Is it
docker run --runtime=nvidia -it -p 7001:8001 -v /var/run/docker.sock:/var/run/docker.sock nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3 /bin/bash
No i am using exec command as given below
docker exec -it taoyolov4 bash
I had created container named taoyolov4 using volume option, but while executing the container everytime i dont use volume option as in docker exec there is no -v option
Officially we recommend users trigger jobs via tao launcher. See TAO Toolkit Launcher — TAO Toolkit 3.22.05 documentation
In this way, please set correct ~/.tao_mounts.json.
Then run something like below.
$ tao yolov4 train xxx
…
And you can also run below to check if the files are available. For example,
$ tao yolov4 run ls xxx
Besides tao launcher, if you trigger docker as below.
$docker run --runtime=nvidia -it -p 7001:8001 -v /var/run/docker.sock:/var/run/docker.sock -v yourlocal_folder:/workspace nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3
/bin/bash
In this way, you will find your files under /workspace . It is not related to ~/.tao_mounts.json.
i wanted a persistent container so i gave it a name called as taoyolov4, this run command will create a new temporary container whose status is not persistent , so i use the exec command to start my already created container named taoyolov4.
Tell me what to do for a persistent container so that i dont have to create a new container everytime.
You can use the 2nd method.
Then there is a name when you run “$docker ps”.
You can use the container id to run “docker exec” every time.
Used the second method as suggested still getting file not found error
yolo_v4 dataset_convert -d $SPECS_DIR/yolo_v4_tfrecords_kitti_train.txt \
> -o $DATA_DOWNLOAD_DIR/training/tfrecords/train
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/yolo_v4/scripts/dataset_convert.py", line 18, in <module>
File "/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/scripts/dataset_convert.py", line 110, in main
FileNotFoundError: [Errno 2] No such file or directory: '/yolo_v4_tfrecords_kitti_train.txt'
As mentioned above, -v yourlocal_folder:/workspace will map your local directory to the docker. Can you check if the file path is correct?
As suggested i have used this command
sudo docker run --runtime=nvidia -it -p 7001:8001 --name abc -v /var/run/docker.sock:/var/run/docker.sock -v tao_project:/workspace nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3 /bin/bash
a folder named tao_project is created on my host pc in /var/lib/docker/volumes path
Now i have updated this tao_project folder name in the jupyter notebook as follows .
# Setting up env variables for cleaner command line commands.
import os
print("Please replace the variable with your key.")
%env KEY=nvidia_tlt
%env USER_EXPERIMENT_DIR=/workspace/tao-experiments/yolo_v4
%env DATA_DOWNLOAD_DIR=/workspace/tao-experiments/data
# Set this path if you don't run the notebook from the samples directory.
# %env NOTEBOOK_ROOT=~/tao-samples/yolo_v4
# Please define this local project directory that needs to be mapped to the TAO docker session.
# The dataset expected to be present in $LOCAL_PROJECT_DIR/data, while the results for the steps
# in this notebook will be stored at $LOCAL_PROJECT_DIR/yolo_v4
%env LOCAL_PROJECT_DIR=tao_project
os.environ["LOCAL_DATA_DIR"] = os.path.join(os.getenv("LOCAL_PROJECT_DIR", os.getcwd()), "data")
os.environ["LOCAL_EXPERIMENT_DIR"] = os.path.join(os.getenv("LOCAL_PROJECT_DIR", os.getcwd()), "yolo_v4")
# The sample spec files are present in the same path as the downloaded samples.
os.environ["LOCAL_SPECS_DIR"] = os.path.join(
os.getenv("NOTEBOOK_ROOT", os.getcwd()),
"specs"
)
%env SPECS_DIR=/workspace/yolo_v4/specs
# Showing list of specification files.
!ls -rlt $LOCAL_SPECS_DIR
Pls check if i m going wrong in setting the Local_Proj_dir or other paths
You can debug in terminal firstly instead of using jupyter notebook.
This output displayed is in the terminal itself not from jupyter notebook
root@3b5ffffef2b0:/workspace# yolo_v4 dataset_convert -d $SPECS_DIR/yolo_v4_tfrecords_kitti_train.txt \
> -o $DATA_DOWNLOAD_DIR/training/tfrecords/train
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/yolo_v4/scripts/dataset_convert.py", line 18, in <module>
File "/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/scripts/dataset_convert.py", line 110, in main
FileNotFoundError: [Errno 2] No such file or directory: '/yolo_v4_tfrecords_kitti_train.txt'
root@3b5ffffef2b0:/workspace# ^C
root@3b5ffffef2b0:/workspace# yolo_v4 dataset_convert -d $SPECS_DIR/yolo_v4_tfrecords_kitti_train.txt \
> -o $DATA_DOWNLOAD_DIR/training/tfrecords/train
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/yolo_v4/scripts/dataset_convert.py", line 18, in <module>
File "/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/scripts/dataset_convert.py", line 110, in main
FileNotFoundError: [Errno 2] No such file or directory: '/yolo_v4_tfrecords_kitti_train.txt'
Where is your yolo_v4_tfrecords_kitti_train.txt ? If it locates at tao_project/yolo_v4_tfrecords_kitti_train.txt, then you can find it in the path /workspace/yolo_v4_tfrecords_kitti_train.txt
On host pc i can see yolo_v4_tfrecords_kitti_train.txt in the following path
/var/lib/docker/volumes/tao_project/_data/cv_samples_v1.3.0/yolo_v4/specs/yolo_v4_tfrecords_kitti_train.txt
when i browse through my docker i can see it in
:/workspace/cv_samples_v1.3.0/yolo_v4/specs/yolo_v4_tfrecords_kitti_train.txt
Can you change your local files into another path instead of /var/lib/docker/ ?
Assume you put the spec files in /home/xxx/tao_project
Then
-v /home/xxx/tao_project:/workspace
Then, you can find all the files will be available in /workspace of the docker.
I am still getting the same error.
My local_project_dir is in /home/pallavi/tao_project and i have executed the docker using the command below
sudo docker run --runtime=nvidia -it -p 8888:9999 --name yolotao -v /var/run/docker.sock:/var/run/docker.sock -v /home/pallavi/tao_project:/workspace nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3 /bin/bash
Error on the command prompt while converting the dataset
root@59eed8d6e529:/workspace# yolo_v4 dataset_convert -d $SPECS_DIR/yolo_v4_tfrecords_kitti_train.txt \
> -o $DATA_DOWNLOAD_DIR/training/tfrecords/train
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/yolo_v4/scripts/dataset_convert.py", line 18, in <module>
File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/scripts/dataset_convert.py", line 110, in main
FileNotFoundError: [Errno 2] No such file or directory: '/yolo_v4_tfrecords_kitti_train.txt'
What is the “$SPECS_DIR” ? Is it correct?
You can change it to explicit path.
I think i have found the error. When i do ls to the host pc project directory it gives me permission denied error.
I am not able to access my host pc folder/file inside the docker container.
Can you now help me how to resolve this problem
Is it by setting UID and GID if yes where to set them
-
Docker run command OR
-
in mounts.json file
Pls guide
No, if you trigger docker via -v yourlocal_folder:/workspace, you can access your hos pc folder/file inside the docker.
I’m closing this topic due to there is no update from you for a period, assuming this issue was resolved.
If still need the support, please open a new topic. Thanks
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