Fix links in hpo tensorflow mnist notebook (#1829)

* Fix links in hpo tensorflow mnist notebook

* Run notebook through sagemaker-upgrade-v2 cli

* Set HyperparameterTuning.fit method wait=False

Co-authored-by: Ajay Karpur <[email protected]>

Author: wiltonwu

hyperparameter_tuning/tensorflow_mnist/hpo_tensorflow_mnist.ipynb

@@ -124,23 +124,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The script here is and adaptation of the [TensorFlow MNIST example](https://github.com/tensorflow/models/tree/master/official/mnist). It provides a ```model_fn(features, labels, mode)```, which is used for training, evaluation and inference. \n",
+    "The script here is and adaptation of the [TensorFlow MNIST example](https://github.com/tensorflow/models/tree/r1.12.0/official/mnist). It provides a ```model_fn(features, labels, mode)```, which is used for training, evaluation and inference. \n",
     "\n",
     "### A regular ```model_fn```\n",
     "\n",
     "A regular **```model_fn```** follows the pattern:\n",
-    "1. [defines a neural network](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py#L96)\n",
-    "- [applies the ```features``` in the neural network](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py#L178)\n",
-    "- [if the ```mode``` is ```PREDICT```, returns the output from the neural network](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py#L186)\n",
-    "- [calculates the loss function comparing the output with the ```labels```](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py#L188)\n",
-    "- [creates an optimizer and minimizes the loss function to improve the neural network](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py#L193)\n",
-    "- [returns the output, optimizer and loss function](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py#L205)\n",
+    "1. [defines a neural network](https://github.com/tensorflow/models/blob/r1.12.0/official/mnist/mnist.py#L103)\n",
+    "- [applies the ```features``` in the neural network](https://github.com/tensorflow/models/blob/r1.12.0/official/mnist/mnist.py#L104)\n",
+    "- [if the ```mode``` is ```PREDICT```, returns the output from the neural network](https://github.com/tensorflow/models/blob/r1.12.0/official/mnist/mnist.py#L108)\n",
+    "- [calculates the loss function comparing the output with the ```labels```](https://github.com/tensorflow/models/blob/r1.12.0/official/mnist/mnist.py#L142)\n",
+    "- [creates an optimizer and minimizes the loss function to improve the neural network](https://github.com/tensorflow/models/blob/r1.12.0/official/mnist/mnist.py#L121)\n",
+    "- [returns the output, optimizer and loss function](https://github.com/tensorflow/models/blob/r1.12.0/official/mnist/mnist.py#L136-L139)\n",
     "\n",
     "### Writing a ```model_fn``` for distributed training\n",
     "When distributed training happens, the same neural network will be sent to the multiple training instances. Each instance will predict a batch of the dataset, calculate loss and minimize the optimizer. One entire loop of this process is called **training step**.\n",
     "\n",
     "#### Syncronizing training steps\n",
-    "A [global step](https://www.tensorflow.org/api_docs/python/tf/train/global_step) is a global variable shared between the instances. It necessary for distributed training, so the optimizer will keep track of the number of **training steps** between runs: \n",
+    "A [global step](https://github.com/tensorflow/docs/blob/r1.12/site/en/api_docs/python/tf/train/global_step.md) is a global variable shared between the instances. It necessary for distributed training, so the optimizer will keep track of the number of **training steps** between runs: \n",
     "\n",
     "```python\n",
     "train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())\n",
@@ -176,14 +176,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "estimator = TensorFlow(entry_point='mnist.py',\n",
-    "                  role=role,\n",
-    "                  framework_version='1.12.0',\n",
-    "                  training_steps=1000, \n",
-    "                  evaluation_steps=100,\n",
-    "                  train_instance_count=1,\n",
-    "                  train_instance_type='ml.m4.xlarge',\n",
-    "                  base_job_name='DEMO-hpo-tensorflow')"
+    "estimator = TensorFlow(py_version='py3',\n",
+    "                       entry_point='mnist.py',\n",
+    "                       role=role,\n",
+    "                       framework_version='1.12.0',\n",
+    "                       training_steps=1000,\n",
+    "                       evaluation_steps=100,\n",
+    "                       instance_count=1,\n",
+    "                       instance_type='ml.m4.xlarge',\n",
+    "                       base_job_name='DEMO-hpo-tensorflow')"
    ]
   },
   {
@@ -268,7 +269,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tuner.fit(inputs)"
+    "tuner.fit(inputs, wait=False)"
    ]
   },
   {