fitnesses_dpctl, distances_dpctl, pop_size)
   fitnesses = dpnp.asnumpy(fitnesses_dpctl)
   chromosomes = next_generation_TSP(chromosomes, fitnesses)
   fitnesses = np.zeros(pop_size, dtype=np.float32)

AI-and-Analytics/Features-and-Functionality/IntelPython_Numpy_Numba_dpex_kNN/IntelPython_Numpy_Numba_dpex_kNN.ipynb

@@ -331,6 +331,7 @@
     "\n",
     "@numba_dpex.kernel\n",
     "def knn_numba_dpex(\n",
+    "    item: numba_dpex.kernel_api.Item,\n",
     "    train,\n",
     "    train_labels,\n",
     "    test,\n",
@@ -339,8 +340,8 @@
     "    votes_to_classes_lst,\n",
     "):\n",
     "    dtype = train.dtype\n",
-    "    i = numba_dpex.get_global_id(0)\n",
-    "    queue_neighbors = numba_dpex.private.array(shape=(3, 2), dtype=dtype)\n",
+    "    i = item.get_id(0)\n",
+    "    queue_neighbors = numba_dpex.kernel_api.PrivateArray(shape=(3, 2), dtype=dtype)\n",
     "\n",
     "    for j in range(k):\n",
     "        x1 = train[j, 0]\n",
@@ -436,8 +437,15 @@
     "y_train_dpt = dpnp.asarray(y_train.values, device=\"cpu\")\n",
     "X_test_dpt = dpnp.asarray(X_test.values, device=\"cpu\")\n",
     "\n",
-    "knn_numba_dpex[numba_dpex.Range(len(X_test.values))](\n",
-    "    X_train_dpt, y_train_dpt, X_test_dpt, 3, predictions, votes_to_classes_lst\n",
+    "numba_dpex.call_kernel(\n",
+    "    knn_numba_dpex,\n",
+    "    numba_dpex.Range(len(X_test.values)),\n",
+    "    X_train_dpt,\n",
+    "    y_train_dpt,\n",
+    "    X_test_dpt,\n",
+    "    3,\n",
+    "    predictions,\n",
+    "    votes_to_classes_lst,\n",
     ")"
    ]
   },

AI-and-Analytics/Features-and-Functionality/IntelPython_Numpy_Numba_dpex_kNN/IntelPython_Numpy_Numba_dpex_kNN.py

@@ -237,6 +237,7 @@ def knn_numba(X_train, y_train, X_test, k):
 
 @numba_dpex.kernel
 def knn_numba_dpex(
+    item: numba_dpex.kernel_api.Item,
     train,
     train_labels,
     test,
@@ -245,8 +246,8 @@ def knn_numba_dpex(
     votes_to_classes_lst,
 ):
     dtype = train.dtype
-    i = numba_dpex.get_global_id(0)
-    queue_neighbors = numba_dpex.private.array(shape=(3, 2), dtype=dtype)
+    i = item.get_id(0)
+    queue_neighbors = numba_dpex.kernel_api.PrivateArray(shape=(3, 2), dtype=dtype)
 
     for j in range(k):
         x1 = train[j, 0]
@@ -333,8 +334,15 @@ def knn_numba_dpex(
 y_train_dpt = dpnp.asarray(y_train.values, device="cpu")
 X_test_dpt = dpnp.asarray(X_test.values, device="cpu")
 
-knn_numba_dpex[numba_dpex.Range(len(X_test.values))](
-    X_train_dpt, y_train_dpt, X_test_dpt, 3, predictions, votes_to_classes_lst
+numba_dpex.call_kernel(
+    knn_numba_dpex,
+    numba_dpex.Range(len(X_test.values)),
+    X_train_dpt,
+    y_train_dpt,
+    X_test_dpt,
+    3,
+    predictions,
+    votes_to_classes_lst,
 )