Saravanan666/pypi-org huggingface dataset:
Package: nnmeta
NNmetaNNmeta is created in order to useNNPackage.InstallationInstall with pip [PyPi]:pip3 install nnmetaInstall from sourceClone the repositorygit clone https://github.com/AlexanderDKazakov/nnmeta
cd nnmetaInstall requirementspip3 install -r requirements.txtInstall NNmetapip3 install .UsageNNmeta support certain convention in data structure and it is required to structure the data in such way:# /path/to/my/base

base
└── xyz
    ├── samples.xyz
    └── samples_for_cc.xyzAfter first run additional folders will be created:models,dbs,splits,testsfromnnmetaimportNNClass# this is a main class for NN traininginfo=dict(runner={# network name# data source [extended xyz file]; used for converting to DB [internal usage]# "filename" : {"range" ex. [from:to:step], epochs should be done}"samples.xyz":{":":20},# train `runner` nn on all samples of `samples.xyz` 20 epochs},runner_features=dict(n_features=64,# details in NN class [default is 128]n_filters=32,#n_gaussians=12,# default 25batch_size=512,#                     [parameter for tuning]lr=1e-4,# learning rate       [parameter for tuning]db_properties=("energy","forces","dipole_moment"),# what can be found in the `samples.xyz` filetraining_properties=("energy","forces","dipole_moment"),# what one wants to trainloss_tradeoff=(0.2,0.8,0.6),n_layers_energy_force=2,# default 2           [parameter for tuning]n_neurons_energy_force=None,# default None        [parameter for tuning]n_layers_dipole_moment=2,# default 2           [parameter for tuning]n_neurons_dipole_moment=None,# default None        [parameter for tuning]loss_function_choice="mse",# "mae", "mse", "sae"train_samples_percent=70,valid_samples_percent=20,predict_each_epoch=200,validate_each_epoch=30,# cross-check with next filescheck_list_files={# this file should lie in the same `xyz` dir"samples_for_cc.xyz":dict(num_points=1000),}))nn=NNClass(info=info,network_name="runner",system_path="/path/to/my/base")nn.prepare_network()ContributionFeel free to contribute to the project, but please create initially an issue with detailed problem and way to resolve it.LicenseMIT
Package: nnmnkwii
nnmnkwii ([nanamin kawaii])Library to build speech synthesis systems designed for easy and fast prototyping.DocumentationSTABLE—most recently tagged version of the documentation.LATEST—in-development version of the documentation.InstallationThe latest release is availabe on pypi. Assuming you have alreadynumpyinstalled, you can install nnmnkwii by:pip install nnmnkwiiIf you want the latest development version, run:pip install git+https://github.com/r9y9/nnmnkwiior:git clone https://github.com/r9y9/nnmnkwii
cd nnmnkwii
python setup.py develop # or installThis should resolve the package dependencies and installnnmnkwiiproperty.At the moment,nnmnkwii.autogradpackage depends onPyTorch.
If you need autograd features, please install PyTorch as well.AcknowledgementsThe library is inspired by the following open source projects:Merlin:https://github.com/CSTR-Edinburgh/merlinLibrosa:https://github.com/librosa/librosaLogo was created by Gloomy Ghost(@740291272) (#40)
Package: nnmt
NNMT - Neuronal Network Meanfield ToolboxNNMT is an open-source, community centered Python package for collecting
reusable implementations of analytical methods for neuronal network model
analysis based on mean-field theory.DocumentationPlease visit ourofficial documentation.In order to compile the documentation on your own, you have to change your
working directory tonnmt/and install and activate the provided conda
environmentconda env create -f environment.yaml
  conda activate nnmtChange you working directory tonnmt/docs/and run the following commandsmake clean
  make htmlThis will compile the documentation and create the folderbuild/.
Now you can access the documentation using your preferred browser by opening
the filebuild/html/index.html.CitationIf you use NNMT for your project, please don't forget tocite NNMT.LicenseGNU General Public License v3.0, seeLICENSEfor
details.AcknowledgmentsSeeacknowledgments.
Package: nn-names
nn-namesA pure python package to generate random names using neural networkInstallationYou can install the package via pip:pipinstallnn-namesUsagefromnn_namesimportgenerate_namefor_inrange(10):name=generate_name()print(name)# Skay# Wendr# Pavie# Axton# Atto# Inll# Winaydn# Gido# Helanangel# XenrobelLicenseThis project is licensed under the terms of the MIT license.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.ContactIf you want to contact me you can reach me atpradishbijukchhe@gmail.com.
Package: nn-numpy
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Description: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/rst
Package: nnogada
Neural Networks Optimized by Genetic Algorithms for Data Analysis (NNOGADA)nnogadais a Python package that performs hyperparemeter tuning for artificial neural networks, particularly for Multi Layer Perceptrons, using simple genetic algorithms. Useful for generate better neural network models for data analysis. Currently, only works with feedforward neural networks in tensorflow.keras (classification and regression) and torch (regression at this moment).You can try to install nnogada in your computer:$ git clonehttps://github.com/igomezv/nnogada$ cd nnogada$ pip3 install -e .then you can delete the cloned repo because you must have nnogada installed locally.Other way to install nnogada (without clonning) is:$ pip3 install -e git+https://github.com/igomezv/nnogada#egg=nnogadaIf you use the code, please cite the paperGómez-Vargas, I., Andrade, J. B., & Vázquez, J. A. (2023). Neural networks optimized by genetic algorithms in cosmology. Physical Review D, 107(4), 043509.Contributions are welcome!
Package: nnoir
NNOIRInstallpip install nnoirExampleCreate & Saveinputs=[nnoir.Value(b'v0',dtype='<f4',shape=(10,10)),nnoir.Value(b'v1',dtype='<f4',shape=(10,10))]outputs=[nnoir.Value(b'v2',dtype='<f4',shape=(10,10))]nodes=inputs+outputsinput_names=[x.nameforxininputs]output_names=[x.nameforxinoutputs]functions=[nnoir.functions.Add(input_names,output_names)]result=nnoir.NNOIR(b'Add',b'add_test','0.1',input_names,output_names,nodes,functions)result.dump('add.nnoir')Loadadd_nnoir=nnoir.load('add.nnoir')Read/Write metadata from command line$nnoir-metadataresnet_50.nnoirname=CaffeFunctiondescription=generator.name=chainer
generator.version=7.7.0
$nnoir-metadataresnet_50.nnoir--write-description"This is resnet_50 (written by nnoir-metada)"$nnoir-metadataresnet_50.nnoirname=CaffeFunctiondescription=Thisisresnet_50(writtenbynnoir-metada)generator.name=chainer
generator.version=7.7.0
$nnoir-metadataresnet_50.nnoir--write-name"CaffeFunction_V2"$nnoir-metadataresnet_50.nnoirname=CaffeFunction_V2description=Thisisresnet_50(writtenbynnoir-metada)generator.name=chainer
generator.version=7.7.0
Package: nnoir-chainer
nnoir-chainerChainer Model from/to NNOIR converterATTENTION: This library goes into the maintenance phasetoo.Installpip install nnoir-chainerExampleImport NNOIRimport chainer
from nnoir_chainer import NNOIRFunction
m = NNOIRFunction('nnoir_file_path')
x = chainer.Variable(np_array)
with chainer.using_config('train', False):
    y = m(x)
    print(y)Export NNOIRm = model.CNN()
chainer.serializers.load_npz('cnn.model', L.Classifier(m))
with chainer.using_config('train', False):
    x = chainer.Variable(np.zeros((1, 28*28)).astype(np.float32))
    y = m(x)
    g = nnoir_chainer.Graph(m, (x,), (y,))
    result = g.to_nnoir()
    with open('model.nnoir', 'w') as f:
        f.buffer.write(result)These layers are supported by nnoir-chainer exporter.chainer.linksBatchNormalizationBiasLinearConvolution2D (DepthwiseConvolution2D, DilatedConvolution2D)ScaleSwishchainer.functionAddAddConstantAveragePooling2DClippedReLUConcatDropoutELULeakyReLUMaxPooling2DMulMulConstantPadReLUReshapeSigmoidSoftmaxSubSumTanhTransposeUnpooling2D
Package: nnoir-onnx
nnoir-onnxnnoir-onnx is a converter from ONNX model to NNOIR model.InstallFromPyPI:pip install nnoir-onnxFromDockerhub:docker pull idein/nnoir-tools:20240208Examplewgethttps://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz
tarxvzfmnist.tar.gz
onnx2nnoir-omodel.nnoirmnist/model.onnxWith docker:docker run --rm -it -u $UID:$GID -v $(pwd):/work idein/nnoir-tools:20240208 onnx2nnoir --graph_name "mobilenet" -o mobilenetv2-1.0.nnoir mobilenetv2-1.0.onnxSupported ONNX OperatorsAddAveragePoolBatchNormalizationscale,B,mean, andvarmust be"constant"Clipmust be opset version 6 or 11if opset version is 11maxmust be"constant"minmust be 0ConcatConvWmust beConstantvalue or have initializer valuebmust beConstantvalue or have initializer valueCosDiv1st input must not be"constant"Dropoutequivalent identity functionEluExpFlattenGemmBmust beConstantvalue or have initializer valueCmust beConstantvalue or have initializer valueGlobalAveragePoolHardSigmoidHardSwishLeakyReluLRNLSTMonlyseq_length == 1directionmust be forwardSupportedactivationsare belowSigmoidTanhReluNot supportclipandinput_forgetMatMulMaxPoolceil_mode = 1is not supporteddilationsis not supportedMulPadmodemust be"constant"Pow2nd input must not be"constant"PReluslopemust be"constant"and a single value tensorReduceMeanReduceSumReluReshapeResizemust be from opset version >= 11modemust be"linear"or"nearest"nearest_modemust be"floor"coordinate_transformation_modemust be either"pytorch_half_pixel"or"align_corners"for"linear"modecoordinate_transformation_modemust be either"asymmetric"for"nearest"modeSigmoidSinSoftmaxSplitSqueezeSub1st input must not be"constant"Sum2 inputsTanTanhTransposeUnsqueeze
Package: nnotes
nnotesnnotes is a command line note taking tool. The goal is to be able to add, view and manipulate notes quickly and easily.InstallationpipinstallnnotesMain commands helpFeatures:multiple notebooksadd notesdelete notes (single, multiple, range)create notebookdelete notebookcustom notebooks directoryUsage (nnotes can be launched by typing 'n' or 'nnotes'):
    n [command]
    nnotes [command]

Available commands:
    add [note]                                  add note to active notebook
    ls                                          list / view notes from active notebook
    rm 2 3                                      delete note(s) 2 and 3
    rm range 3 7                                delete notes 3 to 7 (included)
    
    add @[notebook] [note]                      add note [note] to notebook [notebook]
    ls @[notebook]                              show notes from [notebook]
    rm @[notebook] 4                            delete note 4 from [notebook]
    rm range @[notebook] 3 7                    delete notes 3 to 7 (included) from [notebook]

    add @[notebook]                             create notebook [notebook]
    ls @                                        list all notebooks
    ls @ path                                   list notebooks with their path
    rm @[notebook]                              delete notebook

    set active [notebook]                       set active notebook [notebook]
    get active                                  show active notebook

    get notebooks path                          show notebooks directory
    set notebooks path [notebooks path]         set notebooks directoryNote for Windows users:Windows users must wrap arguments starting with @ with single or double quotes (ex. windows users should typen ls '@'instead ofn ls @), or escape commands starting with @ with a backtick (ex.n ls `@instead ofn ls @)n add '@school' new notewould be the way to add 'new note' to the 'school' notebook. Or alternatively:n add `@school new notewould also work.Example usagenandnnotescan both be used:$naddnewnoteis the same as:$nnotesaddnewnoteOther example commands:$nls$nadd@mainanothernote$nls@etc.
Package: nnprof
nnprofIntroductionnnprof is a profile tool for pytorch neural networks.Featuresmulti profile mode: nnprof support 4 profile mode: Layer level, Operation level, Mixed level, Layer Tree level. Please check below for detail usage.time and memory profile: nnprof support both time and memory profile now. But since memory profile is first supported in pytorch 1.6, please use torch version >= 1.6 for memory profile.support sorted by given key and show profile percent: user could print table with percentage and sorted profile info using a given key,  which is really helpful for optimiziing neural network.RequirementsPython >= 3.6PyTorchNumpyGet Startedinstall nnprofpip install:pipinstallnnproffrom source:python-mpipinstall'git+https://github.com/FateScript/nnprof.git'# or install after clone this repogitclonehttps://github.com/FateScript/nnprof.git
pipinstall-ennprofuse nnprffromnnprofimportprofile,ProfileModeimporttorchimporttorchvisionmodel=torchvision.models.alexnet(pretrained=False)x=torch.rand([1,3,224,224])# mode could be anyone in LAYER, OP, MIXED, LAYER_TREEmode=ProfileMode.LAYERwithprofile(model,mode=mode)asprof:y=model(x)print(prof.table(average=False,sorted_by="cpu_time"))# table could be sorted by presented header.Part of presented table looks like table below, Note that they are sorted by cpu_time.╒══════════════════════╤═══════════════════╤═══════════════════╤════════╕
│ name                 │ self_cpu_time     │ cpu_time          │   hits │
╞══════════════════════╪═══════════════════╪═══════════════════╪════════╡
│ AlexNet.features.0   │ 19.114ms (34.77%) │ 76.383ms (45.65%) │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.3   │ 5.148ms (9.37%)   │ 20.576ms (12.30%) │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.8   │ 4.839ms (8.80%)   │ 19.336ms (11.56%) │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.6   │ 4.162ms (7.57%)   │ 16.632ms (9.94%)  │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.10  │ 2.705ms (4.92%)   │ 10.713ms (6.40%)  │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤You are welcomed to try diffierent profile mode and more table format.ContributionAny issues and pull requests are welcomed.AcknowledgementSome thoughts of nnprof are inspired bytorchprofandtorch.autograd.profile.
Many thanks to the authors.
Package: nnpy
Is what it says on the tin. Stay tuned for more. Dependencies:Python 2.7 or 3.4cffi (http://cffi.readthedocs.org/en/latest/) and its dependenciesnanomsg (tested with 1.0)How to installThe usual should work:$sudopipinstallnnpyGetting startedimportnnpypub=nnpy.Socket(nnpy.AF_SP,nnpy.PUB)pub.bind('inproc://foo')sub=nnpy.Socket(nnpy.AF_SP,nnpy.SUB)sub.connect('inproc://foo')sub.setsockopt(nnpy.SUB,nnpy.SUB_SUBSCRIBE,'')pub.send('hello, world')print(sub.recv())Related Projectsnanomsgaionn- asyncio messaging library based on nnpy
Package: nnpy-bundle
Is what it says on the tin. Stay tuned for more. Dependencies:Python 2.7 or 3.4cffi (http://cffi.readthedocs.org/en/latest/) and its dependenciesnanomsg (tested with 1.0)How to installThe usual should work:$sudopipinstallnnpyGetting startedimportnnpypub=nnpy.Socket(nnpy.AF_SP,nnpy.PUB)pub.bind('inproc://foo')sub=nnpy.Socket(nnpy.AF_SP,nnpy.SUB)sub.connect('inproc://foo')sub.setsockopt(nnpy.SUB,nnpy.SUB_SUBSCRIBE,'')pub.send('hello, world')print(sub.recv())Related Projectsnanomsgaionn- asyncio messaging library based on nnpy
Package: nnsmith
NNSmith🌟NNSmith🌟 is a random DNN generator and a fuzzing infrastructure, primarily designed for automatically validating deep-learning frameworks and compilers.Support TableModelstvmonnxruntimetensorrttflitexlatorchjitONNX✅✅✅TensorFlow🔨✅✅PyTorch🔨🔨✅✅: Supported; 🔨: Coming soon;Quick StartInstall latest code (GitHub HEAD):pipinstall"git+https://github.com/ise-uiuc/nnsmith@main#egg=nnsmith[torch,onnx]"--upgrade# [optional] add more front- and back-ends such as [tensorflow] and [tvm,onnxruntime,xla,...] in "[...]"Install latest stable release[click]pipinstall"nnsmith[torch,onnx]"--upgradeInstall latest pre-release[click]pipinstall"nnsmith[torch,onnx]"--upgrade--preSetting up graphviz for debugging[click]Graphviz providesdotfor visualizing graphs in nice pictures. But it needs to be installed via the following methods:sudoapt-getinstallgraphvizgraphviz-dev# Linuxbrewinstallgraphviz# MacOScondainstall--channelconda-forgepygraphviz# Condachocoinstallgraphviz# Windowspipinstallpygraphviz# Final step.Also seepygraphviz install guidance.# Generate a random model in "nnsmith_outputs/*"nnsmith.model_genmodel.type=onnxdebug.viz=trueLearning MoreBugs:links to reports.Documentation:CLI,concept,logging, andknown issues.Contributions:doc/CONTRIBUTING.mdWe usehydrato manage configurations. Seennsmith/config/main.yaml.Papers📜 NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers.[click :: citation]@inproceedings{liu2023nnsmith,title={Nnsmith: Generating diverse and valid test cases for deep learning compilers},author={Liu, Jiawei and Lin, Jinkun and Ruffy, Fabian and Tan, Cheng and Li, Jinyang and Panda, Aurojit and Zhang, Lingming},booktitle={Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},pages={530--543},year={2023}}
Package: nnspt
Neural Network Signal Processing on TorchNNSPT is a Python library for neural network signal processing on PyTorch.Table of contentsAuthorsInstallationA simple exampleAvailable componentsCitingAuthorsRostislav Epifanov— Researcher in NovosibirskInstallationInstallation from PyPI:pip install nnsptInstallation from GitHub:pip install git+https://github.com/rostepifanov/nnsptA simple examplefromnnspt.segmentation.unetimportUnetmodel=Unet(encoder='tv-resnet34')Available componentsEncodersResNettv-resnet18tv-resnet34tv-resnet50tv-resnet101tv-resnet152ResNeXttv-resnext50_32x4dtv-resnext101_32x4dtv-resnext101_32x8dtv-resnext101_32x16dtv-resnext101_32x32dtv-resnext101_32x48dDenseNettv-densenet121tv-densenet169tv-densenet201tv-densenet161EfficientNetV1timm-efficientnet-b0timm-efficientnet-b1timm-efficientnet-b2timm-efficientnet-b3timm-efficientnet-b4timm-efficientnet-b5timm-efficientnet-b6timm-efficientnet-b7EfficientNetLitetimm-efficientnet-lite0timm-efficientnet-lite1timm-efficientnet-lite2timm-efficientnet-lite3timm-efficientnet-lite4PretrainingAutoencoderSegmentationUnet [paper]CitingIf you find this library useful for your research, please consider citing:@misc{epifanov2023ecgmentations,
  Author = {Rostislav Epifanov},
  Title = {NNSTP},
  Year = {2023},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/rostepifanov/nnspt}}
}
Package: nnstorm-cloud
NNstorm cloudInstallInstall latest stable release from PyPI:pipinstallnnstorm-cloudSet up development versiongitclonehttps://github.com/NNstorm/nnstorm-cloud.gitcdnnstorm-cloud
pipinstall-e.Set up for usageCreate Azure ssh keys:ssh-keygen-trsa-b4096-f$HOME/.ssh/azure_key.pem-P""Set these in$HOME/.profileor source it in your shell's.rcfile or just in the current shell.exportNNSTORM_AZURE_AUTH_LOCATION=${HOME}/.azure/NNSTORM_azure_credentials.jsonexportNNSTORM_AZURE_KEYVAULT=<yourazurekeyvaultname>Run this to store service credentials locally:azadspcreate-for-rbac--sdk-auth>$NNSTORM_AZURE_AUTH_LOCATIONclient_id=$(cat$NNSTORM_AZURE_AUTH_LOCATION|python3-c"import sys, json; print(json.load(sys.stdin)['clientId'])")azkeyvaultset-policy-n$NNSTORM_AZURE_KEYVAULT--spn${client_id}--secret-permissionsdeletegetlistset--key-permissionscreatedecryptdeleteencryptgetlistunwrapKeywrapKey
Package: nnsvs
NNSVSNeural network-based singing voice synthesis library for researchDocumentation can be found athttps://nnsvs.github.io.Citation@article{yamamoto2022nnsvs,
  title={NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit},
  author={Yamamoto, Ryuichi and Yoneyama, Reo and Toda, Tomoki},
  journal={arXiv preprint arXiv:2210.15987},
  year={2022}
}AcknowledgementsThe inference implementation of uSFGAN was adapted fromchomeyama/HN-UnifiedSourceFilterGAN.The code for diffusion models was adapted fromDiffSinger.
Package: nntecture
NNtectureis a Python package for Neural Network architecture visualization.InstallNNtecture package runs under Python 3.6+. It can be installed fromPyPI:pip install nntectureTo render the generated DOT source code, you also need to installGraphviz(download page).Make sure that the directory containing thedotexecutable is on your
systems' path.TutorialBasic UsageAfter installation, the package is ready to be imported.from nntecture import DrawNNCreating your Neural Network architecture with NNtecture is designed to be as easy and intuitive as possible. Drawing a simple architecture can be done in a single line of code (see below).DrawNN([2, 4, 4, 2]).draw()Built In CustomizationThe capabilities are not limited into this base form. The architecture can be easily customised as demonstrated in below example.# init
nn = DrawNN([3, 3, 3, 3], nn_type='RNN')

# draw
nn.draw(fillcolor='#AF628F',
        graph_label='Recurrent Neural Network (RNN)',
        linewidth=0.5,
        fontname='times',
        node_labels=True,
        node_fontcolor='#ffffff')Saving ResultsOnce satisfied with the architecture, the results can be saved into your desired form.# create your architecture
perceptron = DrawNN([2, 1])
perceptron.draw(graph_label='Perceptron (P)', fillcolor='lightblue', linewidth=4)

# save to file
perceptron.save(filename='perceptron', output_format='jpg', size='5,5!', view=True)Advanced Customization CapabilitiesStyling your architecture is not limited into the built-in capabilities. In addition, by accessinggraph_object, one can further customise the drawing withgraphviz(see example below). For more instructions, seegraphviz documentation.# init
nn = DrawNN([4, 3, 2, 1])
nn.draw(direction='UD', fillcolor='blue')

# further customization
nn.graph_object.edge_attr["style"] = "setlinewidth(2)"
nn.graph_object.edge_attr['color'] = 'purple'
nn.graph_object.graph_attr['bgcolor'] = 'black'
nn.graph_object.node_attr["color"] = "white"

# display
nn.graph_object
Package: nntoolQ
nn-pip-tools工具集合安装(Python 版本>=3.8.0)pip install --upgrade nntoolQ使用发送邮件from tmtool.tools import Tool
tool = Tool()

tool.mail_from_user_host = '发件地址host'
tool.mail_from_user = '发件人邮箱号'
tool.mail_from_user_pwd = '发件人密码'

tool.send_mail_msg(to_user='收件人邮箱地址（这里是列表，可填写多个）', title='邮件标题', content='邮件内容')