I have tried installing pytorch on jupyter and spyder but it is not working. I have install it in conda env, from pytorch documentation tried everything.
From pytorch documentation and it shows pytorch installed 100%
In conda env. Installed for jupyter and spyder
In conda list pytorch is not showing
I have tried to open from app after installing in conda env. But it is Not opening here in jupyter, a pop up window appears each time for 1-2 seconds then disappears.
Is there any other way to install it ?? Please help
Your issue is simply that conda fails to install pytorch; this can have multiple causes. Forget about spyder and jupyter, they're not the issue.
1. From pytorch documentation and it shows pytorch installed 100%
No it doesn't. Look at the bottom line: Rolling back transaction: ...working... done. conda did not successfully install the package. You need to debug further why (permissions issue on directory? installing under userid? dependency conflict?) Follow my suggestions at bottom.
2. In conda env. Installed for jupyter and spyder
No they don't! They only show that conda has (previously) installed the jupyter and spyder executables. Those don't tell you which conda packages are installed. That's what conda list does.
3. In conda list pytorch is not showing
Correct. Because it never installed it in the first place, as per 1. above.
You must debug why step 1. fails. There are many possible causes and you haven't given enough debugging information that we can tell which.
Please post extra information on why it fails. Suggest you do the following:
Try just conda install pytorch -c pytorch by itself, not all the other packages, they make constraint solving harder.
Try verbose mode: conda install -v pytorch -c pytorch
verbose mode should show you why conda thinks it needs to roll back the transaction (e.g. conflicting dependencies for 2+ different packages)
Check which user and permissions you're doing conda install under, are they the same as the Anaconda install? (often if you use the GUI installer under Windows/Linux/Mac, it assumes it can/should as admin or else user, without asking you).
In rare cases, you might need to nuke your conda environment and create a new env. I'd do this if you can't debug it after say 48+ hours. If you install every package under 'base', in particular for R as well as Python, this is a known issue. Even if doesn't break things, using 'base' environment for too many things tends to slow down constraint solving to take many hours, or hang forever.
Finally, after you do finally get the package installed, see if you need to set PYTHONPATH (either/both inside the Anaconda GUI package manager, and in the Unix shell .bashrc/Windows environment settings).
See the anaconda doc and also SO and the many good third-party blogs out there.
I've had to install a package with pip in a conda environment to get it to work for my application (link).
The package works fine. However, every time I modify the virtual environment in any way, conda tries to install the "missing" package - which would effectively result in downgrading it.
Question: is there a way to mark the pip package as 'manually installed' in the conda venv (e.g. in the same way apt-mark would handle it)? The intention is to get miniconda to leave it alone while still handling the remaining dependencies for the desired additional package.
The pip installed package indeed shows up when typing conda list, with Channel "pypi".
Can give any additional information if needed.
Thanks in advance for any help.
I am using Anaconda. I would like to know how to remove or uninstall unwanted packages from the base environment. I am using another environment for my coding purpose.
I tried to update my environment by using yml file (Not base environment). Unexpectedly some packages installed by yml into the base environment. So now it has 200 python packages which have another environment also. I want to clear unwanted packages in the base environment and I am not using any packages in the base environment. Also, my memory is full because of this.
Please give me a solution to remove unwanted packages in the base environment in anaconda.
It is very hard to remove one by one each package, therefore, I am looking for a better solution.
It is the same as for other environments:
run conda deactivate to be sure you are in the base environment.
run conda list to see the packages that you have installed
run conda remove {package_name} to remove packages
Update:
WARNING: Be careful because there are bugs around this new functionality
https://github.com/conda/conda/issues/6316
I have not tested it myself, so try at your own risk
To reset your base environment you should:
conda list --revisions
This will show you a list of revisions. At this moment, the latest is:
2019-02-11 21:58:57 (rev 19)
conda {4.6.2 -> 4.6.3}
Now run, where 19 can be replaced for the revision you want to use:
conda install --rev 19
If you use:
conda install --rev 1
Then it should reset the base environment to how it looked like when you installed anaconda.
Reference: https://github.com/conda/conda/issues/1032
According to the tickets, if you are not using conda 4.4 release or upwards, you may end up removing conda too because of this bug: https://github.com/conda/conda/issues/6316
conda remove <package_name>
More info can be found: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#removing-packages
Please use the below code:
conda uninstall -n base <Package_name>
I currently have scikit-learn 0.19 installed. I'd like to test my code using the latest development version as there seems to be a fix for Incremental PCA.
How do I go about installing this new version if I've previously installed scikit-learn using anaconda?
Also, how would I revert back to the stable release in the event that 0.20 does not solve my problem?
I am in need of some hand holding here, as I've read the docs on the website and not sure I completely understand the process (especially being able to revert back to the stable version if needed).
The whole point of the Anaconda Python distribution (apart from the convenience of having a bunch of useful packages included) is that you get the conda environment manager, which exists to meet exactly this sort of requirement.
What you want to do is to create a new conda environment by launching the Anaconda prompt and typing
conda create -n myenv scikit-learn other-package other-package2 etc
where myenv is the name you want to give the new environment and other-package other-package2 etc are the names of any other packages you will want to use (import) in your code. conda will figure out any dependencies of these packages and show you a list of what is going to be installed before it proceeds.
If you want to specify that a package should be a particular version, add that to the package name e.g. other-package=1.1.0, otherwise conda will install the latest versions of each package that are mutually compatible. You can also specify a particular version of Python by including it in the package list, e.g. python=3.4. You can check what versions of a package are available with conda search package-name (where package-name is the name of the package you want, obviously).
To run your code in the newly created environment, first activate the environment at the Anaconda prompt. If you use the Spyder IDE, launch it after activating the correct environment, or use the start menu shortcut specific to that environment if you have one. Other IDEs may have their own method of selecting a specific environment to work in.
To revert to the version(s) you were using before, activate the environment containing those versions - if you've never created a new environment before, that'll be root.
Just in case someone comes here looking for a solution without conda:
The website recommends that you download the latest code via
git clone git://github.com/scikit-learn/scikit-learn.git
and then include it in pip via (after changing to the directory)
pip install --editable .
You can also add the --user flag to have pip install to a local directory. Then, uninstalling should be as easy as pip uninstall sklearn.
I tried the conda search --outdated, there are lots of outdated packages, for example the scipy is 0.17.1 but the latest is 0.18.0. However, when I do the conda update --all. It will not update any packages.
update 1
conda update --all --alt-hint
Fetching package metadata .......
Solving package specifications: ..........
# All requested packages already installed.
# packages in environment at /home/user/opt/anaconda2:
#
update 2
I can update those packages separately. I can do conda update scipy. But why I cannot update all of them in one go?
TL;DR: dependency conflicts: Updating one requires (by it's requirements) to downgrade another
You are right:
conda update --all
is actually the way to go1. Conda always tries to upgrade the packages to the newest version in the series (say Python 2.x or 3.x).
Dependency conflicts
But it is possible that there are dependency conflicts (which prevent a further upgrade). Conda usually warns very explicitly if they occur.
e.g. X requires Y <5.0, so Y will never be >= 5.0
That's why you 'cannot' upgrade them all.
Resolving
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. 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.
To add: maybe it could work but a newer version of X working with Y > 5.0 is not available in conda. It is possible to install with pip, since more packages are available in pip. 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. If you do that, do it as a last resort and after all packages have been installed with conda. It's rather a hack.
A 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. This way conda does also search in this places for available packages.
Considering 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. Say, to add to the example above:
X > 2.0 requires Y < 5.0, X < 2.0 requires Y > 5.0
So upgrading Y > 5.0 implies downgrading X to < 2.0 and vice versa.
(this is a pedagogical example, of course, but it's the same in reality, usually just with more complicated dependencies and sub-dependencies)
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. 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. But that is only to hack your way around issues, definitely not the normal-user case!
1 If you actually want to update the packages of your installation, which you usually don't. 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). Executing conda update --all inside such an environment will update the packages inside this environment. However, since the base environment is also an environment, the answer applies to both cases in the same way.
To answer more precisely to the question:
conda (which is conda for miniconda as for Anaconda) updates all but ONLY within a specific version of a package -> major and minor. That's the paradigm.
In the documentation you will find "NOTE: Conda updates to the highest version in its series, so Python 2.7 updates to the highest available in the 2.x series and 3.6 updates to the highest available in the 3.x series."
doc
If Wang does not gives a reproducible example, one can only assist.
e.g. is it really the virtual environment he wants to update or could Wang get what he/she wants with
conda update -n ENVIRONMENT --all
*PLEASE read the docs before executing "update --all"!
This does not lead to an update of all packages by nature. Because conda tries to resolve the relationship of dependencies between all packages in your environment, this can lead to DOWNGRADED packages without warnings.
If you only want to update almost all, you can create a pin file
echo "conda ==4.0.0" >> ~/miniconda3/envs/py35/conda-meta/pinned
echo "numpy 1.7.*" >> ~/miniconda3/envs/py35/conda-meta/pinned
before running the update. conda issues not pinned
If later on you want to ignore the file in your env for an update, you can do:
conda update --all --no-pin
You should not do update --all. If you need it nevertheless you are saver to test this in a cloned environment.
First step should always be to backup your current specification:
conda list -n py35 --explicit
(but even so there is not always a link to the source available - like for jupyterlab extensions)
Next you can clone and update:
conda create -n py356 --clone py35
conda activate py356
conda config --set pip_interop_enabled True # for conda>=4.6
conda update --all
conda config
update:
Currently I would use mamba (or micromamba) as conda pkg-manager replacement
update:
Because the idea of conda is nice but it is not working out very well for complex environments I personally prefer the combination of nix-shell (or lorri) and poetry [as superior pip/conda .-)] (intro poetry2nix).
Alternatively you can use nix and mach-nix (where you only need you requirements file. It resolves and builds environments best.
On Linux / macOS you could use nix like
nix-env -iA nixpkgs.python37
to enter an environment that has e.g. in this case Python3.7 (for sure you can change the version)
or as a very good Python (advanced) environment you can use mach-nix (with nix) like
mach-nix env ./env -r requirements.txt
(which even supports conda [but currently in beta])
or via api like
nix-shell -p nixFlakes --run "nix run github:davhau/mach-nix#with.ipython.pandas.seaborn.bokeh.scikit-learn "
Finally if you really need to work with packages that are not compatible due to its dependencies, it is possible with technologies like NixOS/nix-pkgs.
Imagine the dependency graph of packages, when the number of packages grows large, the chance of encountering a conflict when upgrading/adding packages is much higher. To avoid this, simply create a new environment in Anaconda.
Be frugal, install only what you need. For me, I installed the following packages in my new environment:
pandas
scikit-learn
matplotlib
notebook
keras
And I have 84 packages in total.
I agree with Mayou36.
For example, I was doing the mistake to install new packages in the base environment using conda for some packages and pip for some other packages.
Why this is bad?
1.None of this is going to help with updating packages that have been > installed >from PyPI via pip, or any packages installed using python
setup.py install. conda list will give you some hints about the
pip-based Python packages you have in an environment, but it won't do
anything special to update them.
And I had all my projects in the same one environment! And I used update all -which is bad and did not update all-.
So, the best thing to do is to create a new environment for each project. Why?
2. A Conda environment is a directory that contains a specific collection of Conda packages that you have installed. For example, you
may be working on a research project that requires NumPy 1.18 and its
dependencies, while another environment associated with an finished
project has NumPy 1.12 (perhaps because version 1.12 was the most
current version of NumPy at the time the project finished). If you
change one environment, your other environments are not affected. You
can easily activate or deactivate environments, which is how you
switch between them.
So, to wrap it up:
Create a new environment for each project
Be aware for the differences in conda and pip
3.Only include the packages that you will actually need and update them properly only if necessary.
if working in MS windows, you can use Anaconda navigator. click on the environment, in the drop-down box, it's "installed" by default. You can select "updatable" and start from there
To update all possible packages I used conda update --update-all
It works!
I solved this problem with conda and pip.
Firstly, I run:
conda uninstall qt and conda uninstall matplotlib and conda uninstall PyQt5
After that, I opened the cmd and run this code that
pip uninstall qt , pip uninstall matplotlib , pip uninstall PyQt5
Lastly, You should install matplotlib in pip by this code that pip install matplotlib