TAO Deploy Installation#

When the tao deploy command is invoked through the TAO launcher, the tao deploy container is pulled from NGC and instantiated. The TAO Deploy container only contains few lightweight python packages such as OpenCV, Numpy, Pillow, and ONNX and is based on the NGC TensorRT container. Along with the NGC container, tao deploy is also released as a public wheel on PyPI. The TensorRT engines generated by tao deploy are specific to the GPU that it is generated on. So, based on the platform that the model is being deployed to, you will need to download the specific version of the tao deploy wheel and generate the engine there after installing the corresponding TensorRT version for your platform.

Invoking the TAO Deploy Container Directly#

To deploy TAO models to TensorRT from the tao-deploy container, you should first identify the latest docker tag associated with the tao launcher by running tao info --verbose.

The following is sample output from TAO 5.0.0:

Configuration of the TAO Instance
task_group:
    deploy:
        dockers:
            nvidia/tao/tao-toolkit-deploy:
                5.0.0-deploy:
                    docker_registry: nvcr.io
                    tasks:
                        1. centerpose
                        2. classification_pyt
                        3. classification_tf1
                        4. classification_tf2
                        5. deformable_detr
                        6. detectnet_v2
                        7. dino
                        8. dssd
                        9. efficientdet_tf1
                        10. efficientdet_tf2
                        11. faster_rcnn
                        12. lprnet
                        13. mask_rcnn
                        14. ml_recog
                        15. multitask_classification
                        16. ocdnet
                        17. ocrnet
                        18. optical_inspection
                        19. retinanet
                        20. segformer
                        21. ssd
                        22. unet
                        23. visual_changenet
                        24. yolo_v3
                        25. yolo_v4
                        26. yolo_v4_tiny
format_version: 3.0
toolkit_version: 5.0.0

The container name associated with the task can be retrieved as $DOCKER_REGISTRY/$DOCKER_NAME:$DOCKER_TAG. For example, from the log above, the Docker name to run detectnet_v2 can be derived as follows:

export DOCKER_REGISTRY="nvcr.io"
export DOCKER_NAME="nvidia/tao/tao-toolkit"
export DOCKER_TAG="5.0.0-deploy"

export DOCKER_CONTAINER=$DOCKER_REGISTRY/$DOCKER_NAME:$DOCKER_TAG

Once you have the Docker name, invoke the container by running the commands defined by the network without the tao deploy prefix. For example, the following command will run detectnet_v2 TensorRT engine generation for FP16.

docker run -it --rm --gpus all \
    -v /path/in/host:/path/in/docker \
    $DOCKER_CONTAINER \
    detectnet_v2 gen_trt_engine -e /path/to/experiment/spec.txt \
    -m /path/to/etlt/file \
    -k $KEY \
    --data_type fp16
    --engine_file /path/to/engine/file

Installing TAO Deploy through wheel#

TAO Deploy is also distributed as a public wheel file at PyPI. The wheel does not include TensorRT or TensorRT OSS as part of its dependencies. Hence, you must either install these dependencies through the official TensorRT website or invoke TensorRT container available on NGC.

Run the following command to install the nvidia-tao-deploy wheel in your python environment.

pip install nvidia-tao-deploy

Then, you can run TAO Deploy tasks with the tao deploy prefix. For example, the following command will run a detectnet_v2 TensorRT engine generation for FP16.

detectnet_v2 gen_trt_engine -e /path/to/experiment/spec.txt \
    -m /path/to/etlt/file \
    -k $KEY \
    --data_type fp16 \
    --engine_file /path/to/engine/file

Installing TAO Deploy on Google Colab#

You can download the nvidia-tao-deploy wheel to Google Colab using the same commands as the x86 platform installation.

Note

The general limitations of Colab are outlined here.

Follow these steps to run TAO Deploy on Google-Colab:

  1. Get the TensorRT TAR archive:

  1. Visit the TensorRT webpage <https://developer.nvidia.com/tensorrt>

  2. Click Download now on the TensorRT webpage. This directs you to the login webpage <https://developer.nvidia.com/nvidia-tensorrt-download>. On this landing page, you have to select either Login or Join Now for NVIDIA Developer Program Membership.

  3. After logging in, choose TensorRT 8 from the available versions.

  4. Agree to the Terms and Conditions.

  5. On the next landing page, click TensorRT 8.5 GA to expand the available options.

  6. Click TensorRT 8.5 GA for Linux x86_64 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7 and 11.8 TAR Package to download the TAR file.

  1. Upload the the TAR file to your Google Drive.

After you upload the TAR file, you can run/view this example Notebook <https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/ptm/tao_deploy.ipynb>, which generates a TRT engine for TAO PTMs and runs inference using TAO Deploy.

Installing TAO Deploy on a Jetson Platform#

You can download the nvidia-tao-deploy wheel to a jetson platform using the same commands as the x86 platform installation. We recommend using the NVIDIA L4T TensorRT Docker container that already includes the TensorRT installation for aarch64. Once you’ve successfully installed TensorRT, run the following command to install the nvidia-tao-deploy wheel in your Python environment.

pip install nvidia-tao-deploy

Due to memory issues, you should first run the gen_trt_engine subtask on the x86 platform to generate the engine; you can then use the generated engine to run inference or evaluation on the Jetson platform and with the target dataset.

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