• Hardware NVIDIA TITAN Xp . Computer has Intel® Xeon(R) CPU X5680 @ 3.33GHz × 12 with 24Gb ram and is running Ubuntu 22.04.5 LTS
• Network Type Detectnet_v2
• TAO Version (Please run “tlt info --verbose” and share “docker_tag” here)
Configuration of the TAO Toolkit Instance
task_group:
model:
dockers:
nvidia/tao/tao-toolkit:
5.5.0-pyt:
docker_registry: nvcr.io
tasks:
1. action_recognition
2. centerpose
3. visual_changenet
4. deformable_detr
5. dino
6. grounding_dino
7. mask_grounding_dino
8. mask2former
9. mal
10. ml_recog
11. ocdnet
12. ocrnet
13. optical_inspection
14. pointpillars
15. pose_classification
16. re_identification
17. classification_pyt
18. segformer
19. bevfusion
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
5.5.0-tf2:
docker_registry: nvcr.io
tasks:
1. classification_tf2
2. efficientdet_tf2
dataset:
dockers:
nvidia/tao/tao-toolkit:
5.5.0-data-services:
docker_registry: nvcr.io
tasks:
1. augmentation
2. auto_label
3. annotations
4. analytics
deploy:
dockers:
nvidia/tao/tao-toolkit:
5.5.0-deploy:
docker_registry: nvcr.io
tasks:
1. visual_changenet
2. centerpose
3. classification_pyt
4. classification_tf1
5. classification_tf2
6. deformable_detr
7. detectnet_v2
8. dino
9. dssd
10. efficientdet_tf1
11. efficientdet_tf2
12. faster_rcnn
13. grounding_dino
14. mask_grounding_dino
15. mask2former
16. lprnet
17. mask_rcnn
18. ml_recog
19. multitask_classification
20. ocdnet
21. ocrnet
22. optical_inspection
23. retinanet
24. segformer
25. ssd
26. trtexec
27. unet
28. yolo_v3
29. yolo_v4
30. yolo_v4_tiny
format_version: 3.0
toolkit_version: 5.5.0
published_date: 08/26/2024
• Training spec file(
detectnet_v2_tfrecords_kitti_trainval.txt (334 Bytes)
)
TFrecords conversion spec file for kitti training
kitti_config {
root_directory_path: “/home/harold/workspace/tao-experiments/data/training”
image_dir_name: “image_2”
label_dir_name: “label_2”
image_extension: “.png”
partition_mode: “random”
num_partitions: 2
val_split: 14
num_shards: 10
}
image_directory_path: “/home/harold/workspace/tao-experiments/data/training”
•I tried to run the next line in the notebook three times, the first two it looked like it was making progress for quite some time (more than 15minutes) but then crashed when I wasn’t watching and I got a Firefox message saying “gah. your tab has crashed” I then disabled the screensaver and tried again and then I get a different message (this doesn’t take long at all)
Creating a new directory for the output tfrecords dump.
print(“Converting Tfrecords for kitti trainval dataset”)
!mkdir -p $LOCAL_DATA_DIR/tfrecords && rm -rf $LOCAL_DATA_DIR/tfrecords/*
!tao model detectnet_v2 dataset_convert
-d $SPECS_DIR/detectnet_v2_tfrecords_kitti_trainval.txt
-o $DATA_DOWNLOAD_DIR/tfrecords/kitti_trainval/kitti_trainval
-r $USER_EXPERIMENT_DIR/
#Output from the above cell:
Converting Tfrecords for kitti trainval dataset
2024-10-09 11:35:27,059 [TAO Toolkit] [INFO] root 160: Registry: [‘nvcr.io’]
2024-10-09 11:35:27,166 [TAO Toolkit] [INFO] nvidia_tao_cli.components.instance_handler.local_instance 360: Running command in container: nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5
2024-10-09 11:35:27,187 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 301: Printing tty value True
2024-10-09 11:35:28,475 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 363: Stopping container.
Here is the output of nvidia-smi:
I am a complete beginner at this as well as this being my first post in this forum. So apologies if I’ve botched this post (it feels too long!) The machine has a fresh install of Ubuntu. Installing Tao and then running this Jupyter notebook has been my first activity with it.
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