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Build EMSAssist docker image from scratch

Build Docker image with all required packages. This step is optional as a ready image will be pulled in absence from Docker hub (step 7). This step involves manual installation of packages and rebuilding the image which will take some time. * Create Dockerfile console touch Dockerfile * Edit Dockerfile with your preffered text editor and paste the following contents in Dockerfile then save it. ```dockerfile #FROM ubuntu:22.04 FROM nvidia/cuda:12.0.1-base-ubuntu22.04 MAINTAINER "Amran Haroon"

ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
ENV PATH /opt/conda/bin:$PATH

RUN set -x && \
	apt-get update --fix-missing && \
	apt-get install -y --no-install-recommends \
		bzip2 \
		ca-certificates \
		git \
		libglib2.0-0 \
		libsm6 \
		libxcomposite1 \
		libxcursor1 \
		libxdamage1 \
		libxext6 \
		libxfixes3 \
		libxi6 \
		libxinerama1 \
		libxrandr2 \
		libxrender1 \
		mercurial \
		openssh-client \
		procps \
		subversion \
		wget \
	&& apt-get clean \
	&& rm -rf /var/lib/apt/lists/* && \
	UNAME_M="$(uname -m)" && \
	if [ "${UNAME_M}" = "x86_64" ]; then \
		ANACONDA_URL="https://repo.anaconda.com/archive/Anaconda3-2023.03-Linux-x86_64.sh"; \
		SHA256SUM="19737d5c27b23a1d8740c5cb2414bf6253184ce745d0a912bb235a212a15e075"; \
	elif [ "${UNAME_M}" = "s390x" ]; then \
		ANACONDA_URL="https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-s390x.sh"; \
		SHA256SUM="f5ccc24aedab1f3f9cccf1945ca1061bee194fa42a212ec26425f3b77fdd943a"; \
	elif [ "${UNAME_M}" = "aarch64" ]; then \
		ANACONDA_URL="https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-aarch64.sh"; \
		SHA256SUM="fbadbfae5992a8c96af0a4621262080eea44e22baee2172e3dfb640f5cf8d22d"; \
	elif [ "${UNAME_M}" = "ppc64le" ]; then \
		ANACONDA_URL="https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-ppc64le.sh"; \
		SHA256SUM="8fdebc79f63b74daad421a2674d43299fa9c5007d85cf00e8dc1a81fbf2787e4"; \
	fi && \
	wget "${ANACONDA_URL}" -O anaconda.sh -q && \
	echo "${SHA256SUM} anaconda.sh" > shasum && \
	sha256sum --check --status shasum && \
	/bin/bash anaconda.sh -b -p /opt/conda && \
	rm anaconda.sh shasum && \
	ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
	echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
	echo "conda activate base" >> ~/.bashrc && \
	find /opt/conda/ -follow -type f -name '*.a' -delete && \
	find /opt/conda/ -follow -type f -name '*.js.map' -delete && \
	/opt/conda/bin/conda clean -afy

RUN set -x && \     
	conda create -yn emsassist-gpu pip python=3.7 

#ENTRYPOINT ["tail", "-f", "/dev/null"]
#CMD [ "/bin/bash" ]
ENTRYPOINT ["tail"]
CMD ["-f","/dev/null"]
```

* Build Docker image and give it a name:
```console
$ docker build -t haroon3rd/anaconda3:base .
```

* Make sure the image was created successfully
```console
$ docker image ls
```

* Run Docker image with gpu enabled in silent mode and execute into terminal :
```console
$ docker run --gpus all -d -t --name base haroon3rd/anaconda3:base
$ docker exec -it base /bin/bash
```

*  Install the required packages inside created conda environment:
```console
# Activate conda env 
$ conda activate emsassist-gpu

# Install gcc
$ apt-get update && apt-get -y install gcc mono-mcs && rm -rf /var/lib/apt/lists/*

# Install the required python modules one by one
$ pip install tensorflow_addons sentencepiece gin-config tflite-support tensorflow_hub natsort scikit-learn fire pyyaml tqdm librosa tensorflow-io==0.26 tensorflow_datasets nltk pydub pandas

# Install the XGBoost-GPU
$ conda install py-xgboost-gpu
# This also installs the CudaToolkit: pkgs/main/linux-64::cudatoolkit-10.0.130-0

# Install the TensorFlow-2.9
$ pip install tensorflow-gpu==2.9

# Install the CUDA ToolKit 11.0 and CuDNN 8.0
$ conda install -c conda-forge cudatoolkit=11.0 cudnn
```

* Save this container (with changes) to a new image (may take up to 10 mins):
```console
$ docker commit base  haroon3rd/anaconda3:nvidia-v1
```

* Make sure you have a new image `haroon3rd/anaconda3:nvidia-v1`:
```console
$ docker image ls
```

  1. Install Docker-Compose

    $ sudo apt update
$ sudo apt-get install docker-compose

  2. install nvidia container tootlkit in local machine (requires Docker dameon to reload). This toolkit allows you to connect the container engine to the bare metal machine's nvidia driver.

    # Add key and repository 
$ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
$ curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu22.04/nvidia-docker.list > /etc/apt/sources.list.d/nvidia-docker.list

# Update repository
$ apt update

# Install nvidia-container-toolkit
$ sudo apt -y install nvidia-container-toolkit

# Restart docker engine
$ systemctl restart docker

  3. Download the data.tar.gz, model.tar.gz and docker-compose.yml files from Google Drive to the cuurent working (i.e., ./home) folder. We expect the downloading and decompressing to take 2-3 hours.

    • decompress the model.tar.gz: tar -xvzf model.tar.gz

    • decompress the data.tar.gz: tar -xvzf data.tar.gz. After this step, make sure we have 4 items in the current directory: data, model, and EMSAssist folder and a file 'docker-compose.yml'. Also make sure there are 3 folders under EMSAssist directory: src, examples, and init_models.

    • Make sure docker-compose.yml has the following content in it:

    version: '3.7'

services:
  emsassist:
    image: haroon3rd/anaconda3:nvidia-v1
      container_name: emsassist
      volumes:
        - ./data:/home/EMSAssist-artifact-evaluation/data
        - ./model:/home/EMSAssist-artifact-evaluation/model
        - ./EMSAssist:/home/EMSAssist-artifact-evaluation/EMSAssist
      #command: [/bin/bash -c "tail -f /dev/null"]
      command: tail -F anything
      #network_mode: "host"
      deploy:
        resources:
          reservations:
            devices:
              - driver: nvidia
                capabilities: [gpu]
volumes:
  emsassist: {}

  4. Clone the git repository of EMSAssist:

    $ git clone --recursive git@github.com:LENSS/EMSAssist.git`
$ cd EMSAssist
$ git clone --recursive git@github.com:tensorflow/examples.git

  5. Run docker-compose in silent mode from the terminal of current folder. For the next step, data, model, and EMSAssist directories along with the docker-compose.yml file need to be in the same folder (i.e., current folder):

    $ docker-compose up -d
# it will pull a docker container image and run it in local machine as "emsassist"

  6. Log in to your running container's terminal (bash)

    $ docker exec -it emsassist /bin/bash

Inside your container:

  1. Activate conda environment and run nvidia-smi to make sure the GPU is working: 
    $ conda activate emsassist-gpu
$ nvidia-smi