Docs Update

README.md

@@ -22,7 +22,7 @@ There are two ways to use it: Automatic mode and configurable mode.
 
 ## Example: Amazon SageMaker Zero-Code-Change
 This example uses a zero-script-change experience, where you can use your training script as-is.
-See the [example notebooks](https://link.com) for more details.
+See the [example notebooks](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger) for more details.
 ```python
 import sagemaker
 from sagemaker.debugger import rule_configs, Rule, CollectionConfig
@@ -88,7 +88,7 @@ print(f"Loss values were {trial.tensor('CrossEntropyLoss:0')}")
 Amazon SageMaker Debugger uses a `hook` to store the values of tensors throughout the training process. Another process called a `rule` job
 simultaneously monitors and validates these outputs to ensure that training is progressing as expected.
 A rule might check for vanishing gradients, or exploding tensor values, or poor weight initialization.
-If a rule is triggered, it will raise a CloudWatch event and stop the training job, saving you time
+If a rule is triggered, it will raise a CloudWatch event, saving you time
 and money.
 
 Amazon SageMaker Debugger can be used inside or outside of SageMaker. There are three main use cases:
@@ -99,9 +99,9 @@ Amazon SageMaker Debugger can be used inside or outside of SageMaker. There are
 The reason for different setups is that SageMaker Zero-Script-Change (via Deep Learning Containers) uses custom framework forks of TensorFlow, PyTorch, MXNet, and XGBoost to save tensors automatically.
 These framework forks are not available in custom containers or non-SM environments, so you must modify your training script in these environments.
 
-See the [SageMaker page](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/sagemaker.md) for details on SageMaker Zero-Code-Change and BYOC experience.\
+See the [SageMaker page](docs/sagemaker.md) for details on SageMaker Zero-Code-Change and BYOC experience.\
 See the frameworks pages for details on modifying the training script:
-- [TensorFlow](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/tensorflow.md)
-- [PyTorch](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/pytorch.md)
-- [MXNet](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/mxnet.md)
-- [XGBoost](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/xgboost.md)
+- [TensorFlow](docs/tensorflow.md)
+- [PyTorch](docs/pytorch.md)
+- [MXNet](docs/mxnet.md)
+- [XGBoost](docs/xgboost.md)

docs/api.md

@@ -1,7 +1,6 @@
 
 # Common API
 These objects exist across all frameworks.
-- [SageMaker Zero-Code-Change vs. Python API](#sagemaker)
 - [Creating a Hook](#creating-a-hook)
     - [Hook from SageMaker](#hook-from-sagemaker)
     - [Hook from Python](#hook-from-python)
@@ -14,8 +13,7 @@ These objects exist across all frameworks.
 
 The imports assume `import smdebug.{tensorflow,pytorch,mxnet,xgboost} as smd`.
 
-**Hook**: The main interface to use training. This object can be passed as a model hook/callback
-in Tensorflow and Keras. It keeps track of collections and writes output files at each step.
+**Hook**: The main class to pass as a callback object, or to create callback functions. It keeps track of collections and writes output files at each step.
 - `hook = smd.Hook(out_dir="/tmp/mnist_job")`
 
 **Mode**: One of "train", "eval", "predict", or "global". Helpful for segmenting data based on the phase
@@ -32,10 +30,10 @@ tensors to include/exclude.
 **ReductionConfig**: Allows you to save a reduction, such as 'mean' or 'l1 norm', instead of the full tensor.
 - `reduction_config = smd.ReductionConfig(reductions=['min', 'max', 'mean'], norms=['l1'])`
 
-**Trial**: The main interface to use when analyzing a completed training job. Access collections and tensors. See [trials documentation](https://link.com).
+**Trial**: The main interface to use when analyzing a completed training job. Access collections and tensors. See [trials documentation](analysis.md).
 - `trial = smd.create_trial(out_dir="/tmp/mnist_job")`
 
-**Rule**: A condition that will trigger an exception and terminate the training job early, for example a vanishing gradient. See [rules documentation](https://link.com).
+**Rule**: A condition that will trigger an exception, for example a vanishing gradient. See [rules documentation](analysis.md).
 
 
 ---
@@ -44,7 +42,7 @@ tensors to include/exclude.
 
 ### Hook from SageMaker
 If you create a SageMaker job and specify the hook configuration in the SageMaker Estimator API
-as described in [AWS Docs](https://link.com),
+as described in [AWS Docs](https://docs.aws.amazon.com/sagemaker/latest/dg/train-model.html),
 the a JSON file will be automatically written. You can create a hook from this file by calling
 ```python
 hook = smd.{hook_class}.create_from_json_file()
@@ -53,10 +51,10 @@ with no arguments and then use the hook Python API in your script. `hook_class`
 
 ### Hook from Python
 See the framework-specific pages for more details.
-* [TensorFlow](https://link.com)
-* [PyTorch](https://link.com)
-* [MXNet](https://link.com)
-* [XGBoost](https://link.com)
+* [TensorFlow](tensorflow.md)
+* [PyTorch](pytorch.md)
+* [MXNet](mxnet.md)
+* [XGBoost](xgboost.md)
 
 ---
 

docs/mxnet.md

@@ -1,6 +1,6 @@
 # MXNet
 
-SageMaker Zero-Code-Change supported container: MXNet 1.6. See [AWS Docs](https://link.com) for more information.\
+SageMaker Zero-Code-Change supported container: MXNet 1.6. See [AWS Docs](https://docs.aws.amazon.com/sagemaker/latest/dg/train-model.html) for more information.\
 Python API supported versions: MXNet 1.4, 1.5, 1.6.
 
 ## Contents

docs/pytorch.md

@@ -1,6 +1,6 @@
 # PyTorch
 
-SageMaker Zero-Code-Change supported containers: PyTorch 1.3. See [AWS Docs](https://link.com) for more information.\
+SageMaker Zero-Code-Change supported containers: PyTorch 1.3. See [AWS Docs](https://docs.aws.amazon.com/sagemaker/latest/dg/train-model.html) for more information.\
 Python API supported versions: 1.2, 1.3.
 
 ## Contents
@@ -71,8 +71,8 @@ for (inputs, labels) in trainloader:
 ```
 
 ## Full API
-See the [Common API](https://link.com) page for details about Collection, SaveConfig, and ReductionConfig.\
-See the [Analysis](https://link.com) page for details about analyzing a training job.
+See the [Common API](api.md) page for details about Collection, SaveConfig, and ReductionConfig.\
+See the [Analysis](analysis.md) page for details about analyzing a training job.
 
 ## Hook
 ```python

docs/sagemaker.md

@@ -13,10 +13,10 @@ These framework forks are not available in custom containers or non-SM environme
 
 This configuration is used for both ZCC and BYOC. The only difference is that with a custom container, you modify your training script as well. See the framework pages below for details on how to modify your training script.
 
-- [TensorFlow](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/tensorflow.md)
-- [PyTorch](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/pytorch.md)
-- [MXNet](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/mxnet.md)
-- [XGBoost](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/xgboost.md)
+- [TensorFlow](tensorflow.md)
+- [PyTorch](pytorch.md)
+- [MXNet](mxnet.md)
+- [XGBoost](xgboost.md)
 
 ```python
 rule = sagemaker.debugger.Rule.sagemaker(
@@ -110,6 +110,7 @@ sagemaker_simple_estimator.fit()
 
 ## List of Builtin Rules
 Full list of rules is:
+
 | Rule Name | Behavior |
 |---|---|
 | `vanishing_gradient` | Detects a vanishing gradient. |