When working with python software, it is customary to create a unique environment that contains only the libraries and tools the software needs. This allows you as a developer to keep track of the requirements of your software and which versions of the libraries are used, making the process more reproducible for other users and for yourself, should you move to a different computer.
Unlike standard python, Miniforge and conda-based installers keep the environments in some centralized location. This should not discourage from using these temporary environments, ensuring some unique names. In my case, I have a project under myproject which requires Numpy, Scipy and other tools:
$ cd c:\Users\me\src\myproject$ conda create -n myproject-env python numpy scipy matplotlib-base libblas=*=*mkl
Note the following peculiarities of the Conda-forge packages:
- Since I am working in a text-based Linux image and do not care about showing graphical plots, I just install
matplotlib-base instead of the full package matplotlib. - We can select which BLAS library Numpy works with by using any of the following options
conda install "libblas=*=*mkl"conda install "libblas=*=*openblas"conda install "libblas=*=*blis"conda install "libblas=*=*accelerate"conda install "libblas=*=*netlib"
FAQs
Virtual environments
During the first installation, Miniforge installed the so called base environment including Python and some useful Python modules (modules are like apps on your smartphone).
How do I install Python from Miniconda? ›
Go to the Miniconda Download page and download the Python 3.7 Windows 64-bit .exe installer. After the download is complete, run the installer and click through the setup steps leaving all the pre-selected installation defaults.
Why does the conda-forge take so long? ›
Installing CUDA-enabled packages like TensorFlow and PyTorch In conda-forge, some packages are available with GPU support. These packages not only take significantly longer to compile and build, but they also result in rather large binaries that users then download.
What is the difference between Miniconda and Anaconda? ›
The main difference between Anaconda and Miniconda lies in their size and the number of pre-installed packages. Anaconda is larger and comes with a vast array of pre-installed packages, while Miniconda is smaller and only includes Conda and Python.
Should I install Python before Miniconda? ›
Even though it is possible to install Python from the Python homepage, we highly recommend using Miniconda to install Python. Miniconda comes with Python and a small number of essential packages. Additional packages can be installed using the package management systems mamba or conda.
What is the difference between miniconda and miniforge? ›
Miniconda / Miniforge
Miniconda is the original conda distribution, whereas Miniforge is a community project around conda — the difference is that Miniforge libraries are often more up-to-date, which is why it's often a preferred choice over Miniconda these days.
Is conda faster than Python? ›
Generally, Anaconda does not significantly affect the performance of Python programs. It primarily acts as a package manager and an environment manager, and any performance differences would likely be due to the specific packages and libraries used rather than Anaconda itself.
How long does it take to install Python on conda? ›
Hello, 3-5 minutes is not a long time to install those packages as a number of package dependencies will need to be installed as well as Python and related packages. Also, the installation time will depend on the speed of your internet connection as well.
How can I make conda install faster? ›
One option for speeding up conda is to reduce the index. The index is reduced by conda based upon the user's input specs. It's likely that your repodata contains package data that is not used in the solving stage. Filtering out these unnecessary packages before solving can save time.
What is better than Anaconda? ›
Other important factors to consider when researching alternatives to Anaconda include user interface and data analysis. The best overall Anaconda alternative is Amazon SageMaker. Other similar apps like Anaconda are Posit, TensorFlow, MATLAB, and Vertex AI.
The decision to use either Anaconda or Python highly depends on the specific requirements and goals of a project. If the project involves data analysis, machine learning, or scientific computing, then Anaconda may be the more suitable choice due to its pre-installed packages and data science tools.
Should I download conda or Anaconda? ›
If Anaconda doesn't include a package that you need, you use conda to download and install it. If Anaconda doesn't have the version of a package you need, you use conda to update it.
Is Python included in Miniconda? ›
Miniconda's installers contain the conda package manager, Python, and a few necessary packages. Once Miniconda is installed, you can use the conda command to install any other packages and create environments.
Does conda also install Python? ›
Conda is a powerful tool that simplifies the management of software packages and their dependencies. With this guide, you should now be able to install Python 3.9 using Conda and start leveraging its features for your data science projects.
Does Anaconda automatically download Python? ›
Introduction. In this mini-tutorial, you will learn how to install Python (a computer programming language) and Anaconda (a distribution that automatically installs Python and a bunch of auxillary packages useful for data science).
Does conda install pandas? ›
The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. The Conda package manager is the recommended installation method for most users.
