OSError: Unable to open file (file signature not found)

Accelerated Computing Intelligent Video Analytics TAO Toolkit
1

2022-01-13 17:38:14,533 [INFO] root: Registry: [‘nvcr.io’]
2022-01-13 17:38:14,706 [INFO] tlt.components.instance_handler.local_instance: Running command in container: nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3
2022-01-13 17:38:15,746 [WARNING] tlt.components.docker_handler.docker_handler:
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the “user”:“UID:GID” in the
DockerOptions portion of the “/home/chenhongzhao/.tao_mounts.json” file. You can obtain your
users UID and GID by using the “id -u” and “id -g” commands on the
terminal.
2022-01-13 09:38:18.074014: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
WARNING:tensorflow:From /root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/tfhooks/checkpoint_saver_hook.py:25: The name tf.train.CheckpointSaverHook is deprecated. Please use tf.estimator.CheckpointSaverHook instead.

2022-01-13 09:38:22,304 [WARNING] tensorflow: From /root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/tfhooks/checkpoint_saver_hook.py:25: The name tf.train.CheckpointSaverHook is deprecated. Please use tf.estimator.CheckpointSaverHook instead.
…
File “/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py”, line 312, in init
fid = make_fid(name, mode, userblock_size, fapl, swmr=swmr)
File “/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py”, line 142, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
File “h5py/_objects.pyx”, line 54, in h5py._objects.with_phil.wrapper
File “h5py/_objects.pyx”, line 55, in h5py._objects.with_phil.wrapper
File “h5py/h5f.pyx”, line 78, in h5py.h5f.open
OSError: Unable to open file (file signature not found)

During handling of the above exception, another exception occurred:
…
fastbuild/bin/magnet/packages/driveix/build_wheel.runfiles/ai_infra/driveix/common/utilities/tlt_utils.py", line 153, in decode_to_keras
OSError: Invalid decryption. Unable to open file (file signature not found). The key used to load the model is incorrect.
Traceback (most recent call last):
…
#################################################################
Running this project: “…/cv_samples_v1.3.0/bpnet” .
All the previous steps are OK until training

this is my API key:OXE5aDZnZmZpYmIwYW90OW12dTVlNXZzZTY6ZDgyYjZmYjItN2U3Yy00ODE5LWFhMmItY2YxZjUyYjM2NWVk
I try to use “tlt_encode”,no effect.

I have set.
docker login nvcr.io
Username: $oauthtoken
Password: OXE5aDZnZmZpYmIwYW90OW12dTVlNXZzZTY6ZDgyYjZmYjItN2U3Yy00ODE5LWFhMmItY2YxZjUyYjM2NWVk

please help me.

3

May I know which network did you run? Can you share the full commandline?

4

!ngc registry model download-version nvidia/tao/bodyposenet:trainable_v1.0
–dest $LOCAL_EXPERIMENT_DIR/pretrained_model

Out:
Downloaded 64.33 MB in 16s, Download speed: 4.02 MB/s

Transfer id: bodyposenet_vtrainable_v1.0 Download status: Completed.
Downloaded local path: …/cv_samples_v1.3.0/bpnet/pretrained_model/bodyposenet_vtrainable_v1-1.0
Total files downloaded: 1
Total downloaded size: 64.33 MB
Started at: 2022-01-13 17:36:15.091512
Completed at: 2022-01-13 17:36:31.115565
Duration taken: 16s

5

Can you share the full commandline when you run the training?

6

!tao bpnet train -e $SPECS_DIR/bpnet_train_m1_coco.yaml \
-r $USER_EXPERIMENT_DIR/models/exp_m1_unpruned \
-k $KEY \
–gpus $NUM_GPUS

7

Please change
-k $KEY

to
-k nvidia_tlt

and retry.

1 Like
8

OK, thank you for your help. But why can it succeed.

9

Because you are using the pretrained bpnet model in ngc, and you run with it as pretrained model. Its loading key is nvidia_tlt.
Similar to gazenet model card. Gaze Estimation | NVIDIA NGC
But the bpnet model card does not mention. It should be the same.

The trainable and deployable models are encrypted and will only operate with the following key:

  • Model load key: nvidia_tlt
1 Like
10

I see.Thank you very much.

12

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