I running in WSL environment
I am doing the efficientdet using the tutorial given in gettingstarted package. I face an issue when running the code below
# convert training data to TFRecords
!sed -i "s|DATADIR|$DATA_DOWNLOAD_DIR|g" $LOCAL_SPECS_DIR/spec_train.yaml
!tao efficientdet_tf2 dataset_convert -e $SPECS_DIR/spec_train.yaml dataset_convert.results_dir=$DATA_DOWNLOAD_DIR
I get the error below:
Error merging 'spec_train.yaml' with schema
Key 'dataset' not in 'ExperimentConfig'
full_key: dataset
object_type=ExperimentConfig
or
DatasetConvertConfig no key result_dir
I did change the the command parameter and directory. And I not sure why it keep saying error, I had checked there is key in yaml file.
I have another question here, what is the ExperimentConfig and DatasetConvertConfig mean. Is it from tao toolkit. Perhaps I can get some guideline to resolve the issue.
Morganh
September 28, 2023, 7:08am
2
Can you share spec_train.yaml?
Here is the SPECS_DIR/spec_train.yaml
dataset_convert:
image_dir: '/mnt/c/Users/Meta/Desktop/workspace/tao-experiments/data/raw-data/train2017/'
annotations_file: '/mnt/c/Users/Meta/Desktop/workspace/tao-experiments/data/raw-data/annotations/instances_train2017.json'
tag: 'train'
num_shards: 256
include_masks: True
dataset:
augmentation:
rand_hflip: True
random_crop_min_scale: 0.1
random_crop_max_scale: 2
loader:
prefetch_size: 4
shuffle_file: False
shuffle_buffer: 10000
cycle_length: 32
block_length: 16
max_instances_per_image: 100
skip_crowd_during_training: True
num_classes: 91
train_tfrecords:
- '/mnt/c/Users/Meta/Desktop/workspace/tao-experiments/data/train-*'
val_tfrecords:
- '/mnt/c/Users/Meta/Desktop/workspace/tao-experiments/data/val-*'
val_json_file: '/mnt/c/Users/Meta/Desktop/workspace/tao-experiments/data/raw-data/annotations/instances_val2017.json'
train:
optimizer:
name: 'sgd'
momentum: 0.9
lr_schedule:
name: 'cosine'
warmup_epoch: 5
warmup_init: 0.0001
learning_rate: 0.2
amp: True
checkpoint: "/workspace/tao-experiments/efficientdet_tf2/pretrained_efficientdet_tf2_vefficientnet_b0"
num_examples_per_epoch: 100
moving_average_decay: 0.999
batch_size: 20
checkpoint_interval: 5
l2_weight_decay: 0.00004
l1_weight_decay: 0.0
clip_gradients_norm: 10.0
image_preview: True
qat: False
random_seed: 42
pruned_model_path: ''
num_epochs: 20
model:
name: 'efficientdet-d0'
input_width: 512
input_height: 512
aspect_ratios: '[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]'
anchor_scale: 4
min_level: 3
max_level: 7
num_scales: 3
freeze_bn: False
freeze_blocks: []
evaluate:
batch_size: 8
num_samples: 500
max_detections_per_image: 100
label_map: "/workspace/tao-experiments/efficientdet_tf2/specs/coco_labels.yaml"
checkpoint: "EVALMODEL"
prune:
checkpoint: "EVALMODEL"
normalizer: 'max'
equalization_criterion: 'union'
granularity: 8
threshold: 0.5
min_num_filters: 16
results_dir: 'RESULTSDIR'
Morganh
September 28, 2023, 7:16am
4
Which TAO version did you install?
Morganh
September 28, 2023, 7:20am
6
Please update to TAO 5.0 version since your yaml file is compatible with TAO 5.0 version.
This command does not work to upgrade to 5.0
bash setup/quickstart_launcher.sh --upgrade
and this do not install 5.0 version too
bash setup/quickstart_launcher.sh --install
Morganh
September 28, 2023, 7:33am
8
Please run below and then run bash setup/quickstart_launcher.sh --upgrade again.https://api.ngc.nvidia.com/v2/resources/nvidia/tao/tao-getting-started/versions/5.0.0/zip -O getting_started_v5.0.0.zip
Morganh:
! tao info --verbose
It is still
format_version: 2.0
toolkit_version: 4.0.1
published_date: 03/06/2023
after i completed the step
Morganh
September 28, 2023, 8:23am
10
Can you run in the terminal again instead of notebook?
Yes, I run it at terminal instead of in notebook.
For your more information, when I execute this line
# convert training data to TFRecords
!sed -i "s|DATADIR|$DATA_DOWNLOAD_DIR|g" $LOCAL_SPECS_DIR/spec_train.yaml
!tao efficientdet_tf2 dataset_convert -e $SPECS_DIR/spec_train.yaml \
dataset_convert.results_dir=$DATA_DOWNLOAD_DIR
it throw
<frozen cv.efficientdet.scripts.dataset_convert>:340: UserWarning:
'spec_train.yaml' is validated against ConfigStore schema with the same name.
This behavior is deprecated in Hydra 1.1 and will be removed in Hydra 1.2.
See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/automatic_schema_matching for migration instructions.
Error merging 'spec_train.yaml' with schema
Key 'results_dir' not in 'DatasetConvertConfig'
full_key: dataset_convert.results_dir
reference_type=DatasetConvertConfig
object_type=DatasetConvertConfig
this is my specs_train.yaml
dataset_convert:
image_dir: 'DATADIR/raw-data/train2017/'
annotations_file: 'DATADIR/raw-data/annotations/instances_train2017.json'
results_dir: 'DATADIR'
tag: 'train'
num_shards: 256
include_masks: True
dataset:
augmentation:
rand_hflip: True
random_crop_min_scale: 0.1
random_crop_max_scale: 2
loader:
prefetch_size: 4
shuffle_file: False
shuffle_buffer: 10000
cycle_length: 32
block_length: 16
max_instances_per_image: 100
skip_crowd_during_training: True
num_classes: 91
train_tfrecords:
- 'DATADIR/train-*'
val_tfrecords:
- 'DATADIR/val-*'
val_json_file: 'DATADIR/raw-data/annotations/instances_val2017.json'
train:
optimizer:
name: 'sgd'
momentum: 0.9
lr_schedule:
name: 'cosine'
warmup_epoch: 5
warmup_init: 0.0001
learning_rate: 0.2
amp: True
checkpoint: "/workspace/tao-experiments/efficientdet_tf2/pretrained_efficientdet_tf2_vefficientnet_b0"
num_examples_per_epoch: 100
moving_average_decay: 0.999
batch_size: 20
checkpoint_interval: 5
l2_weight_decay: 0.00004
l1_weight_decay: 0.0
clip_gradients_norm: 10.0
image_preview: True
qat: False
random_seed: 42
pruned_model_path: ''
num_epochs: 20
model:
name: 'efficientdet-d0'
input_width: 512
input_height: 512
aspect_ratios: '[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]'
anchor_scale: 4
min_level: 3
max_level: 7
num_scales: 3
freeze_bn: False
freeze_blocks: []
evaluate:
batch_size: 8
num_samples: 500
max_detections_per_image: 100
label_map: "/workspace/tao-experiments/efficientdet_tf2/specs/coco_labels.yaml"
checkpoint: "EVALMODEL"
prune:
checkpoint: "EVALMODEL"
normalizer: 'max'
equalization_criterion: 'union'
granularity: 8
threshold: 0.5
min_num_filters: 16
results_dir: 'RESULTSDIR'
Morganh
September 28, 2023, 8:35am
12
I mean the terminal instead.
instead of notebook
This ran from terminal, and this is the output from terminal for your reference.
(launcher) jingjie@DESKTOP-751VIB1:/mnt/d/nrode/getting_started_v5.0.0$ wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tao/tao-getting-started/versions/5.0.0/zip -O getting_started_v5.0.0.zip
--2023-09-28 16:39:23-- https://api.ngc.nvidia.com/v2/resources/nvidia/tao/tao-getting-started/versions/5.0.0/zip
Resolving api.ngc.nvidia.com (api.ngc.nvidia.com)... 35.165.149.147, 54.200.153.123
Connecting to api.ngc.nvidia.com (api.ngc.nvidia.com)|35.165.149.147|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://prod-model-registry-ngc-bucket.s3.us-west-2.amazonaws.com/org/nvidia/team/tao/recipes/tao-getting-started/versions/5.0.0/files.zip?response-content-disposition=attachment%3B%20filename%3D%22files.zip%22&response-content-type=application%2Fzip&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230928T083925Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3600&X-Amz-Credential=AKIA3PSNVSIZ42OUKYPX%2F20230928%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=abe90e5c8999ea456ddabf4f1c36d8b5f47b17316d9e22ea993c50f416bd7ff1 [following]
--2023-09-28 16:39:24-- https://prod-model-registry-ngc-bucket.s3.us-west-2.amazonaws.com/org/nvidia/team/tao/recipes/tao-getting-started/versions/5.0.0/files.zip?response-content-disposition=attachment%3B%20filename%3D%22files.zip%22&response-content-type=application%2Fzip&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230928T083925Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3600&X-Amz-Credential=AKIA3PSNVSIZ42OUKYPX%2F20230928%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=abe90e5c8999ea456ddabf4f1c36d8b5f47b17316d9e22ea993c50f416bd7ff1
Resolving prod-model-registry-ngc-bucket.s3.us-west-2.amazonaws.com (prod-model-registry-ngc-bucket.s3.us-west-2.amazonaws.com)... 52.218.204.65, 52.218.248.113, 3.5.77.200, ...
Connecting to prod-model-registry-ngc-bucket.s3.us-west-2.amazonaws.com (prod-model-registry-ngc-bucket.s3.us-west-2.amazonaws.com)|52.218.204.65|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4878608 (4.7M) [application/zip]
Saving to: ‘getting_started_v5.0.0.zip’
getting_started_v5.0.0.zip 100%[=======================================================================================================>] 4.65M 2.18MB/s in 2.1s
2023-09-28 16:39:28 (2.18 MB/s) - ‘getting_started_v5.0.0.zip’ saved [4878608/4878608]
(launcher) jingjie@DESKTOP-751VIB1:/mnt/d/nrode/getting_started_v5.0.0$ unzip -u getting_started_v5.0.0.zip -d ./getting_started_v5.0.0 && rm -rf getting_started_v5.0.0.zip && cd ./getting_started_v5.0.0
Archive: getting_started_v5.0.0.zip
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inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/locals.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/networking.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/nfs.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/provider.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/user-data/configure-nfs-server.sh
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/user-data/install-nfs-common.sh
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/variables.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy-spec/versions.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/deploy.yml
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inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/modules/networking/output.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/modules/networking/variables.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/modules/networking/versions.tf
inflating: ./getting_started_v5.0.0/setup/quickstart_api_gcp_gke/setup.sh
inflating: ./getting_started_v5.0.0/setup/quickstart_launcher.sh
(launcher) jingjie@DESKTOP-751VIB1:/mnt/d/nrode/getting_started_v5.0.0/getting_started_v5.0.0$ bash setup/quickstart_launcher.sh --upgrade
INFO: Check requirements
INFO: Checking Python installation
INFO: python3 found.
Python 3.6.13 :: Anaconda, Inc.
INFO: Python version:
INFO: pip3 found.
INFO: Pip version: pip 21.2.2 from /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages/pip (python 3.6)
INFO: Docker found. Checking additional requirements for docker.
INFO: Checking nvidia-docker2 installation
ERROR: nvidia-docker not found.
INFO: NGC CLI found.
INFO: NGC CLI 3.29.0
INFO: Requirements check satisfied. Installing TAO Toolkit.
By installing the TAO Toolkit CLI, you accept the terms and conditions of this license: https://developer.nvidia.com/tao-toolkit-software-license-agreement
Would you like to continue? (y/n): y
INFO: EULA accepted.
INFO: Installing TAO Toolkit CLI
INFO: jupyter installation was found.
Selected Jupyter core packages...
IPython : 7.16.3
ipykernel : 5.5.6
ipywidgets : 7.8.1
jupyter_client : 7.1.2
jupyter_core : 4.9.2
jupyter_server : 1.13.1
jupyterlab : 3.2.9
nbclient : 0.5.9
nbconvert : 6.0.7
nbformat : 5.1.3
notebook : 6.4.10
qtconsole : 5.2.2
traitlets : 4.3.3
INFO: TAO Toolkit was found
INFO: Upgrading installed nvidia-tao to the latest version.
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: nvidia-tao in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (4.0.1)
Collecting nvidia-tao
Downloading nvidia_tao-5.0.0-py3-none-any.whl (35 kB)
Collecting certifi>=2022.12.07
Downloading certifi-2023.7.22-py3-none-any.whl (158 kB)
|████████████████████████████████| 158 kB 7.8 MB/s
Requirement already satisfied: websocket-client==0.57.0 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (0.57.0)
Requirement already satisfied: docker==4.3.1 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (4.3.1)
Requirement already satisfied: requests in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (2.27.1)
Requirement already satisfied: urllib3>=1.26.5 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (1.26.16)
Requirement already satisfied: certifi in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (2021.5.30)
Requirement already satisfied: six==1.15.0 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (1.15.0)
Requirement already satisfied: chardet==3.0.4 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (3.0.4)
Requirement already satisfied: docker-pycreds==0.4.0 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (0.4.0)
Requirement already satisfied: tabulate==0.8.7 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (0.8.7)
Requirement already satisfied: idna==2.10 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from nvidia-tao) (2.10)
Requirement already satisfied: charset-normalizer~=2.0.0 in /home/jingjie/miniconda3/envs/launcher/lib/python3.6/site-packages (from requests->nvidia-tao) (2.0.12)
INFO: 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 some suspicious error
I had tried few way include apt-install,
(launcher) jingjie@DESKTOP-751VIB1:/mnt/d/nrode/getting_started_v5.0.0/getting_started_v5.0.0$ sudo apt-get install -y nvidia-docker2
Reading package lists... Done
Building dependency tree
Reading state information... Done
nvidia-docker2 is already the newest version (2.14.0-1).
0 upgraded, 0 newly installed, 0 to remove and 41 not upgraded.
but still fail. Not sure weather it cause problem. The final version still 4.0
(launcher) jingjie@DESKTOP-751VIB1:/mnt/d/nrode/getting_started_v5.0.0/getting_started_v5.0.0$ tao info
Configuration of the TAO Toolkit Instance
dockers: ['nvidia/tao/tao-toolkit']
format_version: 2.0
toolkit_version: 4.0.1
published_date: 03/06/2023
Morganh
September 29, 2023, 3:20am
14
Please try to uninstall nvidia-driver2 and install it again.
Thanks @Morganh – it looks like nvidia-docker2 was missing from the NVIDIA Deep Learning AMI 23.03.0.
Once I installed that, running bash setup/quickstart_launcher.sh --install from the 5.0.0 Getting Started package on a miniconda env with Python=3.6 resulted in the correct version of the Tao Toolkit being installed.
I’m closing this topic due to there is no update from you for a period, assuming this issue was resolved.
Please use python 3.7 or 3.8 virtual environment instead of 3.6. Then it will upgrade to TAO 5.0.