Jianyuzh support win10

AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/README.md

@@ -36,7 +36,7 @@ performance to see the benefit of Intel® Neural Compressor.
 
 | Optimized for                     | Description
 |:---                               |:---
-| OS                                | Linux* Ubuntu* 18.04 or later
+| OS                                | Linux* Ubuntu* 18.04 or later, Windows 10*
 | Hardware                          | The Second Generation Intel® Xeon® Scalable processor family or newer Xeon® processors
 | Software                          | Intel® oneAPI AI Analytics Toolkit 2021.1 or later
 | What you will learn               | How to use Intel® Neural Compressor tool to quantize the AI model based on TensorFlow* and speed up the inference on Intel® Xeon® CPUs
@@ -92,6 +92,8 @@ git checkout 2021.1-beta10
 
 ## Prepare Software Environment
 
+### Linux (Ubuntu)
+
 You can run this sample in a Jupyter notebook on your local computer
 or in the Intel® DevCloud.
 
@@ -184,6 +186,19 @@ https://software.intel.com/content/www/us/en/develop/articles/installation-guide
 
    This step is optional if you plan to open the notebook on your local server.
 
+### Windows 10
+
+Setup the Conda running environment **user_tensorflow** by following commands:
+
+```
+conda deactivate
+conda env remove -n user_tensorflow
+conda create -n user_tensorflow python=3.9 -y
+conda activate user_tensorflow
+conda install -n user_tensorflow pycocotools -c esri -y
+conda install -n user_tensorflow neural-compressor tensorflow -c conda-forge -c intel -y
+conda install -n user_tensorflow jupyter runipy notebook -y
+```
 
 ## Run the Sample <a name="running-the-sample"></a>
 

AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/inc_sample_tensorflow.ipynb

@@ -55,7 +55,9 @@
     "        print(\"iLiT version {}\".format(inc.__version__))       \n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "\n",
+    "from IPython import display"
    ]
   },
   {
@@ -148,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "epochs = 3\n",
+    "epochs = 1\n",
     "\n",
     "alexnet.train_mod(model, data, epochs)"
    ]
@@ -194,7 +196,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!ls -la fp32_frezon.pb"
+    "%ls -la fp32_frezon.pb"
    ]
   },
   {
@@ -257,7 +259,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!cat alexnet.py"
+    "display.Code('alexnet.py')"
    ]
   },
   {
@@ -280,7 +282,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!cat alexnet.yaml"
+    "display.Code('alexnet.yaml')"
    ]
   },
   {
@@ -299,7 +301,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "def auto_tune(input_graph_path, yaml_config, batch_size):    \n",
     "    fp32_graph = alexnet.load_pb(input_graph_path)\n",
     "    quan = inc.Quantization(yaml_config)\n",
@@ -341,7 +342,7 @@
    },
    "outputs": [],
    "source": [
-    "!cat inc_quantize_model.py"
+    "display.Code('inc_quantize_model.py')"
    ]
   },
   {
@@ -388,7 +389,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!cat profiling_inc.py"
+    "display.Code('profiling_inc.py')"
    ]
   },
   {
@@ -435,9 +436,9 @@
    },
    "outputs": [],
    "source": [
-    "!cat 32.json\n",
+    "display.Code('32.json')\n",
     "!echo \" \"\n",
-    "!cat 8.json"
+    "display.Code('8.json')"
    ]
   },
   {
@@ -574,7 +575,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,