Documented alternative way to attach SSH Helper through requirements.txt

Author: ivan-khvostishkov

README.md

@@ -73,7 +73,7 @@ pip install sagemaker-ssh-helper
 
 ### Step 2: Modify your start training job code
 1. Add import for SSHEstimatorWrapper
-2. Add a `dependencies` parameter to the Estimator object.
+2. Add a `dependencies` parameter to the Estimator object. Alternatively, add `sagemaker_ssh_helper` into `requirements.txt`.
 3. Add an `SSHEstimatorWrapper.create(estimator,...)` call before calling `fit()` and add SageMaker SSH Helper 
 as `dependencies`.
 4. Add a call to `ssh_wrapper.get_instance_ids()` to get the SSM instance(s) id. We'll use this as the target 
@@ -82,26 +82,33 @@ to connect to later on.
 For example:
 
 ```python
+import logging
 from sagemaker.pytorch import PyTorch
 from sagemaker_ssh_helper.wrapper import SSHEstimatorWrapper  # <--NEW--
 
 role = ...
 
-estimator = PyTorch(entry_point='train.py',
-                    source_dir='source_dir/training/',
-                    dependencies=[SSHEstimatorWrapper.dependency_dir()],  # <--NEW--
-                    role=role,
-                    framework_version='1.9.1',
-                    py_version='py38',
-                    instance_count=1,
-                    instance_type='ml.m5.xlarge')
+estimator = PyTorch(
+    entry_point='train.py',
+    source_dir='source_dir/training/',
+    dependencies=[SSHEstimatorWrapper.dependency_dir()],  # <--NEW 
+    # (alternatively, add sagemaker_ssh_helper into requirements.txt 
+    # inside source dir) --
+    role=role,
+    framework_version='1.9.1',
+    py_version='py38',
+    instance_count=1,
+    instance_type='ml.m5.xlarge'
+)
 
 ssh_wrapper = SSHEstimatorWrapper.create(estimator, connection_wait_time_seconds=600)  # <--NEW--
 
 estimator.fit(wait=False)
 
 instance_ids = ssh_wrapper.get_instance_ids()  # <--NEW--
-print(f'To connect over SSM run: aws ssm start-session --target {instance_ids[0]}')  # <--NEW--
+
+logging.info(f"To connect over SSM run: aws ssm start-session --target {instance_ids[0]}")
+logging.info(f"To connect over SSH run: sm-local-ssh-training connect {ssh_wrapper.latest_training_job_name()}")
 ```
 
 *Note:* `connection_wait_time_seconds` is the amount of time the SSH helper will wait inside SageMaker before it continues normal execution. It's useful for training jobs, when you want to connect before training starts.
@@ -138,7 +145,7 @@ and will appear in the job's CloudWatch log like this:
 Successfully registered the instance with AWS SSM using Managed instance-id: mi-1234567890abcdef0
 ``` 
 
-To fetch the instance IDs from the logs in an automated way, call the Python method of `ssh_wrapper`, 
+To fetch the instance IDs in an automated way, call the Python method of `ssh_wrapper`, 
 as mentioned in the previous step:
 
 ```python
@@ -218,21 +225,29 @@ Adding SageMaker SSH Helper to inference endpoint is similar to training with th
 1. Wrap your model into `SSHModelWrapper` before calling `deploy()` and add SSH Helper to `dependencies`:
 
 ```python
+from sagemaker import Predictor
 from sagemaker_ssh_helper.wrapper import SSHModelWrapper  # <--NEW--
 
 estimator = ...
 ...
 endpoint_name = ... 
 
-model = estimator.create_model(entry_point='inference.py',
-                               source_dir='source_dir/inference/',
-                               dependencies=[SSHModelWrapper.dependency_dir()])  # <--NEW--
+model = estimator.create_model(
+    entry_point='inference_ssh.py',
+    source_dir='source_dir/inference/',
+    dependencies=[SSHModelWrapper.dependency_dir()]  # <--NEW 
+    # (alternatively, add sagemaker_ssh_helper into requirements.txt 
+    # inside source dir) --
+)
 
 ssh_wrapper = SSHModelWrapper.create(model, connection_wait_time_seconds=0)  # <--NEW--
 
-predictor = model.deploy(initial_instance_count=1,
-                         instance_type='ml.m5.xlarge',
-                         endpoint_name=endpoint_name)
+predictor: Predictor = model.deploy(
+    initial_instance_count=1,
+    instance_type='ml.m5.xlarge',
+    endpoint_name=endpoint_name,
+    wait=True
+)
 
 predicted_value = predictor.predict(data=...)
 ```
@@ -561,7 +576,7 @@ Note, that if you stop the waiting loop, SageMaker will run your training script
 
 But there's a useful trick: submit a dummy script `train_placeholder.py` with the infinite loop, and while this loop will be running, you can 
 run your real training script again and again with the remote interpreter.
-Setting `max_run` parameter of the estimator is highly recommended in this case.
+Setting `max_run` parameter of the estimator is highly recommended in this case. 
 
 The dummy script may look like this:
 
@@ -581,6 +596,8 @@ The method `is_last_session_timeout()` will help to prevent unused resources and
 
 Keep in mind that SSM sessions will [terminate automatically due to user inactivity](https://docs.aws.amazon.com/systems-manager/latest/userguide/session-preferences-timeout.html), but SSH sessions will keep running until either a user terminates them or network timeout occurs (e.g., when local machine hibernates).
 
+Consider also sending e-mail notifications for users of the long-running jobs, so the users don't forget to shut down unused resources.
+
 Make also sure that you're aware of [SageMaker Managed Warm Pools](https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html) 
 feature, which is also helpful in the scenario when you need to rerun your code multiple times.