TF 2.2 documentation update

README.md

@@ -63,23 +63,21 @@ The following frameworks are available AWS Deep Learning Containers with the dee
 
 | Framework | Version |
 | --- | --- |
-| [TensorFlow](docs/tensorflow.md) | 1.15, 2.1, 2.2 |
+| [TensorFlow](docs/tensorflow.md) | 1.15, 2.1.0, 2.2.0, 2.3.0 |
 | [MXNet](docs/mxnet.md) | 1.6 |
-| [PyTorch](docs/pytorch.md) | 1.4, 1.5 |
+| [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**: Limited support of the zero script change experience for TensorFlow 2.2. The tensor collections `layers`, `inputs`, `outputs`, and `gradients` are currently not available.
-
 ### 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.
 
 | Framework | Versions |
 | --- | --- |
-| [TensorFlow](docs/tensorflow.md) | 1.13, 1.14, 1.15, 2.1, 2.2 |
+| [TensorFlow](docs/tensorflow.md) | 1.13, 1.14, 1.15, 2.1.0, 2.2.0, 2.3.0 |
 | Keras (with TensorFlow backend) | 2.3 |
 | [MXNet](docs/mxnet.md) | 1.4, 1.5, 1.6 |
-| [PyTorch](docs/pytorch.md) | 1.2, 1.3, 1.4, 1.5 |
+| [PyTorch](docs/pytorch.md) | 1.2, 1.3, 1.4, 1.5, 1.6 |
 | [XGBoost](docs/xgboost.md) |  0.90-2, 1.0-1 (As a framework)|
 
 ### Debugger on custom containers or local machines

docs/api.md

@@ -163,6 +163,7 @@ Note that `smd` import below translates to `import smdebug.{framework} as smd`.
 |`create_from_json_file(`<br/>`  json_file_path=None)` | `json_file_path (str)` | Takes the path of a file which holds the json configuration of the hook, and creates hook from that configuration. This is an optional parameter. <br/> If this is not passed it tries to get the file path from the value of the environment variable `SMDEBUG_CONFIG_FILE_PATH` and defaults to `/opt/ml/input/config/debughookconfig.json`. When training on SageMaker you do not have to specify any path because this is the default path that SageMaker writes the hook configuration to.
 |`close()` | - | Closes all files that are currently open by the hook |
 | `save_scalar()` | `name (str)` <br/> `value (float)` <br/> `sm_metric (bool)`| Saves a scalar value by the given name. Passing `sm_metric=True` flag also makes this scalar available as a SageMaker Metric to show up in SageMaker Studio. Note that when `sm_metric` is False, this scalar always resides only in your AWS account, but setting it to True saves the scalar also on AWS servers. The default value of `sm_metric` for this method is False. |
+| `save_tensor()`| `tensor_name (str)`, `tensor_value (numpy.array or numpy.ndarray)`, `collections_to_write (str or list[str])` | Manually save metrics tensors. The `record_tensor_value()` API is deprecated in favor or `save_tensor()`.|
 
 
 ### TensorFlow specific Hook API
@@ -178,7 +179,6 @@ The following hook APIs are specific to training scripts using the TF 2.x Gradie
 | Method | Arguments | Returns | Behavior |
 | --- | --- | --- | --- |
 | `wrap_tape(tape)` | `tape` (tensorflow.python.eager.backprop.GradientTape) | Returns a tape object with three identifying markers to help `smdebug`. This returned tape should be used for training. | When not using Zero Script Change environments, calling this method on your tape is necessary for SageMaker Debugger to identify and save gradient tensors. Note that this method returns the same tape object passed.
-| `save_tensor()`| tensor_name (str), tensor_value (float), collections_to_write (str) | - | Manually save metrics tensors while using TF 2.x GradientTape. Note: `record_tensor_value()` is deprecated.|
 
 ### MXNet specific Hook API