![]() Now we are in the environment, so let’s start installing important machine learning libraries and frameworks. Open anaconda prompt and Activate the environment Once you did.Restart the whole Pc Once again This happened to me sometimes, especially when I am in hurry, I open anaconda/miniconda and without activating my environment I would start to work directly on the root, which sometimes costs me by messing other environments that I created for other projects. Please don’t forget to always activate your environment before you install anything or start to work on your project. Open ‘anaconda’ prompt window from the start bar and run the following command.Īctivate the environment you just created by running the following command Lets first create an environment with a name “ML_env”. To Checkĥ.Above you can see the Path of Cudnn and cuda, add path to both cuddn path and path below and above Following paths: C:\cudnn-windows-x86_64–8.6.0.163 C:\cudnn-windows-x86_64–8.6.0.163\bin C:\cudnn-windows-x86_64–8.6.0.163\include C:\cudnn-windows-x86_64–8.6.0.163\lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\extras\CUPTI\lib64 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\extras\CUPTI\includeĪnd You’re Done : ) Creating the machine learning anaconda environment Head over to and download the Python.Make sure to download the “Python 3.7 or Python 3.8 Version” for the appropriate architecture.Īfter the installation check Python is installed on your machine. Operating System: Windows 11 Home Edition.I use the below environment for my setup: Since it’s relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances Environment of my setup Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Package versions are managed by the package management system conda. Anaconda works for R and Python programming language. ![]() Using virtual environment we can switch between both applications easily and get them running.Īnaconda is an open source software that contains Jupyter, spyder, etc that are used for large data processing, data analytics, heavy scientific computing. Virtual environments makes it easy to ideally separate different applications and avoid problems with different dependencies. ![]() The application needs to run on a specific version of the language because it requires a certain dependency that is present in older versions but changes in newer versions. Like many other languages Python requires a different version for different kind of applications. Why do we need to set up a virtual environment ? If you have a vanilla Python installation or other Python distribution see virtualenv. The conda command is the preferred interface for managing installations and virtual environments with the Anaconda Python distribution. Virtual environmets make it easy to cleanly separate different projects and avoid problems with different dependencies and version requiremetns across components. How to set up a virtual environment using the Anaconda Python distributionĪ virtual environment is a named, isolated, working copy of Python that that maintains its own files, directories, and paths so that you can work with specific versions of libraries or Python itself without affecting other Python projects.
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