Docker

docker run -p 5005:5005 -i -t jhfjhfj1/tamuta2:latest _bin_bash
python -m grpc_tools.protoc -I . --python_out=. --grpc_python_out=. core.proto
docker build -t tamuta2:latest .
docker build -f Dockerfile_dry_run -t ta2image .
docker run -i --entrypoint _bin_bash tamuta2 -c 'ta2_search $CONFIG_JSON_PATH'
docker build -f Dockerfile_base -t mybase .
docker run -i --entrypoint _bin_bash ta2image d-c '_resources_executable/1.sh $CONFIG_JSON'
docker run  -p 5005:5005 -it --entrypoint _bin_bash jhfjhfj1/tamuta2:latest -c 'ta2_serve $CONFIG_JSON'

Create a docker image:

docker build -t autokeras:latest .

run docker as a shell:

docker run -it --entrypoint /bin/bash image_name

You save the image as a tar archive, using docker save -o:

docker save -o archive.tar sheffien/rim

Then you load it (on a different computer) with

docker load -i archive.tar

and now it will show up in your docker images list:

docker images

The Docker.

Use ENV to set environment variable.

docker build -t registry.datadrivendiscovery.org/ta2/texas-anm-university_tamu_1.0.0:latest .
docker login registry.datadrivendiscovery.org
docker push registry.datadrivendiscovery.org/ta2/texas-anm-university_tamu_1.0.0
docker tag tamuta2 jhfjhfj1/tamuta2
docker push jhfjhfj1/tamuta2
docker exec tamuta2 _bin_bash

Remove docker images.

docker rmi IMAGE_ID

Remove all docker images.

docker rmi $(docker images -a -q)

Remove all docker images with pattern

docker images -a | grep "pattern" | awk '{print $3}' | xargs docker rmi

Use gpus in docker containers

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && curl -s -L https://nvidia.github.io/libnvidia-container/gpgkey | sudo apt-key add -       && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update

sudo apt-get install -y nvidia-docker2

sudo systemctl restart docker

docker run --rm --gpus all tensorflow/tensorflow:latest-gpu nvidia-smi