Saravanan666/pypi-org huggingface dataset:
Package: onnxruntime-openvino
OpenVINO™ Execution Provider for ONNX Runtimeis a product designed for ONNX Runtime developers who want to get started with OpenVINO™ in their inferencing applications. This product deliversOpenVINO™inline optimizations which enhance inferencing performance with minimal code modifications.OpenVINO™ Execution Provider for ONNX Runtime accelerates inference across manyAI modelson a variety of Intel® hardware such as:Intel® CPUsIntel® integrated GPUsIntel® discrete GPUsInstallationRequirementsUbuntu 18.04, 20.04, RHEL(CPU only) or Windows 10 - 64 bitPython 3.8 or 3.9 or 3.10 for Linux and only Python3.10 for WindowsThis package supports:Intel® CPUsIntel® integrated GPUsIntel® discrete GPUspip3 installonnxruntime-openvinoPlease install OpenVINO™ PyPi Package separately for Windows.
For installation instructions on Windows please refer toOpenVINO™ Execution Provider for ONNX Runtime for Windows.OpenVINO™ Execution Provider for ONNX RuntimeLinux Wheels comes with pre-built libraries of OpenVINO™ version 2023.0.0 eliminating the need to install OpenVINO™ separately. The OpenVINO™ libraries are prebuilt with CXX11_ABI flag set to 0.For more details on build and installation please refer toBuild.UsageBy default, Intel® CPU is used to run inference. However, you can change the default option to either Intel® integrated or discrete GPU.
Invokethe provider config device type argumentto change the hardware on which inferencing is done.For more API calls and environment variables, seeUsage.SamplesTo see what you can do withOpenVINO™ Execution Provider for ONNX Runtime, explore the demos located in theExamples.LicenseOpenVINO™ Execution Provider for ONNX Runtimeis licensed underMIT.
By contributing to the project, you agree to the license and copyright terms therein
and release your contribution under these terms.SupportPlease submit your questions, feature requests and bug reports viaGitHub Issues.How to ContributeWe welcome community contributions toOpenVINO™ Execution Provider for ONNX Runtime. If you have an idea for improvement:Share your proposal viaGitHub Issues.Submit aPull Request.
Package: onnxruntime-powerpc64le
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.12.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.12.01.11.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.11.11.11.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.11.01.10.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.10.01.9.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.9.01.8.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.21.8.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.11.8.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.01.7.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.01.6.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.01.5.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.31.5.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.21.5.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.11.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.01.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: onnxruntime-silicon
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.16.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.16.31.16.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.16.21.16.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.16.11.16.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.16.01.15.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.15.01.14.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.14.01.13.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.13.01.12.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.12.01.11.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.11.01.10.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.10.01.9.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.9.01.8.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.21.8.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.11.8.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.01.7.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.01.6.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.01.5.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.31.5.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.21.5.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.11.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.01.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: onnxruntime-training
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.17.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.17.11.17.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.17.01.16.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.16.01.15.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.15.01.14.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.14.01.13.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.13.01.12.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.12.01.11.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.11.01.10.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.10.01.9.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.9.01.8.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.21.8.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.11.8.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.01.7.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.01.6.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.01.5.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.31.5.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.21.5.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.11.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.01.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: onnxruntime-training-cpu
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.16.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.16.01.15.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.15.01.14.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.14.01.13.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.13.01.12.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.12.01.11.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.11.01.10.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.10.01.9.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.9.01.8.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.21.8.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.11.8.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.01.7.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.01.6.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.01.5.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.31.5.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.21.5.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.11.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.01.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: onnx-safetensors
onnx-safetensorsONNX extension for saving to and loading from safetensors 🤗.Installpip install --upgrade onnx-safetensorsUsageLoad tensors to an ONNX modelimportonnx_safetensors# Provide your ONNX model heremodel:onnx.ModelPrototensor_file="model.safetensors"# Apply weights from the safetensors file to the modelonnx_safetensors.load_file(model,tensor_file)Save weights to a safetensors fileimportonnx_safetensors# Provide your ONNX model heremodel:onnx.ModelPrototensor_file="model.safetensors"# Save weights from to the safetensors fileonnx_safetensors.save_file(model,tensor_file,convert_attributes=True)# Save weights from to the safetensors file and clear the raw_data fields of the ONNX model to reduce its size# model will be updated inplaceonnx_safetensors.save_file(model,tensor_file,convert_attributes=True,strip_data=True)
Package: onnxsim
ONNX SimplifierONNX is great, but sometimes too complicated.BackgroundOne day I wanted to export the following simple reshape operation to ONNX:importtorchclassJustReshape(torch.nn.Module):def__init__(self):super(JustReshape,self).__init__()defforward(self,x):returnx.view((x.shape[0],x.shape[1],x.shape[3],x.shape[2]))net=JustReshape()model_name='just_reshape.onnx'dummy_input=torch.randn(2,3,4,5)torch.onnx.export(net,dummy_input,model_name,input_names=['input'],output_names=['output'])The input shape in this model is static, so what I expected isHowever, I got the following complicated model instead:Our solutionONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph
and then replaces the redundant operators with their constant outputs (a.k.a. constant folding).Web versionWe have published ONNX Simplifier onconvertmodel.com. It works out of the box anddoesn't need any installation. Note that it runs in the browser locally and your model is completely safe.Python versionpip3 install -U pip && pip3 install onnxsimThenonnxsim input_onnx_model output_onnx_modelFor more advanced features, try the following command for help messageonnxsim -hDemonstrationAn overall comparison betweena complicated modeland its simplified version:In-script workflowIf you would like to embed ONNX simplifier python package in another script, it is just that simple.importonnxfromonnxsimimportsimplify# load your predefined ONNX modelmodel=onnx.load(filename)# convert modelmodel_simp,check=simplify(model)assertcheck,"Simplified ONNX model could not be validated"# use model_simp as a standard ONNX model objectYou can see more details of the API inonnxsim/onnx_simplifier.pyProjects Using ONNX SimplifierMXNetMMDetectionYOLOv5ncnn...ChatWe created a Chinese QQ group for ONNX!ONNX QQ Group (Chinese): 1021964010, verification code: nndab. Welcome to join!For English users, I'm active on theONNX Slack. You can find and chat with me (daquexian) there.
Package: onnx-simplifier
ONNX SimplifierONNX is great, but sometimes too complicated.BackgroundOne day I wanted to export the following simple reshape operation to ONNX:importtorchclassJustReshape(torch.nn.Module):def__init__(self):super(JustReshape,self).__init__()defforward(self,x):returnx.view((x.shape[0],x.shape[1],x.shape[3],x.shape[2]))net=JustReshape()model_name='just_reshape.onnx'dummy_input=torch.randn(2,3,4,5)torch.onnx.export(net,dummy_input,model_name,input_names=['input'],output_names=['output'])The input shape in this model is static, so what I expected isHowever, I got the following complicated model instead:Our solutionONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph
and then replaces the redundant operators with their constant outputs (a.k.a. constant folding).Web versionWe have published ONNX Simplifier onconvertmodel.com. It works out of the box anddoesn't need any installation. Note that it runs in the browser locally and your model is completely safe.Python versionpip3 install -U pip && pip3 install onnxsimThenonnxsim input_onnx_model output_onnx_modelFor more advanced features, try the following command for help messageonnxsim -hDemonstrationAn overall comparison betweena complicated modeland its simplified version:In-script workflowIf you would like to embed ONNX simplifier python package in another script, it is just that simple.importonnxfromonnxsimimportsimplify# load your predefined ONNX modelmodel=onnx.load(filename)# convert modelmodel_simp,check=simplify(model)assertcheck,"Simplified ONNX model could not be validated"# use model_simp as a standard ONNX model objectYou can see more details of the API inonnxsim/onnx_simplifier.pyProjects Using ONNX SimplifierMXNetMMDetectionYOLOv5ncnn...ChatWe created a Chinese QQ group for ONNX!ONNX QQ Group (Chinese): 1021964010, verification code: nndab. Welcome to join!For English users, I'm active on theONNX Slack. You can find and chat with me (daquexian) there.
Package: onnxslim
OnnxSlimInstallationUsing PrebuiltpipinstallonnxslimBuild From Sourcepip install .How to useonnxslim your_onnx_model slimmed_onnx_modelFor more usage, see onnxslim -h or refer to ourexamplesReferencesonnx-graphsurgeonPolygraphyonnx-simplifiertabulateContactQQ Group: 873569894
Package: onnx-vis
ONNX VisualizerThis package help you visualize the ONNX model graph. Client-Server based architecture lets you share the model, using just a url instead of sharing the entire model.Why not netron?Netronis a viewer for neural network, deep learning and machine learning models. Netron also supports more formats than just ONNX.But the problem with netron, you can't visualize the models in remote / virtual machines environments, where most of the time GUI is not given. So the only way is you need to download and run the netron locally or use the web app, where model parsing happens in the web browser, which is super slow.This package parses the onnx model and serves only required data, using the server-client model, which is highly bandwidth effecient.UsageInstallpip install onnx-visVisualizingRun the following command to visualize the modelpython3-monnx_vis<pathtotheonnxfile>-p<porttoruntheserver>Example:python3-monnx_visresnet18.onnx-p63325you can access the visualization onhttp://127.0.0.1:63325TODOMake Visualization graph of ONNX NodesAdd node attributes like kernels shape, bias shape to node metadataAdd Sidebar to get more detailed information about nodesAdd search and span to node featureContribution & IssuesContirbution of any kind PR's, Discussions etc. is very much appreciated :). If you face any issues feel free to open a issue.
Package: onoff
onoff is a Python module that provides theOnOffMixinclass which can be used
to addon(),off(), andtrigger()style events to any Python class.NoteFor the latest, complete documentation:http://liftoff.github.io/onoff/For the latest code:https://github.com/liftoff/onoffExample>>>fromonoffimportOnOffMixin>>>classFoo(OnOffMixin):...def__init__(self):...self.on("hello",self.hello)...defhello(self,*args):...print("Hello:%s"%args)...deftest(self,*args):...self.trigger("hello",*args)...>>>f=Foo()>>>f.test("Triggered events rock!")Hello:Triggeredeventsrock!
Package: onoffmonitordevice
On/Off Monitor DeviceInstallationCryptographyOn Linux you might need to install Rust to compile the Cryptography library. See theirinstallation instructionsFor installing Rust on Raspberry Pi seehttps://forums.raspberrypi.com/viewtopic.php?t=289963For when testing on a non-Raspberry PiInstead of installing theRPi.GPIOmodule, you can installrpi-gpio-emu.
Installing with pipenv won't work asRPi.GPIOonly works with Raspberry Pi.
Package: onoffmonitorserver
On/Off Monitor ServerInstallationInstall from PyPI:pip install onoffmonitorserverSetupAdd to projecturls.py:urlpatterns=[...path('api/onoffmonitor/',include('onoffmonitorserver.urls')),path('api/',include('knox.urls')),...]Add to projectsettings.py:INSTALLED_APPS=['onoffmonitorserver',...'rest_framework','knox']Migrate database changes:python manage.py migrate
Package: onomancer
onomanceronomanceris an open source python package for inferring gender from first names for feature engineering. Name-Gender mappings are based on lookups in theF.txt, M.txt, N.txtfiles located in thedatafolder created from combining data provided from theSocial Security AdministrationandWorld Intellectual Property Organization. If a Name-Gender mapping does not exist, the gender is predicted from a pre-trained model namedmodel.binalso located in thedatafolder.Requirementsonomancer requires:python >= 3.6.x.fasttext == 0.9.2 (to load pre-trained model)Installing$ pip install onomancerUsage$ python
>>> import onomancer as ono
>>> ono.predict(['GALADRIEL', 'GanDALF'])
{'GANDALF': 'M', 'GALADRIEL': 'F'}
Package: onos-api
onos-apigRPC API definitions for the µONOS platformOverviewThis repository houses not only the.protofiles, but also a number of automatically generated artifacts, e.g..pb.go,.pyfiles and similarly for other languages that may be added in the future.The source tree structure is paritioned intoproto, which contains the canonical protobuf definitions and a top-level directory for each of the supported languages. The structure within each of the language-specific directories reflects the idoms and conventions appropriate to each language.ProtoThe top-level package for the protobuf definitions isonosand the next level subpackage is the name of the particular platform subsystem, such asconfig,topo, etc. This directory tree should contain exclusively the.protofiles and not be tainted by any other artifacts, especially any language-specific ones.The proto files are compiled and processed viabuild/bin/compile-protos.shscript, which is invoked by theMakefile. All protofiles here should follow the established guidelines and must pass the protobuf lint checker enforcing these conventions.GolangThegosource tree holds the automatically-generated.pb.goartifacts and also any manually authored.gosource files written in support of the Golang bindings. To minimize the churn and to exercise tighter control over versioning, the generated files are also versioned and maintained in the SCM repo.The root package of the module isgithub.com/onosproject/onos-api/go/onos, with subpackages being named after each of the platform subsystems, mirroring the structure of theprotopackages. Golang projects that wish to import µONOS API packages should include the following in the requirements section of theirgo.modfile:require(...github.com/onosproject/onos-api/gov0.6.1...)Additionally, Go bindings are generated with mocks for testing withgomock. Mocks of Protobuf interfaces can be constructed via the same package as the interfaces they mock:importtopoapi"github.com/onosproject/onos-api/go/onos/topo"...mockClient:=topoapi.NewMockTopoClient(ctrl)PythonThepythonsource tree holds gRPC bindings are generated forPython. Pyhton bindings are generated with thepython-betterprotoprotoc plugin and support Python3'sasyncioframework.
Package: onot
onotonotis a tool that automatically creates open source software notices based on SPDX documents.onotis an open source project co-developed byKakaoandSK telecom. We welcome your contributions.InstallationUse the package managerpipto install foobar.pipinstallonotor you can install latest verson from source code.gitclonehttps://github.com/sktelecom/onot.git~/onotcd~/onot;pythonsetup.pyinstallUsageCommand LinePrepare your input file. The input file is anExcel format SPDX document, and refer to the next page forhow to prepare it.Run onot command with two arguments.-ior--input: SPDX document in Excel format containing open source information to be included in the OSS notice-oor--output_format: File type of OSS notice to be generated (htmlortext)Sample output :output/OSS_Notice_SPDX-Tools-v2.0_20221009_180948.htmlonot--inputsample/SPDXRdfExample-v2.3.xlsx--output_formathtmlGUI for windowsPrepare your input file. The input file is anExcel format SPDX document, and refer to the next page forhow to prepare it.Run the command below or download zip file from release assets.$pyinstaller-wonot/gui/onot_app.pyRun the onot_app.exe file. Executable file is located in the onot_app directory.Testpython-munittestContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.MaintainerNameCompanyEmailRogers(한현민)Kakaoum4825@gmail.comHaksung(장학성)SK telecomhakssung@gmail.comContributorNameCompanyEmailCindy(이승아)Kakaoblackstrida@gmail.comLicenseApache-2.0
Package: onpremgpusdk
On Prem GPU SDKProvides Utitlities to support and build Machine Learning and Analytics scripts to run on navi premise GPU cluster.Installationpip3 install onpremgpusdkExampleimport pandas as pd
from onpremgpusdk.data import DataInstance


'''
Create data instance
'''
du = DataInstance("local-stack-bucket") # Instantiate DataInstance with bucket name to use


'''
    Upload File
'''
df = pd.read_csv("./stocks.csv")
du.write_csv(df, "output_test/stock_output.csv")


'''
Read Data
'''
du.read_csv("output_test/stock_output.csv")