The docker file was added for NNCF TF (#837)

- The `cpu` and `gpu` folders for NNCF PT were moved from `nncf/docker` to `nncf/docker/torch`
- The docker files were updated for NNCF PT
    - Proxy env variables were removed
- The docker file was added for NNCF TF
- README.md was updated
    - Refactoring.
    - The additional settings  `--shm-size=1g` and `--ulimit memlock=-1` were added (see [link](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/troubleshooting.html#sharing-data))


Related to 59465

docker/README.md

@@ -1,30 +1,40 @@
 ## Step 1. Install docker
-Review the instructions for installation docker [here](https://docs.docker.com/engine/install/ubuntu/) and configure HTTP or HTTPS proxy behavior as [here](https://docs.docker.com/config/daemon/systemd/).
+
+Review the instructions for installation docker [here](https://docs.docker.com/engine/install/ubuntu/) and configure Docker
+to use a proxy server as [here](https://docs.docker.com/network/proxy/#configure-the-docker-client).
 
 ## Step 2. Install nvidia-docker
 
 *Skip this step if you don't have GPU.*
 
-Review the instructions for installation docker [here](https://github.com/NVIDIA/nvidia-docker)
+Review the instructions for installation docker [here](https://github.com/NVIDIA/nvidia-docker).
 
 ## Step 3. Build image
+
 In the project folder run in terminal:
- ```
- sudo docker image build --network=host --build-arg http_proxy=http://example.com:80 --build-arg https_proxy=http://example.com:81 --build-arg 
-ftp_proxy=http://example.com:80 <PATH_TO_DIR_WITH_DOCKERFILE>
- ```
+```
+sudo docker image build --network=host <PATH_TO_DIR_WITH_DOCKERFILE>
+```
+
+Use `--network` to duplicate the network settings of your localhost into context build.
 
-*Use `--http_proxy` , `--https_proxy`, `--ftp_proxy`, `--network` to duplicate the network settings of your localhost into context build*
-
 ## Step 4. Run container
 Run in terminal:
 ```
-sudo docker run --name <NAME_CONTAINER> --runtime=nvidia -it --network=host --mount type=bind,source=<PATH_TO_DATASETS_ON_HOST>,target=<PATH_TO_DATSETS_IN_CONTAINER>  --mount type=bind,source=<PATH_TO_NNCF_HOME_ON_HOST>,target=/home/nncf/ <ID_IMAGE>
+sudo docker run \
+-it \
+--name=<CONTAINER_NAME> \
+--runtime=nvidia \
+--network=host \
+--shm-size=1g \
+--ulimit memlock=-1 \
+--mount type=bind,source=<PATH_TO_DATASETS_ON_HOST>,target=<PATH_TO_DATSETS_IN_CONTAINER> \
+--mount type=bind,source=<PATH_TO_NNCF_HOME_ON_HOST>,target=/home/nncf \
+<IMAGE_ID>
  ```
 
-*You should not use `--runtime=nvidia` if you want to use `--cpu-only` mode.* 
+You should not use `--runtime=nvidia` if you want to use `--cpu-only` mode.
 
-*Use `--shm-size` to increase the size of the shared memory directory.*
+Use `--shm-size` to increase the size of the shared memory directory.
 
 Now you have a working container and you can run examples.
-

docker/tensorflow/gpu/Dockerfile

@@ -0,0 +1,42 @@
+FROM nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04
+
+RUN apt-get update \
+        && apt-get install -y --no-install-recommends \
+            apt-transport-https \
+            git \
+        && rm -rf /var/lib/apt/lists/*
+
+RUN apt-get update \
+        && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+            build-essential \
+            libgl1-mesa-glx \
+            libglib2.0-dev \
+            wget \
+            curl \
+            zip \
+            unzip \
+            nano \
+            openssh-server \
+            openssh-client \
+            sudo \
+            python3 \
+            python3-dev \
+            python3-pip \
+        && cd /usr/bin \
+        && ln -s python3.8 python \
+        && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 install --upgrade pip \
+        && pip3 install setuptools
+
+ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
+ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
+
+# nvidia-container-runtime
+ENV NVIDIA_VISIBLE_DEVICES all
+ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
+
+ENTRYPOINT cd /home/nncf \
+               && python setup.py install --tf \
+               && pip3 install -r examples/tensorflow/requirements.txt \
+               && bash

docker/cpu/Dockerfile → docker/torch/cpu/Dockerfile

@@ -1,7 +1,8 @@
 FROM ubuntu:20.04
+
 RUN apt-get update && apt-get install -y --no-install-recommends \
         apt-transport-https \
-        git  && \
+        git && \
 rm -rf /var/lib/apt/lists/*
 
 
@@ -16,16 +17,14 @@ RUN apt-get update && \
         openssh-server \
         openssh-client \
         sudo \
-		python3 \
-		python3-dev \
-		python3-pip &&\
-	cd /usr/bin && \
-	ln -s python3.8 python && \
+        python3 \
+        python3-dev \
+        python3-pip && \
+    cd /usr/bin && \
+    ln -s python3.8 python && \
     rm -rf /var/lib/apt/lists/*
 
-ARG https_proxy
-ARG no_proxy
-ENV https_proxy=$https_proxy
-ENV no_proxy=$no_proxy
-
-ENTRYPOINT cd /home/nncf/ && python setup.py install && bash
+ENTRYPOINT cd /home/nncf \
+               && python setup.py install --torch \
+               && pip3 install -r examples/torch/requirements.txt \
+               && bash

docker/gpu/Dockerfile → docker/torch/gpu/Dockerfile

@@ -21,11 +21,11 @@ RUN apt-get update && \
         openssh-server \
         openssh-client \
         sudo \
-		python3 \
-		python3-dev \
-		python3-pip &&\
-	cd /usr/bin && \
-	ln -s python3.8 python && \
+        python3 \
+        python3-dev \
+        python3-pip && \
+    cd /usr/bin && \
+    ln -s python3.8 python && \
     rm -rf /var/lib/apt/lists/*
 
 ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
@@ -35,9 +35,7 @@ ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
 ENV NVIDIA_VISIBLE_DEVICES all
 ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
 
-ARG https_proxy
-ARG no_proxy
-ENV https_proxy=$https_proxy
-ENV no_proxy=$no_proxy
-
-ENTRYPOINT cd /home/nncf/ && python setup.py install && bash
+ENTRYPOINT cd /home/nncf \
+               && python setup.py install --torch \
+               && pip3 install -r examples/torch/requirements.txt \
+               && bash