• Issue Description
Hello,
I have followed this tutorial from Nvidia to train and use HeartRateNet:
After, opening Jupyter notebook with this command:
jupyter notebook --ip=0.0.0.0 --no-browser --allow-root
I saw that as output, i haven’t given a URL with ip address of “0.0.0.0” as seen in the tutorial. Instead the output gave me two URLs:
one with “127.0.0.1” and another with “jetsonnano”.
After opening the browser with the URL with “jetsonnano”, i have found and opened the notebook “heartratenet.ipynb”. As i have opened the notebook, i saw following lines on the terminal:
[I 13:11:26.717 NotebookApp] 302 GET /?token=************************************************ (127.0.0.1) 2.130000ms
[I 13:12:28.066 NotebookApp] Writing notebook-signing key to /home/User/.local/share/jupyter/notebook_secret
[W 13:12:28.073 NotebookApp] Notebook heartratenet/heartratenet.ipynb is not trusted
[I 13:12:33.379 NotebookApp] Kernel started: 7260a7ef-a39c-45f1-9cff-1b688f2cd965, name: python3
[W 13:12:33.510 NotebookApp] 404 GET /nbextensions/widgets/notebook/js/extension.js?v=20231003131032 (127.0.0.1) 65.270000ms referer=http://jetsonnano:8888/notebooks/heartratenet/heartratenet.ipynb
[W 13:12:55.655 NotebookApp] Notebook heartratenet/heartratenet.ipynb is not trusted
Then, from the browser i have clicked on the button reads something like “not trusted” to make it trust the notebook and after few seconds the text in the button turned to “trusted”. As it turns to trusted, i have seen these lines on my terminal:
[W 13:12:55.730 NotebookApp] Trusting notebook /heartratenet/heartratenet.ipynb
[I 13:12:57.423 NotebookApp] Starting buffering for 7260a7ef-a39c-45f1-9cff-1b688f2cd965:f87b39e513e64cedb85d86894420f290
[W 13:12:58.894 NotebookApp] 404 GET /nbextensions/widgets/notebook/js/extension.js?v=20231003131032 (127.0.0.1) 7.630000ms referer=http://jetsonnano:8888/notebooks/heartratenet/heartratenet.ipynb
After that, i have run the notebook by clicking on “Cell” and then on “Run All”, just as shown in the tutorial.
Then, i saw as the output of the “!mkdir -p $LOCAL_EXPERIMENT_DIR/model/” command which says permission denied.
Thanks in advance for all the help you offer.
P.S. I just begun to use the TAO toolkit (except for tao-converter) and i am a complete beginner on TAO, docker and jupyter notebooks. So even if it’s something simple, please explain.
• System Specs
• Hardware: Jetson Nano
• Network Type: HeartRateNet
• TAO Version: 4.0.1 (I have shared complete output of “!tao info --verbose” under “Notes” section below.)
• Training spec file: Please see “Notes” section below. (I have shared the complete output of "!cat $LOCAL_TRAIN_SPEC under “Notes” section below.)
• Jetpack Version: 4.6.4
• Python Version: 3.6.9
• How to reproduce the issue ?
-
Open an NGC account and get the API key
-
Enter the command:
sudo docker login nvcr.io
Then enter “$oauthtoken” to username and the API key as password.
- Enter these commands one by one:
$pip3 install virtualenv
$pip3 install virtualenvwrapper
$export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
$export WORKON_HOME=~/Envs
$source ~/.local/bin/virtualenvwrapper.sh
$mkvirtualenv launcher
$deactivate
$wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tao/cv_samples/versions/v1.4.1/zip -O cv_samples_v1.4.1.zip
--2023-10-03 11:47:36-- https://api.ngc.nvidia.com/v2/resources/nvidia/tao/cv_samples/versions/v1.4.1/zip
$unzip -u cv_samples_v1.4.1.zip -d ./cv_samples_v1.4.1 && rm -rf cv_samples_v1.4.1.zip && cd ./cv_samples_v1.4.1
$workon launcher
$pip3 install notebook
$jupyter notebook --ip=0.0.0.0 --no-browser --allow-root
-
See that you haven’t received a URL with “0.0.0.0” as seen in the guide and instead you have received two URLs one with “127.0.0.1” and “jetsonnano”.
-
Right click on the URL with “jetsonnano” and select “Open Link”.
-
As you see a list of folders with names of models on the browser navigate to “heartratenet” folder and open the notebook there.
-
As you open the notebook notice the log with “
404 GET” and the one reads “Notebook heartratenet/heartratenet.ipynb is not trusted” on the terminal. -
Set the notebook as trusted by clicking a button reads something like “not trusted” on the jupyter notebook page in the browser.
-
Notice the log reads “
Trusting notebook /heartratenet/heartratenet.ipynb” and the one with “404 GET”. -
Click on “Cell” and select “Run All”.
-
Move to the first code block (under “
0. Set up env variables, map drives and install dependencies”) and at the output of the section see permision failed at the end of output for “!mkdir -p $LOCAL_EXPERIMENT_DIR/model/”.
• Notes:
Here is the complete output of the “!tao info --verbose” command:
Configuration of the TAO Toolkit Instance
dockers:
nvidia/tao/tao-toolkit:
4.0.0-tf2.9.1:
docker_registry: nvcr.io
tasks:
1. classification_tf2
2. efficientdet_tf2
4.0.0-tf1.15.5:
docker_registry: nvcr.io
tasks:
1. augment
2. bpnet
3. classification_tf1
4. detectnet_v2
5. dssd
6. emotionnet
7. efficientdet_tf1
8. faster_rcnn
9. fpenet
10. gazenet
11. gesturenet
12. heartratenet
13. lprnet
14. mask_rcnn
15. multitask_classification
16. retinanet
17. ssd
18. unet
19. yolo_v3
20. yolo_v4
21. yolo_v4_tiny
22. converter
4.0.1-tf1.15.5:
docker_registry: nvcr.io
tasks:
1. mask_rcnn
2. unet
4.0.0-pyt:
docker_registry: nvcr.io
tasks:
1. action_recognition
2. deformable_detr
3. segformer
4. re_identification
5. pointpillars
6. pose_classification
7. n_gram
8. speech_to_text
9. speech_to_text_citrinet
10. speech_to_text_conformer
11. spectro_gen
12. vocoder
13. text_classification
14. question_answering
15. token_classification
16. intent_slot_classification
17. punctuation_and_capitalization
format_version: 2.0
toolkit_version: 4.0.1
published_date: 03/06/2023
Here is the full output of the “!cat $LOCAL_TRAIN_SPEC” command:
__class_name__: HeartRateNetTrainer
checkpoint_dir: /workspace/tao-experiments/heartratenet/model/
results_dir: /workspace/tao-experiments/heartratenet/
random_seed: 32
log_every_n_secs: 20
checkpoint_n_epoch: 1
num_epoch: 20
summary_every_n_steps: 1
infrequent_summary_every_n_steps: 0
last_step: 1
evaluation_window: 10
low_freq_cutoff: 0.67
high_freq_cutoff: 4.0
fps: 20.0
model_type: HRNet_release
dataloader:
__class_name__: HeartRateNetDataloader
image_info:
num_channels: 3
image_height: 72
image_width: 72
data_format: channels_first
dataset_info:
tfrecords_directory_path: /workspace/tao-experiments/heartratenet/data
tfrecord_folder_name: ''
tfrecord_train_file_name: train.tfrecord
tfrecord_test_file_name: test.tfrecord
tfrecord_validation_file_name: validation.tfrecord
model_info:
model_type: HRNet_release
model:
__class_name__: HeartRateNet
model_parameters:
input_size: 72
data_format: channels_first
conv_dropout_rate: 0.0
fully_connected_dropout_rate: 0.0
use_batch_norm: False
model_type: HRNet_release
frozen_blocks: 0 # Freeze up the `nth` layer. Must be in range of [0, 5).
pretrained_model: /workspace/tao-experiments/heartratenet/pretrain_models/heartratenet_vtrainable_v2.0/model.tlt
loss:
__class_name__: HeartRateNetLoss
loss_function_name: MSE
optimizer:
__class_name__: AdadeltaOptimizer
rho: 0.95
epsilon: 1.0e-07
learning_rate_schedule:
__class_name__: ConstantLearningRateSchedule
learning_rate: 1.0