Working With the Containers

TAO Toolkit encapsulates DNN training pipelines that may be developed across different training frameworks. To isolate dependencies and training environments, these DNN applications are housed in different containers. The TAO Toolkit Launcher abstracts the details of which network is associated with which container. However, it requires you to run TAO Toolkit from an environment where docker containers can be instantiated by the launcher. This requires elevated user privileges or a Docker IN Docker (DIND) setup to call a Docker from within your container. This may not be ideal in several scenarios, such as:

  • Running on a remote cluster where the SLURM instantiates a container on the provisioned cluster node

  • Running on a machine without elevated user privileges

  • Running multi-node training jobs

  • Running on an instance of a Multi-Instanced supported GPU (MiG)

To run the DNNs from one of the multiple enclosed containers, you first need to know which networks are housed in which container. A simple way to get this information is to install the TAO Toolkit Launcher on your local machine and running tao info –verbose, enclosed across multiple containers.

The following is sample output from TAO 5.0.1:

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Configuration of the TAO Toolkit Instance task_group: model: dockers: nvidia/tao/tao-toolkit: 5.0.0-tf2.9.1: docker_registry: nvcr.io tasks: 1. classification_tf2 2. efficientdet_tf2 5.0.0-tf1.15.5: docker_registry: nvcr.io tasks: 1. bpnet 2. classification_tf1 3. converter 4. detectnet_v2 5. dssd 6. efficientdet_tf1 7. faster_rcnn 8. fpenet 9. lprnet 10. mask_rcnn 11. multitask_classification 12. retinanet 13. ssd 14. unet 15. yolo_v3 16. yolo_v4 17. yolo_v4_tiny 5.0.0-pyt: docker_registry: nvcr.io tasks: 1. action_recognition 2. centerpose 3. classification_pyt 4. deformable_detr 5. dino 6. mal 7. ml_recog 8. ocdnet 9. ocrnet 10. optical_inspection 11. pointpillars 12. pose_classification 13. re_identification 14. re_identification_transformer 15. segformer 16. visual_changenet dataset: dockers: nvidia/tao/tao-toolkit: 5.0.0-dataservice: docker_registry: nvcr.io tasks: 1. augmentation 2. auto_label 3. annotations 4. analytics deploy: dockers: nvidia/tao/tao-toolkit: 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 published_date: 05/31/2023

The container name associated with the task can be derived 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:

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export DOCKER_REGISTRY="nvcr.io" export DOCKER_NAME="nvidia/tao/tao-toolkit" export DOCKER_TAG="5.0.0-tf1.15.5" 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 :code:`tao` prefix. For example, the following command will run a detectnet_v2 training job with 4 GPUs:

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docker run -it --rm --gpus all \ -v /path/in/host:/path/in/docker \ $DOCKER_CONTAINER \ detectnet_v2 train -e /path/to/experiment/spec.txt \ -r /path/to/results/dir \ -k $KEY --gpus 4

From 3.0-21.11, TAO Toolkit supports multi-node training for the following CV models:

  • Image classification

  • Multi-task classification

  • Detectnet_v2

  • FasterRCNN

  • SSD

  • DSSD

  • YOLOv3

  • YOLOv4

  • YOLOv4-Tiny

  • RetinaNet

  • MaskRCNN

  • EfficientDet

  • UNet

For these networks, the only task that can run multi-node training is train. To invoke multi-node training, simply add the --multi-node argument to the train command.

For example, the multi-GPU training command given above can be issued as a multi-node command:

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detectnet_v2 train -e /path/to/experiment/spec.txt \ -r /path/to/results/dir \ -k $KEY \ --gpus 4 \ --multi-node

TAO uses OPEN-MPI + HOROVOD to orchestrate multi-GPU and multi-node training. By default, the following arguments are appended to the mpirun command:

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-x NCCL_IB_HCA=mlx5_4,mlx5_6,mlx5_8,mlx5_10 -x NCCL_SOCKET_IFNAME=^lo,docker

To add more arguments to the mpirun command, add them to the --mpirun-arg of the train command, as shown in the following example:

Note

When running multi-node training, the entire dataset must be visible to all nodes running the training. If the data is not present, training jobs may crash with errors stating that the data couldn’t be found.

For example, if you have a .tar dataset that has been downloaded to one of the nodes (rank 0) in a multi-node job with two nodes and eight GPUs each, a simple way to extract the data would be to run it as a multi node process using mpirun:.

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mpirun -np 16 --allow-run-as-root bash -c 'if [[ $OMPI_COMM_WORLD_LOCAL_RANK -eq 0 ]]; then set -x && tar -xf dataset.tar -C /raid; fi '

NVIDIA Multi-Instance GPU (MIG) expands the performance and value of data-center class GPUs–namely, the NVIDIA H100, A100 and A30 Tensor Core GPUs–by allowing users to partition a single GPU into as many as seven instances, each with its own fully isolated high-bandwidth memory, cache, and compute cores. For more information on setting up MIG, please refer the NVIDIA Multi-Instance GPU User Guide.

Note

Read the supported configurations in the MIG document to understand the best way to split and improve utilization.

The following sample command runs a DetectNet_v2 training session on a MIG-enabled GPU.

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docker run -it --rm --runtime=nvidia \ -e NVIDIA_VISIBLE_DEVICES="MIG-<DEVICE_UUID>,MIG-<DEVICE_UUID>" \ -v /path/in/host:/path/in/container \ nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5 \ detectnet_v2 train -e /path/to/experiment.txt \ -k <key> \ -r /path/to/store/results \ -n <name of trained model>

Note

You must add the --runtime=nvidia flag to the docker command and export the NVIDIA_VISIBLE_DEVICES environment variable with the UUID of the GPU instance. You can get the UUID of the specific MIG instance by running the nvidia-smi -L command.

Running TAO Toolkit via the TAO Toolkit Launcher requires the user to have docker-ce installed since the launcher interacts with the Docker service on the local host to run the commands. Installing Docker requires elevated user privileges to run as root. If you don’t have elevated user privileges on your compute machine, you may run TAO Toolkit using Singularity. This requires you to bypass the tao-launcher and interact directly with the component docker containers. For information on which tasks are implemented in different Dockers, run the tao info --verbose command. Once you have derived the task-to-Docker mapping, you may run the tasks using the following steps:

  1. Pull the required Docker using the following singularity command:

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    singularity pull tao-toolkit-tf:5.0.0-tf1.15.5 py3.sif docker://nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5 .. Note:: For this command to work, the latest version of singularity must be installed.

  2. Instantiate the Docker using the following command:

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    singularity run --nv -B /path/to/workspace:/path/to/workspace tao-toolkit-tf:5.0.0-tf1.15.5.sif

  3. Run the commands inside the container without the tao prefix. For example, to run a detectnet_v2 training in the tao-toolkit-tf container, use the following command:

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detectnet_v2 train -e /path/to/workspace/specs/file.txt \ -k $KEY \ -r /path/to/workspace/results \ -n name_of_final_model \ --gpus $NUM_GPUS


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