Conda installs packages into the anaconda/pkgs directory. If conda cannot find the file, try using an absolute path name instead of a relative path name. Installing packages directly from the file does not resolve dependencies.
This will install pip to your venv directory. Find your anaconda directory, and find the actual venv folder. It should be somewhere like /anaconda/envs/venv_name/ . Install new packages by doing /anaconda/envs/venv_name/bin/pip install package_name .
Environments created with conda , by default, live in the envs/ folder of your miniconda3 (or anaconda3 ) directory the absolute path to which will look something the following: /Users/$USERNAME/miniconda3/envs or C:\Users\$USERNAME\Anaconda3 . You can see the location of your conda environments by running the command.
The conda packages are kept in %LOCALAPPDATA%\Continuum\Anaconda3\pkgs . Specifically, I found my version of graphviz in %LOCALAPPDATA%\Continuum\Anaconda3\pkgs\graphviz-2.38-hfd603c8_2 . In order to determine where files are located, I can recommend Search Everything. The tool which was searched is dot.exe .
Enter conda list . If Anaconda is installed and working, this will display a list of installed packages and their versions.
Locally installed Python and all packages will be installed under a directory similar to ~/. local/bin/ for a Unix-based system, or \Users\Username\AppData\Local\Programs\ for Windows.
To test your installation, in your Terminal window or Anaconda Prompt, run the command conda list . For a successful installation, a list of installed packages appears.
The various CUDA Toolkit components are installed in the conda environment at: $CONDA_PREFIX/bin - CUDA executables: nvcc, cuda-memcheck, cuda-gdb, etc. $CONDA_PREFIX/lib64 - libraries for runtime and building applications.
Pip installs Python packages whereas conda installs packages which may contain software written in any language. Conda has the ability to create isolated environments that can contain different versions of Python and/or the packages installed in them.
Conda is an open source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer.
Conda directory structure
Also referred to as PKGS_DIR. This directory contains decompressed packages, ready to be linked in conda environments. Each package resides in a subdirectory corresponding to its canonical name. The system location for additional conda environments to be created.
Where Anaconda is the hardware store of data science tools and Miniconda is the workbench (software distributions), Conda is the assistant (package manager) who helps you get new tools and customise your hardware store or workbench. The following are some helpful Conda commands you'll want to remember.
By default, pip installs packages located in the Python Package Index (PyPI), but can also install from other indexes.
Installing Cuda Toolkit and cudaDNN
The advantage of using anaconda is you can have multiple versions of the Cuda toolkit in your System in different virtual environments. To install Cuda Toolkit, Open Anaconda Prompt, activate the virtual environment. It will install all the dependent packages.
How To Remove a Package in Conda. You can remove a package in the current environment by running the conda remove package-name command. The command above removes Matplotlib from the current environment. When you run the conda list command, Matplotlib will no longer be listed as a package.
Go to Environments tab just below the Home tab and from there we can check what all packages are installed and what is not. It is very easy to install any package through anaconda navigator, simply search the required package, select package and click on apply to install it.
One alternative approach to check if a Python package is installed is to use the pkgutil module. The pkgutil module provides utilities for working with packages, and specifically includes a find_loader function that can be used to check if a package is installed.
Method 2: Using pip list. The pip list method also works in a similar manner. It will list all the python modules installed in your system followed by their versions that are locally available on your system.
To see all modules installed on the system, use the Get-Module -ListAvailable command.