Set Up Anaconda for Python

Recently, python is getting more popular, because it can complete a project in a short time. However, setting up virtual environment is crucial for programming several projects. In this article, I will introduce how I setting up a anaconda environment for python.

Create Conda Environment

When you start learning Python, it is a good starting point to install the newest Python version with the latest versions of the packages you need or want to play around with. Then, most likely, you immerse yourself in this world, and download Python applications from GitHub, Kaggle or other sources. These applications may need other versions of Python/packages than the ones you have been currently using.

1. Install python version 3.7.3

2. Install Anaconda 3 for win10

3. Create a virtual environment and change the name of the environment <ENV>:

   conda update conda -y
   conda create --name <ENV> python=3.7.3
   conda activate <ENV>
   conda install ipykernel ipywidgets -y
   python -m ipykernel install --user --name <ENV> --display-name "<ENV>"
   pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install

4. GPU support software requirements:

• NVIDIA® GPU drivers
• CUDA® Toolkit
• cuDNN SDK
• (Optional) TensorRT 5.0

5. Windows setup

• Add the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environmental variable. For example, if the CUDA Toolkit is installed to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0 and cuDNN to C:\tools\cuda, update your %PATH% to match:

  $ export PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin;%PATH%
  $ export PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\extras\CUPTI\libx64;%PATH%
  $ export PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include;%PATH%
  $ export PATH=C:\tools\cuda\bin;%PATH%

• Add the absolute path to the TensorRTlib directory to the environment variable LD_LIBRARY_PATH