Unable to build docker image on Mac

[ncoop57]

When attempting to build the docker image on MacOS Mojave v10.14.6 with docker desktop 3.1.0:
docker build -f Dockerfile.prod -t tango .

The following error is produced:

[+] Building 117.5s (6/6) FINISHED                                                                                                                            
 => [internal] load build definition from Dockerfile.prod                                                                                                0.0s
 => => transferring dockerfile: 381B                                                                                                                     0.0s
 => [internal] load .dockerignore                                                                                                                        0.0s
 => => transferring context: 2B                                                                                                                          0.0s
 => [internal] load metadata for docker.io/tensorflow/tensorflow:2.3.0rc0                                                                                0.7s
 => [1/3] FROM docker.io/tensorflow/tensorflow:2.3.0rc0@sha256:2abeb6f52645a3a2b0500773a397a8ed3618106f3f8c270aade242d429812abe                         27.9s
 => => resolve docker.io/tensorflow/tensorflow:2.3.0rc0@sha256:2abeb6f52645a3a2b0500773a397a8ed3618106f3f8c270aade242d429812abe                          0.0s
 => => sha256:d7c3167c320d7a820935f54cf4290890ea19567da496ecf774e8773b35d5f065 26.69MB / 26.69MB                                                         0.7s
 => => sha256:131f805ec7fd68d45a887e2ef82de61de0247b4eb934ab03b7c933650e854baa 35.37kB / 35.37kB                                                         0.2s
 => => sha256:322ed380e680a77f30528ba013e3a802a7b44948a0609c7d1d732dd46a9a310d 844B / 844B                                                               0.2s
 => => sha256:2abeb6f52645a3a2b0500773a397a8ed3618106f3f8c270aade242d429812abe 2.62kB / 2.62kB                                                           0.0s
 => => sha256:5ac4ef45782ea01bfc1d85931e3cf4b8d84bbbed45ba8eeffd5e58521fc19e21 5.42kB / 5.42kB                                                           0.0s
 => => sha256:6ac240b130982ad1c3ba3188abbf18ba4e54bdd9e504ce2d5c2eff6d3e86b8dd 163B / 163B                                                               0.2s
 => => sha256:0a18ca147880a5d174fe1715bbac24c092cf3001878e830b99d7ca436d3a2dfd 27.27MB / 27.27MB                                                         2.0s
 => => sha256:9421cc76cbdd3e88586954465f4683fa12a243538c7d0af366fcd728faba8c6f 154.75MB / 154.75MB                                                       6.5s
 => => sha256:93571a8619cba97e40af3e85ecf77d05a3327b014aa389f85bd44c75f7474909 3.61MB / 3.61MB                                                           1.0s
 => => extracting sha256:d7c3167c320d7a820935f54cf4290890ea19567da496ecf774e8773b35d5f065                                                                1.1s
 => => sha256:0c436df34f94d31bef3332be85addf961e1e9507c1658162a92c3162aea85f68 171B / 171B                                                               1.1s
 => => sha256:276640e8901ff9b54d9bcbbe6785ce3ae2db39e17ef0988c7e19b8e22754db0c 393.66MB / 393.66MB                                                      11.8s
 => => extracting sha256:131f805ec7fd68d45a887e2ef82de61de0247b4eb934ab03b7c933650e854baa                                                                0.1s
 => => sha256:aa84896a64ae1531e48a82230ff7d3fb8077ac4b782bb200f423e8603abd411c 1.09kB / 1.09kB                                                           2.2s
 => => extracting sha256:322ed380e680a77f30528ba013e3a802a7b44948a0609c7d1d732dd46a9a310d                                                                0.0s
 => => extracting sha256:6ac240b130982ad1c3ba3188abbf18ba4e54bdd9e504ce2d5c2eff6d3e86b8dd                                                                0.0s
 => => sha256:0f91f149d4199fe4c73415a09091af14da2e0f0c2d8bd84b42c94563bf85f54e 1.09kB / 1.09kB                                                           2.4s
 => => extracting sha256:0a18ca147880a5d174fe1715bbac24c092cf3001878e830b99d7ca436d3a2dfd                                                                0.8s
 => => extracting sha256:9421cc76cbdd3e88586954465f4683fa12a243538c7d0af366fcd728faba8c6f                                                                5.4s
 => => extracting sha256:93571a8619cba97e40af3e85ecf77d05a3327b014aa389f85bd44c75f7474909                                                                0.2s
 => => extracting sha256:0c436df34f94d31bef3332be85addf961e1e9507c1658162a92c3162aea85f68                                                                0.0s
 => => extracting sha256:276640e8901ff9b54d9bcbbe6785ce3ae2db39e17ef0988c7e19b8e22754db0c                                                               14.7s
 => => extracting sha256:aa84896a64ae1531e48a82230ff7d3fb8077ac4b782bb200f423e8603abd411c                                                                0.0s
 => => extracting sha256:0f91f149d4199fe4c73415a09091af14da2e0f0c2d8bd84b42c94563bf85f54e                                                                0.0s
 => [2/3] RUN apt-get update -y && apt-get install git wget vim libsm6 libxext6 libxrender-dev libx264-dev ffmpeg tesseract-ocr libtesseract-dev openj  49.4s
 => ERROR [3/3] RUN pip install two-to-tango                                                                                                            39.3s
------
 > [3/3] RUN pip install two-to-tango:
#6 0.707 Collecting two-to-tango
#6 0.755   Downloading two_to_tango-0.0.3-py3-none-any.whl (32 kB)
#6 0.805 Collecting fastprogress==0.2.1
#6 0.828   Downloading fastprogress-0.2.1-py3-none-any.whl (11 kB)
#6 0.996 Collecting scikit-learn==0.22.1
#6 1.005   Downloading scikit_learn-0.22.1-cp36-cp36m-manylinux1_x86_64.whl (7.0 MB)
#6 1.671 Collecting pandas==0.25.3
#6 1.679   Downloading pandas-0.25.3-cp36-cp36m-manylinux1_x86_64.whl (10.4 MB)
#6 2.375 Collecting matplotlib==3.1.1
#6 2.382   Downloading matplotlib-3.1.1-cp36-cp36m-manylinux1_x86_64.whl (13.1 MB)
#6 3.273 Collecting ffmpeg-python==0.2.0
#6 3.281   Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)
#6 3.497 Collecting opencv-python==3.4.1.15
#6 3.505   Downloading opencv_python-3.4.1.15-cp36-cp36m-manylinux1_x86_64.whl (24.9 MB)
#6 5.343 Collecting pytesseract==0.3.5
#6 5.352   Downloading pytesseract-0.3.5.tar.gz (13 kB)
#6 5.632 Collecting requests==2.25.0
#6 5.644   Downloading requests-2.25.0-py2.py3-none-any.whl (61 kB)
#6 5.689 Collecting more-itertools==8.0.2
#6 5.700   Downloading more_itertools-8.0.2-py3-none-any.whl (40 kB)
#6 5.756 Collecting pytorch-lightning==0.9.0
#6 5.763   Downloading pytorch_lightning-0.9.0-py3-none-any.whl (408 kB)
#6 5.941 Collecting torch==1.6.0
#6 5.948   Downloading torch-1.6.0-cp36-cp36m-manylinux1_x86_64.whl (748.8 MB)
#6 38.53 Killed
------
executor failed running [/bin/sh -c pip install two-to-tango]: exit code: 137

[ncoop57]

Current work around is to skip build all together and have users pull a prebuilt image from Docker Hub.