You can read more about conda and tensorflow here. One of my favorites is their virtual environment features. Replace tensorflow with tensorflow-gpu if you want the GPU enabled version.īesides being faster and simpler to use for Tensorflow, conda provides other sets of tools that makes it so much easier to integrate into your workflow.

Miniconda is just installing conda and it’s dependencies while Anaconda will pre-install a lot of packages for you. Install Anaconda or Miniconda if you haven’t already. If you’re convinced here are the steps to get started. So I hope those two reasons are good enough for you to switch over to using conda. Everybody likes a one step process, especially when it comes to downloading libraries. The pip install will require you to do that manually. The conda install will automatically install the CUDA and CuDNN libraries needed for GPU support. Not only does the MKL library speed up your Tensorflow packages, it also speeds up other widely used libraries like NumPy, NumpyExr, SciPy, and Scikit-Learn! See how you can get that set up from links below. I also do a lot of inference on a CPU when I can, so this will help my models performance. This increase in speed will help me iterate faster. As a Machine Learning Engineer, I use my CPU to run a test train on my code before pushing it to a GPU enabled machine. That is great for people who still frequently use their CPU for training and inferencing. As you can see, the performance of the conda installation can give over 8X the speed boost compared to the pip installation.

Pip install tensorflow