# TensorBoard in Amazon SageMaker AI
Amazon SageMaker AI with TensorBoard is a capability of Amazon SageMaker AI that brings the [TensorBoard](https://www.tensorflow.org/tensorboard) visualization tools to SageMaker AI and integrated with SageMaker Training and domain. It provides options to administer your AWS account and users belonging to the account through [SageMaker AI domain](https://docs.aws.amazon.com/sagemaker/latest/dg/sm-domain.html), to give the domain users access to the TensorBoard data with appropriate permissions to Amazon S3, and help the domain users perform model debugging tasks using the TensorBoard visualization plugins. SageMaker AI with TensorBoard is extended with the SageMaker AI Data Manager plugin, with which domain users can access a number of training jobs in one place within the TensorBoard application.
**Note**
This feature is for debugging the training of deep learning models using PyTorch or TensorFlow.
**For data scientists**
Training large models can have scientific problems that require data scientists to debug and resolve them in order to improve model convergence and stabilize gradient descent processes.
When you encounter model training issues, such as loss not converging, or vanishing or exploding weights and gradients, you need to access tensor data to dive deep and analyze the model parameters, scalars, and any custom metrics. Using SageMaker AI with TensorBoard, you can visualize model output tensors extracted from training jobs. As you experiment with different models, multiple training runs, and model hyperparameters, you can select multiple training jobs in TensorBoard and compare them in one place.
**For administrators**
Through the TensorBoard landing page in the SageMaker AI console or [SageMaker AI domain](https://docs.aws.amazon.com/sagemaker/latest/dg/sm-domain.html), you can manage TensorBoard application users if you are an administrator of an AWS account or SageMaker AI domain. Each domain user can access their own TensorBoard application given the granted permissions. As a SageMaker AI domain administrator and domain user, you can create and delete the TensorBoard application given the permission level you have.
**Note**
You cannot share the TensorBoard application for collaboration purposes because SageMaker AI domain does not allow application sharing among users. Users can share the output tensors saved in an S3 bucket, if they have access to the bucket.
## Supported frameworks and AWS Regions
The TensorBoard application in SageMaker AI is available for the following machine learning frameworks and AWS Regions.
**Frameworks**
+ PyTorch
+ TensorFlow
+ Hugging Face Transformers
**AWS Regions**
+ US East (N. Virginia) (`us-east-1`)
+ US East (Ohio) (`us-east-2`)
+ US West (Oregon) (`us-west-2`)
+ Europe (Frankfurt) (`eu-central-1`)
+ Europe (Ireland) (`eu-west-1`)
**Note**
Amazon SageMaker AI with TensorBoard runs on an `ml.r5.large` instance and incurs charges after the SageMaker AI free tier or the free trial period of the feature. For more information, see [Amazon SageMaker AI Pricing](https://aws.amazon.com/sagemaker/pricing/).
**Topics**
+ [Supported frameworks and AWS Regions](#debugger-htb-support)
+ [Prepare a training job to collect TensorBoard output data](debugger-htb-prepare-training-job.md)
+ [Accessing the TensorBoard application on SageMaker AI](debugger-htb-access-tb.md)
+ [Load and visualize output tensors using the TensorBoard application](debugger-htb-access-tb-data.md)
+ [Delete unused TensorBoard applications](debugger-htb-delete-app.md)
# Prepare a training job to collect TensorBoard output data
A typical training job for machine learning in SageMaker AI consists of two main steps: preparing a training script and configuring a SageMaker AI estimator object of the SageMaker AI Python SDK. In this section, you'll learn about the required changes to collect TensorBoard-compatible data from SageMaker training jobs.
## Prerequisites
The following list shows the prerequisites to start using SageMaker AI with TensorBoard.
+ A SageMaker AI domain that's set up with Amazon VPC in your AWS account.
For instructions on setting up a domain, see [Onboard to Amazon SageMaker AI domain using quick setup](https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html). You also need to add domain user profiles for individual users to access the TensorBoard on SageMaker AI. For more information, see [Add user profiles](domain-user-profile-add.md).
+ The following list is the minimum set of permissions for using TensorBoard on SageMaker AI.
+ `sagemaker:CreateApp`
+ `sagemaker:DeleteApp`
+ `sagemaker:DescribeTrainingJob`
+ `sagemaker:Search`
+ `s3:GetObject`
+ `s3:ListBucket`
## Step 1: Modify your training script with open-source TensorBoard helper tools
Make sure you determine which output tensors and scalars to collect, and modify code lines in your training script using any of the following tools: TensorBoardX, TensorFlow Summary Writer, PyTorch Summary Writer, or SageMaker Debugger.
Also make sure that you specify the TensorBoard data output path as the log directory (`log_dir`) for callback in the training container.
For more information about callbacks per framework, see the following resources.
+ For PyTorch, use [torch.utils.tensorboard.SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html#module-torch.utils.tensorboard). See also the [Using TensorBoard in PyTorch](https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html#using-tensorboard-in-pytorch) and [Log scalars](https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html#log-scalars) sections in the *PyTorch tutorials*. Alternatively, you can use [TensorBoardX Summary Writer](https://tensorboardx.readthedocs.io/en/latest/tutorial.html).
```
LOG_DIR="/opt/ml/output/tensorboard"
tensorboard_callback=torch.utils.tensorboard.writer.SummaryWriter(log_dir=LOG_DIR)
```
+ For TensorFlow, use the native callback for TensorBoard, [tf.keras.callbacks.TensorBoard](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard).
```
LOG_DIR="/opt/ml/output/tensorboard"
tensorboard_callback=tf.keras.callbacks.TensorBoard(
log_dir=LOG_DIR, histogram_freq=1)
```
+ For Transformers with PyTorch, you can use [transformers.integrations.TensorBoardCallback](https://huggingface.co/docs/transformers/main/en/main_classes/callback#transformers.integrations.TensorBoardCallback).
For Transformers with TensorFlow, use the `tf.keras.tensorboard.callback`, and pass that to the keras callback in transformers.
**Tip**
You can also use a different container local output path. However, in [Step 2: Create a SageMaker training estimator object with the TensorBoard output configuration](#debugger-htb-prepare-training-job-2), you must map the paths correctly for SageMaker AI to successfully search the local path and save the TensorBoard data to the S3 output bucket.
+ For guidance on modifying training scripts using the SageMaker Debugger Python library, see [Adapting your training script to register a hook](debugger-modify-script.md).
## Step 2: Create a SageMaker training estimator object with the TensorBoard output configuration
Use the `sagemaker.debugger.TensorBoardOutputConfig` while configuring a SageMaker AI framework estimator. This configuration API maps the S3 bucket you specify for saving TensorBoard data with the local path in the training container (`/opt/ml/output/tensorboard`). Pass the object of the module to the `tensorboard_output_config` parameter of the estimator class. The following code snippet shows an example of preparing a TensorFlow estimator with the TensorBoard output configuration parameter.
**Note**
This example assumes that you use the SageMaker Python SDK. If you use the low-level SageMaker API, you should include the following to the request syntax of the [CreateTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API.
```
"TensorBoardOutputConfig": {
"LocalPath": "/opt/ml/output/tensorboard",
"S3OutputPath": "s3_output_bucket"
}
```
```
from sagemaker.tensorflow import TensorFlow
from sagemaker.debugger import TensorBoardOutputConfig
# Set variables for training job information,
# such as s3_out_bucket and other unique tags.
...
LOG_DIR="/opt/ml/output/tensorboard"
output_path = os.path.join(
"s3_output_bucket", "sagemaker-output", "date_str", "your-training_job_name"
)
tensorboard_output_config = TensorBoardOutputConfig(
s3_output_path=os.path.join(output_path, 'tensorboard'),
container_local_output_path=LOG_DIR
)
estimator = TensorFlow(
entry_point="train.py",
source_dir="src",
role=role,
image_uri=image_uri,
instance_count=1,
instance_type="ml.c5.xlarge",
base_job_name="your-training_job_name",
tensorboard_output_config=tensorboard_output_config,
hyperparameters=hyperparameters
)
```
**Note**
The TensorBoard application does not provide out-of-the-box support for SageMaker AI hyperparameter tuning jobs, as the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html) API is not integrated with the TensorBoard output configuration for the mapping. To use the TensorBoard application for hyperparameter tuning jobs, you need to write code for uploading metrics to Amazon S3 in your training script. Once the metrics are uploaded to an Amazon S3 bucket, you can then load the bucket into the TensorBoard application on SageMaker AI.
# Accessing the TensorBoard application on SageMaker AI
You can access TensorBoard by two methods: programmatically using the `sagemaker.interactive_apps.tensorboard` module that generates an unsigned or a presigned URL, or using the TensorBoard landing page in the SageMaker AI console. After you open TensorBoard, SageMaker AI runs the TensorBoard plugin and automatically finds all training job output data in a TensorBoard-compatible file format.
**Topics**
+ [Open TensorBoard using the `sagemaker.interactive_apps.tensorboard` module](debugger-htb-access-tb-url.md)
+ [Open TensorBoard using the `get_app_url` function as an `estimator` class method](debugger-htb-access-tb-get-app-url-estimator-method.md)
+ [Open TensorBoard through the SageMaker AI console](debugger-htb-access-tb-console.md)
# Open TensorBoard using the `sagemaker.interactive_apps.tensorboard` module
The `sagemaker.interactive_apps.tensorboard` module provides a function called `get_app_url` that generates unsigned or presigned URLs to open the TensorBoard application in any environment in SageMaker AI or Amazon EC2. This is to provide a unified experience for both Studio Classic and non-Studio Classic users. For the Studio environment, you can open TensorBoard by running the `get_app_url()` function as it is, or you can also specify a job name to start tracking as the TensorBoard application opens. For non-Studio Classic environments, you can open TensorBoard by providing your domain and user profile information to the utility function. With this functionality, regardless of where or how you run training code and launch training jobs, you can directly access TensorBoard by running the `get_app_url` function in your Jupyter notebook or terminal.
**Note**
This functionality is available in the SageMaker Python SDK v2.184.0 and later. To use this functionality, make sure that you upgrade the SDK by running `pip install sagemaker --upgrade`.
**Topics**
+ [Option 1: For SageMaker AI Studio Classic](#debugger-htb-access-tb-url-unsigned)
+ [Option 2: For non-Studio Classic environments](#debugger-htb-access-tb-url-presigned)
## Option 1: For SageMaker AI Studio Classic
If you are using SageMaker Studio Classic, you can directly open the TensorBoard application or retrieve an unsigned URL by running the `get_app_url` function as follows. As you are already within the Studio Classic environment and signed in as a domain user, `get_app_url()` generates unsigned URL because it is not necessary to authenticate again.
**To open the TensorBoard application**
The following code automatically opens the TensorBoard application from the unsigned URL that the `get_app_url()` function returns in the your environment's default web browser.
```
from sagemaker.interactive_apps import tensorboard
region = "us-west-2"
app = tensorboard.TensorBoardApp(region)
app.get_app_url(
training_job_name="your-training_job_name" # Optional. Specify the job name to track a specific training job
)
```
**To retrieve an unsigned URL and open the TensorBoard application manually**
The following code prints an unsigned URL that you can copy to a web browser and open the TensorBoard application.
```
from sagemaker.interactive_apps import tensorboard
region = "us-west-2"
app = tensorboard.TensorBoardApp(region)
print("Navigate to the following URL:")
print(
app.get_app_url(
training_job_name="your-training_job_name", # Optional. Specify the name of the job to track.
open_in_default_web_browser=False # Set to False to print the URL to terminal.
)
)
```
Note that if you run the preceding two code samples outside the SageMaker AI Studio Classic environment, the function will return a URL to the TensorBoard landing page in the SageMaker AI console, because these do not have sign-in information to your domain and user profile. For creating a presigned URL, see Option 2 in the following section.
## Option 2: For non-Studio Classic environments
If you use non-Studio Classic environments, such as SageMaker Notebook instance or Amazon EC2, and want to open TensorBoard directly from the environment you are in, you need to generate a URL presigned with your domain and user profile information. A *presigned* URL is a URL that's signed in to Amazon SageMaker Studio Classic while the URL is being created with your domain and user profile, and therefore granted access to all of the domain applications and files associated with your domain. To open TensorBoard through a presigned URL, use the `get_app_url` function with your domain and user profile name as follows.
Note that this option requires the domain user to have the `sagemaker:CreatePresignedDomainUrl` permission. Without the permission, the domain user will receive an exception error.
**Important**
Do not share any presigned URLs. The `get_app_url` function creates presigned URLs, which automatically authenticates with your domain and user profile and gives access to any applications and files associated with your domain.
```
print(
app.get_app_url(
training_job_name="your-training_job_name", # Optional. Specify the name of the job to track.
create_presigned_domain_url=True, # Reguired to be set to True for creating a presigned URL.
domain_id="your-domain-id", # Required if creating a presigned URL (create_presigned_domain_url=True).
user_profile_name="your-user-profile-name", # Required if creating a presigned URL (create_presigned_domain_url=True).
open_in_default_web_browser=False, # Optional. Set to False to print the URL to terminal.
optional_create_presigned_url_kwargs={} # Optional. Add any additional args for Boto3 create_presigned_domain_url
)
)
```
**Tip**
The `get_app_url` function runs the [https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_presigned_domain_url.html](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_presigned_domain_url.html) API in the AWS SDK for Python (Boto3) in the backend. As the Boto3 `create_presigned_domain_url` API creates presigned domain URLs that expire in 300 seconds by default, presigned TensorBoard application URLs also expire in 300 seconds. If you want to extend the expiration time, pass the `ExpiresInSeconds` argument to the `optional_create_presigned_url_kwargs` argument of the `get_app_url` function as follows.
```
optional_create_presigned_url_kwargs={"ExpiresInSeconds": 1500}
```
**Note**
If any of your input passed to the arguments of `get_app_url` is invalid, the function outputs a URL to the TensorBoard landing page instead of opening the TensorBoard application. The output message would be similar to the following.
```
Navigate to the following URL:
https://us-west-2.console.aws.amazon.com/sagemaker/home?region=us-west-2#/tensor-board-landing
```
# Open TensorBoard using the `get_app_url` function as an `estimator` class method
If you are in the process of running a training job using the `estimator` class of the SageMaker Python SDK and have an active object of the `estimator` class, you can also access the [`get_app_url` function as a class method](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.EstimatorBase.get_app_url) of the `estimator` class. Open the TensorBoard application or retrieve an unsigned URL by running the `get_app_url` method as follows. The `get_app_url` class method pulls the training job name from the estimator and opens the TensorBoard application with the specified job.
**Note**
This functionality is available in the SageMaker Python SDK v2.184.0 and later. To use this functionality, make sure that you upgrade the SDK by running `pip install sagemaker --upgrade`.
**Topics**
+ [Option 1: For SageMaker Studio Classic](#debugger-htb-access-tb-get-app-url-estimator-method-studio)
+ [Option 2: For non-Studio Classic environments](#debugger-htb-access-tb-get-app-url-estimator-method-non-studio)
## Option 1: For SageMaker Studio Classic
**To open the TensorBoard application**
The following code automatically opens the TensorBoard application from the unsigned URL that the `get_app_url()` method returns in the your environment's default web browser.
```
estimator.get_app_url(
app_type=SupportedInteractiveAppTypes.TENSORBOARD # Required.
)
```
**To retrieve an unsigned URL and open the TensorBoard application manually**
The following code prints an unsigned URL that you can copy to a web browser and open the TensorBoard application.
```
print(
estimator.get_app_url(
app_type=SupportedInteractiveAppTypes.TENSORBOARD, # Required.
open_in_default_web_browser=False, # Optional. Set to False to print the URL to terminal.
)
)
```
Note that if you run the preceding two code samples outside the SageMaker AI Studio Classic environment, the function will return a URL to the TensorBoard landing page in the SageMaker AI console, because these do not have sign-in information to your domain and user profile. For creating a presigned URL, see Option 2 in the following section.
## Option 2: For non-Studio Classic environments
If you use non-Studio Classic environments, such as SageMaker Notebook instance and Amazon EC2, and want to generate a presigned URL to open the TensorBoard application, use the `get_app_url` method with your domain and user profile information as follows.
Note that this option requires the domain user to have the `sagemaker:CreatePresignedDomainUrl` permission. Without the permission, the domain user will receive an exception error.
**Important**
Do not share any presigned URLs. The `get_app_url` function creates presigned URLs, which automatically authenticates with your domain and user profile and gives access to any applications and files associated with your domain.
```
print(
estimator.get_app_url(
app_type=SupportedInteractiveAppTypes.TENSORBOARD, # Required
create_presigned_domain_url=True, # Reguired to be set to True for creating a presigned URL.
domain_id="your-domain-id", # Required if creating a presigned URL (create_presigned_domain_url=True).
user_profile_name="your-user-profile-name", # Required if creating a presigned URL (create_presigned_domain_url=True).
open_in_default_web_browser=False, # Optional. Set to False to print the URL to terminal.
optional_create_presigned_url_kwargs={} # Optional. Add any additional args for Boto3 create_presigned_domain_url
)
)
```
# Open TensorBoard through the SageMaker AI console
You can also use the SageMaker AI console UI to open the TensorBoard application. There are two options to open the TensorBoard application through the SageMaker AI console.
**Topics**
+ [Option 1: Launch TensorBoard from the domain details page](#debugger-htb-access-tb-console-domain-detail)
+ [Option 2: Launch TensorBoard from the TensorBoard landing page](#debugger-htb-access-tb-console-landing-pg)
## Option 1: Launch TensorBoard from the domain details page
**Navigate to the domain details page**
The following procedure shows how to navigate to the domain details page.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **domains**.
1. From the list of domains, select the domain in which you want to launch the TensorBoard application.
**Launch a user profile application**
The following procedure shows how to launch a Studio Classic application that is scoped to a user profile.
1. On the domain details page, choose the **User profiles** tab.
1. Identify the user profile for which you want to launch the Studio Classic application.
1. Choose **Launch** for your selected user profile, then choose **TensorBoard**.
## Option 2: Launch TensorBoard from the TensorBoard landing page
The following procedure describes how to launch a TensorBoard application from the TensorBoard landing page.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. On the left navigation pane, choose **TensorBoard**.
1. Under **Get started**, select the domain in which you want to launch the Studio Classic application. If your user profile only belongs to one domain, you do not see the option for selecting a domain.
1. Select the user profile for which you want to launch the Studio Classic application. If there is no user profile in the domain, choose **Create user profile**. For more information, see [Add and Remove User Profiles](https://docs.aws.amazon.com/sagemaker/latest/dg/domain-user-profile-add.html).
1. Choose **Open TensorBoard**.
The following screenshot shows the location of TensorBoard in the left navigation pane of the SageMaker AI console and the SageMaker AI with TensorBoard landing page in the main pane.
![\[The TensorBoard landing page\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-landing-page.png)
# Load and visualize output tensors using the TensorBoard application
You can conduct an online or offline analysis by loading collected output tensors from S3 buckets paired with training jobs during or after training.
When you open the TensorBoard application, TensorBoard opens with the **SageMaker AI Data Manager** tab. The following screenshot shows the full view of the SageMaker AI Data Manager tab in the TensorBoard application.
**Note**
The visualization plugins might not appear when you first launch the TensorBoard application. After you select training jobs in the SageMaker AI Data Manager plugin, the TensorBoard application loads the TensorBoard data and populates the visualization plugins.
**Note**
The TensorBoard application automatically shuts down after 1 hour of inactivity. If you want to shut the application down when you are done using it, make sure to manually shut down TensorBoard to avoid paying for the instance hosting it. For instructions on deleting the application, see [Delete unused TensorBoard applications](debugger-htb-delete-app.md).
![\[The SageMaker AI Data Manager tab view.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-sagemaker-manager-tab.png)
In the **SageMaker AI Data Manager** tab, you can select any training job and load TensorBoard-compatible training output data from Amazon S3.
1. In the **Search training jobs** section, use the filters to narrow down the list of training jobs you want to find, load, and visualize.
1. In the **List of training jobs** section, use the check boxes to choose training jobs from which you want to pull data and visualize for debugging.
1. Choose **Add selected jobs**. The selected jobs should appear in the **Tracked training jobs** section, as shown in the following screenshot.
![\[The Tracked training jobs section.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-sagemaker-manager-tab-tracked-jobs.png)
**Note**
The **SageMaker AI Data Manager** tab only shows training jobs configured with the `TensorBoardOutputConfig` parameter. Make sure you have configured the SageMaker AI estimator with this parameter. For more information, see [Step 2: Create a SageMaker training estimator object with the TensorBoard output configuration](debugger-htb-prepare-training-job.md#debugger-htb-prepare-training-job-2).
**Note**
The visualization tabs might not appear if you are using SageMaker AI with TensorBoard for the first time or no data is loaded from a previous use. After adding training jobs and waiting for a few seconds, refresh the viewer by choosing the clockwise circular arrow on the upper-right corner. The visualization tabs should appear after the job data are successfully loaded. You can also set to auto-refresh using the **Settings** button next to the refresh button in the upper right corner.
## Visualization of the output tensors in TensorBoard
In the graphics tabs, you can find the list of the loaded training jobs in the left pane. You can also use the check boxes of the training jobs to show or hide visualizations. The TensorBoard dynamic plugins are activated dynamically depending on how you have set your training script to include summary writers and pass callbacks for tensor and scalar collection, and therefore the graphics tabs also appear dynamically. The following screenshots show example views of each tab with visualization of two training jobs that collected metrics for time series, scalar, graph, distribution, and histogram plugins.
**The TIME SERIES tab view**
![\[The TIME SERIES tab view.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-time-series.png)
**The SCALARS tab view**
![\[The SCALARS tab view.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-scalars.png)
**The GRAPHS tab view**
![\[The GRAPHS tab view.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-graphs.png)
**The DISTRIBUTIONS tab view**
![\[The DISTRIBUTIONS tab view.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-distribution.png)
**The HISTOGRAMS tab view**
![\[The HISTOGRAMS tab view.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/htb-histogram.png)
# Delete unused TensorBoard applications
After you are done with monitoring and experimenting with jobs in TensorBoard, shut the TensorBoard application down.
1. Open the SageMaker AI console.
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **domains**.
1. Choose your domain.
1. Choose your user profile.
1. Under **Apps**, choose **Delete App** for the TensorBoard row.
1. Choose **Yes, delete app**.
1. Type **delete** in the text box, then choose **Delete**.
1. A blue message should appear at the top of the screen: **default is being deleted**.