REST API

The TAO Toolkit API exposes dataset and model endpoints for setting up and running actions.

If using Python requests for making API calls, it is required to use Python >= 3.10 to avoid the upload of large files issue.

User authentication is based on NGC API KEY.

Within the cluster, one can use the following command to know the node-port for the ingress controller. The node port would be the number after the 443 cluster port 443:<NodePort>.

            

            
kubectl get services ingress-nginx-controller
        

the IP address would be the external IP of one of the cluster nodes.

..code:

            

            
hostname -i
        

A curl command to do user authentication would look like the following, where 172.17.169.89 is one of the node’s external IP, 30108 is the NodePort for the ingress controller and the NGC API KEY is zZYtczM5amdtdDcwNjk0cnA2bGU2bXQ3bnQ6NmQ4NjNhMDItMTdmZS00Y2QxLWI2ZjktNmE5M2YxZTc0OGyM. This will return the user ID and token.

            

            
curl https://172.17.169.89:30108/api/v1/login/zZYtczM5amdtdDcwNjk0cnA2bGU2bXQ3bnQ6NmQ4NjNhMDItMTdmZS00Y2QxLWI2ZjktNmE5M2YxZTc0OGyM
        

The following is a curl example of an API call with user authentication.

            

            
curl https://172.17.169.89:30108/api/v1/user/6eba330c-1019-4644-82cc-80eb5273b867/model \
-H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJsb2dnZWRJbkFzIjoiYWRtaW4iLCJpYXQiOjE0MjI3Nzk2Mz"
        

The TAO Toolkit API service includes methods for dealing with the content of experimental workspaces, such as user datasets and models. It also includes methods for executing TAO actions applicable to data and specifications stored in such experimental workspaces.

A basic scenario would be for a user to first create a Dataset for a specific network type, create a Model experiment that is pointing to the dataset and a pretrained model, and customize specs before executing network-related actions.

See the TAO Toolkit API Reference for more details.

The tutorial notebooks provide examples that demonstrate the various workflows.

1. Download the resource using the NGC CLI.

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   ngc registry resource download-version "nvidia/tao/tao-getting-started:5.0.0"
           

   
   

2. Find tutorial notebooks (.ipynb files).

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   cd tao-getting-started_v5.0.0/notebooks/tao_api_starter_kit/api
           

   
   

3. Serve these Notebook examples using Jupyter-lab pods.

   Warning

   Jupyter-lab pods are not multi-tenant and present a security risk, as the user could gain access to manage the whole GPU cluster within the pod.

   Also, the instructions mentioned below to forward the port and use the Jupyter-lab pod is only meant to show how to launch the TAO API notebooks across different Cloud Service Providers (CSPs) in a cloud agnostic way. Each organization will have it’s own specific security policies regarding opening cloud service ports publicly, so make sure you review them and comply before executing the below mentioned steps.

   TAO Toolkit version 5.0 provides a Jupyter-lab pod with the dependencies required to launch notebooks pre-installed.

   1. On the remote machine where the one-click deployment scripts are run, execute the following command. Note that this command will not terminate automatically, therefore to run any new commands on this machine, you will need to open a new terminal.

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      kubectl port-forward service/tao-toolkit-api-jupyterlab-service :8888
              

      Example output

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      Forwarding from 127.0.0.1:33465 -> 8888
      
      Forwarding from [::1]:33465 -> 8888
              

      
      

   2. Copy the port number following the address 127.0.0.1 from output of previous step.

   3. In a new terminal window on your local machine, launch ssh tunneling of the remote machine via the following command. user_name and IP_address corresponds to the machine where the deployment scripts are run. Note that this command will not terminate automatically.

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      ssh -N -L <port_number_copied>:localhost:<port_number_copied> <user_name>@<IP_address>
              

      
      

   4. On the browser of the local machine, visit the address localhost:<port_number_copied>/notebook/lab

      Now, the jupyterlab session inside the jupyter-lab pod is accessible to you on the browser.
      You can run api or TAO-Client notebooks inside the tao_end2end folder.

   5. Inside the notebook, for FIXME of ip_address, use ingress-nginx-controller and for FIXME of port_number, use 80.

   You can view the pod name using kubectl get pods -n default | grep "jupyter".

   You can copy files into the pod using kubectl cp <path to local file system> <jupyterlab-pod pod name>:<path inside jupyterlab-pod>

   You can also launch the notebooks without the Jupyter-lab pod by installing the Jupyter notebook pip package and using that package to launch notebooks from the getting started directory:

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   pip3 install jupyter notebook
   jupyter notebook --ip 0.0.0.0
           

   Note

   TAO-Client notebooks need to be run from the machine where the API is installed, whereas notebooks under the api folder, which use direct REST API calls, can be run from any machine.