munkres | Munkres algorithm for Python | Machine Learning library
kandi X-RAY | munkres Summary
kandi X-RAY | munkres Summary
The Munkres module provides an O(n^3) implementation of the Munkres algorithm (also called the Hungarian algorithm or the Kuhn-Munkres algorithm). The algorithm models an assignment problem as an NxM cost matrix, where each element represents the cost of assigning the ith worker to the jth job, and it figures out the least-cost solution, choosing a single item from each row and column in the matrix, such that no row and no column are used more than once. This particular implementation is based on See the docs on the project page for more details. WARNING: As of version 1.1.0, munkres no longer supports Python 2. If you need to use this package with Python 2, install an earlier version. See the installation instructions for details.
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QUESTION
I have created a Python 3.7 conda virtual environment and installed the following packages using this command:
conda install pytorch torchvision torchaudio cudatoolkit=11.3 matplotlib scipy opencv -c pytorch
They install fine, but then when I come to run my program I get the following error which suggests that a CUDA enabled device is not found:
...ANSWER
Answered 2022-Feb-18 at 14:52I beleive I had the following things wrong that prevented me from using Cuda. Despite having cuda installed the nvcc --version command indicated that Cuda was not installed and so what I did was add it to the path using this answer.
Despite doing that and deleting my original conda environment and using the conda install pytorch torchvision torchaudio cudatoolkit=11.3 matplotlib scipy opencv -c pytorch command again I still got False when evaluating torch.cuda.is_available().
I then used this command conda install pytorch torchvision torchaudio cudatoolkit=10.2 matplotlib scipy opencv -c pytorch changing cudatoolkit from verison 11.3 to version 10.2 and then it worked!
Now torch.cuda.is_available() evaluates to True
Unfortunately, Cuda version 10.2 was incompatible with my RTX 3060 gpu (and I'm assuming it is not compatible with all RTX 3000 cards). Cuda version 11.0 was giving me errors and Cuda version 11.3 only installs the CPU only versions for some reason. Cuda version 11.1 worked perfectly though!
This is the command I used to get it to work in the end:
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
QUESTION
I have a local python project called jive that I would like to use in an another project. My current method of using jive in other projects is to activate the conda env for the project, then move to my jive directory and use python setup.py install. This works fine, and when I use conda list, I see everything installed in the env including jive, with a note that jive was installed using pip.
But what I really want is to do this with full conda. When I want to use jive in another project, I want to just put jive in that projects environment.yml.
So I did the following:
- write a simple
meta.yamlso I could use conda-build to buildjivelocally - build jive with
conda build . - I looked at the tarball that was produced and it does indeed contain the
jivesource as expected - In my other project, add jive to the dependencies in
environment.yml, and add 'local' to the list of channels. - create a conda env using that environment.yml.
When I activate the environment and use conda list, it lists all the dependencies including jive, as desired. But when I open python interpreter, I cannot import jive, it says there is no such package. (If use python setup.py install, I can import it.)
How can I fix the build/install so that this works?
Here is the meta.yaml, which lives in the jive project top level directory:
ANSWER
Answered 2022-Feb-05 at 04:16The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.
I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).
Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").
Try adjusting the meta.yaml to something like
QUESTION
I install new modules via the following command in my miniconda
...ANSWER
Answered 2022-Jan-06 at 20:11Consider creating a separate environment, e.g.,
QUESTION
I created a new environment and added it to jupyter like this:
...ANSWER
Answered 2021-Nov-22 at 07:31Going by the SO answer here the virtual environment named tf_plot needs to be activated first before import. i.e,
QUESTION
import matplotlib.pyplot as plt
plt.plot([1,2,3])
plt.show()
input("Press enter to continue...")
ANSWER
Answered 2021-Nov-03 at 13:32As of late, conda and matplotlib have been having issues.
You can try to downgrade freetype from 2.11.0 to 2.10.4 by doing conda install freetype=2.10.4
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Install munkres
You can use munkres like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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