TAO Toolkit Launcher
TAO Toolkit encapsulates DNN training pipelines that may be developed across different
training platforms. In order to abstract the details from TAO Toolkit users, TAO Toolkit now is packaged with a launcher CLI.
The CLI is a python3 wheel package that may be installed using the python-pip. When installed, the
launcher CLI abstracts the user from having to instantiate and run several TAO containers and map the commands accordingly.
In this release of TAO Toolkit, the TAO package includes multiple underlying Docker containers based on each training framework.
Each Docker container includes entrypoints to tasks that run the sub-tasks associated with them. The tasks in the containers are
grouped into different task_groups, which are divided into the following categories:
modeldatasetdeploy
The tasks under model contain routines to perform train, evaluate, and inference on one of any number
of DNN models supported by TAO Toolkit. The tasks under dataset contain routines to manipulate datasets, such as augment
and auto_label, while the tasks under deploy optimize and deploy models to TensorRT.
The tasks are broadly divided into model, deploy, and dataset. For example, DetectNet_v2 is a
computer vision task for object detection in TAO Toolkit, which supports subtasks such as train, prune,
evaluate, export etc. When the user executes a command, for example tao model detectnet_v2 train --help,
the TAO Toolkit Launcher does the following:
Pulls the required TAO container with the entrypoint for DetectNet_v2
Creates an instance of the container
Runs the
detectnet_v2entrypoint with thetrainsub-task
You may visualize the user interaction diagram for the TAO Launcher when running training and evaluation for a DetectNet_v2 model as follows:
Similarly, the interaction diagram for training a DINO model is as follows:
The following sections cover supported commands and configuring the launcher.
The sample jupyter notebooks in the tao_launcher_starter_kit directory of the TAO Getting started resource
on NGC covers the steps to install the launcher CLI.
Once the launcher has been installed, the workflow to run the launcher is as follows.
Listing the
task_groupssupported in the toolkit.After installing the launcher, you will now be able to list the tasks that are supported in the TAO Toolkit Launcher. The output of
tao model --helpcommand is as follows:usage: tao model [-h] {list,stop,info,dataset,deploy,model} ... Launcher for TAO Toolkit. optional arguments: -h, --help show this help message and exit task_groups: {list,stop,info,dataset,deploy,model}
Listing the
taskssupported under atask_group.Once you have listed the
task_groupsincluded as part of the toolkit, you can list thetaskassociated with this group using the--helpoption in cascade. For example, to list thetaskundermodel, you can run the following commandtao model --helpusage: tao model [-h] {list,stop,info,dataset,deploy,model} ... {action_recognition,classification_pyt,deformable_detr,dino,mal,ml_recog,ocdnet,ocrnet,optical_inspection,pointpillars,pose_classification,re_identification,re_identification_transformer,segformer} ... optional arguments: -h, --help show this help message and exit task_groups: {list,stop,info,dataset,deploy,model} task: {action_recognition,classification_pyt,deformable_detr,dino,mal,ml_recog,ocdnet,ocrnet,optical_inspection,pointpillars,pose_classification,re_identification,re_identification_transformer,segformer}
Configuring the launcher instance.
Running Deep Neural Networks implies working on large datasets. These datasets are usually present network share drives with significantly higher storage capacity. Since the TAO Toolkit Launcher users docker containers under the hood, these drives/mount points need to be mapped to the docker. The launcher instance can be configured in the
~/.tao_mounts.jsonfile.The launcher config file consists of three sections:
MountsEnvsDockerOptions
The
Mountsparameter defines the paths in the local machine, that should be mapped to the docker. This is a list ofjsondictionaries containing thesourcepath in the local machine and thedestinationpath that is mapped for the TAO Toolkit commands.The
Envsparameter defines the environment variables to be set to the respective TAO Toolkit docker. This is also a list of dictionaries. Each dictionary entry has 2 key-value pairs defined.variable: The name of the environment variable you would like to setvalue: The value of the environment variable
The
DockerOptionsparameter defines the options to be set when invoking the docker instance. This parameter is a dictionary containing key-value pair of the parameter and option to set. Currently, the TAO Toolkit Launcher only allows users to configure the following parameters.shm_size: Defines the shared memory size of the docker. If this parameter isn’t set, then the TAO Toolkit instance allocates 64MB by default. We recommend setting this as"16G", thereby allocating 16GB of shared memory.ulimits: Defines the user limits in the docker. This parameter corresponds to the ulimit parameters in/etc/security/limits.conf. We recommend users setmemlockto-1andstackto67108864.user: Defines the user id and group id of the user to run the commands in the docker. By default, if this parameter isn’t defined in the~/.tao_mounts.jsonthe uid and gid of the root user. However, this would mean that when directories created by the TAO dockers would be set to root permissions. If you would like to set the user in the docker to be the same as the host user, please set this parameter as “UID:GID”, where UID and GID can be obtained from the command line by runningid -uandid -g.ports: This parameter defines the ports in the docker to be mounted to the host.You may specify this parameter as a dictionary containing the map between the port in the docker to the port in the host machine. For example, if you wish to expose port 8888 and port 8000, this parameter would look as follows:
"ports":{ "8888":"8888", "8000":"8000" }
Please use the following code block as a sample for the
~/.tao_mounts.jsonfile. In this mounts sample, we define 3 drives, an environment variable calledCUDA_DEVICE_ORDER. ForDockerOptionswe set shared memory size of16G, user limits and set the host user’s permission. We also bind the port 8888 from the docker to the host.{ "Mounts": [ { "source": "/path/to/your/data", "destination": "/workspace/tao-experiments/data" }, { "source": "/path/to/your/local/results", "destination": "/workspace/tao-experiments/results" }, { "source": "/path/to/config/files", "destination": "/workspace/tao-experiments/specs" } ], "Envs": [ { "variable": "CUDA_DEVICE_ORDER", "value": "PCI_BUS_ID" } ], "DockerOptions": { "shm_size": "16G", "ulimits": { "memlock": -1, "stack": 67108864 }, "user": "1000:1000", "ports": { "8888": 8888 } } }
Similarly, a sample config file containing 2 mount points and no docker options is as below.
{ "Mounts": [ { "source": "/path/to/your/experiments", "destination": "/workspace/tao-experiments" }, { "source": "/path/to/config/files", "destination": "/workspace/tao-experiments/specs" } ] }
Running a task.
Once you have installed the TAO Toolkit Launcher, you may now run the tasks supported by TAO Toolkit using the following command format.
tao model <task_group> <task> <subtask> <cli_args>
To view the sub-tasks supported by a certain task, you may run the command following the template
For example: Listing the tasks of
detectnet_v2, the outputs is as follows:$ tao model detectnet_v2 --help usage: detectnet_v2 [-h] [--num_processes NUM_PROCESSES] [--gpus GPUS] [--gpu_index GPU_INDEX [GPU_INDEX ...]] [--use_amp] [--log_file LOG_FILE] {train,prune,inference,export,evaluate,dataset_convert,calibration_tensorfile} ... TAO Toolkit optional arguments: -h, --help show this help message and exit --num_processes NUM_PROCESSES, -np NUM_PROCESSES The number of horovod child processes to be spawned. Default is -1(equal to --gpus). --gpus GPUS The number of GPUs to be used for the job. --gpu_index GPU_INDEX [GPU_INDEX ...] The indices of the GPU's to be used. --use_amp Flag to enable Auto Mixed Precision. --log_file LOG_FILE Path to the output log file. tasks: {train,prune,inference,export,evaluate,dataset_convert,calibration_tensorfile} 2023-06-01 08:52:50,522 [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 326: Stopping container.
The TAO Toolkit Launcher also supports a
runcommand associated with every task to allow you to run custom scripts in the docker. This provides you the opporturnity bring in your own data pre-processing scripts and leverage the prebuilt dependencies and isolated dev environements in the TAO Toolkit dockers.For example, assume you have a shell script to download and preprocess COCO dataset into TFRecords for MaskRCNN, which requires TensorFlow as a dependency. You can simply map the directory containing that script to the docker using the steps mentioned in step 4 below with the
~/.tao_mounts.jsonand run it astao model mask_rcnn run /path/to/download_and_preprocess_coco.sh <script_args>
The
taolauncher CLI allows you to interactively run commands inside the docker associated with the tasks you wish to run. This is a useful tool for debugging commands inside the docker and viewing the filesystems from inside the container, as opposed to viewing the end output in the host system. To invoke an interactive session, run thetaocommand with the task and no other arguments. For example, to run interactive commands inside the docker containing the detectnet_v2 task, run the following command:tao model detectnet_v2
This command opens up an interactive session inside the
tao-toolkit-tfdocker.NoteThe interactive command uses the
~/.tao_mounts.jsonfile to configure the launcher and mount paths in the host file system to the docker.Once you are inside the interactive session, you may run the command task and its associated subtask by calling the
<task> <subtask> <cli_args>commands without thetaoprefix.For example, to train a detectnet_v2 model in the interactive session, run the following command after invoking an interactive session using
tao model detectnet_v2detectnet_v2 train -e /path/to/experiment_spec.txt -k <key> -r /path/to/train/output --gpus <number of GPUs>
TAO Toolkit Launcher allows users to list all the processes that were launched by an instance of the TAO Toolkit Launcher on the host machine
and kill any jobs the user may deem unnecessary using the list and stop command.
Listing TAO launched processes
The list command, as the name suggests prints out a tabulated list of running processes with the command that was used to invoke the process.
A sample output of
tao model listcommand when you have 2 processes running is as below.============== ================== ============================================================================================================================================================================================= container_id container_status command ============== ================== ============================================================================================================================================================================================= 5316a70139 running detectnet_v2 train -e /workspace/tao-experiments/detectnet_v2/experiment_dir_unpruned/experiment_spec.txt -k tlt_encode -r /workspace/tao-experiments/detectnet_v2/experiment_dir_unpruned ============== ================== =============================================================================================================================================================================================
Killing running TAO instances
The
tao stopcommand helps in killing the running containers should the users wish to abort the respective session. The usage for thetao stopcommand is as mentioned below.usage: tao stop [-h] [--container_id CONTAINER_ID [CONTAINER_ID ...]] [--all] {info,list,stop,augment,classification,classifynet,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,heartratenet,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,retinanet,speech_to_text,ssd,text_classification,converter,token_classification,yolo_v3,yolo_v4,yolo_v4_tiny} ... optional arguments: -h, --help show this help message and exit --container_id CONTAINER_ID [CONTAINER_ID ...] Ids of the containers to be stopped. --all Kill all TAO Toolkit running TAO Toolkit containers. tasks: {info,list,stop,augment,classification,classifynet,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,heartratenet,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,retinanet,speech_to_text,ssd,text_classification,converter,token_classification,yolo_v3,yolo_v4,yolo_v4_tiny}
With
tao stop, you may choose to eitherKill a subset of the containers shown by the
tao model listcommand by providing multiple container id’s to the launcher’s--container_idarg
A sample output of the
tao model listcommand after runningtao stop --container_id 5316a70139, is as below.============== ================== ============================================================================================================================================================================================= container_id container_status command ============== ================== ============================================================================================================================================================================================= 5316a70139 running detectnet_v2 train -e /workspace/tao-experiments/detectnet_v2/experiment_dir_unpruned/experiment_spec.txt -k tlt_encode -r /workspace/tao-experiments/detectnet_v2/experiment_dir_unpruned ============== ================== =============================================================================================================================================================================================
Force kill all the containers by using the
tao stop --allcommand.
A sample output of
tao model listcommand after running thetao stop --allcommand is as below.============== ================== ========= container_id container_status command ============== ================== ========= ============== ================== =========
Retrieving information for the underlying TAO components
The
tao infocommand allows users to retrieve information about the underlying components in the launcher. To retrieve options for thetao infocommand, you can use thetao info --helpcommand. The sample usage for the command is as follows:usage: tao info [-h] [--verbose] {info,list,stop,info,augment,classification,detectnet_v2, ... ,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny} ... optional arguments: -h, --help show this help message and exit --verbose Print information about the TAO Toolkit instance. tasks: {info,list,stop,info,augment,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,heartratenet,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,retinanet,speech_to_text,ssd,text_classification,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}
When you run
tao info, the launcher returns concise information about the launcher, namely the docker container names, format version of the launcher config, TAO Toolkit version, and publishing date.Configuration of the TAO Toolkit Instance task_group: ['model', 'dataset', 'deploy'] format_version: 3.0 toolkit_version: 5.0.0 published_date: 05/31/2023
For more information about the dockers and the tasks supported by the docker, you may use the
--verboseoption of thetao infocommand. A sample output of thetao info --verbosecommand is shown below.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 7. ocdnet 8. ocrnet 9. optical_inspection 10. pointpillars 11. pose_classification 12. re_identification 13. segformer 14. 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. yolo_v3 24. yolo_v4 25. yolo_v4_tiny 26. visual_changenet format_version: 3.0 toolkit_version: 5.0.0 published_date: 05/31/2023
The TAO Toolkit Launcher watches the following environment variables to override certain configurable parameters.
LAUNCHER_MOUNTS: This environment variable defines the path to the default launcher configuration.jsonfile. If not set, the launcher configuration path is picked up from~/.tao_mounts.json.OVERRIDE_REGISTRY: This environment variable defines the registry to pull the TAO dockers from. By default, the TAO docker is hosted in NGC under the repositorynvcr.io. For example, if you set theOVERRIDE_REGISTRYenvironment variables as shown below,export OVERRIDE_REGISTRY="dockerhub.io"
the dockers would be
dockerhub.io/nvidia/tao/tao-toolkit:<docker_tag>
dockerhub.io/nvidia/tao/tao-toolkit:<docker_tag>
- When using the
OVERRIDE_REGISTRYvariable, use thedocker logincommand to log in to this registry.
docker login $OVERRIDE_REGISTRY