Nano: Update examples and tutorials (#6888)

docs/readthedocs/source/doc/Nano/Howto/install_in_colab.md

@@ -56,7 +56,7 @@ To enable OpenVINO acceleration, or use POT for quantization, you need to instal
 
 ```eval_rst
 .. note::
-    If you meet ``ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject`` when using ``Trainer.trace`` or ``Trainer.quantize`` function, you could try to solve it by upgrading ``numpy`` through:
+    If you meet ``ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject`` when using ``InferenceOptimizer.trace`` or ``InferenceOptimizer.quantize`` function, you could try to solve it by upgrading ``numpy`` through:
     
     .. code-block:: python
 

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_onnxruntime.md

@@ -78,8 +78,8 @@ When you're ready, you can simply append the following part to enable your ONNXR
 # you have run `trainer.fit` before trace
 # Model has `example_input_array` set
 # Model is a LightningModule with any dataloader attached.
-from bigdl.nano.pytorch import Trainer
+from bigdl.nano.pytorch import InferenceOptimizer
-ort_model = Trainer.trace(model_ft, accelerator="onnxruntime", input_sample=torch.rand(1, 3, 224, 224))
+ort_model = InferenceOptimizer.trace(model_ft, accelerator="onnxruntime", input_sample=torch.rand(1, 3, 224, 224))
 
 # The usage is almost the same with any PyTorch module
 y_hat = ort_model(x)

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_openvino.md

@@ -78,8 +78,8 @@ When you're ready, you can simply append the following part to enable your OpenV
 # The argument `input_sample` is not required in the following cases:
 # you have run `trainer.fit` before trace
 # The Model has `example_input_array` set
-from bigdl.nano.pytorch import Trainer
+from bigdl.nano.pytorch import InferenceOptimizer
-ov_model = Trainer.trace(model_ft, accelerator="openvino", input_sample=torch.rand(1, 3, 224, 224))
+ov_model = InferenceOptimizer.trace(model_ft, accelerator="openvino", input_sample=torch.rand(1, 3, 224, 224))
 
 # The usage is almost the same with any PyTorch module
 y_hat = ov_model(x)

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_quantization_inc.md

@@ -74,12 +74,13 @@ y_hat.argmax(dim=1)
 ```
 
 ### Step 3: Quantization using Intel Neural Compressor
-Quantization is widely used to compress models to a lower precision, which not only reduces the model size but also accelerates inference. BigDL-Nano provides `Trainer.quantize()` API for users to quickly obtain a quantized model with accuracy control by specifying a few arguments.
+Quantization is widely used to compress models to a lower precision, which not only reduces the model size but also accelerates inference. BigDL-Nano provides `InferenceOptimizer.quantize()` API for users to quickly obtain a quantized model with accuracy control by specifying a few arguments.
 
-Without extra accelerator, `Trainer.quantize()` returns a pytorch module with desired precision and accuracy. You can add quantization as below:
+Without extra accelerator, `InferenceOptimizer.quantize()` returns a pytorch module with desired precision and accuracy. You can add quantization as below:
 ```python
+from bigdl.nano.pytorch import InferenceOptimizer
 from torchmetrics.functional import accuracy
-q_model = trainer.quantize(model, calib_dataloader=train_dataloader, metric=accuracy)
+q_model = InferenceOptimizer.quantize(model, calib_data=train_dataloader, metric=accuracy)
 
 # run simple prediction
 y_hat = q_model(x)

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_quantization_inc_onnx.md

@@ -73,12 +73,13 @@ y_hat.argmax(dim=1)
 ```
 
 ### Step 3: Quantization with ONNXRuntime accelerator
-With the ONNXRuntime accelerator, `Trainer.quantize()` will return a model with compressed precision but running inference in the ONNXRuntime engine.
+With the ONNXRuntime accelerator, `InferenceOptimizer.quantize()` will return a model with compressed precision but running inference in the ONNXRuntime engine.
 
 you can add quantization as below:
 ```python
+from bigdl.nano.pytorch import InferenceOptimizer
 from torchmetrics.functional import accuracy
-ort_q_model = trainer.quantize(model, accelerator='onnxruntime', calib_dataloader=train_dataloader, metric=accuracy)
+ort_q_model = InferenceOptimizer.quantize(model, accelerator='onnxruntime', calib_data=train_dataloader, metric=accuracy)
 
 # run simple prediction
 y_hat = ort_q_model(x)

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_quantization_openvino.md

@@ -76,8 +76,9 @@ y_hat.argmax(dim=1)
 ### Step 3: Quantization using Post-training Optimization Tools
 Accelerator='openvino' means using OpenVINO POT to do quantization. The quantization can be added as below:
 ```python
+from bigdl.nano.pytorch import InferenceOptimizer
 from torchmetrics import Accuracy
-ov_q_model = trainer.quantize(model, accelerator="openvino", calib_dataloader=data_loader)
+ov_q_model = InferenceOptimizer.quantize(model, accelerator="openvino", calib_data=data_loader)
 
 # run simple prediction
 batch = torch.stack([data_set[0][0], data_set[1][0]])