Installing condor_tensorflow

Requirements

condor_tensorflow has been tested with the following software and packages:

  • Python == 3.9.6
  • Tensorflow == 2.4.1
  • sklearn == 0.24.2
  • numpy == 1.19.5 (newer versions currently incompatibile with above tensorflow version)

PyPI

You can install the latest stable release of condor_tensorflow directly from Python's package index via pip by executing the following code from your command line: 

pip install condor-tensorflow

Latest GitHub Source Code


You want to try out the latest features before they go live on PyPI? Install the condor_tensorflow dev-version latest development version from the GitHub repository by executing

pip install git+git://github.com/GarrettJenkinson/condor_tensorflow.git

Docker

This package relies on Python 3.6+, Tensorflow 2.2+, sklearn, and numpy. For convenience we provide a Dockerfile that will build a container with condor_tensorflow as well as its dependencies. This can be used as

# Create a docker image
docker build -t cpu_tensorflow -f cpu.Dockerfile ./

# run image to serve a jupyter notebook
docker run -it -p 8888:8888 --rm cpu_tensorflow

# how to run bash inside container (with python that will have deps)
docker run -u $(id -u):$(id -g) -it -p 8888:8888 --rm cpu_tensorflow bash

Assuming a GPU enabled machine with the NVIDIA drivers installed replace cpu above with gpu.