Saravanan666/pypi-org huggingface dataset:
Package: cups-printers
cups_printerRetrieve all printer from a CUPS server. It's a simple command-line tool that
allows one to output the existing printers or write them to a file.InstallationIt may require that the development package for CUPS is installed, e.g.,$sudodnf-yinstallcups-develPyPIThe package is hosted onPyPI.
It depends onclickandpycups.$pip3installcups_printerManual setup$python3-mvenv
$sourcebin/activate
$python3setup.pyDevelopment setupFor development, clone the Git repository and create a Python virtual
environment.$python3-mvenv
$sourcebin/activate
$python3setup.pydevelopUsageThe default CUPS instance which is used islocalhost.$cups_printers--help
Usage:cups_printers[OPTIONS]COMMAND[ARGS]...AcommandlineinterfaceforCUPSprinters.

Options:--versionShowtheversionandexit.-s,--serverTEXTTheserverURLofCUPSinstance.[default:localhost:631]--timeoutINTEGERTimeoutfornetworkoperations.--helpShowthismessageandexit.

Commands:outputOutputtheretrieveddatafromaCUPSinstance.statePrintthecurrentstateofallprinters.To get the details from a remote CUPS instance, use--server.$cups_printers--server192.168.191.1:631outputjsonLicensecups_printersìs license under MIT.
Package: cupy
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This package (cupy) is a source distribution.
For most users, use of pre-build wheel distributions are recommended:cupy-cuda12x(for CUDA 12.x)cupy-cuda11x(for CUDA 11.2 ~ 11.x)cupy-rocm-5-0(for ROCm 5.0)cupy-rocm-4-3(for ROCm 4.3)Please seeInstallation Guidefor the detailed instructions.
Package: cupy-cuda100
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 10.0.
You need to installCUDA Toolkit 10.0to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda101
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 10.1.
You need to installCUDA Toolkit 10.1to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda102
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 10.2.
You need to installCUDA Toolkit 10.2to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda110
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.0.
You need to installCUDA Toolkit 11.0to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda111
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.1.
You need to installCUDA Toolkit 11.1to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda112
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.2.
You need to installCUDA Toolkit 11.2to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda113
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.3.
You need to installCUDA Toolkit 11.3to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda114
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.4.
You need to installCUDA Toolkit 11.4to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda115
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.5.
You need to installCUDA Toolkit 11.5to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda116
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.6.
You need to installCUDA Toolkit 11.6to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda117
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.7.
You need to installCUDA Toolkit 11.7to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda11x
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 11.2 ~ 11.x.
You need to installCUDA Toolkit 11.2 ~ 11.xto use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda12x
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 12.x.
You need to installCUDA Toolkit 12.xto use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda80
CuPy : NumPy-like API accelerated with CUDACuPyis an implementation of NumPy-compatible multi-dimensional array on CUDA.This is a CuPy wheel (precompiled binary) package for CUDA 8.0.
You need to installCUDA Toolkit 8.0to use these packages.If you have another version of CUDA, please seeInstallation Guidefor instructions.
If you want to build CuPy fromsource distribution, usepip install cupyinstead.
Package: cupy-cuda90
CuPy : A NumPy-compatible array library accelerated by CUDACuPyis an implementation of NumPy-compatible multi-dimensional array on CUDA.This is a CuPy wheel (precompiled binary) package for CUDA 9.0.
You need to installCUDA Toolkit 9.0to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-cuda91
CuPy : NumPy-like API accelerated with CUDACuPyis an implementation of NumPy-compatible multi-dimensional array on CUDA.This is a CuPy wheel (precompiled binary) package for CUDA 9.1.
You need to installCUDA Toolkit 9.1to use these packages.If you have another version of CUDA, please seeInstallation Guidefor instructions.
If you want to build CuPy fromsource distribution, usepip install cupyinstead.
Package: cupy-cuda92
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for CUDA 9.2.
You need to installCUDA Toolkit 9.2to use these packages.If you have another version of CUDA, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-knn
Cupy-KNNThis package provides a fast nearest neighbor index for 3-D points
using a Cupy implementation of a left balanced BVH-tree.InstallationUsing pippip install cupy-knnFrom sourcegit clone https://github.com/mortacious/cupy-knn.git
cd cupy-knn
python setup.py installAcknowledgementsThis package is inspired by the approach presented in the following paper:@inproceedings{jakob2021optimizing,title={Optimizing LBVH-Construction and Hierarchy-Traversal to accelerate kNN Queries on Point Clouds using the GPU},author={Jakob, Johannes and Guthe, Michael},booktitle={Computer Graphics Forum},volume={40},number={1},pages={124--137},year={2021},organization={Wiley Online Library}}
Package: cupyopt
A package ofPrefectTasks and helpers.Minimum Python 3.6The following task ‘families’ are included:SFTP TasksORADB (Oracle Database) TasksObject Storage TasksInstallationFrom pip: pip install git+https://github.com/CUBoulder-OIT/cupyopt.git@master#egg=cupyoptAlternatively, and importantly for development, clone this into an existing project and install in “development mode” with -e.git clonehttps://github.com/CUBoulder-OIT/cupyopt.gitpip install -e cupyopt/NB, The ‘x.y.z’ (e.g. 0.12.6) in the versioning matches the versioning in Prefect. However the 4th section is ours to be unique within that version.UseImport the Task you need similar to:from cupyopt.objectstore_tasks import ObjstrClientTests and Lintingmake install
make lint
make testContributing?Create an issue, fork the repo, fix an issue, submit a PR. All are welcome.Flow practices very roughly hew to one-flow:https://reallifeprogramming.com/git-process-that-works-say-no-to-gitflow-50bf2038ccf7
Package: cupy-rocm-4-0
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for AMD ROCm 4.0.
You need to installROCm 4.0to use these packages.If you have another version of ROCm, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-rocm-4-2
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for AMD ROCm 4.2.
You need to installROCm 4.2to use these packages.If you have another version of ROCm, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-rocm-4-3
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for AMD ROCm 4.3.
You need to installROCm 4.3to use these packages.If you have another version of ROCm, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-rocm-5-0
CuPy : NumPy & SciPy for GPUCuPyis a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.This is a CuPy wheel (precompiled binary) package for AMD ROCm 5.0.
You need to installROCm 5.0to use these packages.If you have another version of ROCm, or want to build from source, refer to theInstallation Guidefor instructions.
Package: cupy-xarray
cupy-xarray⚠️ This project is looking for maintainers and contributors. Come help out!Interface for using cupy in xarray, providing convenience accessors.InstallationFrom anaconda:conda install cupy-xarray -c conda-forgeFrom PyPI:pip install cupy-xarrayThe latest version from Github:pip install git+https://github.com/xarray-contrib/cupy-xarray.gitUsageimportxarrayasxrimportcupy_xarray# This registers the `DataSet.cupy` and `DataArray.cupy` namespaces but is not used directlyds=xr.tutorial.load_dataset("air_temperature.nc")type(ds.air.data)# numpy.ndarray%timeitds.air.mean()# 8.56 ms ± 15.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)ds=ds.cupy.as_cupy()# Also available via convenience method ds.as_cupy()type(ds.air.data)# cupy.core.core.ndarray%timeitds.air.mean()# 2.14 ms ± 21.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)ds=ds.as_numpy()type(ds.air.data)# numpy.ndarray
Package: cuquantum
NVIDIA cuQuantum SDKis a high-performance library for quantum information science and beyond.
Currently its primary target isquantum circuit simulationsand it consists of two major components:cuStateVec: a high-performance library for state vector computationscuTensorNet: a high-performance library for tensor network computationsIn addition to C APIs, cuQuantum also provides Python APIs viacuQuantum Python.DocumentationPlease refer tohttps://docs.nvidia.com/cuda/cuquantum/index.htmlfor the cuQuantum documentation.Installationpipinstall-v--no-cache-dircuquantumNoteStarting cuQuantum 22.11, this package is a meta package pointing tocuquantum-cuXX,
where XX is the CUDA major version (currently CUDA 11 & 12 are supported).
The meta package will attempt to infer and install the correct-cuXXwheel. However,
in situations where the auto-detection fails, this package currently points tocuquantum-cu11with a warning raised (if the verbosity flag-vis set, as shown above). This behavior
is subject to change in the future, and users are encouraged to install the new wheels that
comewiththe-cuXXsuffix.The argument--no-cache-diris required for pip 23.1+. It forces pip to execute the
auto-detection logic.NoteTo use cuQuantum’s Python APIs, please directly installcuQuantum Python.Citing cuQuantumPleae click this Zenodo badge to see the citation format:
Package: cuquantum-cu11
NVIDIA cuQuantum SDKis a high-performance library for quantum information science and beyond.
Currently its primary target isquantum circuit simulationsand it consists of two major components:cuStateVec: a high-performance library for state vector computationscuTensorNet: a high-performance library for tensor network computationsIn addition to C APIs, cuQuantum also provides Python APIs viacuQuantum Python.DocumentationPlease refer tohttps://docs.nvidia.com/cuda/cuquantum/index.htmlfor the cuQuantum documentation.InstallationThe cuQuantum wheel can be installed as follows:pipinstallcuquantum-cuXXwhere XX is the CUDA major version (currently CUDA 11 and 12 are supported).
We encourage users to install packagewiththe-cuXXsuffix;
the packagecuquantum(without the-cuXXsuffix) currently points tocuquantum-cu11and is subject to change in the future.NoteThis package is a “meta”-package – installing it will install bothcustatevec-cuXXandcutensornet-cuXXthat are released in the installed cuQuantum SDK version.NoteTo use cuQuantum’s Python APIs, please directly installcuQuantum Python.Citing cuQuantumPleae click this Zenodo badge to see the citation format:
Package: cuquantum-cu12
NVIDIA cuQuantum SDKis a high-performance library for quantum information science and beyond.
Currently its primary target isquantum circuit simulationsand it consists of two major components:cuStateVec: a high-performance library for state vector computationscuTensorNet: a high-performance library for tensor network computationsIn addition to C APIs, cuQuantum also provides Python APIs viacuQuantum Python.DocumentationPlease refer tohttps://docs.nvidia.com/cuda/cuquantum/index.htmlfor the cuQuantum documentation.InstallationThe cuQuantum wheel can be installed as follows:pipinstallcuquantum-cuXXwhere XX is the CUDA major version (currently CUDA 11 and 12 are supported).
We encourage users to install packagewiththe-cuXXsuffix;
the packagecuquantum(without the-cuXXsuffix) currently points tocuquantum-cu11and is subject to change in the future.NoteThis package is a “meta”-package – installing it will install bothcustatevec-cuXXandcutensornet-cuXXthat are released in the installed cuQuantum SDK version.NoteTo use cuQuantum’s Python APIs, please directly installcuQuantum Python.Citing cuQuantumPleae click this Zenodo badge to see the citation format:
Package: cuquantum-python
NVIDIA cuQuantum Python provides Python bindings and high-level object-oriented models for accessing the full functionalities of NVIDIA cuQuantum SDK from Python.Installationpipinstall-v--no-cache-dircuquantum-pythonNoteStarting cuQuantum 22.11, this package is a meta package pointing tocuquantum-python-cuXX,
where XX is the CUDA major version (currently CUDA 11 & 12 are supported).
The meta package will attempt to infer and install the correct-cuXXwheel. However,
in situations where the auto-detection fails, this package currently points tocuquantum-python-cu11with a warning raised (if the verbosity flag-vis set, as shown above). This behavior
is subject to change in the future, and users are encouraged to install the new wheels that
comewiththe-cuXXsuffix.The argument--no-cache-diris required for pip 23.1+. It forces pip to execute the
auto-detection logic.Citing cuQuantumPleae click this Zenodo badge to see the citation format:
Package: cur
CUR DecompositionCUR Decomposition as described inMining of Massive Datasets, page 406.Currently it only works with Numpy arrays but the point of CUR is to keep C and R sparse if M is sparse so I plan to add support for Scipy Sparse arrays.UsageM = np.array([ ... ])
r = int
from cur import cur_decomposition
C, U, R = cur_decomposition(M, r)
Package: curatedapi-wm
Example Package: Curated APIThis package provides a Python interface for the Curated API. With it, you can easily manage issues, links, and subscribers within publications using theCuratedApiclass.InstallationTo install the package, you can use pip:pip install curatedapi_wmUsageTo use theCuratedApiclass within your Python project:fromcuratedapiimportCuratedApiapi_key="YOUR_API_KEY"curated_api=CuratedApi(api_key)Make sure to replace"YOUR_API_KEY"with your actual Curated API key in order to gain access to your account via the API. Please see the documentation provided by Curated forgetting started with the Curated APISetting publication idYou must perform this step or you cannot communicate with the Curated APITo target any individual publication within the API you'll need to get its id from within Curated. You can also achieve this by reviewing the results from the following API call:print(curated_api.request_all_publications())This will return a JSON response containing all publications within your account. Choose the ID from the one you want to target and assign that to thecurated_apiinstance you have created.publication_id="YOUR_PUBLICATION_ID"curated_api.set_publication_id(publication_id)TestsThe package comes with a set of tests. To run the tests:Navigate to the package directory.Run the test files using Python:python -m unittest tests/test_curated_api.py
python -m unittest tests/test_curated_category.py
python -m unittest tests/test_curated_link.pyLicenseThis package is licensed under the MIT license. See the LICENSE file for more details.
Package: curatedmetagenomicdata3
# curatedMetagenomicData3-pythonPython Wrapper for curatedMetagenomicData3# InstallationThis package has been tested on python-3.7+. You can install it from PyPi[https://pypi.org/project/curatedmetagenomicdata3/]:pip install curatedmetagenomicdata3# UsageSeedemo.pyfor a full working demo. You can follow along the below in a IPython terminal or Jupyter notebook## Pre-requisites# Decide on a folder to store files in
# This package downloads files containing raw annotation data based on your queriesimport curatedmetagenomicdata3 as cmd3
source_dir = “test” # This folder needs to exist beforehand## 1) Open Metadata# This function returns a DataFrame containing all sample metadata
df = cmd3.get_metadata()## 2) Decide What Samples You Want Annotations for# For example, let’s say you want to download taxonomic relative
# abundances data for all samples for Type 2 Diabetes patientst2d = df[df[‘disease’] == ‘T2D’]## 3) Compile OTU Data for Chosen Samples# Taxonomic Relative Abundances
taxa = cmd3.assemble_taxa_dataset(t2d, source_dir)That’s it!taxawill be a dataframe containing the relative abundances data for the samples you chose. All of the data for that will be downloaded and cached tosource_dirautomatically for future use.You can also get pathway abundances as well# Pathway Relative Abundances
pathways = cmd3.assemble_pathways_dataset(t2d, source_dir)
Package: curatedmetagenomicspipeline
InstallationFrom PyPi:pip install curatedmetagenomicspipelineFrom source code:git clone git@github.com:waldronlab/curatedmetagenomics.git
cd python_pipeline
pip install .Usagecmd_pipeline --demo TEST_SAMPLE ERR262957 /outputThe--demoargument causes use of a small demo FASTQ file and mini ChocoPhlAn, and UniRef databases (although ERR262957 is still downloaded to testfasterq-dumpcmd_pipeline MV_FEI4_t1Q14 "SRR4052038" /outputThe command above download the specified SRR run for sampleMV_FEI4_t1Q14from the AsnicarF_2017 dataset. Reads are then processed for:Taxonomic profiling with MetaPhlAn 3.0Markers abundance and presence profiles are computed alongside the species-level profilingStrain-level profiling with StrainPhlAn 3.0 (only the consensus_marker step is done)Functional potential profiling with HUMAnN 3.0Pathways and gene families abundances are normalized using species' relative abundances and CPMFor the full list of options and commands, please runcmd_utilities_cli --helpand/orcmd_utilities_cli COMMAND --help
Package: curated-tokenizers
🥢 Curated TokenizersThis Python library provides word-/sentencepiece tokenizers. The following
types of tokenizers are currenty supported:TokenizerBindingExample modelBPEsentencepieceByte BPENativeRoBERTa/GPT-2UnigramsentencepieceXLM-RoBERTaWordpieceNativeBERT⚠️ Warning: experimental packageThis package is experimental and it is likely that the APIs will change in
incompatible ways.⏳ InstallCurated tokenizers is availble through PyPI:pipinstallcurated_tokenizers🚀 QuickstartThe best way to get started with curated tokenizers is through thecurated-transformerslibrary.curated-transformersalso provides functionality to load tokenization
models from Huggingface Hub.
Package: curator
Curator is a powerful script that allows one to generate Web page image
galleries with the intent of displaying photographic images on the Web, or for a
CD-ROM presentation and archiving.It generates static Web pages only - no special configuration or running scripts
are required on the server. The script supports many file formats, hierarchical
directories, thumbnail generation and update, per-image description file with
any attributes, and ‘tracks’ of images spanning multiple directories. The
templates consist of HTML with embedded Python. Running this script only
requires a recent Python interpreter and the Python Imaging Library OR the
ImageMagick tools. If you’ve been looking for a simple yet very powerful script
to do this task you’ve come to the right place.