Intel® Neural Compressor TensorFlow* Getting Started Sample readme update

AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/IntelPyTorch_TrainingOptimizations_AMX_BF16.ipynb

@@ -311,6 +311,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5eea6ae7",
    "metadata": {},
    "source": [
     "The training times for the 3 cases are printed out and shown in the figure above. Using BF16 should show significant reduction in training time. However, there is little to no change using AVX512 with BF16 and AMX with BF16 because the amount of computations required for one batch is too small with this dataset.   "
@@ -348,15 +349,16 @@
    "id": "b6ea2aeb",
    "metadata": {},
    "source": [
-    "This figure shows the relative performance speedup of AMX compared to FP32 and BF16 with AVX512. The expected behavior is that AMX with BF16 should have about a 1.5X improvement over FP32 and about the same performance as BF16 with AVX512. To see more performance improvement between AVX-512 BF16 and AMX BF16, increase the amount of required computations in one batch. This can be done by increasing the batch size with CIFAR10 or using another dataset.  "
+    "This figure shows the relative performance speedup of AMX compared to FP32 and BF16 with AVX512."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0da073a6",
+   "id": "7bf01080",
    "metadata": {},
    "source": [
-    "This code sample shows how to enable and disable AMX during runtime, as well as the performance improvements using AMX BF16 for training the ResNet50 model. There will be additional significant performance improvements if AMX INT8 is used in inference, which is covered in a related oneAPI sample."
+    "## Conclusion\n",
+    "This code sample shows how to enable and disable AMX during runtime, as well as the performance improvements using AMX BF16 for training on the ResNet50 model. Performance will vary based on your hardware and software versions. To see more performance improvement between AVX-512 BF16 and AMX BF16, increase the amount of required computations in one batch. This can be done by increasing the batch size with CIFAR10 or using another dataset. For even more speedup, consider using the Intel® Extension for PyTorch* [Launch Script](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/launch_script.html). "
    ]
   },
   {

AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/README.md

@@ -148,11 +148,9 @@ If you receive an error message, troubleshoot the problem using the **Diagnostic
 
 ## Example Output
 
-If successful, the sample displays `[CODE_SAMPLE_COMPLETED_SUCCESSFULLY]`. Additionally, the sample generates performance and analysis diagrams for comparison.
+If successful, the sample displays `[CODE_SAMPLE_COMPLETED_SUCCESSFULLY]`. Additionally, the sample will print out the runtimes and charts of relative performance with the FP32 model without any optimizations as the baseline. 
 
-The following image shows approximate performance speed increases using AMX BF16 with auto-mixed precision during training. To see more performance improvement between AVX-512 BF16 and AMX BF16, increase the amount of required computations in one batch. This can be done by increasing the batch size with CIFAR10 or using another dataset.  
-
-![comparison images](assets/amx_relative_speedup.png)
+The performance speedups using AMX BF16 are approximate on ResNet50. Performance will vary based on your hardware and software versions. To see more performance improvement between AVX-512 BF16 and AMX BF16, increase the amount of required computations in one batch. This can be done by increasing the batch size with CIFAR10 or using another dataset. For even more speedup, consider using the Intel® Extension for PyTorch* [Launch Script](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/launch_script.html). 
 
 ## License