diff --git a/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_onnx.ipynb b/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_onnx.ipynb
index 9f39c132aa2..294e40fcd01 100644
--- a/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_onnx.ipynb
+++ b/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_onnx.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can use ``Trainer.trace(..., accelerator='onnxruntime')`` API to enable the ONNXRuntime acceleration for PyTorch inference. It only takes a few lines."
+    "You can use ``InferenceOptimizer.trace(..., accelerator='onnxruntime')`` API to enable the ONNXRuntime acceleration for PyTorch inference. It only takes a few lines."
    ]
   },
   {
@@ -109,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To enable ONNXRuntime acceleration for your PyTorch inference pipeline, **the only change you need to made is to import BigDL-Nano Trainer, and trace your PyTorch model to convert it into an ONNXRuntime accelerated module for inference**:"
+    "To enable ONNXRuntime acceleration for your PyTorch inference pipeline, **the only change you need to made is to import BigDL-Nano** `InferenceOptimizer`**, and trace your PyTorch model to convert it into an ONNXRuntime accelerated module for inference**:"
    ]
   },
   {
@@ -119,11 +119,11 @@
    "outputs": [],
    "source": [
     "import torch\n",
-    "from bigdl.nano.pytorch import Trainer\n",
+    "from bigdl.nano.pytorch import InferenceOptimizer\n",
     "\n",
-    "ort_model = Trainer.trace(model_ft,\n",
-    "                          accelerator=\"onnxruntime\",\n",
-    "                          input_sample=torch.rand(1, 3, 224, 224))"
+    "ort_model = InferenceOptimizer.trace(model_ft,\n",
+    "                                     accelerator=\"onnxruntime\",\n",
+    "                                     input_sample=torch.rand(1, 3, 224, 224))"
    ]
   },
   {
@@ -134,7 +134,7 @@
     "> \n",
     "> `input_sample` is the parameter for ONNXRuntime accelerator to know the **shape** of the model input. So both the batch size and the specific values are not important to `input_sample`. If we want our test dataset to consist of images with $224 \\times 224$ pixels, we could use `torch.rand(1, 3, 224, 224)` for `input_sample` here. \n",
     "> \n",
-    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.Trainer.trace) for more information on `Trainer.trace`."
+    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.InferenceOptimizer.trace) for more information on `InferenceOptimizer.trace`."
    ]
   },
   {
diff --git a/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_openvino.ipynb b/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_openvino.ipynb
index c5460527101..c66f13a0622 100644
--- a/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_openvino.ipynb
+++ b/python/nano/tutorial/notebook/inference/pytorch/accelerate_pytorch_inference_openvino.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can use `Trainer.trace(..., accelerator='openvino')` API to enable the OpenVINO acceleration for PyTorch inference. It only takes a few lines."
+    "You can use `InferenceOptimizer.trace(..., accelerator='openvino')` API to enable the OpenVINO acceleration for PyTorch inference. It only takes a few lines."
    ]
   },
   {
@@ -79,7 +79,7 @@
    "source": [
     "> ⚠️ **Warning**\n",
     ">\n",
-    "> Errors may occur when using `Trainer.trace(accelerator='openvino')` API in CentOS, becuase the latest version of `openvino-dev` is not supported in CentOS."
+    "> Errors may occur when using `InferenceOptimizer.trace(..., accelerator='openvino')` API in CentOS, becuase the latest version of `openvino-dev` is not supported in CentOS."
    ]
   },
   {
@@ -120,7 +120,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To enable OpenVINO acceleration for your PyTorch inference pipeline, **the only change you need to made is to import BigDL-Nano Trainer, and trace your PyTorch model to convert it into an OpenVINO accelerated module for inference**:"
+    "To enable OpenVINO acceleration for your PyTorch inference pipeline, **the only change you need to made is to import BigDL-Nano** `InferenceOptimizer`**, and trace your PyTorch model to convert it into an OpenVINO accelerated module for inference**:"
    ]
   },
   {
@@ -130,9 +130,9 @@
    "outputs": [],
    "source": [
     "import torch\n",
-    "from bigdl.nano.pytorch import Trainer\n",
+    "from bigdl.nano.pytorch import InferenceOptimizer\n",
     "\n",
-    "ov_model = Trainer.trace(model_ft,\n",
+    "ov_model = InferenceOptimizer.trace(model_ft,\n",
     "                         accelerator=\"openvino\",\n",
     "                         input_sample=torch.rand(1, 3, 224, 224))"
    ]
@@ -145,7 +145,7 @@
     "> \n",
     "> `input_sample` is the parameter for OpenVINO accelerator to know the **shape** of the model input. So both the batch size and the specific values are not important to `input_sample`. If we want our test dataset to consist of images with $224 \\times 224$ pixels, we could use `torch.rand(1, 3, 224, 224)` for `input_sample` here.\n",
     "> \n",
-    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.Trainer.trace) for more information on `Trainer.trace`."
+    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.InferenceOptimizer.trace) for more information on `InferenceOptimizer.trace`."
    ]
   },
   {
diff --git a/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_inc.ipynb b/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_inc.ipynb
index dcc94cfddea..3955fcfdb0b 100644
--- a/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_inc.ipynb
+++ b/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_inc.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With Intel Neural Compressor (INC) as quantization engine, you can apply `Trainer.quantize` API to realize post-training quantization on your PyTorch `nn.Module`. `Trainer.quantize` also supports ONNXRuntime acceleration at the meantime through specifying `accelerator='onnxruntime'`. All acceleration takes only a few lines."
+    "With Intel Neural Compressor (INC) as quantization engine, you can apply `InferenceOptimizer.quantize` API to realize post-training quantization on your PyTorch `nn.Module`. `InferenceOptimizer.quantize` also supports ONNXRuntime acceleration at the meantime through specifying `accelerator='onnxruntime'`. All acceleration takes only a few lines."
    ]
   },
   {
@@ -201,7 +201,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To enable quantization using INC for inference, you could simply **import BigDL-Nano Trainer, and use Trainer to quantize your PyTorch model**:"
+    "To enable quantization using INC for inference, you could simply **import BigDL-Nano** `InferenceOptimizer`**, and use** `InferenceOptimizer` **to quantize your PyTorch model**:"
    ]
   },
   {
@@ -210,10 +210,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from bigdl.nano.pytorch import Trainer\n",
+    "from bigdl.nano.pytorch import InferenceOptimizer\n",
     "\n",
-    "q_model = Trainer.quantize(model, \n",
-    "                           calib_dataloader=DataLoader(train_dataset, batch_size=32))"
+    "q_model = InferenceOptimizer.quantize(model, \n",
+    "                                      calib_dataloader=DataLoader(train_dataset, batch_size=32))"
    ]
   },
   {
@@ -229,11 +229,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from bigdl.nano.pytorch import Trainer\n",
+    "from bigdl.nano.pytorch import InferenceOptimizer\n",
     "\n",
-    "q_model = Trainer.quantize(model,\n",
-    "                            accelerator='onnxruntime',\n",
-    "                            calib_dataloader=DataLoader(train_dataset, batch_size=32))"
+    "q_model = InferenceOptimizer.quantize(model,\n",
+    "                                      accelerator='onnxruntime',\n",
+    "                                      calib_dataloader=DataLoader(train_dataset, batch_size=32))"
    ]
   },
   {
@@ -242,11 +242,11 @@
    "source": [
     "> 📝 **Note**\n",
     "> \n",
-    "> `Trainer` will by default quantize your PyTorch `nn.Module` through **static** post-training quantization. For this case, `calib_dataloader` (for calibration data) is required. Batch size is not important to ``calib_dataloader``, as it intends to read 100 samples. And there could be no label in calibration data.\n",
+    "> `InferenceOptimizer` will by default quantize your PyTorch `nn.Module` through **static** post-training quantization. For this case, `calib_dataloader` (for calibration data) is required. Batch size is not important to ``calib_dataloader``, as it intends to read 100 samples. And there could be no label in calibration data.\n",
     "> \n",
     "> If you would like to implement dynamic post-training quantization, you could set parameter `approach='dynamic'`. In this case, `calib_dataloader` should be `None`. Compared to dynamic quantization, static quantization could lead to faster inference as it eliminates the data conversion costs between layers.\n",
     "> \n",
-    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.Trainer.quantize) for more information on `Trainer.quantize`."
+    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.InferenceOptimizer.quantize) for more information on `InferenceOptimizer.quantize`."
    ]
   },
   {
diff --git a/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_pot.ipynb b/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_pot.ipynb
index c011c39b84b..0ab1a4c4a7a 100644
--- a/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_pot.ipynb
+++ b/python/nano/tutorial/notebook/inference/pytorch/quantize_pytorch_inference_pot.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As Post-training Optimization Tools (POT) is provided by OpenVINO toolkit, OpenVINO acceleration will be enabled in the meantime when using POT for quantization. You can call `Trainer.quantize` API with `accelerator='openvino'` to use POT for your PyTorch `nn.Module`. It only takes a few lines."
+    "As Post-training Optimization Tools (POT) is provided by OpenVINO toolkit, OpenVINO acceleration will be enabled in the meantime when using POT for quantization. You can call `InferenceOptimizer.quantize` API with `accelerator='openvino'` to use POT for your PyTorch `nn.Module`. It only takes a few lines."
    ]
   },
   {
@@ -79,7 +79,7 @@
    "source": [
     "> ⚠️ **Warning**\n",
     ">\n",
-    "> Errors may occur when using `Trainer.quantize(..., accelerator='openvino')` API in CentOS, becuase the latest version of `openvino-dev` is not supported in CentOS."
+    "> Errors may occur when using `InferenceOptimizer.quantize(..., accelerator='openvino')` API in CentOS, becuase the latest version of `openvino-dev` is not supported in CentOS."
    ]
   },
   {
@@ -193,7 +193,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To enable quantization using POT for inference, you could simply **import BigDL-Nano Trainer, and use Trainer to quantize your PyTorch model**:"
+    "To enable quantization using POT for inference, you could simply **import BigDL-Nano** `InferenceOptimizer`**, and use** `InferenceOptimizer` **to quantize your PyTorch model**:"
    ]
   },
   {
@@ -202,11 +202,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from bigdl.nano.pytorch import Trainer\n",
+    "from bigdl.nano.pytorch import InferenceOptimizer\n",
     "\n",
-    "q_model = Trainer.quantize(model,\n",
-    "                           accelerator='openvino',\n",
-    "                           calib_dataloader=DataLoader(train_dataset, batch_size=32))"
+    "q_model = InferenceOptimizer.quantize(model,\n",
+    "                                      accelerator='openvino',\n",
+    "                                      calib_dataloader=DataLoader(train_dataset, batch_size=32))"
    ]
   },
   {
@@ -219,7 +219,7 @@
     "> \n",
     "> For `calib_dataloader`, batch size is not important as it intends to read 100 samples. And there could be no label in calibration data.\n",
     "> \n",
-    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.Trainer.quantize) for more information on `Trainer.quantize`."
+    "> Please refer to [API documentation](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl.nano.pytorch.InferenceOptimizer.quantize) for more information on `InferenceOptimizer.quantize`."
    ]
   },
   {