image3c 0.1.4

Classifier for Image3c

Instructions for training and prediction

Installation of python

• For best performance an NVIDIA GPU with CUDA is recommended

Install requirements for TensorFlow

• tensorflow_gpu-1.15.0 3.3-3.7
  • NVIDIA drivers
    • Linux : >= 410.48
    • Windows : >= 411.31
  • cuDNN 7.6 (See below to install with conda)
  • CUDA 10.2 (See below to install with conda)

The steps below install the CUDA libraries during the conda environment setup, but if you want more information about it see these links:

• For more detail about using conda to install CUDA see this article: Install CUDA with conda

• For more information regarding installation of CUDA, see this document: NVIDIA CUDA

Install miniconda (recommended) or anaconda

We recommend using conda as an environment and package manager. It will allow easily creating python environments with specific version and package needs. If you already have Anaconda installed it will work and the instructions below will be the same.

1. Download miniconda for your platform.
2. Follow the installation documentation for the target operating system:
   • Windows
   • Linux
   • Mac

After installation, open a terminal to start installing packages. On Windows find the Anaconda Prompt command using the search tool.

Create a conda environment

Image3c requires version 3.7 of python and TensorFlow version 1.15, so a fresh conda enviroment is recommended. We have written an environment file the takes care of creating the conda environment and installing all needed dependencies. If you are on MacOS or don't have an NVIDA GPU with CUDA use environment.yml. If you are on Windows or Linux and have a CUDA GPU then use environment_gpu.yml

Creating the conda environment in this way also installs the correct CUDA libraries in the conda python environment.

In the following command, a conda environment named image3c is created with python 3.7:

conda env create -f environment.yml

if on windows or linux with a CUDA gpu

conda env create -f environment_gpu.yml

To activate this environment, use this command: conda activate image3c

The image3c python package is installed during the creation of the conda environment, so no other installation command are needed.

Install image3c from pip

If you don't want to create an environment as described above, image3c can be installed with pip:

pip install image3c

How to use Image3c

The main documentation can be found at: Image3c Github

Jupyter notebooks giving details about training and predicting data from the ImageStream can be found in the main pages of this github repository: Classifier Notebooks.