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Docker_CPU.md

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Docker Usage (CPU version)

This document describes how to setup and use Docker. The following commands were executed on an Ubuntu 16.04 base installation on an EC2 instance on AWS.

Docker Installation

sudo apt-get update

sudo apt-get install -y \
    apt-transport-https \
    ca-certificates \
    curl \
    software-properties-common

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"

sudo apt-get update
sudo apt-get install -y docker-ce

# test installation
sudo docker run hello-world

Install general programs

# update
sudo apt-get update

# install some basics
sudo apt-get install -y git unzip wget zip screen vim

Build Container

This builds the CPU version of the container.

# install docker container
mkdir ~/code
cd ~/code
git clone https://github.com/marco-willi/camera-trap-classifier.git
cd camera-trap-classifier
sudo docker build . -f Dockerfile.cpu -t camera-trap-classifier:latest-cpu

Start Container

Now we run the container and map /my_data/ from the host computer to /data/ inside the container. It is important to adapt these paths to where the actual data is on the host.

# run docker image
# maps /my_data/ on host to /data/ in container
sudo docker run --name ctc -v /my_data/:/data/ -itd camera-trap-classifier:latest-cpu

Run Scripts

# run scripts with data on host
sudo docker exec ctc ctc.create_dataset_inventory dir -path /data/images \
-export_path /data/dataset_inventory.json

# create directory for tfr-files on host
sudo mkdir /my_data/tfr_files

# create tfr-files
sudo docker exec ctc ctc.create_dataset -inventory /data/dataset_inventory.json \
-output_dir /data/tfr_files/ \
-image_save_side_smallest 200 \
-split_percent 0.5 0.25 0.25 \
-overwrite


# create directory for log files and model saves
sudo mkdir /my_data/run1 /my_data/save1

# train model
sudo docker exec ctc ctc.train \
-train_tfr_path /data/tfr_files/ \
-val_tfr_path /data/tfr_files/ \
-test_tfr_path /data/tfr_files/ \
-class_mapping_json /data/tfr_files/label_mapping.json \
-run_outputs_dir /data/run1/ \
-model_save_dir /data/save1/ \
-model small_cnn \
-labels class \
-batch_size 16 \
-n_cpus 4 \
-n_gpus 0 \
-buffer_size 16 \
-max_epochs 70 \
-color_augmentation full_randomized

# predict from model
sudo docker exec ctc ctc.predict \
  -image_dir /data/images \
  -results_file /data/output.csv \
  -model_path /data/save1/best_model.hdf5 \
  -class_mapping_json /data/save1/label_mappings.json \
  -pre_processing_json /data/save1/image_processing.json

# export model
sudo mkdir /my_data/save1/my_model_exports/ /my_data/save1/my_estimators/
sudo docker exec ctc ctc.export -model /data/save1/best_model.hdf5 \
  -class_mapping_json /data/save1/label_mappings.json \
  -pre_processing_json /data/save1/image_processing.json \
  -output_dir /data/save1/my_estimators/ \
  -estimator_save_dir /data/save1/my_estimators/keras/