Saravanan666/pypi-org huggingface dataset:
Package: hyaline
Hyaline Discord API Wrapper.WARNING: Hyaline is not easy to use library.Hyaline is a discord api wrapper for python. hyaline is created for discord developers and hyaline makes everything harder.Its in alpha-release and still not finished.Check./docsfolder for documentation and more informations about hyaline. Documentation is not finished. You can use pythondir()function for get all functions, attrs in the object. Also usehelp()for get help about a function / class.InstallationJust typepip install hyalineand you are ready to go!Current Version- 0.1.1Change Log:Added Webhook Support.Example Ping-Pong Discord Bot# Import the library and intent helper.importhyalinefromhyaline.helpers.IntentsimportALL# Setup session configurations.# NOTE: If you want to limit message cache add MAX_MESSAGESsession=hyaline.Session({"TOKEN":"token","INTENTS":ALL})asyncdefon_ready(_packet):print(f"Bot Started with name:{session.client.username}.")asyncdefon_message(msg):ifnotmsg.author.id==session.client.idandmsg.content.lower()=="ping":awaitmsg.reply({"content":":ping_pong: Pong!"})# Configure the events.# Also you can configure multiple event.session.event("MESSAGE_CREATE",on_message)session.event("READY",on_ready)# Start the session.session.start()
Package: hyalus
Hyalus is a testing tool for software verification, designed as a standalone tool as well as a tool that integrates withpytest.Getting StartedSeeIntro to hyalus.ContributingSeeContribution Guidelines.Code of ConductSeeCode of Conduct.LicenseCopyright David McConnell and others, 2023.Distributed under the terms of theMIT license, hyalus is free and open source software.
Package: hybo
HyboUnofficial pure-python module for iLidar, solid state lidar byHyboInstall$pipinstallhyboExampleexample/simple.pyimporttimeimporthybowithhybo.Lidar('/dev/cu.usbserial-D3095E6S')ashybo:# waiting for connectiontime.sleep(1)# retrieve latest frameframe=hybo.get_latest_frame()print(frame)Result:{'sequence':53247,'time_peak':529.675937,'points':array([[0,0,0],[0,0,0],[0,0,0],...,[0,0,0],[0,0,0],[0,0,0]],dtype=int16)}TODOcallback-style frame capturingframe queuingasync mode
Package: hybrid-attributes
Hybrid-Attributes implements ahybrid_propertyandhybrid_methoddescriptor, which call the underlying function both in a class and instance
context.Assume you have a class like:class Foo:
    @classmethod
    def spam(self):
        return 'spam'

    @property
    def eggs(self):
        return 'eggs'

    def ham(self):
        return 'ham'You can get the values returned by calling.spam()on the class, accessing
‘.eggs’ or calling.ham()on an instance:>>> Foo.spam()
'spam'
>>> foo = Foo()
>>> foo.eggs
'eggs'
>>> foo.ham()
'ham'If you access these in a different context,.spam()will still be
called with the class as first argument,.eggswill return a property object
only useful for introspection and.ham()will raise an exception because
there is no instance for it to be called with.Hybrid-Attributes are different. They don’t care about the object are accessed
through, the underlying function will always be called with whatever object
it’s being accessed on. As an example, take this class:class HybridFoo:
    @hybrid_property
    def spam(self):
        return 'spam'

    @hybrid_method
    def eggs(self):
        return 'eggs'You can access the property and call the method on the class or on an instance:>>> Foo.spam
'spam'
>>> Foo.eggs()
'eggs'
>>> foo = Foo()
>>> foo.spam
'spam'
>>> foo.eggsHybrid-Attributes is BSD licensed and available for Python 3.4 and later.
Package: hybridbackend-cpu-legacy
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataInstallUsing pip packagesGLIBCCUDAPythonTensorflowCommand>= 2.4-3.6>=1.15, < 2.0pip install hybridbackend-cpu>= 2.4-3.6>=1.14, < 1.15pip install hybridbackend-cpu-legacyBuild from sourceSeeBuilding Instructions.UsagePlease seedocumentationfor
more information.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discuss new feature
requirements with registered adopters in advance.Please cite HybridBackend in your publications if it helps:@article{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  journal={arXiv preprint arXiv:2204.04903},
  year={2022}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-deeprec2208-cu114
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.ParquetDataset(filenames,batch_size=batch_size)ds=ds.apply(hb.data.parse())# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Install from PyPIpip install {PACKAGE}{PACKAGE}DependencyPythonCUDAGLIBCColumnar Data LoadingEmbedding OrchestrationHybrid Parallelismhybridbackend-deeprec2208-cu114DeepRec 22.083.611.4>=2.27✓✓✓hybridbackend-tf115-cu116TensorFlow 1.153.811.6>=2.31✓✓✓hybridbackend-tf115-cu100TensorFlow 1.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpuTensorFlow 1.153.6->=2.24✓✗✗Method 2: Build from sourceSeeBuilding Instructions.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-deeprec2212-cu114
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.Dataset.from_parquet(filenames)ds=ds.batch(batch_size)# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Install from PyPIpip install {PACKAGE}{PACKAGE}DependencyPythonCUDAGLIBCData Opt.Embedding Opt.Parallelism Opt.hybridbackend-tf115-cu118TensorFlow 1.1513.811.8>=2.31✓✓✓hybridbackend-tf115-cu100TensorFlow 1.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpuTensorFlow 1.153.6->=2.24✓✗✗hybridbackend-deeprec2212-cu114DeepRec 22.1223.611.4>=2.27✓✓✓1: Suggested docker image:nvcr.io/nvidia/tensorflow:23.02-tf1-py32: Suggested docker image:registry.cn-shanghai.aliyuncs.com/pai-dlc/tensorflow-training:deeprec2212-gpu-py36-cu114-ubuntu18.04Method 2: Build from sourceSeeBuilding Instructions.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-tf115-cpu
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.Dataset.from_parquet(filenames)ds=ds.batch(batch_size)# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Install from PyPIpip install {PACKAGE}{PACKAGE}DependencyPythonCUDAGLIBCData Opt.Embedding Opt.Parallelism Opt.hybridbackend-tf115-cu121TensorFlow 1.153.812.1>=2.31✓✓✓hybridbackend-tf115-cu100TensorFlow 1.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpuTensorFlow 1.153.6->=2.24✓✗✗Method 2: Build from sourceSeeBuilding Instructions.We also provide built docker images for latestDeepRec:registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:1.0.0-deeprec-py3.6-cu114-ubuntu18.04LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-tf115-cu100
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.ParquetDataset(filenames,batch_size=batch_size)ds=ds.apply(hb.data.parse())# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Pull container images fromPAI DLCdocker pull registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:{TAG}{TAG}TensorFlowPythonCUDAOSColumnar Data LoadingEmbedding OrchestrationHybrid Parallelism0.7-tf1.15-py3.8-cu114-ubuntu20.041.153.811.4Ubuntu 20.04✓✓✓Method 2: Install from PyPIpip install {PACKAGE}{PACKAGE}TensorFlowPythonCUDAGLIBCColumnar Data LoadingEmbedding OrchestrationHybrid Parallelismhybridbackend-tf115-cu114*1.153.811.4>=2.31✓✓✓hybridbackend-tf115-cu1001.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpu1.153.6->=2.24✓✗✗*nvidia-pyindexmust be installed firstMethod 3: Build from sourceSeeBuilding Instructions.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-tf115-cu114
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.Dataset.from_parquet(filenames)ds=ds.batch(batch_size)# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Install from PyPIpip install {PACKAGE}{PACKAGE}DependencyPythonCUDAGLIBCData Opt.Embedding Opt.Parallelism Opt.hybridbackend-tf115-cu118TensorFlow 1.1513.811.8>=2.31✓✓✓hybridbackend-tf115-cu100TensorFlow 1.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpuTensorFlow 1.153.6->=2.24✓✗✗hybridbackend-deeprec2212-cu114DeepRec 22.1223.611.4>=2.27✓✓✓1: Suggested docker image:nvcr.io/nvidia/tensorflow:23.02-tf1-py32: Suggested docker image:registry.cn-shanghai.aliyuncs.com/pai-dlc/tensorflow-training:deeprec2212-gpu-py36-cu114-ubuntu18.04Method 2: Build from sourceSeeBuilding Instructions.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-tf115-cu116
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.ParquetDataset(filenames,batch_size=batch_size)ds=ds.apply(hb.data.parse())# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Pull container images fromPAI DLCdocker pull registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:{TAG}{TAG}TensorFlowPythonCUDAOSColumnar Data LoadingEmbedding OrchestrationHybrid Parallelism0.7-tf1.15-py3.8-cu116-ubuntu20.041.153.811.6Ubuntu 20.04✓✓✓Method 2: Install from PyPIpip install {PACKAGE}{PACKAGE}TensorFlowPythonCUDAGLIBCColumnar Data LoadingEmbedding OrchestrationHybrid Parallelismhybridbackend-tf115-cu114*1.153.811.4>=2.31✓✓✓hybridbackend-tf115-cu1001.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpu1.153.6->=2.24✓✗✗*nvidia-pyindexmust be installed firstMethod 3: Build from sourceSeeBuilding Instructions.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-tf115-cu118
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.Dataset.from_parquet(filenames)ds=ds.batch(batch_size)# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Install from PyPIpip install {PACKAGE}{PACKAGE}DependencyPythonCUDAGLIBCData Opt.Embedding Opt.Parallelism Opt.hybridbackend-tf115-cu118TensorFlow 1.1513.811.8>=2.31✓✓✓hybridbackend-tf115-cu100TensorFlow 1.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpuTensorFlow 1.153.6->=2.24✓✗✗hybridbackend-deeprec2208-cu114DeepRec 22.0823.611.4>=2.27✓✓✓1: Suggested docker image:nvcr.io/nvidia/tensorflow:22.12-tf1-py32: Suggested docker image:dsw-registry.cn-shanghai.cr.aliyuncs.com/pai/tensorflow-training:1.15PAI-gpu-py36-cu114-ubuntu18.04Method 2: Build from sourceSeeBuilding Instructions.LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbackend-tf115-cu121
HybridBackendHybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.FeaturesMemory-efficient loading of categorical dataGPU-efficient orchestration of embedding layersCommunication-efficient training and evaluation at scaleEasy to use with existing AI workflowsUsageA minimal example:importtensorflowastfimporthybridbackend.tensorflowashbds=hb.data.Dataset.from_parquet(filenames)ds=ds.batch(batch_size)# ...withtf.device('/gpu:0'):embs=tf.nn.embedding_lookup_sparse(weights,input_ids)# ...Please seedocumentationfor
more information.InstallMethod 1: Install from PyPIpip install {PACKAGE}{PACKAGE}DependencyPythonCUDAGLIBCData Opt.Embedding Opt.Parallelism Opt.hybridbackend-tf115-cu121TensorFlow 1.153.812.1>=2.31✓✓✓hybridbackend-tf115-cu100TensorFlow 1.153.610.0>=2.27✓✓✗hybridbackend-tf115-cpuTensorFlow 1.153.6->=2.24✓✗✗Method 2: Build from sourceSeeBuilding Instructions.We also provide built docker images for latestDeepRec:registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:1.0.0-deeprec-py3.6-cu114-ubuntu18.04LicenseHybridBackend is licensed under theApache 2.0 License.CommunityPlease seeContributing Guidebefore your first contribution.Pleaseregister as an adopterif your organization is interested in adoption. We will discussRoadMapwith
registered adopters in advance.Please citeHybridBackendin your publications if it helps:@inproceedings{zhang2022picasso,
  title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
  author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}Contact UsIf you would like to share your experiences with others, you are welcome to
contact us in DingTalk:
Package: hybridbosse
HybridBOSSEHybrids Balance-of-System System Engineering (HybridBOSSE) is a tool for estimating BOS costs for hybrid power plants that share infrastructure. The development of this tool was funded by the Department of Energy, and developed by researchers at the National Renewable Energy Laboratory (NREL).Free software: MIT licenseDocumentation:https://hybrids-shared-infrastructure.readthedocs.io.Features![hybrid infrastructure](https://user-images.githubusercontent.com/41651551/90398485-c0856b80-e056-11ea-81bc-50d0faa1e80c.png)History1 (2020-04-28)First release on PyPI.
Package: hybridcontents
HybridContentsTheHybridContentManagerwas originally created byQuantopianas part ofpgcontents; however, the usage ofHybridContentsManagerwas restricted to the compatibility requirements ofpgcontents. These restrictions includedpostgresdependencies and no support for the latestnotebookversion (>6).AtViaductwe usedpgcontentsexclusively for theHybridContentsManagerand wanted to extend its functionality, so we created this forkhybridcontents.See relatedpgcontentsissues:https://github.com/quantopian/pgcontents/issues/66https://github.com/quantopian/pgcontents/issues/50https://github.com/quantopian/pgcontents/issues/28Getting StartedPrerequisites:A Python installation withJupyter Notebook>= 4.0.Installation:pippipinstallhybridcontentsAnacondacondainstall-cviaducthybridcontentsconda-forgeSee instructionshereFeaturesMix and match different content managers for different directoriesEasily move files between different content managers (i.e local files to s3 backed manager)Path validation to keep consistent naming scheme and/or prevent illegal charactersUsageFor a detailed example see,hybrid_manager_example.pyThe following code snippet creates a HybridContentsManager with two directories with different content managers.c=get_config()c.NotebookApp.contents_manager_class=HybridContentsManagerc.HybridContentsManager.manager_classes={# NOTE: LargFileManager only exists in notebook > 5# If using notebook < 5, use FileContentManager instead"":LargeFileManager,"shared":S3ContentsManager}# Each item will be passed to the constructor of the appropriate content manager.c.HybridContentsManager.manager_kwargs={# Args for root LargeFileManager"":{"root_dir":read_only_dir},# Args for the shared S3ContentsManager directory"shared":{"access_key_id":...,"secret_access_key":...,"endpoint_url":...,"bucket":...,"prefix":...},}defonly_allow_notebooks(path):returnpath.endswith('.ipynb')# Only allow notebook files to be stored in S3c.HybridContentsManager.path_validators={"shared":only_allow_notebooks}TestingTo run unit tests,toxThis will run all unit tests for python versions 2.7, 3.6, 3.7 and jupyter notebook versions 4, 5, and 6.Publishing a ReleaseCreate a new release on GithubUpdate the version insetup.pyRun ./scripts/pip_publish.shUpdate the versionmeta.yamlUpdate thesha256 in meta.yamlRun ./scripts/anaconda_publish.shUpdate on Conda Forge