Saravanan666/pypi-org huggingface dataset:
Package: torchwisdom
torchwisdomSonar Cloud AnalysisThis library is built on the top of pytorch as one of the most popular deep learning frameworks. This library got its inspiration from keras, fastai and torchtools. The purpose of this library is to make deep learning engineer or scientist easier to train the neural network.Some of the package that is available in this library are:tabularaudiotextvisionThe package of the library represents a toolset to train a kind of data, from tabular, audio, text and vision kind of dataset. This library is still under construction to version 0.1 alpha.
Package: torch-workflow-archiver
Torch Workflow Archiver is a tool used for creating archives of workflow designed using trained neural net models that can be consumed by TorchServe inference.Use the Torch Workflow Archiver CLI to create a.warfile.Torch Workflow Archiver is part ofTorchServe.
However, you can install Torch Workflow Archiver stand alone.Detailed documentation and examples are provided in theREADME.Installationpip install torch-workflow-archiverSource codeYou can check the latest source code as follows:git clone https://github.com/pytorch/serve.gitCitationIf you use TorchServe in a publication or project, please cite TorchServe:https://github.com/pytorch/serve
Package: torch-workflow-archiver-ag
Torch Workflow Archiver is a tool used for creating archives of workflow designed using trained neural net models that can be consumed by TorchServe inference.Use the Torch Workflow Archiver CLI to start create a.warfile.Torch Workflow Archiver is part ofTorchServe.
However, you can install Torch Workflow Archiver stand alone.Detailed documentation and examples are provided in theREADME.Installationpip install torch-workflow-archiverSource codeYou can check the latest source code as follows:git clone https://github.com/pytorch/serve.gitCitationIf you use TorchServe in a publication or project, please cite TorchServe:https://github.com/pytorch/serve
Package: torch-workflow-archiver-nightly
Torch Workflow Archiver is a tool used for creating archives of workflow designed using trained neural net models that can be consumed by TorchServe inference.Use the Torch Workflow Archiver CLI to create a.warfile.Torch Workflow Archiver is part ofTorchServe.
However, you can install Torch Workflow Archiver stand alone.Detailed documentation and examples are provided in theREADME.Installationpip install torch-workflow-archiverSource codeYou can check the latest source code as follows:git clone https://github.com/pytorch/serve.gitCitationIf you use TorchServe in a publication or project, please cite TorchServe:https://github.com/pytorch/serve
Package: torchwrapper
TorchWrapperA wrapper class for a PyTorhc Model using fit and predict functions that are
familiar to those who use Keras and Sklearn.Reduces the need to write fit and evaluation functions for basic models.Quick Start# import the modulefromtorchwrapperimportWrapper# create your module, optimizer, and criterion functionmodel=Model()optimizer=torch.optim.Adam(model.parameters())criterion=torch.nn.MSELos()# wrap the modelmodel=Wrapper(model)# train the networkmodel.fit(dataloader,optimizer,criterion,epochs=50)With a trained model, you can predict using a PyTorch dataloader:preds=model.predict(dataloader)This will return a numpy array of the predictions.
Package: torchx
TorchXTorchX is a universal job launcher for PyTorch applications.
TorchX is designed to have fast iteration time for training/research and support
for E2E production ML pipelines when you're ready.TorchX currently supports:Kubernetes (EKS, GKE, AKS, etc)SlurmAWS BatchDockerLocalRay (prototype)GCP Batch (prototype)Need a scheduler not listed?Let us know!QuickstartSee thequickstart guide.DocumentationStable DocumentationNightly DocumentationRequirementstorchx:python3 (3.8+)PyTorchoptional:Docker(needed for docker based schedulers)Certain schedulers may require scheduler specific requirements. See installation
for info.InstallationStable# install torchx sdk and CLI -- minimum dependenciespipinstalltorchx# install torchx sdk and CLI -- all dependenciespipinstall"torchx[dev]"# install torchx kubeflow pipelines (kfp) supportpipinstall"torchx[kfp]"# install torchx Kubernetes / Volcano supportpipinstall"torchx[kubernetes]"# install torchx Ray supportpipinstall"torchx[ray]"# install torchx GCP Batch supportpipinstall"torchx[gcp_batch]"Nightly# install torchx sdk and CLIpipinstalltorchx-nightly[dev]Source# install torchx sdk and CLI from source$pipinstall-egit+https://github.com/pytorch/torchx.git#egg=torchx# install extra dependencies$pipinstall-egit+https://github.com/pytorch/torchx.git#egg=torchx[dev]DockerTorchX provides a docker container for using as as part of a TorchX role.See:https://github.com/pytorch/torchx/pkgs/container/torchxContributingWe welcome PRs! See theCONTRIBUTINGfile.LicenseTorchX is BSD licensed, as found in theLICENSEfile.
Package: torchxai
torchxaiVisualzation methods that help developers to realize the deep networkGetting Start# clone the repogitclonehttps://github.com/jimmylin0979/torchxai.gitcdtorchxai# install requirements and CVNets packagepipinstall-rrequirements.txt
pipinstall--editable.Visualization Method1. Centered Kernel Alignement (CKA)Reference: ICML 2019 Similarity of Neural Network Representations RevisitedCKA can reveal pathology in neural networks representations.According to this fetaure, it's recommend to use CKA to analyze whether two networks acts similar, or use to reveal how the layer interacts with each other in one network.Please have a look on example atexample/CKA.ipynb2. Loss LandscapeReference: NIPS 2018 Visualizing the Loss Landscape of Neural NetsTopographically map of the loss function in the parameter space.The generated loss landscape can help visualize the optimization process, identify local minima and saddle points, and understand the generalization ability of the model.Please have a look on example atexample/Landscape.ipynb3. Receptive FieldReference: NIPS 2016 Understanding the Effective Receptive Field in Deep Convolutional Neural NetworksReceptive field refers to the portion of an input image that a neuron in a CNN is sensitive to. In other words, it represents the area of the input image that contributes to the activation of a particular neuron.The receptive field of a neuron can be visualized as a region in the input image that, when activated, causes the neuron to fire.Please have a look on example atexample/ERF.ipynbRoadMapFinish setup.py, and register on pypi platformAllow project to install with-eflags inpip installcommandAutomatic deploy with Github ActionsMethod CKALinear-based CKAKernel-based CKAMethod Loss LandscapeMethod Receptive FieldMethod CAMReferenceCKA-Centered-Kernel-Alignmentloss-landscapes
Package: torchx-applovin
to install forkpip install torchx-applovinto updaterm dist/* &&  && python3.9 setup.py sdist bdist_wheel && twine upload dist/* --verboseTorchXTorchX is a universal job launcher for PyTorch applications.
TorchX is designed to have fast iteration time for training/research and support
for E2E production ML pipelines when you're ready.TorchX currently supports:Kubernetes (EKS, GKE, AKS, etc)SlurmAWS BatchDockerLocalRay (prototype)GCP Batch (prototype)Need a scheduler not listed?Let us know!QuickstartSee thequickstart guide.DocumentationStable DocumentationNightly DocumentationRequirementstorchx:python3 (3.8+)PyTorchoptional:Docker(needed for docker based schedulers)Certain schedulers may require scheduler specific requirements. See installation
for info.InstallationStable# install torchx sdk and CLI -- minimum dependenciespipinstalltorchx# install torchx sdk and CLI -- all dependenciespipinstall"torchx[dev]"# install torchx kubeflow pipelines (kfp) supportpipinstall"torchx[kfp]"# install torchx Kubernetes / Volcano supportpipinstall"torchx[kubernetes]"# install torchx Ray supportpipinstall"torchx[ray]"# install torchx GCP Batch supportpipinstall"torchx[gcp_batch]"Nightly# install torchx sdk and CLIpipinstalltorchx-nightly[dev]Source# install torchx sdk and CLI from source$pipinstall-egit+https://github.com/pytorch/torchx.git#egg=torchx# install extra dependencies$pipinstall-egit+https://github.com/pytorch/torchx.git#egg=torchx[dev]DockerTorchX provides a docker container for using as as part of a TorchX role.See:https://github.com/pytorch/torchx/pkgs/container/torchxContributingWe welcome PRs! See theCONTRIBUTINGfile.LicenseTorchX is BSD licensed, as found in theLICENSEfile.
Package: torchx-nightly
TorchXTorchX is a universal job launcher for PyTorch applications.
TorchX is designed to have fast iteration time for training/research and support
for E2E production ML pipelines when you're ready.TorchX currently supports:Kubernetes (EKS, GKE, AKS, etc)SlurmAWS BatchDockerLocalRay (prototype)GCP Batch (prototype)Need a scheduler not listed?Let us know!QuickstartSee thequickstart guide.DocumentationStable DocumentationNightly DocumentationRequirementstorchx:python3 (3.8+)PyTorchoptional:Docker(needed for docker based schedulers)Certain schedulers may require scheduler specific requirements. See installation
for info.InstallationStable# install torchx sdk and CLI -- minimum dependenciespipinstalltorchx# install torchx sdk and CLI -- all dependenciespipinstall"torchx[dev]"# install torchx kubeflow pipelines (kfp) supportpipinstall"torchx[kfp]"# install torchx Kubernetes / Volcano supportpipinstall"torchx[kubernetes]"# install torchx Ray supportpipinstall"torchx[ray]"# install torchx GCP Batch supportpipinstall"torchx[gcp_batch]"Nightly# install torchx sdk and CLIpipinstalltorchx-nightly[dev]Source# install torchx sdk and CLI from source$pipinstall-egit+https://github.com/pytorch/torchx.git#egg=torchx# install extra dependencies$pipinstall-egit+https://github.com/pytorch/torchx.git#egg=torchx[dev]DockerTorchX provides a docker container for using as as part of a TorchX role.See:https://github.com/pytorch/torchx/pkgs/container/torchxContributingWe welcome PRs! See theCONTRIBUTINGfile.LicenseTorchX is BSD licensed, as found in theLICENSEfile.
Package: torchy-nn
OverviewTorchy is a neural network framework implemented only using NumPy and based on PyTorch API but with manual backpropogation calculations. The main idea was to build a neural network from scratch for educational purposes.Installationpipinstalltorchy-nnGetting startedI suggest you to take a look atcurrently implemented stuffto be familiar with current possibilities for building neural network models with Torchy. Also I've createdpackage structurein case if you stuck where to get specific layers.Example usageFirst we can define our model using Torchy with its PyTorch-like APIfromnn.modules.sequentialimportSequential# Same as nn.Sequentialimportnn.modules.moduleaslayer# Define 2-layer wtth 100 neurons hidden layer.model=Sequential(layer.Linear(n_input=10,n_output=100),layer.BatchNorm1d(n_output=100),layer.ReLU(),layer.Linear(n_input=100,n_output=2))Next step is to create instances of optimizer and criterion for loss function and scheduler for funimportnn.modules.lossaslossimportoptimizers.optimasoptimimportoptimizers.schedulerasschedoptimizer=optim.SGD(model.params(),lr=1e-3)criterion=loss.CrossEntropyLoss()scheduler=sched.StepLR(optimizer,step_size=10)I won't cover whole training process like loops and stuff, just show you main differences while training...predictions=model(X)# Nothing changedloss,grad=criterion(predictions,y)# Now return tuple of (loss, grad) instead of only lossoptimizer.zero_grad()model.backward(grad)# Call backward on model object and pass gradient from loss as argumentoptimizer.forward_step()DemonstrationThedemo notebookshowcases what Torchy currently can do.RoadmapThere is still a lot of work to be done, but here are the main points that will be completed soonDocstring every entity & add type hintingAdd evaluation & inference for modelResourcesThe opportunity to create such a project was given to me thanks to these peoplePyTorchCS231n - 2016Deep Learning на пальцах - 2019Neural Networks: Zero to HeroLicenseMIT License
Package: torchzq
TorchZQ: A PyTorch experiment runner based onZouqiInstallationInstall from PyPI:pip install torchzqInstall the latest version:pip install git+https://github.com/enhuiz/torchzq@mainAn Example for MNIST Classificationimporttorchimporttorch.nnasnnimporttorch.nn.functionalasFfromtorchvisionimportdatasets,transformsimporttorchzqclassNet(nn.Module):def__init__(self):super(Net,self).__init__()self.conv1=nn.Conv2d(1,32,3,1)self.conv2=nn.Conv2d(32,64,3,1)self.dropout1=nn.Dropout2d(0.25)self.dropout2=nn.Dropout2d(0.5)self.fc1=nn.Linear(9216,128)self.fc2=nn.Linear(128,10)defforward(self,x):x=self.conv1(x)x=F.relu(x)x=self.conv2(x)x=F.relu(x)x=F.max_pool2d(x,2)x=self.dropout1(x)x=torch.flatten(x,1)x=self.fc1(x)x=F.relu(x)x=self.dropout2(x)x=self.fc2(x)output=F.log_softmax(x,dim=1)returnoutputclassRunner(torchzq.Runner):def__init__(self,*args,**kwargs):super().__init__(*args,**kwargs)defcreate_model(self):returnNet()defcreate_dataset(self):returndatasets.MNIST("../data",train=self.training,download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,)),]),)defprepare_batch(self,batch):x,y=batchx=x.to(self.args.device)y=y.to(self.args.device)returnx,ydeftraining_step(self,batch,optimizer_index):x,y=self.prepare_batch(batch)loss=F.nll_loss(self.model(x),y)returnloss,{"nll_loss":loss.item()}@torch.no_grad()deftesting_step(self,batch,batch_index):x,y=self.prepare_batch(batch)y_=self.model(x).argmax(dim=-1)return{"accuracy":(y_==y).float().mean().item()}if__name__=="__main__":torchzq.start(Runner)Run an ExampleTraining$ tzq example/config/mnist.yml trainTesting$ tzq example/config/mnist.yml testTensorBoard$ tensorboard --logdir runsSupported FeaturesModel checkpointsLoggingGradient accumulationConfiguration fileTensorBoardFP16
Package: torcms_app
安装源代码部署。首次安装Building the environment~/usr/python36/bin/python3 -m venv ~/vpy_yunsuan
source ~/vpy_yunsuan/bin/activate
pip3 install -r doc/requirements.txt
git clone https://github.com/bukun/torcms_modules_bootstrap.git templates/modulesDatabase\set dbname yunsuan
CREATE USER :dbname WITH PASSWORD '131322' ;
CREATE DATABASE :dbname OWNER :dbname ;
GRANT ALL PRIVILEGES ON DATABASE :dbname to :dbname ;
\c :dbname ;
create extension hstore;
\q
Package: torcms_maplet
Source codes forhttp://www.maplet.orgIntroductionThe CMS was desinged as the container for GIS showing as the very beginning.
It has the basic map showing, map overlaying, GIS data editing funtion now.
And, the CMS could used to publish different kind the information,
such as basic HTML page, JavaScript app, maps, multimedias.Building the environmentpython3 -m venv ~/vpy_maplet
source ~/vpy_maplet/bin/activate
pip3 install -r doc/requirements.txt
git clone https://github.com/bukun/torcms_f2elib.git static/f2elib
git clone https://github.com/bukun/torcms_modules_bootstrap.git templates/modulesSetupDatabase\set dbname maplet
CREATE USER :dbname WITH PASSWORD '131322' ;
CREATE DATABASE :dbname OWNER :dbname ;
GRANT ALL PRIVILEGES ON DATABASE :dbname to :dbname ;
\c :dbname ;
create extension hstore;
\q
Package: tord
Asynchronous Websocket + Pubsub based web framework built over Tornado, Redis, ZMQ, SockJSGoals:Micro FrameworkREST API over HTTP and Websocket (over same TCP port)Template supportStatic serving supportPubSub support using Redis or ZMQAsynchronous + Tornado event loopSee [example](https://github.com/abhinavsingh/tord/tree/master/example) for usage and documentation.
Package: tordjango
TorDjango=====================This is a simple management command that serves a Django project using Tornado.python manage.py runtornado [8000, 8001, ...]Requirements------------- Django- Tornado`runtornado` Command--------The management command `runtornado` takes the following arguments:- `--reload`: Use code change auto-reloader- `--static`: Serve static files- `--multiple`: Use multiple processes (one per CPU core; `--reload`will be ignored)Note: `--multiple` requires that the Tornado ioloop be uninitializedbefore the management command completes. If it has already beeninitialized (say, by an application that's adding callbacks somewhere),the server will fail to start. `--multiple` is incompatible with thecode reloader, and `--reload` will be ignored.TODO----- Configuration option for specifying additional Tornado handlers.
Package: tordle
Tordle!Have you  fallen for the recentWordletrend? Want to play some more, and don't want to leave your terminal? Welltordleis for you!Tordle does not connect to wordle. Instead, words are selected on the fly. Additionally, the game can be customized, if you're looking to expand the game.Installation:pip install tordleUsagetordleOptions--target-length: Specify the size of the taget word.--total-guesses: Specify the number of tries to guess the correct word.--no-alphabet: Hide the "hint alphabet" that shows which letters you still
have available.Roadmap:Add a hard mode, which requires future guesses to use past hints.Add an evil mode.
Package: tor-downloader
TorDownloaderDownload files from Tor websites. Used to download data from ransomware leak sites.Uses streamed file downloads and restarts if a file is not fully downloaded.
Gets the list of URLs to download either from a JSON file or from another URL (work in progress).Installing TorDownloaderUsing PipTorDownloader can be easily installed through pip:First, create a virtual environment:python -m venv .venvThen activate it:.venv\Scripts\activateon Windows or.venv/activateon LinuxFinally install the tor_downloader package:python -m pip install tor-downloaderIf installing TorDownloader through pip, it will be installed in your Python path. This is important as the path is used for default options such as the output directory, input directory, and log directory.
Because of this, you can give TorDownloader the argumentpathto find the folder TorDownloader was installed into.
Example of this command:python -m tor_downloader pathRunning TorDownloaderLinks must be given to TorDownloader in a JSON file with a single list of links. By default, TorDownloader looks for alinks.jsonfile in thetor_downloader/data/inputdirectory.Configuration options can be given in either a JSON file or as command line arguments.
The config file must be a JSON file with a single dictionary.
The command line arguments must be formatted like so:CONFIG=SETTINGConfiguration options:
socks_port: Port of Tor Socks5 proxy.
max_downloads: Maximum number of downloads to run at once.
max_tor_checks: Number of times the Tor proxy will be checked to ensure Tor is working before crashing. Default is 5.
tor_path: Path to the Tor executable (tor.exe). Often found in Tor Browser if installed (Tor Browser\Browser\TorBrowser\Tor\tor.exe).
links_file: Path to the file containing the list of URLs to download. Must be a .json file with a single list of URLs.
log_file: Path to the log file. Log file will be created if it does not exist.
output_dir: Path to the directory to download the files to.
config: Path to the configuration file. Only usable through the command line arguments.Example command:python -m tor_downloader max_downloads=7 tor_path="Tor Browser\\Browser\\TorBrowser\\Tor\\tor.exe" links_file=links.json output_directory=outputGetting ResultsAll files will be installed into the output directory specified in the config file or command line argument. By default, the output directory istor_downloader/data/input.
Package: toredis-fork
TOREDIS=======This is minimalistic, but feature rich redis client for Tornado built on top of `hiredis <https://github.com/pietern/hiredis-py>`_ protocol parser.Supports all redis commands, which are auto-generated from the redis `JSON documentation file <https://github.com/antirez/redis-doc/blob/master/commands.json>`_.Key design points:1. While toredis attempts to add some syntactical sugar to the API, all responses are returned "as is". For example, if command returnslist of items and developer requested only one key, list with one entry will be returned. For example::def handle(self, result):print(len(result))conn.hkeys('test1', handle)2. Most redis commands accept one or more keys. Toredis adds a bit of logic to handle single key or array of keys. Due to pythonlimitations, it is not possible to use ``*args`` with named ``callback`` argument, so you will have to pass array of key names::# This will workconn.blpop('test', callback=callback)conn.blpop(['test', 'test2'], callback=callback)# This won't workconn.blpop('test', 'test2', callback=callback)3. If redis connection will be dropped while waiting for response, callback will be triggered with `None` as a value.4. Toredis does not provide reconnection feature, but you can override :meth:`~toredis.Client.on_disconnect` method and implement your reconnection logic.You can find command `documentation here <https://github.com/mrjoes/toredis/blob/master/toredis/commands.py>`_ (will be moved to rtd later).Pipelining is also supported::# For more than one pipeline for connection create it with Pipeline(conn)pipeline = conn.pipeline()pipeline.set('foo', 'bar')pipeline.get('foo')pipeline.send(callback=callback)For more examples please refer to tests.More on `redis pipelining <http://redis.io/topics/pipelining>`_.Things missing:* Backport pure-python redis protocol parser (for PyPy support)* Connection pools
Package: toree
This package will install Apache Toree as a Jupyter kernel.Apache Toree is an effort undergoing incubation at the Apache Software
Foundation (ASF), sponsored by the Apache Incubator PMC.Incubation is required of all newly accepted projects until a further review
indicates that the infrastructure, communications, and decision making process
have stabilized in a manner consistent with other successful ASF projects.While incubation status is not necessarily a reflection of the completeness
or stability of the code, it does indicate that the project has yet to be
fully endorsed by the ASF.
Package: to-requirements.txt
to-requirements.txt | Automatically update requirements.txtto-requirements.txtallows to automatically manage dependencies in requirements.txt usingpipas a package manager.BenefitsEasy to setup.The installation process include only two steps: install the package using pip
and setup up it using script provided by the package. That's it.Setup in one command.You can set up VirtualEnv-based project in one command. It will create virtual environment.
And installto-requirements.txtautomatically.Customizable.Customize it the way you like: use it only in git repositories, allow or disallow
automated requirements.txt file creation, enable or disable the package itself.Easy to use.After installing the package, running setup command and (optionally) customizing it
the package is ready. There is no additional conditions to use. Just install,
uninstall or upgrade packages usingpipas you usually do.Always in sync.Withto-requirements.txtthe project's requirements.txt will always stay in sync
with packages that you install usingpip.InstallationTo install the package run the following command:pipinstallto-requirements.txtAnd after that run the command below to initialize the package:requirements-txtinstallIt will update your currentpipscripts to execute the functionality of
this package. Also, if you want to enable all the available functionality of
the package you should put the lines below to your .bashrc, .zshrc or other
.*rc file:aliasrt=". rt"aliasrequirements-txt=". requirements-txt"Or simply use the cli command:rtaliasIt will enable sourced mode of the cli execution and the cli will be able
to activate your virtual environment / deactivate it if required.The changes made topipscripts will not affect ordinarypipworkflow after
uninstallingto-requirements.txt.Setup projectTo set up VirtualEnv based project just type:requirements-txtinitThe same effect could be achieved much easier with aliases:rtiDocumentationThe detailed documentation is available onrequirements-txt.readthedocs.io.ContributingVisit the fileCONTRIBUTING.md.LicenseVisit the fileMIT.