Conda is useful for any packaging process but it stands out from otherpackage and environment management systems through its utility for datascience.
![Conda Install Tensorflow Conda Install Tensorflow](https://images2018.cnblogs.com/blog/1433065/201808/1433065-20180814173240072-1000672465.png)
Jun 26, 2019 conda create -name tf-gpu conda activate tf-gpu conda install tensorflow-gpu That gives you a full install including the needed CUDA and cuDNN libraries all nicely contained in that env. I was looking at the install documentation for the TensorFlow 2.0.0-beta1 and saw that it was still being built with links to CUDA 10.0 and cuDNN 7.x.
Conda’s benefits include:
- Providing prebuilt packages which avoid the need to deal with compilers orfiguring out how to set up a specific tool.
- Managing one-step installation of tools thatare more challenging to install (such as TensorFlow or IRAF).
- Allowing you to provide your environment to other people across differentplatforms, which supports the reproducibility of research workflows.
- Allowing the use of other package management tools, such as pip, insideconda environments where a library or tools are not already packaged forconda.
- Providing commonly used data science libraries and tools, such as R, NumPy,SciPy, and TensorFlow. These are built using optimized, hardware-specificlibraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performancewithout code changes.
Read more about how conda supports data scientists.