Saravanan666/pypi-org huggingface dataset:
Package: tensora-taco
TACO CLI interface for TensoraThis Python package provides a Python wrapper around the CLI tool of theTensor Algebra Compiler(TACO). This package exists to support thetensora[taco]extra ofTensora. The maintensorapackage is pure Python, and including TACO by default would complicate its distribution.Thetensora-tacoPython package contains one functiontaco_clithat takes thetacoCLI arguments of as a list of strings, and returns aResult.fromtensora_tacoimporttaco_cliresult=taco_cli(["y(i) = A(i,j) * x(j)","-print-nocolor","-print-compute"])kernel=result.unwrap()# // Generated by the Tensor Algebra Compiler (tensor-compiler.org)## int compute(taco_tensor_t *y, taco_tensor_t *A, taco_tensor_t *x) {#   int y1_dimension = (int)(y->dimensions[0]);#   double* restrict y_vals = (double*)(y->vals);#   int A1_dimension = (int)(A->dimensions[0]);#   int A2_dimension = (int)(A->dimensions[1]);#   double* restrict A_vals = (double*)(A->vals);#   int x1_dimension = (int)(x->dimensions[0]);#   double* restrict x_vals = (double*)(x->vals);##   #pragma omp parallel for schedule(runtime)#   for (int32_t i = 0; i < A1_dimension; i++) {#     double tjy_val = 0.0;#     for (int32_t j = 0; j < x1_dimension; j++) {#       int32_t jA = i * A2_dimension + j;#       tjy_val += A_vals[jA] * x_vals[j];#     }#     y_vals[i] = tjy_val;#   }#   return 0;# }Contributingtensora-tacois a free and open source project developed under an MIT license. Development occurs at theGitHub project.Cloning the repoTo make a local copy oftensora-taco, clone the repository with git. This project depends on TACO at build time as a submodule, so it must be cloned as well in order to build the project:gitclone--recurse-submoduleshttps://github.com/drhagen/tabeline.gitInstalling from sourcetensora-tacouses Poetry as its packaging and dependency manager. In whatever Python environment you prefer,install Poetryand then use Poetry to install this package and its dependencies:pipinstallpoetry
poetryinstallTestingtensora-tacouses pytest to run the tests in thetests/directory. The test command is encapsulated with Nox:poetryrunnox-stestThis will try to test with all compatible Python versions thatnoxcan find. To run the tests with only a particular version, run something like this:poetryrunnox-stest-3.11It is good to run the tests locally before making a PR, but it is not necessary to have all Python versions run. It is rare for a failure to appear in a single version, and the CI will catch it anyway.Code qualitytensora-tacouses Ruff to ensure a minimum standard of code quality. The code quality commands are encapsulated with Nox:poetryrunnox-sruffMaking a releaseBumpIncrement version inpyproject.tomlCommit with message "Bump version number to X.Y.Z"Push commit to GitHubCheckCIto ensure all tests passTagTag commit with "vX.Y.Z"Push tag to GitHubWait forbuildto finishCheckPyPIfor good uploadDocumentCreateGitHub releasewith name "Tensora TACO X.Y.Z" and major changes in body
Package: tensorbank
TensorBankTensorBank is a collection of assorted algorithms expressed in Tensors.We do not intend to limit ourselves to a specific domain.  The initial batch of
algorithms is focused on point and box gemoetry for some object detection
tasks, but more algorithms will be added later.We are open to all backends, including Tensorflow, Pytorch, and NumPy.Primarily, this project is to avoid copy-pasting the "utils" directory from one
project to the next :-)Installation$ pip install tensorbankUsageIf you're using TensorFlow, import TensorBank as follows and use thetb.prefix:importtensorbank.tfastbtb.axis_aligned_boxes.area([[1,1,2,2],[-1,-1,1,2]])>>>tf.Tensor([16],shape=(2,),dtype=int32)SeeAPI Reference on Readthedocsfor the full list of the algorithms
offered and comprehensive usage examples.Development versionIf you want to install the push-on-green development version, do thisinstead:$ pip install tensorbank-dev
Package: tensorbank-dev
TensorBankTensorBank is a collection of assorted algorithms expressed in Tensors.We do not intend to limit ourselves to a specific domain.  The initial batch of
algorithms is focused on point and box gemoetry for some object detection
tasks, but more algorithms will be added later.We are open to all backends, including Tensorflow, Pytorch, and NumPy.Primarily, this project is to avoid copy-pasting the "utils" directory from one
project to the next :-)Installation$ pip install tensorbankUsageIf you're using TensorFlow, import TensorBank as follows and use thetb.prefix:importtensorbank.tfastbtb.axis_aligned_boxes.area([[1,1,2,2],[-1,-1,1,2]])>>>tf.Tensor([16],shape=(2,),dtype=int32)SeeAPI Reference on Readthedocsfor the full list of the algorithms
offered and comprehensive usage examples.Development versionIf you want to install the push-on-green development version, do thisinstead:$ pip install tensorbank-dev
Package: tensorbay
TensorBay Python SDKIMPORTANT: TensorBay system underwent a huge refactoring, which broke the downward compatibility
of OpenAPI and SDK. As a result, the SDK under version v1.6.0 does not work anymore.Please update tensorbay SDK to v1.6.0 or a higher version.TensorBay Python SDK is a python library to accessTensorBayand manage your datasets.It provides:A pythonic way to access your TensorBay resources by TensorBay OpenAPI.An easy-to-use CLI toolgas(Graviti AI service) to communicate with TensorBay.A consistent dataset format to read and write your datasets.Installationpip3 install tensorbayDocumentationMore information can be found on thedocumentation siteUsageAnAccessKeyis needed to communicate with TensorBay.
Please visitthis pageto get anAccessKeyfirst.Authorize a client objectfromtensorbayimportGASgas=GAS("<YOUR_ACCESSKEY>")Create a Datasetgas.create_dataset("<DATASET_NAME>")List Dataset namesdataset_names=gas.list_dataset_names()Upload images to the Datasetfromtensorbay.datasetimportData,Dataset# Organize the local dataset by the "Dataset" class before uploading.dataset=Dataset("<DATASET_NAME>")# TensorBay uses "segment" to separate different parts in a dataset.segment=dataset.create_segment("<SEGMENT_NAME>")segment.append(Data("0000001.jpg"))segment.append(Data("0000002.jpg"))dataset_client=gas.upload_dataset(dataset,jobs=8)# TensorBay provides dataset version control feature, commit the uploaded data before using it.dataset_client.commit("Initial commit")Read images from the DatasetfromPILimportImagedataset=Dataset("<DATASET_NAME>",gas)segment=dataset[0]fordatainsegment:withdata.open()asfp:image=Image.open(fp)width,height=image.sizeimage.show()Delete the Datasetgas.delete_dataset("<DATASET_NAME>")
Package: tensorboard-chainer
tensorboard-chainerWrite tensorboard events with simple command.
including scalar, image, histogram, audio, text, graph and embedding.This is based ontensorboard-pytorch.UsageInstall tensorflow.pip install tensorflowExecute demo.py and tensorboard.
Access "localhost:6006" in your browser.cd examples
python demo.py
tensorboard --logdir runsScalar exampleHistogram exampleGraph exampleName scopeLike tensorflow, nodes in the graph can be grouped together in the namespace to make it easy to see.importchainerimportchainer.functionsasFimportchainer.linksasLfromtb_chainerimportname_scope,within_name_scopeclassMLP(chainer.Chain):def__init__(self,n_units,n_out):super(MLP,self).__init__()withself.init_scope():self.l1=L.Linear(None,n_units)# n_in -> n_unitsself.l2=L.Linear(None,n_units)# n_units -> n_unitsself.l3=L.Linear(None,n_out)# n_units -> n_out@within_name_scope('MLP')def__call__(self,x):withname_scope('linear1',self.l1.params()):h1=F.relu(self.l1(x))withname_scope('linear2',self.l2.params()):h2=F.relu(self.l2(h1))withname_scope('linear3',self.l3.params()):o=self.l3(h2)returnoHow to save the logs using this model is shown below.add_all_variable_imagesis the function that saves the Variable's data in the model that matches the pattern as an images.add_all_parameter_histogramsis the function that save histograms of the Parameter's data in the model that match the pattern.fromdatetimeimportdatetimefromtb_chainerimportSummaryWritermodel=L.Classifier(MLP(1000,10))res=model(chainer.Variable(np.random.rand(1,784).astype(np.float32)),chainer.Variable(np.random.rand(1).astype(np.int32)))writer=SummaryWriter('runs/'+datetime.now().strftime('%B%d%H:%M:%S'))writer.add_graph([res])writer.add_all_variable_images([res],pattern='.*MLP.*')writer.add_all_parameter_histograms([res],pattern='.*MLP.*')writer.close()Referencetensorboard-pytorchtensorboard_loggertfchain
Package: tensorboard-easy
A small logger that lets you write logs readable by Tensorboard but
doesn’t require Tensorflow.UsageYou can use the logger as a context manager:fromtensorboard_easyimportLoggerimportnumpyasnpwithLogger('/path/to/logs/folder/')aslog:log.log_scalar('my_scalar',100,step=1)log.log_image('my_images',np.random.rand(3,20,20),step=1)or you can close the logger explicitly:log=Logger('/some/other/logs')log.log_text('my_text',"Let's throw in some text",0)log.log_text('my_text',[['Some','tensor'],['with','text!']],1)log.log_histogram('my_histogram',np.random.rand(500),step=0)log.close()It supports scalars, images, text and histograms.You can also create functions, that write to a specific tag and automatically
increase the step:withLogger('/path/to/logs/folder/')aslog:write_loss=log.make_log_scalar('loss')foriinrange(1,100):write_loss(1/i)InstallationIt can be installed via pip:pip installtensorboard-easyThetensorflowortensorflow-tensorboardpackages are not
required, however you will need one of them to visualize your logs.
Package: tensorboard-plugin-3d
TensorBoardPlugin3D - 3D Tensor VisualizationIntroductionTensorBoardPlugin3D is an open-source TensorBoard plugin that supports
visualizing 3D output within the TensorBoard application, JupyterLab,
Google Colab, and SageMaker.InstallationInstall thePyPI package:pipinstalltensorboard-plugin-3dMONAI Notebook ExamplesThe Spleen Segmentation 3D tutorial shows how to integrate MONAI into an existing PyTorch medical DL program and demonstrates using TensorBoardPlugin3D to view an image with a label to show the input data (shown in the first image) or to compare the model output with the input.Get started with a quick view of the output data with the cached notebook.The UNet Segmentation 3D Ignite notebook provides a simple, fast-running notebook with demo data. If you're looking for a quick way to get started and see the plugin in action this is a great place to start.The BRATS Segmentation 3D notebook demonstrates brain tumor 3D segmentation with MONAI. The tutorial shows how to construct a training workflow of multi-labels segmentation task.Get started with a quick view of the output data with the cached notebook.DocumentationSee thedocumentationfor a guide on getting started.
Package: tensorboard-plugin-customizable-plots
Customizable Plots - Tensorboard PluginThis plugin is a simple Tensorboard dashboard to visualize, customize
and export ready-to-use scalar plots for publication.Built withplotly.js,reactandmaterial-ui.Installationpipinstalltensorboard-plugin-customizable-plotsAfter running Tensorboard, a new tab labeledCUSTMOZIABLE PLOTSwill be added to the Tensorboard dashboard.FeaturesThe plugin has almost the same features as theTIME SERIESor theSCALARSdashboards,
plusplotly.jsfeatures:The ability to customize the plot title and the axis labels.The ability to customize the colors.Legends are attached with each plot.X-axis and Y-axis both supportlogscale.The customized plots can be exported to many image formats includingsvgandpng.Checkplotly.js documentationfor the full list of features and options.LimitationsNot as good and stylish as theTIME SERIESplugin.The settings with a text field are not applied untilEnteris pressed (I tried to implementonChangebut it makes the plugin slower)So until the Tensorboard authors implement the above features,
one might use theTIME SERIESdashboard to track the progress of the plots (as it is more responsive)
and use this plugin to customize and export them.LicenseThe plugin is licensed under the Apache License, Version 2.0. SeeLICENSEfor the full license text.
Package: tensorboard-plugin-wit
What-If Tool TensorBoard Plugin.The tensorboard-plugin-wit package adds theWhat-If Toolto the standard distribution of the TensorBoard toolkit, as a plugin.The What-If Tool is an interactive visual probe for ML model understanding.The source code for the plugin, and the entire What-If Tool, can be found athttps://github.com/pair-code/what-if-tool.Previously, the What-If Tool source code was part of the TensorBoard repository,
but it has since been moved to its own repository. For that reason, the tensorboard
pip package depends on this package.
Package: tensorboard-projects
TensorBoard ProjectsManagement UI for Tensorboard DashboardsTensorBoard is a powerful visualization tool when building machine learning models. However it can quickly become quite unwieldy as the number of model versions and runs increases.What is TensorBoard ProjectsTensorBoard Projects provides a UI to manage Tensorboard runs and allows you to easily:Visualize runs for a given modelArchive RunsDelete RunsAdd metadata to individual runssStart TensorBoard dashboard for a subset of model runsWrite documentation for a modelAs the project is still very much in development, please report any issues or features you would like to see added !What TensorBoard Projects isn'tTensorBoard projects is not an experiment tracking solution, it simply allows you to better manage TensorBoard runs.If you are looking for a fully fledgeed experiment tracking solution, you can look into:ClearMLCometMLFlowWeights and Biasesand othersUsing TensorBoard ProjectsTensorboard Projects can be installed from PyPI usingpip install tensorboard-projects.Once Tensorboard Projects is installed, you can start the UI using:tensorboard-projects uiIn order to assist with running this on a remote machine, the following arguments are supported:--backend-store-uri: Storage location of metadata, defaults to~/.tensorboard_projects--ip: Host for the API, use 0.0.0.0 to access from a remote machine--port: Port to run UI on
remote machien
Package: tensorboard-pytorch
History=======0.7 (2017-08-22)-----------------* remove tensorflow dependency for embedding function* fixed incorrect image<->label pairing in embedding function (#12)* unifies API call and adds docstring. Documentation is available at: http://tensorboard-pytorch.readthedocs.io/0.6.5 (2017-07-30)-----------------* add travis test (py2.7, py3.6)* add support for python2 (in PyPI)0.6 (2017-07-18)-----------------* supports embedding0.5 (2017-07-18)-----------------* supports graph summary* fixed np.histogram issue0.4 (2017-07-12)-----------------* supports text summary0.3 (2017-07-03)-----------------* supports audio summary0.2 (2017-06-24)-----------------* simplifies add_image API* speed up add_histogram API by 35x0.1 (2017-06-13)------------------* First commit. Reference:https://github.com/TeamHG-Memex/tensorboard_loggerhttps://github.com/dmlc/tensorboard
Package: tensorboard-reporter
TensorBoard ReporterGet reports for your training process via Slack.SetupInstall TensorBoard Reporter from pippipinstalltensorboard-reporterConfigure your reportSLACK_BOT_TOKEN="xoxb-abc-1232"tensorboard-reporter\--run_dirruns--all_time\--tag"Test/loss"--tag"Train/loss"\--interval_hour0.5--slack_channels"#tensorboard-reports"And get reports to your Slack periodicallyCommand line usageusage:tensorboard-reporter[-h]--run_dirRUN_DIR--interval_hourINTERVAL_HOUR--slack_channelsSLACK_CHANNELS[--tagTAG][--all_time]GetreportsforyourtrainingprocessviaSlack

optionalarguments:-h,--helpshowthishelpmessageandexitrequiredarguments:--run_dirRUN_DIRtensorboardrundir--interval_hourINTERVAL_HOURreportintervalhour--slack_channelsSLACK_CHANNELSslackchannels(commaseparated)whichmessageswillbesent(canbeuserids)--tagTAGtagstoreport.Default:alltags--all_timeincludeallsummaries(evenoldones).Default:False
Package: tensorboard-termplot
Terminal-plot for TensorboardA plotter for tensorboard, directly within your terminal. This is useful when you are training your neural network on a remote server, and you just want to quickly peek at the training curve without launching a tensorboard instance and mess with forwarding ports.InstallYou can install the package published in PyPI with$pipinstalltensorboard-termplot# or install with an isolated environment# $ pipx install tensorboard-termplotUsage$tensorboard-termplotFOLDERFor example,$tensorboard-termplot~/my_amazing_nn/runswhererunsis the folder that tensorboard had created.ExampleRunning the executable on a tensorboard output folder:Or with the consolidate flag-c/--consolidateto combine multiple stats with a common prefix (most stats in the bottom figure had empty values):Help$ tensorboard-termplot --help
usage: tensorboard-termplot [-h] [--canvas-color CANVAS_COLOR]
                            [--axes-color AXES_COLOR]
                            [--ticks-color TICKS_COLOR] [--colorless] [-d]
                            [--grid] [--plotsize WIDTH,HEIGHT] [--force-label]
                            [--no-iter-color] [-c] [-f] [-n secs]
                            [--log_interval LOG_INTERVAL]
                            FOLDER

positional arguments:
  FOLDER                Folder of a tensorboard runs

optional arguments:
  -h, --help            show this help message and exit
  --canvas-color CANVAS_COLOR
                        set the color of the plot canvas (the area where the
                        data is plotted)
  --axes-color AXES_COLOR
                        sets the background color of all the labels
                        surrounding the actual plot, i.e. the axes, axes
                        labels and ticks, title and legend, if present
  --ticks-color TICKS_COLOR
                        sets the (full-ground) color of the axes ticks and of
                        the grid lines.
  --colorless           Remove color.
  -d, --dark-theme      A collection of flags. If set, it is equivalent to
                        setting canvas-color and axes-color to black, and
                        setting ticks-color to red. Can be overwritten
                        individually.
  --grid                Show grid.
  --plotsize WIDTH,HEIGHT
                        Manually set the size of each subplot, e.g., 50,20.
  --force-label         Force showing label even for plot with one series.
  --no-iter-color       Stop iterating through different colors per plot.
  -c, --consolidate     Consolidate based on prefix.
  -f, --follow          Run in a loop to update display periodic.
  -n secs, --interval secs
                        seconds to wait between updates
Package: tensorboard-wrapper
TensorboardThis is a wrapper class for the Tensorflow's Tensorboard application. It is still in early development, and should be used with a grain of salt.
If you want to help be sure to contact me or fork this repository and work on our ToDo list from the development branch.
Tensorboard.py is the Wrapper that contains all functions needed to interact, more documentation should come in the future, but as for now, the requirements for this project are:PyTorchThe project does not install PyTorch so it doesn't force a different version from your environment, so before using the wrapper, be sure to install:torchvision
torchDevelopment dependenciessetuptools
wheel
twine
numpy
tensorboard==1.15
torch>=1.6.0 
torchvision>=0.7.0installYou can now install this project by using:pip install tensorboard-wrapperYou can run the tests by executing:python -m unittest test.pyAnd the list of the functions already implemented:tensorboard.writer.SummaryWritteradd_scalaradd_scalar*add_histogram*add_imageadd_images*add_figureadd_videoadd_audioadd_textadd_graphadd_embeddingadd_pr_curveadd_custom_scalarsadd_meshadd_hparamsflush*close**Altered in some shape or form. All changes will be in the documentation in the future.
Package: tensorboardX-hparams
History1.7.2 (2019-07-24)Add hparams support1.6 (2019-01-02)Many graph related bug is fixed in this version.New function: add_images(). This function accepts 4D iamge tensor. See documentation.Make add_image_with_boxes() usable.API change: add_video now accepts BxTxCxHxW instead of BxCxTxHxW tensor.1.5 (2018-12-10)Add API for Custom scalarAdd support for logging directly to S3Add support for Caffe2 graphPytorch 1.0.0 JIT graph support (alpha-release)1.4 (2018-08-09)Made add_text compatible with tensorboard>1.6Fix the issue of strange histogram if default binning method is usedSupports passing matplotlib figures to add_image()Resolve namespace confliction with TF tensorboardadd_image_boxes functionSupports custom timestamp for event1.2 (2018-04-21)Supports tensorshape information in graph visualization. Drop support for 0.3.1Adds add_video function1.1 (2018-02-21)Supports pytorch 0.3.1 (hacky)1.0 (2018-01-18)Supports graph (the pretty one)0.9 (2017-11-11)Supports markdown for add_text functionIt's ready to log precision recall curve (needs tensorboard>=0.4)Adds context manager for the SummaryWriter class0.8 (2017-09-25)Package name renamed to tensorboardX to fix namespace confliction with tensorflow's tensorboardSupports multi-scalars and JSON exportMultiple Embeddings in One ExperimentSupports Chainer and mxnet0.7 (2017-08-22)remove tensorflow dependency for embedding functionfixed incorrect image<->label pairing in embedding function (#12)unifies API call and adds docstring. Documentation is available at:http://tensorboard-pytorch.readthedocs.io/0.6.5 (2017-07-30)add travis test (py2.7, py3.6)add support for python2 (in PyPI)0.6 (2017-07-18)supports embedding0.5 (2017-07-18)supports graph summaryfixed np.histogram issue0.4 (2017-07-12)supports text summary0.3 (2017-07-03)supports audio summary0.2 (2017-06-24)simplifies add_image APIspeed up add_histogram API by 35x0.1 (2017-06-13)First commit. Reference:https://github.com/TeamHG-Memex/tensorboard_loggerhttps://github.com/dmlc/tensorboard
Package: tensorboardY
tensorboardYThe easier it is to interact with ML models, the faster we can determine their current limitations. This library seeks to automate the creation of cool ML demo websites likestyle transferMegadepthTalk to transformerGLTRWayve demoFUNITDocumentation ishere(generated usingpdoc3).
The github repo ishere.Installpip install tensorboardYExamplespython examples/simple_example.pyimport tensorboardY as ty
import matplotlib.pyplot as plt

def forward(x, title):
    plt.imshow(x)
    plt.title(title)
    return plt.gcf()

inputs = [ty.Image(var='x', exs=['imgs/curve.jpg']),
          ty.Text(var='title', exs=["EXAMPLE"])]

ty.show(forward, inputs)python examples/full_example.pyimport tensorboardY as ty
import os

def forward(z):
    return z

inputs = [ty.Widget("z", name="Choose your input",
                    camera=True,
                    image_upload=True,
                    image_list=['imgs/curve.jpg'], image_names=['Curve example'],
                    text_input=True,
                    text_list=['This is an example text!'], text_names=['Random'],
                    option_list=["This is an example option!"], option_names=['Resnet50'],
                    boolean=True,
                    slider=(5, 20, 0.5), slider_default=10.3)]

ty.show(forward, inputs)