docker_train_prod_model

#!/bin/bash

# Builds a new model within a docker container using production settings.
#
# Example
#  $ mkdir ~/models
#  $ ./docker_train_prod_model ~/models

# Exit build script on first failure
set -e

# Echo commands to stdout.
set -x

if [ -z "$1" ];
  then echo "Usage: ./docker_train_prod_model output_dir" && exit 1;
fi
OUTPUT_DIR="$1"

IMAGE_NAME="ingredient-phrase-tagger-image"
CONTAINER_NAME="ingredient-phrase-tagger-model-builder"

docker version

docker rm -f "$CONTAINER_NAME" || true

docker build \
  --tag "$IMAGE_NAME" \
  .

docker run \
  --tty \
  --detach \
  --name "$CONTAINER_NAME" \
  "$IMAGE_NAME"

DOCKER_MODEL_DIR=/models

docker exec "$CONTAINER_NAME" mkdir "${DOCKER_MODEL_DIR}"
docker exec "$CONTAINER_NAME" bin/train-prod-model "${DOCKER_MODEL_DIR}"
FIND_MODEL_CMD="ls ${DOCKER_MODEL_DIR}/*.crfmodel"
DOCKER_MODEL_PATH=$(docker exec "$CONTAINER_NAME" sh -c "$FIND_MODEL_CMD")
docker cp "${CONTAINER_NAME}:${DOCKER_MODEL_PATH}" "${OUTPUT_DIR}/"

MODEL_FILE="$(ls ${OUTPUT_DIR}/*.crfmodel)"
echo "Trained new model: $MODEL_FILE"