I want to use the package "Dask", but there is one problem.
"Dask dataframe requirements are not installed."
Obviously, we can use pip install "dask[dataframe]" or pip install "dask[complete]".
However, in the secured server where I work, there is no internet connection.
So, I transfer the file of package and install manually.
But, I cannot find the package dask[dataframe] for downloading.
How can I install the rest of packages manually without internet connection?
Thank you
You should look at the setup.py requirements file in the dask repository to see which dependencies it requires.
Related
I was trying to install Ursina but I was having trouble getting all the required packages I needed to run my code properly. Come to find out, there's a package that refuses to install called 'psd-tools3' that won't install, no matter what I do.
I've been using cmd commands like 'pip install psd-tools3' and 'pip3 install psd-tools3' but no other commands work (i.e. 'sudo pip install psd-tools3' doesn't work because my PC doesn't know what 'sudo' means and doesn't run). I've tried installing required packages for this package, but nothing works. It just keeps giving me this error:
enter image description here
I would really appreciate the help with this problem. All I can really assume is that the Python file '_version' hasn't been created and that's what's throwing the whole program off. If there is a way to add this manually and then install it, I would appreciate steps to do that as well.
I was running this on a Lenovo Thinkpad (Windows 10) on Python 3.10 (I also have Python 3.8.3 but that was installed with the 3.10) and I made sure all packages and pip are up-to-date. Still having this problem and I don't know why.
Seems to me like the issue is on the side of the maintainers of psd-tools3.
For example, looking at the content of the latest source distribution on PyPI, we can see that it does not contain any _version.py file.
This needs to be solved by the project's maintainers, but they do not have a ticket tracker. On the other hand there seems to be an "Author" email address on the project's PyPI page as well as in the project's setup.py script.
A solution might be to clone the project's source code repository (with git), and try to install from the local clone.
Just simply try
pip install psd-tools3==1.9.0
Or
pip install psd-tools3==1.8.2
This should work on your pc as well. I was having same issue, and then I tried this It worked for me
I have to install pyez using pip. This at least is stated as requirement to use the role Juniper.junos to run ansible commands against Juniper routers. I'm using cygwin on a windows laptop without any adminrights in a corporate network. The problem is, I cannot use the corporate proxy because of that proprietary Microsoft NTLM protocol so a simple "pip install junos-eznc" is not possible. But what I could do is download the software, tarball or whatever and run pip against that downloaded stuff. I tried several things and failed miserably and google doesn't return anything useful. Can anyone help? What am I supposed to do to get that installed successfully? Many thanks in advance.
Youcan download required package and install it like this
pip install <file_name>
I solved it.
First issue: Proxy
px.exe on https://github.com/genotrance/px/releases.
Second issue: missing libs:
I had to install several libs and make on cygwin in order to get junos-eznc installed:
libxml2-devel
libxslt-devel
libffi-devel
libssl-devel
make
Third issue: cryptography
Don't use latest version of cryptography because it'll need rust which is pita on cygwin
pip install cryptography==3.2
... and then ...
pip install junos-eznc
After that, installation of ...
ansible-galaxy install Juniper.junos
... was quite smooth
I would like to create a Conda environment from a .yaml file on an offline machine (i.e. no Internet access). On an online machine this works perfectly fine:
conda env create -f environment.yaml
However, it doesn't work on an offline machine as the packages are then not found. How do I do this?
If that's not possible is there another easy way to get my complete Conda environment to an offline machine (including both Conda and pip installed packages)?
Going through the packages one by one to install them from the .tar.bz2 files works, but it is quite cumbersome, so I would like to avoid that.
If you can use pip to install the packages, you should take a look at devpi, particutlarily its server. devpi can cache packages normally installed from PyPI, so only on first install it actually retrieves them. You have to configure pip to retrieve the packages from the devpi server.
As you don't want to list all the packages and their dependencies by hand you should, on a machine connected to the internet:
install the devpi server (I have that running in a Docker container)
run your installation
examine the devpi repository and gathered all the .tar.bz2 and .whl files out of there (you might be able to tar the whole thing)
On the non-connected machine:
Install the devpi server and client
use the devpi client to upload all the packages you gathered (using devpi upload) to the devpi server
make sure you have pip configured to look at the devpi server
run pip, it will find all the packages on the local server.
devpi has a small learning curve, which already worth traversing because of the speed up and the ability to install private packages (i.e. not uploaded to PyPI) as a normal dependency, by just generating the package and upload it to your local devpi server.
I guess that Anthon's solution above is pretty good but just in case anybody is interested in an easy solution that worked for me:
I first created a .yaml file specifying the environment using conda env export > file.yaml. Following the instructions on http://support.esri.com/en/technical-article/000014951, I automatically downloaded all the necessary installation files for conda installed packages and created a channel from the files. For that, I just adapted the code from the link above to work with my .yaml file instead of the conda list file they used. In addition, I automatically downloaded the necessary files for the pip installed packages by looping through the pip entries in the .yaml file and using pip download for downloading each of them. Furthermore, I automatically created separate conda and pip requirement lists from the .yaml file. Then I created the environment using conda create with the offline flag, the file with the conda requirements and my custom channel. Finally, I installed the pip requirements using pip install with the pip requirements file and the folder containing the pip installation files for the option --find-links.
That worked for me. The only problem is that you can only download binaries with pip download if you need to specify a different operating system than the one you are running, and for some packages no binaries are available. That was okay for me now as the target machine has the some characteristics but might be problem in the future, so I am planning to look into the solution suggested by Anthon.
I use a jupyter notebook as part of an anaconda installation. I'd like to install a Python package hosted on Github (i.e., via a https://... or git://... URL), along with all requirements.txt dependencies. This is a custom package developed in-house, not a package published to PyPI.
I'm not clear on whether I would want to do it in a conda or a pip/virtualenv environment, or how I would do it. What is the best way to do this?
This may not be the best way but you can use Ipython console to install Github package (if supported by pip). Like for TwitterAPI you can use below command
!pip install TwitterAPI
I am working on a station where internet communication is forbidden. Is it possible to install snakemake without conda?
Thanks,
Michelle
Yes, it can be installed via pip as well. However, that still needs a download. With conda, you should also be able to download the package and all dependencies via anaconda.org, and then copy it over to that machine. But it certainly will require some manual steps. There is also a docker container. This is probably easier, because it is only a single file.