![]() The command run in the base environment will update the packages in this, but usually you should work with virtual environments ( conda create -n myenv and then conda activate myenv). But that is only to hack your way around issues, definitely not the normal-user case!ġ If you actually want to update the packages of your installation, which you usually don't. Or break the compatibility of the packages (which you usually don't want!), which is only possible by explicitly invoking an ignore-dependencies and force-command. So you still cannot upgrade them all by doing the upgrades separately the dependencies are just not satisfiable so earlier or later, an upgrade will downgrade an already upgraded package again. ( this is a pedagogical example, of course, but it's the same in reality, usually just with more complicated dependencies and sub-dependencies) So upgrading Y > 5.0 implies downgrading X to < 2.0 and vice versa. ![]() This way conda does also search in this places for available packages.Ĭonsidering your update: You can upgrade them each separately, but doing so will not only include an upgrade but also a downgrade of another package as well. It's rather a hack.Ī safe way you can try is to add conda-forge as a channel when upgrading (add -c conda-forge as a flag) or any other channel you find that contains your package if you really need this new version. If you do that, do it as a last resort and after all packages have been installed with conda. But be aware that pip also installs packages if dependency conflicts exist and that it usually breaks your conda environment in the sense that you cannot reliably install with conda anymore. It is possible to install with pip, since more packages are available in pip. To add: maybe it could work but a newer version of X working with Y > 5.0 is not available in conda. A way to invoke it without installing mamba is via the -solver=libmamba flag (requires conda-libmamba-solver), as pointed out by matteo in the comments. Update 1: since a while, mamba has proven to be an extremely powerful drop-in replacement for conda in terms of dependency resolution and (IMH experience) finds solutions to problems where conda fails. That's why you 'cannot' upgrade them all. Conda usually warns very explicitly if they occur. Dependency conflictsīut it is possible that there are dependency conflicts (which prevent a further upgrade). Conda always tries to upgrade the packages to the newest version in the series (say Python 2.x or 3.x). # All requested packages already installed.Īccording to cloud, _nb_ext_conf in my machine is the latest installed and would certainly not hinder python's update.TL DR: dependency conflicts: Updating one requires (by its requirements) to downgrade another Warning: 8 possible package resolutions (only showing differing packages): The environment is inconsistent, please check the package plan carefully The following packages are causing the inconsistency:` ![]() My conda is latest installed, instead I get different set of messages/warnings that I'm unable to resolve. Running Python 3.6.8 and unable to update to latest 3.7.3. Scikit-learn 0.20.0 p圓5heebcf9a_1 defaults # packages in environment at C:\Program Files\Anaconda3: Package cache : C:\Program Files\Anaconda3\pkgsĬ:\Users\hazzaldo\AppData\Local\conda\conda\pkgsĮnvs directories : C:\Program Files\Anaconda3\envsĬ:\Users\hazzaldo\AppData\Local\conda\conda\envs Populated config files : C:\Users\hazzaldo\.condarcīase environment : C:\Program Files\Anaconda3 (writable) User config file : C:\Users\hazzaldo\.condarc Active env location : C:\Program Files\Anaconda3 ![]()
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