I wanted to find the last package in the 3.6 series, so I found the announcement of the python 3.7 package build (September 4, 2018) and then looked that package up by date in https://repo.continuum.io/miniconda/ (Miniconda3-4.5.11), then traced it back one release. The end result:
Miniconda3-4.5.4 was the last Python 3.6 miniconda package.
Miniconda3-4.5.11 is the first that uses 3.7.
There are no releases 4.5.5->4.5.10 in that repository.
Answer from gremwell on Stack OverflowI wanted to find the last package in the 3.6 series, so I found the announcement of the python 3.7 package build (September 4, 2018) and then looked that package up by date in https://repo.continuum.io/miniconda/ (Miniconda3-4.5.11), then traced it back one release. The end result:
Miniconda3-4.5.4 was the last Python 3.6 miniconda package.
Miniconda3-4.5.11 is the first that uses 3.7.
There are no releases 4.5.5->4.5.10 in that repository.
I just downloaded Miniconda3-4.3.31 for Windows 64bit and it came with Python 3.6.3.
https://repo.continuum.io/miniconda/ Binary: https://repo.continuum.io/miniconda/Miniconda3-4.3.31-Windows-x86_64.exe
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Find older version of conda from here: https://repo.anaconda.com/miniconda/
https://repo.anaconda.com/miniconda/Miniconda3-4.3.11-Linux-x86_64.sh is python 3.6.0
Stick with the miniconda latest, so in your case Miniconda3-latest-Windows-x86_64.exe. Unlike pip, Conda is a very intelligent package manager and will try to match dependency versions so that your installed packages will all work in a given virtual environment. My suggestion is to create a conda virtual environment, and then install TensorFlow inside. This is what I do at work.
Also, FYI, the conda version of TensorFlow has been optimized to run faster than the pip version in many situations see this post from Anaconda.
Example:
conda create -n tf
conda activate tf
conda install tensorflow
## alternatively if you have a compatible nvidia gpu:
conda install tensorflow-gpu