#Anaconda install stuck download#
Lastly, I'd add that the newer versions of conda introduced parallel downloading, but, in my experience, the main bottleneck is not the download but solving the environment. If you know of any other potential solutions or suggestions, do share! But be mindful of the licence terms whereby commercial users require a licence to access the anaconda repository - but not, if I understand correctly, to access conda-forge This is one of the reasons why, with default settings, miniconda is faster than miniforge: because miniconda searches te conda repository, while miniforge the conda-forge, which is much much larger.
For the reason above, if you know most of your packages are available in two channels, put the smaller channel first.condarc file, so it won't search the 2nd channel if it found all it needed in the 1st This means that conda will search channels in the order you specified in your. Smaller environments with fewer packages means less chance for conflicts and faster solving times for conda. a set of datascience projects and one of web-related stuff, maintain two separate environments. If you have projects of different kind, e.g. For the same reason, avoid Anaconda, if you can, and just build the environments you need with miniconda, miniforge or mambaforge. In general, try to avoid huge environments with loads of packages you do not need.If even that takes forever, you can build the environment with mamba then export a requirements file in the format required by pip, and install all the packages with pip in the slower PC. Solution 4 still requires conda to solve a problem, even if a more limited one.This way, the slower conda doesn't have to solve that package x is at version 2.3 but only version 1.9 works with package y etc You can then create a new environment on the computer with conda only, based on the more detailed yml file. numpy and pandas) as well as the exact versions of each package. pandas) once the environment is created, you can export the yml file, which will contain all the dependencies you hadn't specified (e.g. If you have access to mamba on one computer but not another, you can: create an environment with mamba based on a yml file which contains only the packages you need (e.g.This is a bug with zscaler, which sends back packets with "_ransfer-Encoding" instead of "Transfer-Encoding". mamba and the libmamba solver do not work behind a corporate proxy with zscaler.If you cannot install mamba, you can use the mamba solver with.The recommended way to install it is toĭownload mambaforge, otherwise you can install it from anaconda or mamba is a kind of conda clone rewritten in C, so much muchįaster.
#Anaconda install stuck code#
Now, with many packages each with many versions, conda's non-optimised code can take forever. In the very early days, with relatively few packages, it was not the end of the world.
The main background point is that conda is solving a boolean satisfiability problem (colloquially: dependency hell ). Many good comments already - let me try to consolidate and add a few more thoughts:
0 kommentar(er)
