Add Nano Known issues and some notebook link (#4667)

* Add Nano Known issues and some notebook link

* fix typo

docs/readthedocs/source/doc/Nano/Overview/known_issues.md

@@ -0,0 +1,53 @@
+# Nano Known Issues
+
+## **PyTorch Issues**
+
+### **AttributeError: module 'distutils' has no attribute 'version'**
+
+This usually is because the latest setuptools does not compatible with PyTorch 1.9.
+
+You can downgrade setuptools to 58.0.4 to solve this problem.
+
+For example, if your `setuptools` is installed by conda, you can run:
+
+```bash
+conda install setuptools==58.0.4
+```
+
+### **error while loading shared libraries: libunwind.so.8**
+
+You may see this error message when running `source bigdl-nano-init`
+```
+ Sed: error while loading shared libraries: libunwind.so.8: cannot open shared object file: No such file or directory.
+```
+You can use the following command to fix this issue.
+
+* `apt-get install libunwind8-dev` 
+
+### **Bus error (core dumped) in multi-instance training with spawn distributed backend**
+
+This usually is because you did not set enough shared memory size in your docker container.
+
+You can increase `--shm-size` to a larger value, e.g. a few GB, to your `docker run` command, or use `--ipc=host`.
+
+If you are running in k8s, you can mount larger storage in `/dev/shm`. For example, you can add the following `volume` and `volumeMount` in your pod and container definition.
+
+```yaml
+spec:
+  containers:
+    ...
+    volumeMounts:
+    - mountPath: /dev/shm
+      name: cache-volume
+  volumes:
+  - emptyDir:
+    medium: Memory
+    sizeLimit: 8Gi
+    name: cache-volume
+```
+
+## **TensorFlow Issues**
+
+### **Nano keras multi-instance training currently does not suport tensorflow dataset.from_generators, numpy_function, py_function**
+
+Nano keras multi-instance training will serialize TensorFlow dataset object into a `graph.pb` file, which does not work with `dataset.from_generators`, `dataset.numpy_function`, `dataset.py_function` due to limitations in TensorFlow.

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

@@ -2,7 +2,7 @@
 
 BigDL-Nano can be used to accelerate PyTorch or PyTorch-Lightning applications on training workloads. The optimizations in BigDL-Nano are delivered through an extended version of PyTorch-Lightning `Trainer`. These optimizations are either enabled by default or can be easily turned on by setting a parameter or calling a method.
 
-We will briefly describe here the major features in BigDL-Nano for PyTorch training. You can find complete examples here [links to be added]().
+We will briefly describe here the major features in BigDL-Nano for PyTorch training. You can find complete examples [here](https://github.com/intel-analytics/BigDL/tree/main/python/nano/notebooks/pytorch).
 
 ### Best Known Configurations
 
@@ -40,13 +40,19 @@ trainer.fit(lightning_module, train_loader)
 
 #### Intel® Extension for PyTorch
 
-Intel Extension for Pytorch (a.k.a. IPEX) extends PyTorch with optimizations for an extra performance boost on Intel hardware. BigDL-Nano integrates IPEX through the `Trainer`. Users can turn on IPEX by setting `use_ipex=True`.
+Intel Extension for Pytorch (a.k.a. IPEX) [link](https://github.com/intel/intel-extension-for-pytorch) extends PyTorch with optimizations for an extra performance boost on Intel hardware. BigDL-Nano integrates IPEX through the `Trainer`. Users can turn on IPEX by setting `use_ipex=True`.
 
 ```python
 from bigdl.nano.pytorch import Trainer
 trainer = Trainer(max_epoch=10, use_ipex=True)
 ```
 
+Note: BigDL-Nano does not install IPEX by default. You can install IPEX using the following command:
+
+```bash
+python -m pip install torch_ipex==1.9.0 -f https://software.intel.com/ipex-whl-stable
+```
+
 #### Multi-instance Training
 
 When training on a server with dozens of CPU cores, it is often beneficial to use multiple training instances in a data-parallel fashion to make full use of the CPU cores. However, using PyTorch's DDP API is a little cumbersome and error-prone, and if not configured correctly, it will make the training even slow.

docs/readthedocs/source/index.rst

@@ -52,6 +52,7 @@ BigDL Documentation
    doc/Nano/QuickStart/tensorflow_train.md
    doc/Nano/QuickStart/tensorflow_inference.md
    doc/Nano/QuickStart/hpo.md
+   doc/Nano/Overview/known_issues.md
 
 .. toctree::
    :maxdepth: 1