TF ZCC limitation footnote (aws#342)

Author: mchoi8739

README.md

@@ -68,6 +68,8 @@ The following frameworks are available AWS Deep Learning Containers with the dee
 | [PyTorch](docs/pytorch.md) | 1.4, 1.5, 1.6 |
 | [XGBoost](docs/xgboost.md) | 0.90-2, 1.0-1 ([As a built-in algorithm](docs/xgboost.md#use-xgboost-as-a-built-in-algorithm))|
 
+**Note**: Debugger with zero script change is partially available for TensorFlow v2.1.0 and v2.3.0. The `inputs`, `outputs`, `gradients`, and `layers` built-in collections are currently not available for these TensorFlow versions.
+
 ### AWS training containers with script mode
 
 The `smdebug` library supports frameworks other than the ones listed above while using AWS containers with script mode. If you want to use SageMaker Debugger with one of the following framework versions, you need to make minimal changes to your training script.

docs/tensorflow.md

@@ -64,6 +64,14 @@ tf_estimator = TensorFlow(
 )
 tf_estimator.fit("s3://bucket/path/to/training/data")
 ```
+>**Note**: The SageMaker TensorFlow estimator and the Debugger collections in the example are based on the SageMaker python SDK v2 and `smdebug` v0.9.2. It is highly recommended to upgrade the packages by executing the following command line.
+```bash
+pip install -U sagemaker
+pip install -U smdebug
+```
+If you are using Jupyter Notebook, put exclamation mark at the front of the code lines and restart your kernel.
+
+#### Available Tensor Collections for TensorFlow
 
 **Note**: The SageMaker TensorFlow estimator and the Debugger collections in this example are based on the latest `smdebug` library. We highly recommend that you upgrade the packages by running the following commands at the command line:
 ```