Tao classification command not pulling the correct version

Please provide the following information when requesting support.

• Hardware (Ubuntu 18.04 , dGPU RTX2080)
• Network Type (Classification)
• TAO Version (format_version: 2.0
toolkit_version: 3.21.11
• Training spec file(If have, please share here)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
Hi guys, I need some help.

Whenever I run the command “tao classification”, it will pull this image “nvcr.io/nvidia/tao/tao-toolkit-tf v3.21.11-tf1.15.5-py3”. Is there a way to pull the image “nvcr.io/nvidia/tao/tao-toolkit-tf v3.21.08-py3” instead?

It is expected. You can check “$tao info --verbose” . The latest tao version is 3.21.11.

For the old version of “nvcr.io/nvidia/tao/tao-toolkit-tf v3.21.08-py3” , you can run "$docker pull nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.08-py3 " and then
$ docker run --runtime -it --rm nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.08-py3 /bin/bash

In order to do classification training, I can only use this image “nvcr.io/nvidia/tao/tao-toolkit-tf v3.21.11-tf1.15.5-py3”? and not other older version?

I have installed nvidia-tlt, yet the toolkit version is 3.21.11.

tlt info --verbose
/home/sapphire/.virtualenvs/launcher/lib/python3.6/site-packages/tlt/init.py:20: DeprecationWarning:
The nvidia-tlt package will be deprecated soon. Going forward please migrate to using the nvidia-tao package.

warnings.warn(message, DeprecationWarning)
Configuration of the TAO Toolkit Instance

docker_registry: nvcr.io
1. augment
2. bpnet
3. classification
4. dssd
5. emotionnet
6. efficientdet
7. fpenet
8. gazenet
9. gesturenet
10. heartratenet
11. lprnet
12. mask_rcnn
13. multitask_classification
14. retinanet
15. ssd
16. unet
17. yolo_v3
18. yolo_v4
19. yolo_v4_tiny
20. converter
docker_registry: nvcr.io
1. detectnet_v2
2. faster_rcnn
docker_registry: nvcr.io
1. speech_to_text
2. speech_to_text_citrinet
3. text_classification
4. question_answering
5. token_classification
6. intent_slot_classification
7. punctuation_and_capitalization
8. spectro_gen
9. vocoder
10. action_recognition
docker_registry: nvcr.io
1. n_gram
format_version: 2.0
toolkit_version: 3.21.11
published_date: 11/08/2021

No. You can use

  • 3.21.08 version
  • 3.0_dp version

But currently you already install latest nvidia-tao package , so if you trigger docker, need to use below way.

$ docker run --runtime -it --rm nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.08-py3 /bin/bash

this command “docker run --runtime -it --rm nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.08-py3 /bin/bash” does not work.

But i can run it without specifying “–runtime”.

Now i am facing another issue. After executing the command"classification" inside the docker container, i had the error below:

root@bf4443e0d0f0:/workspace# classification
Using TensorFlow backend.
2022-03-10 03:39:05,810 [WARNING] modulus.export._tensorrt: Failed to import TRT and/or CUDA. TensorRT optimization and inference will not be available.
Traceback (most recent call last):
File “/usr/local/bin/classification”, line 8, in
File “/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/makenet/entrypoint/makenet.py”, line 12, in main
File “/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/common/entrypoint/entrypoint.py”, line 256, in launch_job
File “/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/common/entrypoint/entrypoint.py”, line 47, in get_modules
File “/usr/lib/python3.6/importlib/init.py”, line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File “”, line 994, in _gcd_import
File “”, line 971, in _find_and_load
File “”, line 955, in _find_and_load_unlocked
File “”, line 665, in _load_unlocked
File “”, line 678, in exec_module
File “”, line 219, in _call_with_frames_removed
File “/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/makenet/scripts/export.py”, line 8, in
File “/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/makenet/export/classification_exporter.py”, line 11, in
File “/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/common/export/keras_exporter.py”, line 22, in
ImportError: cannot import name ‘ONNXEngineBuilder’
root@bf4443e0d0f0:/workspace# " docker run -it --rm nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.08-py3 /bin/bash"

Need to use “--runtime=nvidia”
Did you install nvidia-docker?

Ok, it is working now!! since I am doing it this way, do i still need to follow the ~/.tao_mounts.json setup for the launcher instance? or do I need to setup the mounting/binding manually myself?

For this way, you can use
-v yourlocalfolder:dockerfolder

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