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The Fast Cross-Platform Package Manager

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Quick Overview

Mamba is a fast, cross-platform package manager for conda environments. It's designed as a drop-in replacement for conda, offering significantly improved performance and parallel downloads while maintaining compatibility with existing conda packages and environments.

Pros

  • Significantly faster than conda, especially for large environments
  • Parallel downloads and installations
  • Compatible with existing conda packages and environments
  • Cross-platform support (Linux, macOS, Windows)

Cons

  • Still a relatively new project, may have some stability issues
  • Not all conda features are fully implemented yet
  • Requires separate installation alongside conda
  • Learning curve for users familiar with conda-only workflows

Code Examples

  1. Creating a new environment:
mamba create -n myenv python=3.9 numpy pandas

This creates a new environment named "myenv" with Python 3.9, NumPy, and pandas installed.

  1. Installing packages in an existing environment:
mamba install -n myenv scikit-learn matplotlib

This installs scikit-learn and matplotlib in the "myenv" environment.

  1. Updating all packages in an environment:
mamba update -n myenv --all

This updates all packages in the "myenv" environment to their latest compatible versions.

Getting Started

To get started with Mamba, follow these steps:

  1. Install Mamba using Conda:
conda install mamba -n base -c conda-forge
  1. Create a new environment:
mamba create -n myenv python=3.9
  1. Activate the environment:
conda activate myenv
  1. Install packages:
mamba install numpy pandas matplotlib
  1. Use your new environment with installed packages:
python -c "import numpy as np; print(np.__version__)"

Competitor Comparisons

conda logo

conda

6,333

A system-level, binary package and environment manager running on all major operating systems and platforms.

Pros of conda

  • Mature and widely adopted ecosystem with extensive documentation
  • Supports a broader range of platforms and architectures
  • More flexible package specification and environment management

Cons of conda

  • Slower package resolution and installation process
  • Higher memory usage during package operations
  • Less efficient handling of large environments

Code comparison

conda:

conda create -n myenv python=3.8
conda activate myenv
conda install numpy pandas

mamba:

mamba create -n myenv python=3.8
mamba activate myenv
mamba install numpy pandas

The syntax for both conda and mamba is nearly identical, with the main difference being the use of mamba instead of conda in the command. Mamba is designed to be a drop-in replacement for conda, offering faster performance while maintaining compatibility with conda's commands and package ecosystem.

conda-forge logo

miniforge

6,124

A conda-forge distribution.

Pros of Miniforge

  • Provides pre-configured Conda environments with Conda-Forge as the default channel
  • Includes a minimal installer for a more lightweight setup
  • Supports multiple architectures and operating systems out-of-the-box

Cons of Miniforge

  • May have slower package resolution and installation compared to Mamba
  • Limited to Conda-Forge packages by default, which may not include all desired packages
  • Lacks some advanced features and optimizations present in Mamba

Code Comparison

Miniforge (using Conda):

conda create -n myenv python=3.9
conda activate myenv
conda install numpy pandas

Mamba:

mamba create -n myenv python=3.9
mamba activate myenv
mamba install numpy pandas

The syntax is similar, but Mamba typically offers faster execution and improved dependency resolution.

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README

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The Fast Cross-Platform Package Manager

part of mamba-org
Package Manager mamba Package Server quetz Package Builder boa

mamba

Build Status Join the Gitter Chat docs

mamba is a reimplementation of the conda package manager in C++.

  • parallel downloading of repository data and package files using multi-threading
  • libsolv for much faster dependency solving, a state of the art library used in the RPM package manager of Red Hat, Fedora and OpenSUSE
  • core parts of mamba are implemented in C++ for maximum efficiency

At the same time, mamba utilizes the same command line parser, package installation and deinstallation code and transaction verification routines as conda to stay as compatible as possible.

Mamba is part of a bigger ecosystem to make scientific packaging more sustainable. You can read our announcement blog post. The ecosystem also consists of quetz, an open source conda package server and boa, a fast conda package builder.

Installation

Please refer to the mamba and micromamba installation guide in the documentation.

Additional features in Mamba and Micromamba

mamba and micromamba come with features on top of stock conda.

repoquery

To efficiently query repositories and query package dependencies you can use mamba repoquery or micromamba repoquery. See the repoquery documentation for details.

Installing lock files

micromamba can be used to install lock files generated by conda-lock without having to install conda-lock. Simply invoke e.g. micromamba create -n my-env -f conda-lock.yml with an environment lockfile named *-lock.yml or *-lock.yaml.

setup-micromamba (setup-miniconda replacement)

setup-micromamba is a replacement for setup-miniconda that uses micromamba. It can significantly reduce your CI setup time by:

  • Using micromamba, which takes around 1 s to install.
  • Caching package downloads.
  • Caching entire conda environments.

micromamba

micromamba is a small, pure-C++ reimplementation of mamba/conda. It strives to be a full replacement for mamba and conda. As such, it doesn't use any conda code (in fact it doesn't require Python at all).

See the documentation on micromamba for details.

Development installation

Please refer to the instructions given by the official documentation.

Support us

For questions, you can also join us on the QuantStack Chat or on the Conda channel (note that this project is not officially affiliated with conda or Anaconda Inc.).

License

We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.

This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.

Biweekly Dev Meeting

We have videoconference meetings every two weeks where we discuss what we have been working on and get feedback from one another.

Anyone is welcome to attend, if they would like to discuss a topic or just listen in.