However, our workflows usually specify a small to medium number of pinned dependencies. The results above aren’t close to being a formal benchmark, but are simply representative of the kinds of gains you can make with mamba out of the box. How much faster is Mamba? We took the environmental dependencies of the Medaka project from Bioconda, unpinned their versions (to make it harder for the package manager to solve the problem) and timed how long Miniconda and Mamba took to re-create the environment respectively: m5.12xLarge (48cpus, ~200gigs RAM)Īs we can see, Mamba’s performance is significantly better when dealing with a complex environment like this.
#Conda python version install
Mamba install python=3.8 jupyter -c conda-forge # Or if you don't have anything, use miniforge's mambaforge
#Conda python version how to
Here’s how to get started with Mamba: # Again, assuming linuxĬonda install mamba -n base -c conda-forge Mamba is most akin to Miniconda, in that it comes with Python, but doesn’t ship with a whole load of extra software. This is where Mamba comes in, the fast drop-in replacement for conda, which reimplements the slow bits in in C++. Whilst Miniconda is small as compared with full-fat anaconda, the latest Miniconda3 Linux 64-bit Python 3.9 download size is 58.6 MiB, could this be better?