# 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)