install nvidia docker digits in ubantu 18. 04 and 18 .10
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Rapidly Create an Image Classifier with Tensor Flow using docker
https://askmacgyver.com/blog/tutorial...
https://github.com/MacgyverCode/Image...
Download the Tensor Flow Docker Container
$ docker pull macgyvertechnology/tensorflow
Stand Up the Container
docker run -it -d macgyvertechnology/tensorflow
List Running Containers
docker ps -a
Log Into Tensor Flow Container
docker exec -it container_id bash
From Within the Tensor Flow Docker Container Shell
Create a folder called "training_images" with subdirectories for each image category we want to include in our model.
mkdir training_images
Structure the folders like the following. The model will create labels from the names of the folders. So in this case there will be three categories: "pens", "laptops", "chairs". The names and file extensions of these images don't matter.
/training_images
/pens
image1.jpg
image2.jpg
...
/laptops
/chairs
Copy this directory from our local machine into our docker container.
Copy this directory from our local machine into our docker container.
docker cp training_images _container_id_:/
saktheeswaran@saktheeswaran:~/Pictures$ sudo docker cp training_images/ 10d9afdd2c0541/:
saktheeswaran@saktheeswaran:~/Pictures$ sudo docker cp test/ 10d9ddas2c0541/:
saktheeswaran@saktheeswaran:~/Pictures$ sudo docker exec -it 10d9dd2ascc0541 bash
root@10d9dd2c0541:~# cd /
root@10d9dd2c0541:/# ls
root@10d9dd2c0541:/# python tensorflow/tensorflow/examples/image_retraining/retrain.py \
--bottleneck_dir=/bottlenecks \
--model_dir=/inception \
--output_labels=/retrained_labels.txt \
--output_graph=/retrained_graph.pb \
--image_dir=/training_images/
Prediction
We run a prediction and pass our trained model "retrained_graph.pb" along with out labels "retrained_labels.txt" as well as our novel test image "test-image.jpeg".
python tensorflow/tensorflow/examples/image_retraining/label_image.py \
--graph=/retrained_graph.pb \
--labels=/retrained_labels.txt \
--image=/test-image.jpeg
for more information on excecuting the tutorial is here
https://stackoverflow.com/questions/2...
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install docker
https://docs.docker.com/v17.09/engine...
install docker
https://github.com/NVIDIA/nvidia-docker
to run digits
https://stackoverflow.com/questions/2...
E: Unable to locate package nvidia-docker
https://nvidia.github.io/nvidia-docker/
https://nvidia.github.io/nvidia-docker/
https://github.com/NVIDIA/nvidia-dock...
https://github.com/NVIDIA/nvidia-dock...
https://chunml.github.io/ChunML.githu...
E: Unable to locate package nvidia-docker
https://nvidia.github.io/nvidia-docker/
https://nvidia.github.io/nvidia-docker/
https://github.com/NVIDIA/nvidia-dock...
https://github.com/NVIDIA/nvidia-dock...
https://chunml.github.io/ChunML.githu...
nvidia-docker run --name digits -d -p 8888:5000 nvcr.io/nvidia/digits
https://stackoverflow.com/questions/2...
https://stackoverflow.com/questions/2...
https://forums.docker.com/t/run-comma...
cudaErrorInsufficientDriver = 35
This indicates that the installed NVIDIA CUDA driver is older than the CUDA runtime library. This is not a supported configuration. Users should install an updated NVIDIA display driver to allow the application to run.
https://github.com/NVIDIA/DIGITS/issu...
https://github.com/NVIDIA/DIGITS/issu...
https://docs.nvidia.com/deeplearning/...
https://docs.nvidia.com/deeplearning/...
https://docs.nvidia.com/deeplearning/...
Image Classification (Keras) For Idiots - Bill Gates vs Jeff Bezos
https://github.com/jrjames83/Keras-Ga...
• Image Classification (Keras) For Idio...
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