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1,341 | data | https://github.com/prefecthq/prefect-aws | ['aws'] | null | [] | [] | null | null | null | prefecthq/prefect-aws | prefect-aws | 83 | 39 | 11 | Python | https://PrefectHQ.github.io/prefect-aws/ | Prefect integrations with AWS. | prefecthq | 2023-12-29 | 2022-01-04 | 108 | 0.768519 | https://avatars.githubusercontent.com/u/39270919?v=4 | Prefect integrations with AWS. | ['aws', 'prefect'] | ['aws', 'prefect'] | 2024-01-05 | [('aws/aws-sdk-pandas', 0.6139408946037292, 'pandas', 1), ('boto/boto3', 0.5230756402015686, 'util', 1), ('pynamodb/pynamodb', 0.5191414952278137, 'data', 1), ('rhinosecuritylabs/pacu', 0.5137910842895508, 'security', 1), ('prefecthq/prefect-dbt', 0.512976348400116, 'ml-ops', 1)] | 34 | 4 | null | 1.42 | 50 | 32 | 25 | 0 | 18 | 15 | 18 | 50 | 43 | 90 | 0.9 | 31 |
1,659 | data | https://github.com/unstructured-io/unstructured-inference | ['unstructured', 'inference', 'pipeline'] | Hosted model inference code for layout parsing models. | [] | [] | null | null | null | unstructured-io/unstructured-inference | unstructured-inference | 61 | 18 | 15 | Python | null | null | unstructured-io | 2024-01-14 | 2022-12-20 | 58 | 1.051724 | https://avatars.githubusercontent.com/u/108372208?v=4 | Hosted model inference code for layout parsing models. | [] | ['inference', 'pipeline', 'unstructured'] | 2024-01-10 | [('optimalscale/lmflow', 0.5030722618103027, 'llm', 0)] | 24 | 3 | null | 3.21 | 70 | 54 | 13 | 0 | 71 | 72 | 71 | 70 | 43 | 90 | 0.6 | 31 |
1,038 | term | https://github.com/manrajgrover/halo | [] | null | [] | [] | null | null | null | manrajgrover/halo | halo | 2,816 | 146 | 24 | Python | null | 💫 Beautiful spinners for terminal, IPython and Jupyter | manrajgrover | 2024-01-11 | 2017-09-03 | 334 | 8.423932 | null | 💫 Beautiful spinners for terminal, IPython and Jupyter | ['async', 'halo', 'ipython', 'jupyter', 'ora', 'spinner'] | ['async', 'halo', 'ipython', 'jupyter', 'ora', 'spinner'] | 2020-11-09 | [('ipython/ipyparallel', 0.5247726440429688, 'perf', 1)] | 31 | 4 | null | 0 | 4 | 0 | 77 | 39 | 0 | 0 | 0 | 4 | 1 | 90 | 0.2 | 30 |
1,783 | diffusion | https://github.com/openai/improved-diffusion | ['denoising', 'diffusion'] | null | [] | [] | null | null | null | openai/improved-diffusion | improved-diffusion | 2,511 | 408 | 116 | Python | null | Release for Improved Denoising Diffusion Probabilistic Models | openai | 2024-01-12 | 2021-02-08 | 155 | 16.185083 | https://avatars.githubusercontent.com/u/14957082?v=4 | Release for Improved Denoising Diffusion Probabilistic Models | [] | ['denoising', 'diffusion'] | 2022-01-12 | [('lllyasviel/controlnet', 0.5762985944747925, 'diffusion', 0), ('tanelp/tiny-diffusion', 0.5728610754013062, 'diffusion', 0), ('divamgupta/stable-diffusion-tensorflow', 0.5380927324295044, 'diffusion', 0)] | 1 | 0 | null | 0 | 28 | 2 | 36 | 24 | 0 | 0 | 0 | 28 | 28 | 90 | 1 | 30 |
800 | web | https://github.com/flipkart-incubator/astra | [] | null | [] | [] | null | null | null | flipkart-incubator/astra | Astra | 2,385 | 389 | 84 | Python | null | Automated Security Testing For REST API's | flipkart-incubator | 2024-01-13 | 2018-01-10 | 315 | 7.550882 | https://avatars.githubusercontent.com/u/7090545?v=4 | Automated Security Testing For REST API's | ['ci-cd', 'owasp', 'penetration-testing', 'penetration-testing-framework', 'postman-collection', 'restapiautomation', 'sdlc', 'security', 'security-automation'] | ['ci-cd', 'owasp', 'penetration-testing', 'penetration-testing-framework', 'postman-collection', 'restapiautomation', 'sdlc', 'security', 'security-automation'] | 2023-02-16 | [('rhinosecuritylabs/pacu', 0.5590912699699402, 'security', 2), ('taverntesting/tavern', 0.552314817905426, 'testing', 0), ('swisskyrepo/payloadsallthethings', 0.5459146499633789, 'security', 2), ('tox-dev/tox', 0.5185132026672363, 'testing', 0), ('tiangolo/fastapi', 0.5062249302864075, 'web', 0)] | 12 | 3 | null | 0.02 | 4 | 0 | 73 | 11 | 0 | 0 | 0 | 4 | 1 | 90 | 0.2 | 30 |
1,328 | ml-dl | https://github.com/google-research/electra | [] | null | [] | [] | null | null | null | google-research/electra | electra | 2,269 | 350 | 61 | Python | null | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | google-research | 2024-01-13 | 2020-03-10 | 203 | 11.17734 | https://avatars.githubusercontent.com/u/43830688?v=4 | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | ['deep-learning', 'nlp', 'tensorflow'] | ['deep-learning', 'nlp', 'tensorflow'] | 2021-03-31 | [('huggingface/text-generation-inference', 0.648766815662384, 'llm', 2), ('minimaxir/textgenrnn', 0.6392484903335571, 'nlp', 2), ('amansrivastava17/embedding-as-service', 0.6076592803001404, 'nlp', 3), ('google/sentencepiece', 0.5957339406013489, 'nlp', 0), ('allenai/allennlp', 0.5719739198684692, 'nlp', 2), ('microsoft/unilm', 0.5669850707054138, 'nlp', 1), ('openai/clip', 0.5659348964691162, 'ml-dl', 1), ('minimaxir/gpt-2-simple', 0.5602125525474548, 'llm', 1), ('infinitylogesh/mutate', 0.553767204284668, 'nlp', 0), ('yueyu1030/attrprompt', 0.5535896420478821, 'llm', 0), ('deepset-ai/farm', 0.5499265789985657, 'nlp', 2), ('alibaba/easynlp', 0.5465848445892334, 'nlp', 2), ('bytedance/lightseq', 0.5465645790100098, 'nlp', 0), ('huggingface/transformers', 0.5317683219909668, 'nlp', 3), ('openai/finetune-transformer-lm', 0.5301187634468079, 'llm', 0), ('graykode/nlp-tutorial', 0.5271270871162415, 'study', 2), ('extreme-bert/extreme-bert', 0.5268334150314331, 'llm', 2), ('keras-team/keras-nlp', 0.5258187651634216, 'nlp', 3), ('qanastek/drbert', 0.5233448147773743, 'llm', 1), ('salesforce/blip', 0.5222206115722656, 'diffusion', 0), ('huggingface/text-embeddings-inference', 0.5215802788734436, 'llm', 0), ('nvidia/deeplearningexamples', 0.5167393684387207, 'ml-dl', 3), ('jonasgeiping/cramming', 0.5113855600357056, 'nlp', 0), ('explosion/spacy-transformers', 0.5100114941596985, 'llm', 1), ('huggingface/neuralcoref', 0.5090615153312683, 'nlp', 1), ('lucidrains/dalle2-pytorch', 0.5081148743629456, 'diffusion', 1), ('huggingface/setfit', 0.5080073475837708, 'nlp', 1), ('minimaxir/aitextgen', 0.5072848796844482, 'llm', 0), ('squeezeailab/squeezellm', 0.5039941072463989, 'llm', 0), ('llmware-ai/llmware', 0.5037044882774353, 'llm', 1), ('sharonzhou/long_stable_diffusion', 0.5007908344268799, 'diffusion', 0), ('plasticityai/magnitude', 0.500442385673523, 'nlp', 1)] | 5 | 2 | null | 0 | 1 | 1 | 47 | 34 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 30 |
848 | profiling | https://github.com/jiffyclub/snakeviz | [] | null | [] | [] | null | null | null | jiffyclub/snakeviz | snakeviz | 2,156 | 133 | 22 | Python | https://jiffyclub.github.io/snakeviz/ | An in-browser Python profile viewer | jiffyclub | 2024-01-11 | 2012-06-26 | 605 | 3.563636 | null | An in-browser Python profile viewer | [] | [] | 2023-05-14 | [('landscapeio/prospector', 0.582079291343689, 'util', 0), ('bokeh/bokeh', 0.5664529204368591, 'viz', 0), ('joerick/pyinstrument', 0.5636839866638184, 'profiling', 0), ('pyutils/line_profiler', 0.563589870929718, 'profiling', 0), ('gaogaotiantian/viztracer', 0.5514275431632996, 'profiling', 0), ('benfred/py-spy', 0.5489193797111511, 'profiling', 0), ('webpy/webpy', 0.5397558808326721, 'web', 0), ('urwid/urwid', 0.5396429300308228, 'term', 0), ('sumerc/yappi', 0.5380180478096008, 'profiling', 0), ('psf/requests', 0.5308777093887329, 'web', 0), ('pympler/pympler', 0.5296313762664795, 'perf', 0), ('roniemartinez/dude', 0.5278874635696411, 'util', 0), ('hoffstadt/dearpygui', 0.5256134867668152, 'gui', 0), ('seleniumbase/seleniumbase', 0.5255619287490845, 'testing', 0), ('eleutherai/pyfra', 0.5175898671150208, 'ml', 0), ('nedbat/coveragepy', 0.5139472484588623, 'testing', 0), ('pythonspeed/filprofiler', 0.5121504664421082, 'profiling', 0), ('scrapy/scrapy', 0.5070153474807739, 'data', 0), ('r0x0r/pywebview', 0.5045416951179504, 'gui', 0), ('pyglet/pyglet', 0.5042890310287476, 'gamedev', 0)] | 26 | 7 | null | 0.23 | 1 | 0 | 141 | 8 | 0 | 2 | 2 | 1 | 0 | 90 | 0 | 30 |
1,049 | util | https://github.com/kalliope-project/kalliope | [] | null | [] | [] | null | null | null | kalliope-project/kalliope | kalliope | 1,683 | 241 | 82 | Python | https://kalliope-project.github.io/ | Kalliope is a framework that will help you to create your own personal assistant. | kalliope-project | 2024-01-13 | 2016-10-11 | 381 | 4.417323 | https://avatars.githubusercontent.com/u/22769353?v=4 | Kalliope is a framework that will help you to create your own personal assistant. | ['bot', 'bot-creation', 'home-automation', 'jarvis', 'linux', 'personal-assistant', 'raspberry', 'speech-recognition', 'speech-synthesis', 'speech-to-text'] | ['bot', 'bot-creation', 'home-automation', 'jarvis', 'linux', 'personal-assistant', 'raspberry', 'speech-recognition', 'speech-synthesis', 'speech-to-text'] | 2022-03-06 | [('rasahq/rasa', 0.5666339993476868, 'llm', 1), ('togethercomputer/openchatkit', 0.5493156909942627, 'nlp', 0), ('cheshire-cat-ai/core', 0.5292161107063293, 'llm', 0), ('speechbrain/speechbrain', 0.5283302664756775, 'nlp', 2), ('gunthercox/chatterbot', 0.518312394618988, 'nlp', 1), ('lucidrains/toolformer-pytorch', 0.5108852982521057, 'llm', 0), ('minimaxir/simpleaichat', 0.5094994902610779, 'llm', 0), ('willmcgugan/textual', 0.5060406923294067, 'term', 0)] | 29 | 2 | null | 0 | 4 | 2 | 88 | 23 | 0 | 3 | 3 | 4 | 4 | 90 | 1 | 30 |
1,544 | util | https://github.com/konradhalas/dacite | [] | null | [] | [] | null | null | null | konradhalas/dacite | dacite | 1,577 | 95 | 14 | Python | null | Simple creation of data classes from dictionaries. | konradhalas | 2024-01-12 | 2018-03-03 | 308 | 5.113015 | null | Simple creation of data classes from dictionaries. | ['dataclasses'] | ['dataclasses'] | 2023-05-12 | [('lidatong/dataclasses-json', 0.630731999874115, 'util', 1), ('fabiocaccamo/python-benedict', 0.5441532731056213, 'util', 0), ('marshmallow-code/marshmallow', 0.5163299441337585, 'util', 0)] | 11 | 4 | null | 0.06 | 5 | 0 | 71 | 8 | 2 | 7 | 2 | 5 | 1 | 90 | 0.2 | 30 |
458 | nlp | https://github.com/google-research/language | [] | null | [] | [] | null | null | null | google-research/language | language | 1,536 | 349 | 62 | Python | https://ai.google/research/teams/language/ | Shared repository for open-sourced projects from the Google AI Language team. | google-research | 2024-01-12 | 2018-10-16 | 276 | 5.565217 | https://avatars.githubusercontent.com/u/43830688?v=4 | Shared repository for open-sourced projects from the Google AI Language team. | ['machine-learning', 'natural-language-processing', 'research'] | ['machine-learning', 'natural-language-processing', 'research'] | 2023-10-19 | [('google-research/google-research', 0.6985517740249634, 'ml', 2), ('alirezadir/machine-learning-interview-enlightener', 0.6070800423622131, 'study', 1), ('googlecloudplatform/vertex-ai-samples', 0.6063291430473328, 'ml', 0), ('antonosika/gpt-engineer', 0.5954734683036804, 'llm', 0), ('rasahq/rasa', 0.5900284051895142, 'llm', 2), ('transformeroptimus/superagi', 0.5880876779556274, 'llm', 0), ('mlflow/mlflow', 0.5879070162773132, 'ml-ops', 1), ('tensorflow/tensorflow', 0.5603903532028198, 'ml-dl', 1), ('tensorflow/tensor2tensor', 0.5586530566215515, 'ml', 1), ('krohling/bondai', 0.5564771294593811, 'llm', 0), ('mindsdb/mindsdb', 0.5518535375595093, 'data', 1), ('argilla-io/argilla', 0.5392791628837585, 'nlp', 2), ('deeppavlov/deeppavlov', 0.5389516949653625, 'nlp', 1), ('unity-technologies/ml-agents', 0.5368104577064514, 'ml-rl', 1), ('aiwaves-cn/agents', 0.5324529409408569, 'nlp', 0), ('yueyu1030/attrprompt', 0.5311223864555359, 'llm', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5293827652931213, 'study', 1), ('rasbt/machine-learning-book', 0.5288284420967102, 'study', 1), ('doccano/doccano', 0.5283976197242737, 'nlp', 2), ('merantix-momentum/squirrel-core', 0.5260434150695801, 'ml', 2), ('bentoml/bentoml', 0.5239987969398499, 'ml-ops', 1), ('mlc-ai/mlc-llm', 0.5201672911643982, 'llm', 0), ('prefecthq/marvin', 0.5198028087615967, 'nlp', 0), ('nltk/nltk', 0.5192822217941284, 'nlp', 2), ('allenai/allennlp', 0.5185959935188293, 'nlp', 1), ('iterative/dvc', 0.5162245631217957, 'ml-ops', 1), ('microsoft/generative-ai-for-beginners', 0.5157017111778259, 'study', 0), ('patchy631/machine-learning', 0.5135775804519653, 'ml', 0), ('openlm-research/open_llama', 0.5121092796325684, 'llm', 0), ('oegedijk/explainerdashboard', 0.5075442790985107, 'ml-interpretability', 0), ('netflix/metaflow', 0.5073051452636719, 'ml-ops', 1), ('embedchain/embedchain', 0.5062578916549683, 'llm', 0), ('databrickslabs/dolly', 0.5051336288452148, 'llm', 0), ('aimhubio/aim', 0.5032878518104553, 'ml-ops', 1), ('lucidrains/toolformer-pytorch', 0.5021114945411682, 'llm', 0), ('microsoft/nni', 0.5012085437774658, 'ml', 1)] | 10 | 3 | null | 0 | 21 | 3 | 64 | 3 | 0 | 0 | 0 | 21 | 3 | 90 | 0.1 | 30 |
1,309 | study | https://github.com/chandlerbang/awesome-self-supervised-gnn | ['awesome'] | null | [] | [] | null | null | null | chandlerbang/awesome-self-supervised-gnn | awesome-self-supervised-gnn | 1,366 | 157 | 50 | Python | null | Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN). | chandlerbang | 2024-01-10 | 2020-05-27 | 191 | 7.119881 | null | Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN). | ['deep-learning', 'graph-mining', 'graph-neural-networks', 'graph-self-supervised-learning', 'machine-learning', 'pre-training', 'pretraining', 'self-supervised-learning'] | ['awesome', 'deep-learning', 'graph-mining', 'graph-neural-networks', 'graph-self-supervised-learning', 'machine-learning', 'pre-training', 'pretraining', 'self-supervised-learning'] | 2023-07-10 | [('stellargraph/stellargraph', 0.6943688988685608, 'graph', 3), ('danielegrattarola/spektral', 0.6770707964897156, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.6452118158340454, 'ml-dl', 2), ('dmlc/dgl', 0.593945324420929, 'ml-dl', 2), ('google-deepmind/materials_discovery', 0.5740145444869995, 'sim', 0), ('rampasek/graphgps', 0.5653940439224243, 'graph', 0), ('googlecloudplatform/vertex-ai-samples', 0.5588842034339905, 'ml', 0), ('benedekrozemberczki/tigerlily', 0.5460976362228394, 'ml-dl', 2), ('microsoft/unilm', 0.5360260605812073, 'nlp', 0), ('accenture/ampligraph', 0.5335105061531067, 'data', 1), ('a-r-j/graphein', 0.5173624753952026, 'sim', 2), ('christoschristofidis/awesome-deep-learning', 0.5087785124778748, 'study', 3), ('graphistry/pygraphistry', 0.5049978494644165, 'data', 0)] | 19 | 5 | null | 0.33 | 1 | 0 | 44 | 6 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 30 |
1,094 | data | https://github.com/eleutherai/the-pile | ['training-data', 'llm'] | The Pile is a large, diverse, open source language modelling data set that consists of many smaller datasets combined together. | [] | [] | null | null | null | eleutherai/the-pile | the-pile | 1,334 | 112 | 31 | Python | null | null | eleutherai | 2024-01-12 | 2020-08-26 | 178 | 7.458466 | https://avatars.githubusercontent.com/u/68924597?v=4 | The Pile is a large, diverse, open source language modelling data set that consists of many smaller datasets combined together. | [] | ['llm', 'training-data'] | 2021-06-16 | [('salesforce/xgen', 0.6535871624946594, 'llm', 1), ('togethercomputer/redpajama-data', 0.6279685497283936, 'llm', 0), ('infinitylogesh/mutate', 0.6196421980857849, 'nlp', 0), ('hannibal046/awesome-llm', 0.6086982488632202, 'study', 0), ('cg123/mergekit', 0.607460081577301, 'llm', 1), ('yueyu1030/attrprompt', 0.5999411940574646, 'llm', 0), ('juncongmoo/pyllama', 0.5945956707000732, 'llm', 0), ('databrickslabs/dolly', 0.5931678414344788, 'llm', 0), ('neuml/txtai', 0.5822166800498962, 'nlp', 1), ('mooler0410/llmspracticalguide', 0.5788910984992981, 'study', 0), ('paddlepaddle/paddlenlp', 0.5773162841796875, 'llm', 1), ('explosion/spacy-llm', 0.5771530270576477, 'llm', 1), ('lianjiatech/belle', 0.5712957978248596, 'llm', 0), ('lm-sys/fastchat', 0.568549394607544, 'llm', 0), ('young-geng/easylm', 0.5631842613220215, 'llm', 0), ('llmware-ai/llmware', 0.5548039078712463, 'llm', 0), ('night-chen/toolqa', 0.5525684952735901, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5507524013519287, 'llm', 0), ('ai21labs/lm-evaluation', 0.5482072830200195, 'llm', 0), ('freedomintelligence/llmzoo', 0.5475942492485046, 'llm', 0), ('bobazooba/xllm', 0.5469703674316406, 'llm', 1), ('argilla-io/argilla', 0.542231023311615, 'nlp', 1), ('bigscience-workshop/biomedical', 0.5412442684173584, 'data', 0), ('salesforce/codet5', 0.5400211811065674, 'nlp', 0), ('thudm/chatglm2-6b', 0.5338200330734253, 'llm', 1), ('deepset-ai/haystack', 0.5290297269821167, 'llm', 0), ('nebuly-ai/nebullvm', 0.528251588344574, 'perf', 1), ('dylanhogg/llmgraph', 0.5278661847114563, 'ml', 1), ('ctlllll/llm-toolmaker', 0.5270031690597534, 'llm', 0), ('epfllm/meditron', 0.5269107818603516, 'llm', 0), ('koaning/embetter', 0.5203182101249695, 'data', 1), ('openlm-research/open_llama', 0.5138996839523315, 'llm', 0), ('aiwaves-cn/agents', 0.5101150274276733, 'nlp', 1), ('nomic-ai/gpt4all', 0.5064859986305237, 'llm', 0), ('huggingface/text-generation-inference', 0.5064693093299866, 'llm', 0), ('optimalscale/lmflow', 0.5063433647155762, 'llm', 0), ('conceptofmind/toolformer', 0.5042285919189453, 'llm', 0), ('bigscience-workshop/petals', 0.5029622316360474, 'data', 0), ('tigerlab-ai/tiger', 0.5005179643630981, 'llm', 1)] | 7 | 3 | null | 0 | 5 | 0 | 41 | 31 | 0 | 0 | 0 | 5 | 8 | 90 | 1.6 | 30 |
1,186 | ml-rl | https://github.com/anthropics/hh-rlhf | ['rlhf', 'dataset'] | null | [] | [] | null | null | null | anthropics/hh-rlhf | hh-rlhf | 1,304 | 99 | 19 | null | https://arxiv.org/abs/2204.05862 | Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback" | anthropics | 2024-01-12 | 2022-04-10 | 94 | 13.830303 | https://avatars.githubusercontent.com/u/76263028?v=4 | Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback" | [] | ['dataset', 'rlhf'] | 2023-09-19 | [] | 4 | 2 | null | 0.04 | 0 | 0 | 21 | 4 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 30 |
650 | web | https://github.com/magicstack/httptools | [] | null | [] | [] | null | null | null | magicstack/httptools | httptools | 1,148 | 76 | 41 | Python | null | Fast HTTP parser | magicstack | 2024-01-04 | 2016-04-25 | 405 | 2.833568 | https://avatars.githubusercontent.com/u/14324950?v=4 | Fast HTTP parser | [] | [] | 2023-10-16 | [('aio-libs/yarl', 0.5892772674560547, 'util', 0), ('psf/requests', 0.5530205965042114, 'web', 0)] | 15 | 6 | null | 0.12 | 5 | 4 | 94 | 3 | 2 | 2 | 2 | 2 | 0 | 90 | 0 | 30 |
219 | template | https://github.com/tezromach/python-package-template | [] | null | [] | [] | 1 | null | null | tezromach/python-package-template | python-package-template | 1,056 | 147 | 9 | Python | null | 🚀 Your next Python package needs a bleeding-edge project structure. | tezromach | 2024-01-13 | 2020-04-15 | 197 | 5.337184 | null | 🚀 Your next Python package needs a bleeding-edge project structure. | ['best-practices', 'codestyle', 'cookiecutter', 'formatters', 'makefile', 'poetry', 'python-packages', 'semantic-versions', 'template'] | ['best-practices', 'codestyle', 'cookiecutter', 'formatters', 'makefile', 'poetry', 'python-packages', 'semantic-versions', 'template'] | 2022-05-18 | [('tedivm/robs_awesome_python_template', 0.647948682308197, 'template', 0), ('python-poetry/poetry', 0.6311818361282349, 'util', 1), ('pypa/hatch', 0.6175110936164856, 'util', 0), ('lyz-code/cookiecutter-python-project', 0.5960633754730225, 'template', 1), ('pypa/flit', 0.5902042984962463, 'util', 0), ('pypa/build', 0.5865764021873474, 'util', 0), ('mitsuhiko/rye', 0.5775073170661926, 'util', 0), ('jazzband/pip-tools', 0.5716038346290588, 'util', 0), ('regebro/pyroma', 0.5707094073295593, 'util', 0), ('pdm-project/pdm', 0.5643466114997864, 'util', 0), ('indygreg/pyoxidizer', 0.5635542273521423, 'util', 0), ('giswqs/pypackage', 0.5540108680725098, 'template', 2), ('pyscaffold/pyscaffold', 0.553695797920227, 'template', 0), ('asottile/reorder-python-imports', 0.5504276156425476, 'util', 0), ('pypa/pipenv', 0.5377501249313354, 'util', 0), ('pypi/warehouse', 0.5265879034996033, 'util', 0), ('tiangolo/poetry-version-plugin', 0.5260007977485657, 'util', 0), ('cookiecutter/cookiecutter', 0.5202876925468445, 'template', 1), ('eugeneyan/python-collab-template', 0.518621563911438, 'template', 1), ('pyodide/micropip', 0.5130401253700256, 'util', 0)] | 13 | 2 | null | 0 | 3 | 0 | 46 | 20 | 0 | 6 | 6 | 3 | 3 | 90 | 1 | 30 |
204 | debug | https://github.com/alexmojaki/snoop | [] | null | [] | [] | null | null | null | alexmojaki/snoop | snoop | 1,042 | 33 | 20 | Python | null | A powerful set of Python debugging tools, based on PySnooper | alexmojaki | 2024-01-07 | 2019-05-13 | 246 | 4.233314 | null | A powerful set of Python debugging tools, based on PySnooper | ['debugger', 'debugging', 'debugging-tools', 'logging'] | ['debugger', 'debugging', 'debugging-tools', 'logging'] | 2022-12-22 | [('samuelcolvin/python-devtools', 0.7110832929611206, 'debug', 0), ('alexmojaki/heartrate', 0.6388193964958191, 'debug', 1), ('gaogaotiantian/viztracer', 0.623710036277771, 'profiling', 2), ('inducer/pudb', 0.61795973777771, 'debug', 1), ('nedbat/coveragepy', 0.6032272577285767, 'testing', 0), ('metachris/logzero', 0.601128876209259, 'util', 1), ('alexmojaki/birdseye', 0.6003603935241699, 'debug', 2), ('pympler/pympler', 0.5967013835906982, 'perf', 0), ('hoffstadt/dearpygui', 0.5897102952003479, 'gui', 0), ('ionelmc/python-hunter', 0.5830564498901367, 'debug', 2), ('beeware/toga', 0.5817451477050781, 'gui', 0), ('reloadware/reloadium', 0.5736259818077087, 'profiling', 0), ('delgan/loguru', 0.5699717998504639, 'util', 1), ('pypy/pypy', 0.5637267827987671, 'util', 0), ('gotcha/ipdb', 0.5553923845291138, 'debug', 1), ('urwid/urwid', 0.5546684265136719, 'term', 0), ('pyston/pyston', 0.5509077310562134, 'util', 0), ('landscapeio/prospector', 0.5469942688941956, 'util', 0), ('trailofbits/pip-audit', 0.5462668538093567, 'security', 0), ('p403n1x87/austin', 0.5450026392936707, 'profiling', 1), ('secdev/scapy', 0.5415452122688293, 'util', 0), ('pyglet/pyglet', 0.5321446061134338, 'gamedev', 0), ('pyutils/line_profiler', 0.5304473638534546, 'profiling', 0), ('python/cpython', 0.5296629667282104, 'util', 0), ('willmcgugan/textual', 0.5284596681594849, 'term', 0), ('ionelmc/pytest-benchmark', 0.5151593685150146, 'testing', 0), ('faster-cpython/ideas', 0.5135080814361572, 'perf', 0), ('pytoolz/toolz', 0.5116428732872009, 'util', 0), ('teamhg-memex/eli5', 0.5058972835540771, 'ml', 0), ('micropython/micropython', 0.505521297454834, 'util', 0), ('pytest-dev/pytest-bdd', 0.5047500133514404, 'testing', 0), ('amaargiru/pyroad', 0.5022026896476746, 'study', 0), ('eleutherai/pyfra', 0.5008596777915955, 'ml', 0), ('klen/pylama', 0.500612735748291, 'util', 0)] | 22 | 5 | null | 0 | 1 | 0 | 57 | 13 | 0 | 1 | 1 | 1 | 1 | 90 | 1 | 30 |
627 | util | https://github.com/pyca/pynacl | [] | null | [] | [] | null | null | null | pyca/pynacl | pynacl | 1,009 | 228 | 28 | C | https://pynacl.readthedocs.io/ | Python binding to the Networking and Cryptography (NaCl) library | pyca | 2024-01-13 | 2013-02-22 | 570 | 1.768403 | https://avatars.githubusercontent.com/u/5615737?v=4 | Python binding to the Networking and Cryptography (NaCl) library | ['cryptography', 'libsodium', 'nacl'] | ['cryptography', 'libsodium', 'nacl'] | 2023-12-17 | [('legrandin/pycryptodome', 0.7229923605918884, 'util', 1), ('pyca/cryptography', 0.659361720085144, 'util', 1), ('1200wd/bitcoinlib', 0.5711807608604431, 'crypto', 0), ('primal100/pybitcointools', 0.56072998046875, 'crypto', 0), ('secdev/scapy', 0.5417189002037048, 'util', 0), ('man-c/pycoingecko', 0.5348667502403259, 'crypto', 0), ('nvidia/cuda-python', 0.5136300921440125, 'ml', 0), ('paramiko/paramiko', 0.5029951930046082, 'util', 0)] | 67 | 2 | null | 0.27 | 9 | 7 | 133 | 1 | 0 | 1 | 1 | 9 | 8 | 90 | 0.9 | 30 |
346 | nlp | https://github.com/norskregnesentral/skweak | [] | null | [] | [] | null | null | null | norskregnesentral/skweak | skweak | 902 | 74 | 28 | Python | null | skweak: A software toolkit for weak supervision applied to NLP tasks | norskregnesentral | 2024-01-09 | 2021-03-16 | 150 | 6.013333 | https://avatars.githubusercontent.com/u/17080513?v=4 | skweak: A software toolkit for weak supervision applied to NLP tasks | ['data-science', 'distant-supervision', 'natural-language-processing', 'nlp-library', 'nlp-machine-learning', 'spacy', 'training-data', 'weak-supervision'] | ['data-science', 'distant-supervision', 'natural-language-processing', 'nlp-library', 'nlp-machine-learning', 'spacy', 'training-data', 'weak-supervision'] | 2023-09-26 | [('alibaba/easynlp', 0.6338717341423035, 'nlp', 0), ('argilla-io/argilla', 0.6235673427581787, 'nlp', 2), ('explosion/spacy', 0.6230126619338989, 'nlp', 4), ('explosion/spacy-models', 0.608527660369873, 'nlp', 2), ('nltk/nltk', 0.608248770236969, 'nlp', 1), ('flairnlp/flair', 0.602255642414093, 'nlp', 1), ('allenai/allennlp', 0.5997143387794495, 'nlp', 2), ('explosion/spacy-llm', 0.5937286615371704, 'llm', 2), ('paddlepaddle/paddlenlp', 0.592394232749939, 'llm', 0), ('sloria/textblob', 0.5448392033576965, 'nlp', 1), ('infinitylogesh/mutate', 0.5382066965103149, 'nlp', 1), ('explosion/spacy-stanza', 0.5375661849975586, 'nlp', 3), ('openai/whisper', 0.5360553860664368, 'ml-dl', 0), ('graykode/nlp-tutorial', 0.5352105498313904, 'study', 1), ('huggingface/text-generation-inference', 0.5326890349388123, 'llm', 0), ('lexpredict/lexpredict-lexnlp', 0.5267290472984314, 'nlp', 0), ('rasahq/rasa', 0.5265606641769409, 'llm', 2), ('deepset-ai/farm', 0.5261529088020325, 'nlp', 1), ('keras-team/keras-nlp', 0.5260323882102966, 'nlp', 1), ('bytedance/lightseq', 0.5218310952186584, 'nlp', 0), ('llmware-ai/llmware', 0.5207846760749817, 'llm', 0), ('makcedward/nlpaug', 0.5184004902839661, 'nlp', 2), ('maartengr/bertopic', 0.5178706645965576, 'nlp', 0), ('databrickslabs/dolly', 0.5073420405387878, 'llm', 0), ('lm-sys/fastchat', 0.5055734515190125, 'llm', 0), ('jonasgeiping/cramming', 0.505377471446991, 'nlp', 0), ('yueyu1030/attrprompt', 0.5007184743881226, 'llm', 1), ('minimaxir/aitextgen', 0.5007104873657227, 'llm', 0)] | 12 | 5 | null | 0.29 | 1 | 0 | 34 | 4 | 0 | 1 | 1 | 1 | 0 | 90 | 0 | 30 |
1,674 | util | https://github.com/fastai/fastcore | [] | null | [] | [] | null | null | null | fastai/fastcore | fastcore | 880 | 256 | 19 | Jupyter Notebook | http://fastcore.fast.ai | Python supercharged for the fastai library | fastai | 2024-01-07 | 2019-12-02 | 217 | 4.052632 | https://avatars.githubusercontent.com/u/20547620?v=4 | Python supercharged for the fastai library | ['data-structures', 'developer-tools', 'dispatch', 'documentation-generator', 'fastai', 'functional-programming', 'languages', 'parallel-processing'] | ['data-structures', 'developer-tools', 'dispatch', 'documentation-generator', 'fastai', 'functional-programming', 'languages', 'parallel-processing'] | 2023-06-25 | [('pypy/pypy', 0.6839970946311951, 'util', 0), ('asacristani/fastapi-rocket-boilerplate', 0.6752527952194214, 'template', 0), ('pyston/pyston', 0.6732315421104431, 'util', 0), ('pytoolz/toolz', 0.6410788297653198, 'util', 0), ('cython/cython', 0.6373258829116821, 'util', 0), ('tiangolo/fastapi', 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731 | perf | https://github.com/zerointensity/pointers.py | [] | null | [] | [] | null | null | null | zerointensity/pointers.py | pointers.py | 851 | 12 | 5 | Python | https://pointers.zintensity.dev/ | Bringing the hell of pointers to Python. | zerointensity | 2024-01-08 | 2022-03-09 | 98 | 8.608382 | null | Bringing the hell of pointers to Python. | ['pointers', 'python-pointers'] | ['pointers', 'python-pointers'] | 2023-11-29 | [('pyston/pyston', 0.523978590965271, 'util', 0), ('google/jax', 0.5109991431236267, 'ml', 0)] | 8 | 2 | null | 0.06 | 1 | 1 | 22 | 2 | 1 | 4 | 1 | 1 | 0 | 90 | 0 | 30 |
418 | util | https://github.com/sethmmorton/natsort | [] | null | [] | [] | null | null | null | sethmmorton/natsort | natsort | 819 | 48 | 17 | Python | https://pypi.org/project/natsort/ | Simple yet flexible natural sorting in Python. | sethmmorton | 2024-01-06 | 2012-05-03 | 612 | 1.336675 | null | Simple yet flexible natural sorting in Python. | ['natsort', 'natural-sort', 'sorting', 'sorting-interface'] | ['natsort', 'natural-sort', 'sorting', 'sorting-interface'] | 2023-06-20 | [('pycqa/isort', 0.5276709794998169, 'util', 0)] | 21 | 4 | null | 0.92 | 2 | 2 | 142 | 7 | 0 | 5 | 5 | 2 | 4 | 90 | 2 | 30 |
981 | llm | https://github.com/muennighoff/sgpt | [] | null | [] | [] | null | null | null | muennighoff/sgpt | sgpt | 761 | 49 | 8 | Jupyter Notebook | https://arxiv.org/abs/2202.08904 | SGPT: GPT Sentence Embeddings for Semantic Search | muennighoff | 2024-01-12 | 2022-02-11 | 102 | 7.41922 | null | SGPT: GPT Sentence Embeddings for Semantic Search | ['gpt', 'information-retrieval', 'language-model', 'large-language-models', 'neural-search', 'retrieval', 'semantic-search', 'sentence-embeddings', 'sgpt', 'text-embedding'] | ['gpt', 'information-retrieval', 'language-model', 'large-language-models', 'neural-search', 'retrieval', 'semantic-search', 'sentence-embeddings', 'sgpt', 'text-embedding'] | 2023-07-06 | [('neuml/txtai', 0.6413739323616028, 'nlp', 6), ('intellabs/fastrag', 0.6058968305587769, 'nlp', 2), ('ddangelov/top2vec', 0.5922024846076965, 'nlp', 1), ('ukplab/sentence-transformers', 0.5446346402168274, 'nlp', 3), ('jina-ai/clip-as-service', 0.5414046049118042, 'nlp', 1), ('llmware-ai/llmware', 0.5382522940635681, 'llm', 3), ('weaviate/demo-text2vec-openai', 0.5382280945777893, 'util', 0), ('paddlepaddle/rocketqa', 0.5352213978767395, 'nlp', 1), ('amansrivastava17/embedding-as-service', 0.5335803627967834, 'nlp', 0), ('ai21labs/in-context-ralm', 0.5289058685302734, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5275580286979675, 'llm', 1), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5272648334503174, 'data', 0), ('hannibal046/awesome-llm', 0.5209395885467529, 'study', 2), ('huggingface/text-generation-inference', 0.519349992275238, 'llm', 1), ('plasticityai/magnitude', 0.5156716704368591, 'nlp', 0), ('jina-ai/finetuner', 0.512883186340332, 'ml', 1), ('koaning/whatlies', 0.5074957013130188, 'nlp', 0), ('sebischair/lbl2vec', 0.5040941834449768, 'nlp', 0), ('bigscience-workshop/megatron-deepspeed', 0.5024375915527344, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5024375915527344, 'llm', 0)] | 3 | 2 | null | 0.12 | 3 | 0 | 23 | 6 | 0 | 0 | 0 | 3 | 5 | 90 | 1.7 | 30 |
473 | viz | https://github.com/holoviz/holoviz | [] | null | [] | [] | 1 | null | null | holoviz/holoviz | holoviz | 756 | 120 | 36 | Shell | https://holoviz.org/ | High-level tools to simplify visualization in Python. | holoviz | 2024-01-13 | 2017-09-22 | 331 | 2.280052 | https://avatars.githubusercontent.com/u/51678735?v=4 | High-level tools to simplify visualization in Python. | ['colorcet', 'datashader', 'geoviews', 'holoviews', 'holoviz', 'hvplot', 'panel'] | ['colorcet', 'datashader', 'geoviews', 'holoviews', 'holoviz', 'hvplot', 'panel'] | 2023-12-04 | [('holoviz/panel', 0.7308956384658813, 'viz', 4), ('holoviz/geoviews', 0.7218708992004395, 'gis', 3), ('altair-viz/altair', 0.7110622525215149, 'viz', 0), ('man-group/dtale', 0.7002979516983032, 'viz', 0), ('residentmario/geoplot', 0.6968002319335938, 'gis', 0), ('pyqtgraph/pyqtgraph', 0.6720275282859802, 'viz', 0), ('holoviz/hvplot', 0.6662247776985168, 'pandas', 3), ('bokeh/bokeh', 0.6567468047142029, 'viz', 0), ('scitools/iris', 0.6550614833831787, 'gis', 0), ('giswqs/geemap', 0.6438645124435425, 'gis', 0), ('contextlab/hypertools', 0.6432879567146301, 'ml', 0), ('kanaries/pygwalker', 0.6368023753166199, 'pandas', 0), ('mwaskom/seaborn', 0.6366784572601318, 'viz', 0), ('enthought/mayavi', 0.6358242630958557, 'viz', 0), ('matplotlib/matplotlib', 0.6354397535324097, 'viz', 0), ('plotly/plotly.py', 0.6298113465309143, 'viz', 0), ('has2k1/plotnine', 0.6090349555015564, 'viz', 0), ('opengeos/leafmap', 0.6019681692123413, 'gis', 0), ('vispy/vispy', 0.6010532379150391, 'viz', 0), ('maartenbreddels/ipyvolume', 0.6008582711219788, 'jupyter', 0), ('holoviz/datashader', 0.5855922698974609, 'gis', 2), ('holoviz/holoviews', 0.585200309753418, 'viz', 2), ('pyglet/pyglet', 0.5789063572883606, 'gamedev', 0), ('pyvista/pyvista', 0.5743590593338013, 'viz', 0), ('alexmojaki/heartrate', 0.573762834072113, 'debug', 0), ('vizzuhq/ipyvizzu', 0.5736362338066101, 'jupyter', 0), ('graphistry/pygraphistry', 0.5680197477340698, 'data', 0), ('gaogaotiantian/viztracer', 0.5649738311767578, 'profiling', 0), ('gregorhd/mapcompare', 0.5640184879302979, 'gis', 0), ('hoffstadt/dearpygui', 0.5614981651306152, 'gui', 0), ('lux-org/lux', 0.5574216246604919, 'viz', 0), ('jakevdp/pythondatasciencehandbook', 0.5567380785942078, 'study', 0), ('cuemacro/chartpy', 0.5522693395614624, 'viz', 0), ('plotly/dash', 0.5496276617050171, 'viz', 0), ('mckinsey/vizro', 0.5490374565124512, 'viz', 0), ('beeware/toga', 0.5480042099952698, 'gui', 0), ('dfki-ric/pytransform3d', 0.5455291867256165, 'math', 0), ('marcomusy/vedo', 0.5432279706001282, 'viz', 0), ('artelys/geonetworkx', 0.5396808385848999, 'gis', 0), ('federicoceratto/dashing', 0.5380876064300537, 'term', 0), ('westhealth/pyvis', 0.5358419418334961, 'graph', 0), ('raphaelquast/eomaps', 0.5355204343795776, 'gis', 0), ('parthjadhav/tkinter-designer', 0.5346206426620483, 'gui', 0), ('scitools/cartopy', 0.5334916114807129, 'gis', 0), ('vaexio/vaex', 0.5222266316413879, 'perf', 0), ('pygraphviz/pygraphviz', 0.5206543207168579, 'viz', 0), ('wesm/pydata-book', 0.5204950571060181, 'study', 0), ('imageio/imageio', 0.5204654335975647, 'util', 0), ('eleutherai/pyfra', 0.5192615985870361, 'ml', 0), ('visgl/deck.gl', 0.5192416310310364, 'viz', 0), ('brandtbucher/specialist', 0.5191949605941772, 'perf', 0), ('bmabey/pyldavis', 0.518200159072876, 'ml', 0), ('ipython/ipyparallel', 0.5176970362663269, 'perf', 0), ('pandas-dev/pandas', 0.5176126956939697, 'pandas', 0), ('nomic-ai/deepscatter', 0.5163466930389404, 'viz', 0), ('geopandas/geopandas', 0.515374481678009, 'gis', 0), ('wxwidgets/phoenix', 0.5125094652175903, 'gui', 0), ('makepath/xarray-spatial', 0.510444700717926, 'gis', 1), ('earthlab/earthpy', 0.5084066987037659, 'gis', 0), ('amaargiru/pyroad', 0.5063855648040771, 'study', 0), ('quantopian/qgrid', 0.5044505596160889, 'jupyter', 0), ('jalammar/ecco', 0.5038740634918213, 'ml-interpretability', 0), ('gradio-app/gradio', 0.5025941729545593, 'viz', 0), ('pyston/pyston', 0.5020092725753784, 'util', 0)] | 23 | 2 | null | 0.4 | 11 | 3 | 77 | 1 | 1 | 13 | 1 | 11 | 10 | 90 | 0.9 | 30 |
1,680 | util | https://github.com/pycqa/mccabe | [] | null | [] | [] | null | null | null | pycqa/mccabe | mccabe | 615 | 58 | 17 | Python | pypi.python.org/pypi/mccabe | McCabe complexity checker for Python | pycqa | 2024-01-12 | 2013-02-20 | 570 | 1.077327 | https://avatars.githubusercontent.com/u/8749848?v=4 | McCabe complexity checker for Python | ['complexity', 'complexity-analysis', 'flake8', 'flake8-extensions', 'flake8-plugin', 'linter-flake8', 'linter-plugin', 'mccabe'] | ['complexity', 'complexity-analysis', 'flake8', 'flake8-extensions', 'flake8-plugin', 'linter-flake8', 'linter-plugin', 'mccabe'] | 2023-12-03 | [('pycqa/flake8', 0.6619266867637634, 'util', 3), ('facebook/pyre-check', 0.5869566202163696, 'typing', 0), ('google/pytype', 0.5844917893409729, 'typing', 0), ('agronholm/typeguard', 0.5824611186981201, 'typing', 0), ('pycqa/pycodestyle', 0.5681533813476562, 'util', 3), ('rubik/radon', 0.5480080842971802, 'util', 0), ('microsoft/pyright', 0.5418702960014343, 'typing', 0), ('pytoolz/toolz', 0.521766722202301, 'util', 0), ('astral-sh/ruff', 0.509772777557373, 'util', 0)] | 24 | 7 | null | 0.04 | 8 | 8 | 133 | 1 | 0 | 1 | 1 | 8 | 6 | 90 | 0.8 | 30 |
1,483 | util | https://github.com/ivankorobkov/python-inject | ['dependency-injection'] | null | [] | [] | null | null | null | ivankorobkov/python-inject | python-inject | 607 | 98 | 17 | Python | null | Python dependency injection | ivankorobkov | 2024-01-12 | 2010-02-08 | 729 | 0.832484 | null | Python dependency injection | [] | ['dependency-injection'] | 2023-11-23 | [('python-injector/injector', 0.7356547713279724, 'util', 1), ('allrod5/injectable', 0.640688955783844, 'util', 1), ('ets-labs/python-dependency-injector', 0.6299859881401062, 'util', 1), ('mitsuhiko/rye', 0.5525853037834167, 'util', 0), ('proofit404/dependencies', 0.547492265701294, 'util', 1), ('python-poetry/poetry', 0.534679651260376, 'util', 0)] | 29 | 5 | null | 0.31 | 10 | 7 | 170 | 2 | 0 | 2 | 2 | 10 | 21 | 90 | 2.1 | 30 |
522 | gis | https://github.com/toblerity/rtree | [] | null | [] | [] | null | null | null | toblerity/rtree | rtree | 582 | 126 | 31 | Python | https://rtree.readthedocs.io/en/latest/ | Rtree: spatial index for Python GIS | toblerity | 2024-01-04 | 2011-06-19 | 658 | 0.884115 | https://avatars.githubusercontent.com/u/859968?v=4 | Rtree: spatial index for Python GIS | [] | [] | 2023-12-19 | [('pysal/pysal', 0.6110436320304871, 'gis', 0), ('uber/h3-py', 0.6043885350227356, 'gis', 0), ('artelys/geonetworkx', 0.597081184387207, 'gis', 0), ('makepath/xarray-spatial', 0.5867227911949158, 'gis', 0), ('pinecone-io/pinecone-python-client', 0.5536699295043945, 'data', 0), ('geopandas/geopandas', 0.5479511618614197, 'gis', 0), ('opengeos/leafmap', 0.5405087471008301, 'gis', 0), ('earthlab/earthpy', 0.5261022448539734, 'gis', 0), ('gregorhd/mapcompare', 0.5135495662689209, 'gis', 0)] | 41 | 3 | null | 0.63 | 13 | 10 | 153 | 1 | 1 | 1 | 1 | 13 | 23 | 90 | 1.8 | 30 |
1,320 | util | https://github.com/pycqa/pylint-django | ['django', 'pylint', 'linter'] | null | [] | [] | null | null | null | pycqa/pylint-django | pylint-django | 575 | 121 | 16 | Python | null | Pylint plugin for improving code analysis for when using Django | pycqa | 2024-01-12 | 2013-10-01 | 539 | 1.06679 | https://avatars.githubusercontent.com/u/121692054?v=4 | Pylint plugin for improving code analysis for when using Django | [] | ['django', 'linter', 'pylint'] | 2023-11-04 | [('psf/black', 0.5581016540527344, 'util', 0), ('pygments/pygments', 0.5438166856765747, 'util', 0), ('grantjenks/blue', 0.5386630892753601, 'util', 0), ('google/pytype', 0.5338144898414612, 'typing', 1), ('pycqa/flake8', 0.5301540493965149, 'util', 1), ('pylons/pyramid', 0.5214040279388428, 'web', 0), ('hhatto/autopep8', 0.5179296731948853, 'util', 0), ('klen/pylama', 0.5170671939849854, 'util', 1), ('landscapeio/prospector', 0.5079518556594849, 'util', 0), ('bottlepy/bottle', 0.5043782591819763, 'web', 0), ('feincms/feincms', 0.5040256381034851, 'web', 0)] | 70 | 3 | null | 0.6 | 23 | 14 | 125 | 2 | 1 | 5 | 1 | 23 | 28 | 90 | 1.2 | 30 |
485 | gis | https://github.com/fatiando/verde | [] | null | [] | [] | null | null | null | fatiando/verde | verde | 550 | 69 | 21 | Python | https://www.fatiando.org/verde | Processing and gridding spatial data, machine-learning style | fatiando | 2024-01-12 | 2018-04-25 | 300 | 1.82811 | https://avatars.githubusercontent.com/u/8174113?v=4 | Processing and gridding spatial data, machine-learning style | ['earth-science', 'fatiando-a-terra', 'geophysics', 'geoscience', 'geospatial', 'interpolation', 'machine-learning', 'scipy', 'scipy-stack'] | ['earth-science', 'fatiando-a-terra', 'geophysics', 'geoscience', 'geospatial', 'interpolation', 'machine-learning', 'scipy', 'scipy-stack'] | 2023-10-25 | [('osgeo/grass', 0.6331599950790405, 'gis', 2), ('krzjoa/awesome-python-data-science', 0.559866726398468, 'study', 1), ('microsoft/torchgeo', 0.5598282217979431, 'gis', 1), ('ddbourgin/numpy-ml', 0.5586436986923218, 'ml', 1), ('automl/auto-sklearn', 0.5549225807189941, 'ml', 0), ('scikit-learn/scikit-learn', 0.5504959225654602, 'ml', 1), ('developmentseed/label-maker', 0.5453664660453796, 'gis', 0), ('sentinel-hub/eo-learn', 0.5445219874382019, 'gis', 1), ('feast-dev/feast', 0.5436317324638367, 'ml-ops', 1), ('remotesensinglab/raster4ml', 0.5404156446456909, 'gis', 1), ('plant99/felicette', 0.533496618270874, 'gis', 2), ('scikit-mobility/scikit-mobility', 0.531174898147583, 'gis', 0), ('online-ml/river', 0.531051754951477, 'ml', 1), ('awslabs/autogluon', 0.5272731781005859, 'ml', 1), ('polyaxon/datatile', 0.5251547694206238, 'pandas', 0), ('opengeos/segment-geospatial', 0.5204988121986389, 'gis', 2), ('scitools/iris', 0.5199081301689148, 'gis', 1), ('firmai/industry-machine-learning', 0.5189895629882812, 'study', 1), ('milvus-io/bootcamp', 0.5155651569366455, 'data', 0), ('skops-dev/skops', 0.513488233089447, 'ml-ops', 1), ('earthlab/earthpy', 0.5133398771286011, 'gis', 0), ('r-barnes/richdem', 0.5110668540000916, 'gis', 1), ('geopandas/geopandas', 0.5108627080917358, 'gis', 1), ('sloria/textblob', 0.5090445876121521, 'nlp', 0), ('gradio-app/gradio', 0.5083851218223572, 'viz', 1), ('raphaelquast/eomaps', 0.5024335384368896, 'gis', 1)] | 13 | 8 | null | 0.13 | 4 | 2 | 70 | 3 | 1 | 2 | 1 | 4 | 4 | 90 | 1 | 30 |
1,797 | jupyter | https://github.com/rapidsai/jupyterlab-nvdashboard | ['gpu'] | null | [] | [] | null | null | null | rapidsai/jupyterlab-nvdashboard | jupyterlab-nvdashboard | 531 | 74 | 16 | TypeScript | null | A JupyterLab extension for displaying dashboards of GPU usage. | rapidsai | 2024-01-04 | 2019-08-12 | 233 | 2.277574 | https://avatars.githubusercontent.com/u/43887749?v=4 | A JupyterLab extension for displaying dashboards of GPU usage. | [] | ['gpu'] | 2024-01-12 | [('federicoceratto/dashing', 0.6266454458236694, 'term', 0), ('nvidia/warp', 0.5572924017906189, 'sim', 1), ('vizzuhq/ipyvizzu', 0.5411252975463867, 'jupyter', 0), ('datapane/datapane', 0.5317434668540955, 'viz', 0), ('holoviz/panel', 0.5303380489349365, 'viz', 0), ('voila-dashboards/voila', 0.5271745920181274, 'jupyter', 0), ('plotly/plotly.py', 0.5129841566085815, 'viz', 0), ('maartenbreddels/ipyvolume', 0.5118504762649536, 'jupyter', 0), ('xiaohk/stickyland', 0.5069721937179565, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.5053638815879822, 'jupyter', 0), ('graphistry/pygraphistry', 0.503430187702179, 'data', 1)] | 19 | 2 | null | 0.27 | 7 | 5 | 54 | 0 | 2 | 6 | 2 | 7 | 4 | 90 | 0.6 | 30 |
1,669 | testing | https://github.com/lundberg/respx | ['mocking', 'httpx'] | null | [] | [] | null | null | null | lundberg/respx | respx | 523 | 38 | 4 | Python | https://lundberg.github.io/respx | Mock HTTPX with awesome request patterns and response side effects 🦋 | lundberg | 2024-01-12 | 2019-11-13 | 219 | 2.378817 | null | Mock HTTPX with awesome request patterns and response side effects 🦋 | ['httpx', 'mock', 'pytest', 'testing'] | ['httpx', 'mock', 'mocking', 'pytest', 'testing'] | 2023-07-20 | [('kevin1024/vcrpy', 0.6465907692909241, 'testing', 2), ('jamielennox/requests-mock', 0.6144221425056458, 'testing', 1), ('pytest-dev/pytest-mock', 0.5953378081321716, 'testing', 2), ('getsentry/responses', 0.5848559737205505, 'testing', 1), ('taverntesting/tavern', 0.5600868463516235, 'testing', 2)] | 24 | 7 | null | 0.15 | 5 | 0 | 51 | 6 | 1 | 11 | 1 | 5 | 6 | 90 | 1.2 | 30 |
921 | util | https://github.com/heuer/segno | [] | null | [] | [] | null | null | null | heuer/segno | segno | 507 | 47 | 13 | Python | https://pypi.org/project/segno/ | Python QR Code and Micro QR Code encoder | heuer | 2024-01-08 | 2016-08-04 | 390 | 1.297623 | null | Python QR Code and Micro QR Code encoder | ['barcode', 'iso-18004', 'matrix-barcode', 'micro-qr-code', 'micro-qrcode', 'python-qrcode', 'qr-code', 'qr-generator', 'qrcode', 'segno', 'structured-append'] | ['barcode', 'iso-18004', 'matrix-barcode', 'micro-qr-code', 'micro-qrcode', 'python-qrcode', 'qr-code', 'qr-generator', 'qrcode', 'segno', 'structured-append'] | 2023-11-30 | [('mnooner256/pyqrcode', 0.7471798658370972, 'util', 0)] | 11 | 3 | null | 1.46 | 12 | 10 | 91 | 1 | 2 | 6 | 2 | 12 | 19 | 90 | 1.6 | 30 |
830 | gis | https://github.com/perrygeo/python-rasterstats | [] | null | [] | [] | null | null | null | perrygeo/python-rasterstats | python-rasterstats | 504 | 165 | 34 | Python | null | Summary statistics of geospatial raster datasets based on vector geometries. | perrygeo | 2024-01-12 | 2013-09-18 | 540 | 0.931854 | null | Summary statistics of geospatial raster datasets based on vector geometries. | [] | [] | 2023-10-05 | [('osgeo/gdal', 0.5707691311836243, 'gis', 0), ('remotesensinglab/raster4ml', 0.5588922500610352, 'gis', 0), ('osgeo/grass', 0.5056399703025818, 'gis', 0), ('makepath/xarray-spatial', 0.5050948262214661, 'gis', 0)] | 31 | 7 | null | 0.38 | 5 | 2 | 126 | 3 | 1 | 2 | 1 | 5 | 9 | 90 | 1.8 | 30 |
291 | util | https://github.com/fastai/ghapi | [] | null | [] | [] | null | null | null | fastai/ghapi | ghapi | 496 | 55 | 9 | Jupyter Notebook | https://ghapi.fast.ai/ | A delightful and complete interface to GitHub's amazing API | fastai | 2024-01-12 | 2020-11-21 | 166 | 2.980258 | https://avatars.githubusercontent.com/u/20547620?v=4 | A delightful and complete interface to GitHub's amazing API | ['api-client', 'github', 'github-api', 'nbdev', 'openapi'] | ['api-client', 'github', 'github-api', 'nbdev', 'openapi'] | 2023-06-14 | [('fauxpilot/fauxpilot', 0.5876653790473938, 'llm', 0), ('vitalik/django-ninja', 0.58127361536026, 'web', 1), ('openai/openai-python', 0.5810590982437134, 'util', 0), ('langchain-ai/opengpts', 0.5734665393829346, 'llm', 0), ('hugapi/hug', 0.5626925230026245, 'util', 0), ('pygithub/pygithub', 0.5488420724868774, 'util', 2), ('github/innovationgraph', 0.5455352067947388, 'data', 1), ('shishirpatil/gorilla', 0.5438264012336731, 'llm', 0), ('starlite-api/starlite', 0.5345987677574158, 'web', 1), ('googleapis/google-api-python-client', 0.5308494567871094, 'util', 0), ('tiangolo/fastapi', 0.5305969715118408, 'web', 1), ('prefecthq/server', 0.5246346592903137, 'util', 0), ('python-restx/flask-restx', 0.5162189602851868, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5124199986457825, 'data', 1), ('kivy/kivy', 0.5002898573875427, 'util', 0)] | 16 | 7 | null | 0.02 | 4 | 1 | 38 | 7 | 0 | 6 | 6 | 4 | 2 | 90 | 0.5 | 30 |
1,273 | ml | https://github.com/intellabs/bayesian-torch | [] | null | [] | [] | null | null | null | intellabs/bayesian-torch | bayesian-torch | 402 | 57 | 17 | Python | null | A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch | intellabs | 2024-01-14 | 2020-12-17 | 162 | 2.470588 | https://avatars.githubusercontent.com/u/1492758?v=4 | A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch | ['bayesian-deep-learning', 'bayesian-inference', 'bayesian-layers', 'bayesian-neural-networks', 'deep-learning', 'deep-neural-networks', 'pytorch', 'stochastic-variational-inference', 'uncertainty-estimation', 'uncertainty-neural-networks', 'uncertainty-quantification'] | ['bayesian-deep-learning', 'bayesian-inference', 'bayesian-layers', 'bayesian-neural-networks', 'deep-learning', 'deep-neural-networks', 'pytorch', 'stochastic-variational-inference', 'uncertainty-estimation', 'uncertainty-neural-networks', 'uncertainty-quantification'] | 2024-01-02 | [('pyro-ppl/pyro', 0.6956607699394226, 'ml-dl', 3), ('pytorch/ignite', 0.6580493450164795, 'ml-dl', 2), ('pytorch/botorch', 0.6108170747756958, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5984524488449097, 'study', 2), ('rasbt/machine-learning-book', 0.5859395861625671, 'study', 2), ('intel/intel-extension-for-pytorch', 0.582083523273468, 'perf', 2), ('pyg-team/pytorch_geometric', 0.5782586932182312, 'ml-dl', 2), ('skorch-dev/skorch', 0.5576133131980896, 'ml-dl', 1), ('denys88/rl_games', 0.5465734004974365, 'ml-rl', 2), ('tensorlayer/tensorlayer', 0.5386697053909302, 'ml-rl', 1), ('thu-ml/tianshou', 0.5311893820762634, 'ml-rl', 1), ('nvidia/apex', 0.5274893045425415, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5250702500343323, 'ml', 1), ('karpathy/micrograd', 0.5212419629096985, 'study', 0), ('keras-team/keras', 0.5210490822792053, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5206497311592102, 'ml-dl', 2), ('aistream-peelout/flow-forecast', 0.518251359462738, 'time-series', 3), ('calculatedcontent/weightwatcher', 0.51715487241745, 'ml-dl', 0), ('rasbt/deeplearning-models', 0.516359806060791, 'ml-dl', 0), ('huggingface/transformers', 0.5125004053115845, 'nlp', 2), ('pytorch/rl', 0.5093467831611633, 'ml-rl', 1), ('udlbook/udlbook', 0.5086169838905334, 'study', 1), ('microsoft/deepspeed', 0.5001977682113647, 'ml-dl', 2)] | 6 | 2 | null | 0.63 | 8 | 7 | 37 | 0 | 2 | 2 | 2 | 8 | 10 | 90 | 1.2 | 30 |
1,230 | perf | https://github.com/dgilland/cacheout | [] | null | [] | [] | null | null | null | dgilland/cacheout | cacheout | 392 | 42 | 13 | Python | https://cacheout.readthedocs.io | A caching library for Python | dgilland | 2024-01-03 | 2018-01-12 | 315 | 1.242191 | null | A caching library for Python | ['caching', 'fifo', 'lfu', 'lifo', 'lru', 'memoization', 'mru', 'rr'] | ['caching', 'fifo', 'lfu', 'lifo', 'lru', 'memoization', 'mru', 'rr'] | 2023-12-22 | [('python-cachier/cachier', 0.7924980521202087, 'perf', 2), ('erotemic/ubelt', 0.6818086504936218, 'util', 0), ('joblib/joblib', 0.6794201135635376, 'util', 2), ('grantjenks/python-diskcache', 0.6435301899909973, 'util', 0), ('pythonspeed/filprofiler', 0.6149056553840637, 'profiling', 0), ('pytoolz/toolz', 0.6084659695625305, 'util', 0), ('pympler/pympler', 0.6028500199317932, 'perf', 0), ('spotify/annoy', 0.5922878980636597, 'ml', 0), ('pypy/pypy', 0.5562092661857605, 'util', 0), ('pythonprofilers/memory_profiler', 0.555606484413147, 'profiling', 0), ('pyston/pyston', 0.5537205338478088, 'util', 0), ('aio-libs/aiocache', 0.5477664470672607, 'data', 0), ('zilliztech/gptcache', 0.540678858757019, 'llm', 0), ('pytables/pytables', 0.5385268926620483, 'data', 0), ('klen/py-frameworks-bench', 0.5362305641174316, 'perf', 0), ('fastai/fastcore', 0.5273230671882629, 'util', 0), ('long2ice/fastapi-cache', 0.5193830728530884, 'web', 0), ('python-trio/trio', 0.5188122987747192, 'perf', 0), ('libtcod/python-tcod', 0.5180550813674927, 'gamedev', 0), ('qdrant/fastembed', 0.5054611563682556, 'ml', 0), ('dosisod/refurb', 0.5029307007789612, 'util', 0), ('sumerc/yappi', 0.5023199915885925, 'profiling', 0)] | 6 | 1 | null | 0.79 | 10 | 10 | 73 | 1 | 0 | 4 | 4 | 10 | 34 | 90 | 3.4 | 30 |
1,548 | llm | https://github.com/eugeneyan/obsidian-copilot | [] | null | [] | [] | null | null | null | eugeneyan/obsidian-copilot | obsidian-copilot | 342 | 23 | 6 | Python | https://eugeneyan.com/writing/obsidian-copilot/ | 🤖 A prototype assistant for writing and thinking | eugeneyan | 2024-01-12 | 2023-06-11 | 33 | 10.274678 | null | 🤖 A prototype assistant for writing and thinking | ['assistant', 'generative-ai', 'large-language-models', 'llm', 'obsidian-plugin', 'retrieval-augmented-generation'] | ['assistant', 'generative-ai', 'large-language-models', 'llm', 'obsidian-plugin', 'retrieval-augmented-generation'] | 2024-01-11 | [('kyegomez/tree-of-thoughts', 0.6086257100105286, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5925748348236084, 'study', 1), ('llmware-ai/llmware', 0.5871189832687378, 'llm', 3), ('paddlepaddle/paddlenlp', 0.5580410361289978, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5504752397537231, 'llm', 0), ('lupantech/chameleon-llm', 0.5486295819282532, 'llm', 1), ('openlmlab/moss', 0.5385111570358276, 'llm', 1), ('intellabs/fastrag', 0.5370295643806458, 'nlp', 2), ('huggingface/text-generation-inference', 0.5260320901870728, 'llm', 0), ('ofa-sys/ofa', 0.5171679854393005, 'llm', 0), ('lm-sys/fastchat', 0.5163758993148804, 'llm', 0), ('srush/minichain', 0.5138395428657532, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5133623480796814, 'llm', 0), ('microsoft/lmops', 0.5132716298103333, 'llm', 1), ('rcgai/simplyretrieve', 0.5125769972801208, 'llm', 3), ('thilinarajapakse/simpletransformers', 0.5094960927963257, 'nlp', 0), ('reasoning-machines/pal', 0.5085762739181519, 'llm', 1), ('deepset-ai/haystack', 0.5081061124801636, 'llm', 2), ('prefecthq/marvin', 0.5069007277488708, 'nlp', 1), ('cheshire-cat-ai/core', 0.5033305287361145, 'llm', 2), ('lianjiatech/belle', 0.5000393390655518, 'llm', 0)] | 5 | 2 | null | 0.35 | 1 | 1 | 7 | 0 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 30 |
306 | crypto | https://github.com/ethereum/eth-utils | [] | null | [] | [] | null | null | null | ethereum/eth-utils | eth-utils | 297 | 151 | 19 | Python | https://eth-utils.readthedocs.io/en/latest/ | Utility functions for working with ethereum related codebases. | ethereum | 2024-01-03 | 2017-02-07 | 364 | 0.815934 | https://avatars.githubusercontent.com/u/6250754?v=4 | Utility functions for working with ethereum related codebases. | ['ethereum', 'utility-library'] | ['ethereum', 'utility-library'] | 2024-01-10 | [('pytoolz/toolz', 0.525811493396759, 'util', 0), ('suor/funcy', 0.5216156244277954, 'util', 0), ('tiiuae/sbomnix', 0.5169852375984192, 'util', 0)] | 37 | 2 | null | 1.9 | 17 | 11 | 84 | 0 | 0 | 10 | 10 | 17 | 8 | 90 | 0.5 | 30 |
1,725 | study | https://github.com/ray-project/ray-educational-materials | [] | null | [] | [] | null | null | null | ray-project/ray-educational-materials | ray-educational-materials | 232 | 42 | 11 | Jupyter Notebook | null | This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray. | ray-project | 2024-01-10 | 2022-09-16 | 71 | 3.241517 | https://avatars.githubusercontent.com/u/22125274?v=4 | This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray. | ['deep-learning', 'distributed-machine-learning', 'generative-ai', 'llm', 'llm-inference', 'llm-serving', 'ray', 'ray-data', 'ray-distributed', 'ray-serve', 'ray-train', 'ray-tune'] | ['deep-learning', 'distributed-machine-learning', 'generative-ai', 'llm', 'llm-inference', 'llm-serving', 'ray', 'ray-data', 'ray-distributed', 'ray-serve', 'ray-train', 'ray-tune'] | 2024-01-09 | [('ray-project/ray', 0.7717517614364624, 'ml-ops', 3), ('ray-project/ray-llm', 0.6102384924888611, 'llm', 4), ('alpa-projects/alpa', 0.5506226420402527, 'ml-dl', 2), ('horovod/horovod', 0.5418562293052673, 'ml-ops', 2), ('aistream-peelout/flow-forecast', 0.5015262365341187, 'time-series', 1)] | 8 | 2 | null | 0.98 | 22 | 19 | 16 | 0 | 2 | 3 | 2 | 22 | 7 | 90 | 0.3 | 30 |
1,478 | web | https://github.com/alirn76/panther | [] | null | [] | [] | null | null | null | alirn76/panther | panther | 226 | 12 | 7 | Python | https://pantherpy.github.io | Fast & Friendly Web Framework For Building Async APIs With Python 3.10+ | alirn76 | 2024-01-13 | 2022-02-23 | 100 | 2.240793 | null | Fast & Friendly Web Framework For Building Async APIs With Python 3.10+ | ['framework', 'panther'] | ['framework', 'panther'] | 2024-01-04 | [('pallets/quart', 0.7484593391418457, 'web', 0), ('neoteroi/blacksheep', 0.7121951580047607, 'web', 1), ('aio-libs/aiohttp', 0.7044265270233154, 'web', 0), ('encode/httpx', 0.6786492466926575, 'web', 0), ('klen/muffin', 0.6565958857536316, 'web', 0), ('python-trio/trio', 0.6534282565116882, 'perf', 0), ('geeogi/async-python-lambda-template', 0.6337937712669373, 'template', 0), ('python-restx/flask-restx', 0.6230086088180542, 'web', 0), ('encode/uvicorn', 0.6067794561386108, 'web', 0), ('huge-success/sanic', 0.6031531095504761, 'web', 1), ('magicstack/uvloop', 0.5940344929695129, 'util', 0), ('encode/starlette', 0.5935547351837158, 'web', 0), ('agronholm/anyio', 0.5871341824531555, 'perf', 0), ('timofurrer/awesome-asyncio', 0.5865374207496643, 'study', 0), ('tiangolo/asyncer', 0.5792798399925232, 'perf', 0), ('falconry/falcon', 0.5779109001159668, 'web', 1), ('samuelcolvin/arq', 0.5734840035438538, 'data', 0), ('klen/py-frameworks-bench', 0.564853310585022, 'perf', 0), ('starlite-api/starlite', 0.5622544288635254, 'web', 0), ('vitalik/django-ninja', 0.5587133169174194, 'web', 0), ('masoniteframework/masonite', 0.5462374091148376, 'web', 1), ('pallets/flask', 0.5419907569885254, 'web', 0), ('airtai/faststream', 0.5416358709335327, 'perf', 0), ('samuelcolvin/aioaws', 0.5380411148071289, 'data', 0), ('sumerc/yappi', 0.5367398858070374, 'profiling', 0), ('tornadoweb/tornado', 0.5357459187507629, 'web', 0), ('tiangolo/fastapi', 0.5322808027267456, 'web', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5177244544029236, 'template', 0), ('hugapi/hug', 0.5171224474906921, 'util', 0), ('ets-labs/python-dependency-injector', 0.516931414604187, 'util', 0), ('jordaneremieff/mangum', 0.5160053372383118, 'web', 0), ('fastai/fastcore', 0.5143014788627625, 'util', 0), ('bottlepy/bottle', 0.5107240676879883, 'web', 0), ('nficano/python-lambda', 0.5092719793319702, 'util', 0), ('pylons/pyramid', 0.5073546767234802, 'web', 0), ('hyperopt/hyperopt', 0.5043962597846985, 'ml', 0)] | 6 | 1 | null | 5.27 | 22 | 18 | 23 | 0 | 0 | 40 | 40 | 22 | 4 | 90 | 0.2 | 30 |
1,173 | data | https://github.com/pinecone-io/pinecone-python-client | ['vector-search'] | null | [] | [] | null | null | null | pinecone-io/pinecone-python-client | pinecone-python-client | 205 | 50 | 21 | Python | https://www.pinecone.io/docs | The Pinecone Python client | pinecone-io | 2024-01-12 | 2021-09-16 | 123 | 1.657044 | https://avatars.githubusercontent.com/u/54333248?v=4 | The Pinecone Python client | [] | ['vector-search'] | 2024-01-14 | [('qdrant/qdrant-client', 0.6546476483345032, 'util', 1), ('weaviate/weaviate-python-client', 0.5670716762542725, 'util', 1), ('toblerity/rtree', 0.5536699295043945, 'gis', 0), ('qdrant/qdrant-haystack', 0.5180879831314087, 'data', 0), ('qdrant/vector-db-benchmark', 0.512586772441864, 'perf', 1), ('facebookresearch/faiss', 0.5026112794876099, 'ml', 1)] | 28 | 2 | null | 2.27 | 71 | 56 | 28 | 0 | 3 | 18 | 3 | 70 | 20 | 90 | 0.3 | 30 |
1,865 | llm | https://github.com/lamini-ai/llm-classifier | ['classifier'] | null | [] | [] | null | null | null | lamini-ai/llm-classifier | llm-classifier | 124 | 13 | 4 | Python | null | Classify data instantly using an LLM | lamini-ai | 2024-01-12 | 2023-09-20 | 18 | 6.575758 | https://avatars.githubusercontent.com/u/130713213?v=4 | Classify data instantly using an LLM | [] | ['classifier'] | 2023-12-14 | [('microsoft/jarvis', 0.5053083300590515, 'llm', 0)] | 6 | 1 | null | 0.96 | 2 | 0 | 4 | 1 | 0 | 0 | 0 | 2 | 6 | 90 | 3 | 30 |
1,557 | util | https://github.com/tiiuae/sbomnix | [] | null | [] | [] | null | null | null | tiiuae/sbomnix | sbomnix | 72 | 18 | 8 | Python | null | A suite of utilities to help with software supply chain challenges on nix targets | tiiuae | 2024-01-04 | 2022-12-08 | 59 | 1.205742 | https://avatars.githubusercontent.com/u/59836348?v=4 | A suite of utilities to help with software supply chain challenges on nix targets | ['bill-of-materials', 'cpe', 'cyclonedx', 'dependencies', 'nix', 'purl', 'sbom', 'sbom-generator', 'sbom-tool', 'security', 'software-bill-of-materials', 'software-supply-chain', 'software-supply-chain-security', 'spdx-sbom', 'static-analysis', 'vulnerability-scanners'] | ['bill-of-materials', 'cpe', 'cyclonedx', 'dependencies', 'nix', 'purl', 'sbom', 'sbom-generator', 'sbom-tool', 'security', 'software-bill-of-materials', 'software-supply-chain', 'software-supply-chain-security', 'spdx-sbom', 'static-analysis', 'vulnerability-scanners'] | 2024-01-03 | [('spack/spack', 0.5559228658676147, 'util', 0), ('trailofbits/pip-audit', 0.5466781258583069, 'security', 1), ('conda/conda', 0.5382207632064819, 'util', 0), ('aquasecurity/trivy', 0.5336388945579529, 'security', 2), ('chaostoolkit/chaostoolkit', 0.5200450420379639, 'util', 0), ('mamba-org/mamba', 0.5184597373008728, 'util', 0), ('ethereum/eth-utils', 0.5169852375984192, 'crypto', 0), ('aswinnnn/pyscan', 0.5072173476219177, 'security', 2)] | 9 | 5 | null | 3.15 | 17 | 16 | 13 | 1 | 12 | 11 | 12 | 17 | 13 | 90 | 0.8 | 30 |
760 | study | https://github.com/fluentpython/example-code-2e | [] | null | [] | [] | null | null | null | fluentpython/example-code-2e | example-code-2e | 2,683 | 763 | 68 | Python | https://amzn.to/3J48u2J | Example code for Fluent Python, 2nd edition (O'Reilly 2022) | fluentpython | 2024-01-13 | 2019-03-21 | 253 | 10.574887 | https://avatars.githubusercontent.com/u/9216311?v=4 | Example code for Fluent Python, 2nd edition (O'Reilly 2022) | ['concurrency', 'iterators', 'metaprogramming', 'special-methods'] | ['concurrency', 'iterators', 'metaprogramming', 'special-methods'] | 2022-04-24 | [('more-itertools/more-itertools', 0.5874441862106323, 'util', 0), ('python-trio/trio', 0.5376577377319336, 'perf', 0), ('python-greenlet/greenlet', 0.514401912689209, 'perf', 0), ('evhub/coconut', 0.5133896470069885, 'util', 0), ('fastai/fastcore', 0.5095949769020081, 'util', 0), ('pytoolz/toolz', 0.5072869062423706, 'util', 0), ('nteract/papermill', 0.5064553022384644, 'jupyter', 0), ('sumerc/yappi', 0.5051793456077576, 'profiling', 0), ('joblib/joblib', 0.5024613738059998, 'util', 0), ('koaning/clumper', 0.5001460909843445, 'util', 0)] | 7 | 1 | null | 0 | 3 | 1 | 59 | 21 | 0 | 0 | 0 | 3 | 1 | 90 | 0.3 | 29 |
1,343 | util | https://github.com/cdgriffith/box | [] | null | [] | [] | null | null | null | cdgriffith/box | Box | 2,308 | 104 | 35 | Python | https://github.com/cdgriffith/Box/wiki | Python dictionaries with advanced dot notation access | cdgriffith | 2024-01-12 | 2017-03-11 | 359 | 6.421304 | null | Python dictionaries with advanced dot notation access | ['addict', 'box', 'bunch', 'dictionaries', 'helper', 'object', 'python-box', 'python-types'] | ['addict', 'box', 'bunch', 'dictionaries', 'helper', 'object', 'python-box', 'python-types'] | 2023-08-26 | [] | 1 | 0 | null | 0.08 | 3 | 0 | 83 | 5 | 9 | 9 | 9 | 3 | 3 | 90 | 1 | 29 |
1,474 | util | https://github.com/ianmiell/shutit | [] | null | [] | [] | null | null | null | ianmiell/shutit | shutit | 2,143 | 130 | 67 | Python | http://ianmiell.github.io/shutit/ | Automation framework for programmers | ianmiell | 2024-01-13 | 2014-03-25 | 514 | 4.169261 | null | Automation framework for programmers | ['docker', 'pexpect', 'vagrant'] | ['docker', 'pexpect', 'vagrant'] | 2022-06-29 | [('tox-dev/tox', 0.5560944080352783, 'testing', 0), ('pypa/pipenv', 0.549948513507843, 'util', 0), ('backtick-se/cowait', 0.5379471182823181, 'util', 1), ('martinheinz/python-project-blueprint', 0.5357551574707031, 'template', 1), ('pexpect/pexpect', 0.5116491317749023, 'util', 0), ('willmcgugan/textual', 0.5011722445487976, 'term', 0)] | 24 | 6 | null | 0 | 0 | 0 | 119 | 19 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 29 |
707 | gis | https://github.com/mcordts/cityscapesscripts | [] | null | [] | [] | null | null | null | mcordts/cityscapesscripts | cityscapesScripts | 2,053 | 608 | 45 | Python | null | README and scripts for the Cityscapes Dataset | mcordts | 2024-01-12 | 2016-02-20 | 414 | 4.953809 | null | README and scripts for the Cityscapes Dataset | [] | [] | 2023-05-07 | [('udst/urbansim', 0.6591488718986511, 'sim', 0), ('pysal/momepy', 0.564961314201355, 'gis', 0), ('gregorhd/mapcompare', 0.5555253624916077, 'gis', 0), ('mattbierbaum/arxiv-public-datasets', 0.5378220677375793, 'data', 0), ('spatialucr/geosnap', 0.5296457409858704, 'gis', 0)] | 18 | 3 | null | 0.04 | 6 | 1 | 96 | 8 | 0 | 0 | 0 | 6 | 1 | 90 | 0.2 | 29 |
1,002 | study | https://github.com/cerlymarco/medium_notebook | [] | null | [] | [] | null | null | null | cerlymarco/medium_notebook | MEDIUM_NoteBook | 1,972 | 966 | 100 | Jupyter Notebook | null | Repository containing notebooks of my posts on Medium | cerlymarco | 2024-01-11 | 2019-04-22 | 249 | 7.915138 | null | Repository containing notebooks of my posts on Medium | ['artificial-intelligence', 'data-science', 'deep-learning', 'machine-learning', 'notebooks'] | ['artificial-intelligence', 'data-science', 'deep-learning', 'machine-learning', 'notebooks'] | 2023-12-17 | [('firmai/industry-machine-learning', 0.6431946158409119, 'study', 2), ('zenodo/zenodo', 0.5398790240287781, 'util', 0), ('tensorflow/tensor2tensor', 0.5338510870933533, 'ml', 2), ('ageron/handson-ml2', 0.525178074836731, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5219733119010925, 'study', 2)] | 1 | 0 | null | 0.67 | 1 | 1 | 58 | 1 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 29 |
9 | ml | https://github.com/contextlab/hypertools | [] | null | [] | [] | null | null | null | contextlab/hypertools | hypertools | 1,796 | 163 | 61 | Python | http://hypertools.readthedocs.io/en/latest/ | A Python toolbox for gaining geometric insights into high-dimensional data | contextlab | 2024-01-13 | 2016-09-27 | 383 | 4.689295 | https://avatars.githubusercontent.com/u/22374976?v=4 | A Python toolbox for gaining geometric insights into high-dimensional data | ['data-visualization', 'data-wrangling', 'high-dimensional-data', 'text-vectorization', 'time-series', 'topic-modeling', 'visualization'] | ['data-visualization', 'data-wrangling', 'high-dimensional-data', 'text-vectorization', 'time-series', 'topic-modeling', 'visualization'] | 2022-02-12 | [('enthought/mayavi', 0.6949652433395386, 'viz', 1), ('residentmario/geoplot', 0.686218798160553, 'gis', 0), ('holoviz/holoviz', 0.6432879567146301, 'viz', 0), ('marcomusy/vedo', 0.6358500719070435, 'viz', 1), ('scitools/iris', 0.6321009993553162, 'gis', 0), ('mwaskom/seaborn', 0.6260726451873779, 'viz', 1), ('pyqtgraph/pyqtgraph', 0.6197599172592163, 'viz', 1), ('altair-viz/altair', 0.6141685843467712, 'viz', 1), ('holoviz/panel', 0.6038259267807007, 'viz', 0), ('holoviz/hvplot', 0.5875470042228699, 'pandas', 0), ('pyvista/pyvista', 0.5857540369033813, 'viz', 1), ('facebookresearch/hiplot', 0.5843124985694885, 'viz', 0), ('man-group/dtale', 0.5685189366340637, 'viz', 2), ('gregorhd/mapcompare', 0.565951406955719, 'gis', 0), ('holoviz/geoviews', 0.5654189586639404, 'gis', 0), ('bmabey/pyldavis', 0.5628495812416077, 'ml', 0), ('matplotlib/matplotlib', 0.5552871227264404, 'viz', 1), ('makepath/xarray-spatial', 0.5519405603408813, 'gis', 0), ('vaexio/vaex', 0.548748791217804, 'perf', 1), ('kanaries/pygwalker', 0.5455514192581177, 'pandas', 1), ('dfki-ric/pytransform3d', 0.5422584414482117, 'math', 1), ('pandas-dev/pandas', 0.5390833616256714, 'pandas', 0), ('bokeh/bokeh', 0.5384601950645447, 'viz', 1), ('earthlab/earthpy', 0.5363619923591614, 'gis', 0), ('pysal/pysal', 0.535285472869873, 'gis', 0), ('wesm/pydata-book', 0.5346917510032654, 'study', 0), ('eleutherai/pyfra', 0.5310801863670349, 'ml', 0), ('jakevdp/pythondatasciencehandbook', 0.5308850407600403, 'study', 0), ('graphistry/pygraphistry', 0.5276858806610107, 'data', 1), ('lux-org/lux', 0.5267577767372131, 'viz', 1), ('artelys/geonetworkx', 0.5169349908828735, 'gis', 0), ('has2k1/plotnine', 0.5157314538955688, 'viz', 0), ('geopandas/geopandas', 0.5144107341766357, 'gis', 0), ('tdameritrade/stumpy', 0.5135495662689209, 'time-series', 0), ('holoviz/datashader', 0.5120874643325806, 'gis', 0), ('pytables/pytables', 0.5119327306747437, 'data', 0), ('blaze/blaze', 0.5032052397727966, 'pandas', 0)] | 21 | 7 | null | 0 | 0 | 0 | 89 | 23 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 29 |
1,682 | util | https://github.com/rubik/radon | [] | null | [] | [] | null | null | null | rubik/radon | radon | 1,566 | 114 | 34 | Python | http://radon.readthedocs.org/ | Various code metrics for Python code | rubik | 2024-01-14 | 2012-09-20 | 592 | 2.642082 | null | Various code metrics for Python code | ['cli', 'code-analysis', 'quality-assurance', 'static-analysis'] | ['cli', 'code-analysis', 'quality-assurance', 'static-analysis'] | 2023-10-06 | [('google/pytype', 0.6680740714073181, 'typing', 1), ('sourcery-ai/sourcery', 0.6180241703987122, 'util', 0), ('psf/black', 0.609166145324707, 'util', 0), ('nedbat/coveragepy', 0.6033869981765747, 'testing', 0), ('grantjenks/blue', 0.6026664972305298, 'util', 0), ('facebook/pyre-check', 0.6015112400054932, 'typing', 1), ('dosisod/refurb', 0.5743918418884277, 'util', 1), ('pyutils/line_profiler', 0.5716840028762817, 'profiling', 0), ('landscapeio/prospector', 0.5695351362228394, 'util', 0), ('pympler/pympler', 0.568540632724762, 'perf', 0), ('pycqa/flake8', 0.5651803612709045, 'util', 1), ('hhatto/autopep8', 0.5594862699508667, 'util', 0), ('pythonprofilers/memory_profiler', 0.5579990148544312, 'profiling', 0), ('regebro/pyroma', 0.5549668669700623, 'util', 0), ('ionelmc/pytest-benchmark', 0.554404079914093, 'testing', 0), ('jendrikseipp/vulture', 0.5527566075325012, 'util', 0), ('klen/py-frameworks-bench', 0.5515268445014954, 'perf', 0), ('python/cpython', 0.5486265420913696, 'util', 0), ('pycqa/mccabe', 0.5480080842971802, 'util', 0), ('klen/pylama', 0.5425077676773071, 'util', 0), ('google/yapf', 0.5423753261566162, 'util', 0), ('agronholm/typeguard', 0.5345003604888916, 'typing', 0), ('ydataai/ydata-quality', 0.5341893434524536, 'data', 0), ('microsoft/pyright', 0.533168375492096, 'typing', 0), ('pypa/hatch', 0.5330091714859009, 'util', 1), ('eugeneyan/python-collab-template', 0.5297834873199463, 'template', 0), ('astral-sh/ruff', 0.528230607509613, 'util', 1), ('samuelcolvin/python-devtools', 0.5275425314903259, 'debug', 0), ('amaargiru/pyroad', 0.5273672342300415, 'study', 0), ('gaogaotiantian/viztracer', 0.5236186385154724, 'profiling', 0), ('cython/cython', 0.5208148956298828, 'util', 0), ('ydataai/ydata-profiling', 0.5186936259269714, 'pandas', 0), ('aswinnnn/pyscan', 0.5182473659515381, 'security', 0), ('mynameisfiber/high_performance_python_2e', 0.5166555643081665, 'study', 0), ('eleutherai/pyfra', 0.514139711856842, 'ml', 0), ('pypy/pypy', 0.5132455825805664, 'util', 0), ('instagram/monkeytype', 0.5105746984481812, 'typing', 0), ('citadel-ai/langcheck', 0.505801796913147, 'llm', 0), ('pypi/warehouse', 0.5054935216903687, 'util', 0), ('facebookincubator/bowler', 0.5027236938476562, 'util', 0), ('microsoft/pycodegpt', 0.5025046467781067, 'llm', 0)] | 60 | 2 | null | 0.33 | 7 | 1 | 138 | 3 | 0 | 4 | 4 | 7 | 1 | 90 | 0.1 | 29 |
390 | data | https://github.com/mchong6/jojogan | [] | null | [] | [] | null | null | null | mchong6/jojogan | JoJoGAN | 1,395 | 207 | 26 | Jupyter Notebook | null | Official PyTorch repo for JoJoGAN: One Shot Face Stylization | mchong6 | 2024-01-08 | 2021-12-17 | 110 | 12.616279 | null | Official PyTorch repo for JoJoGAN: One Shot Face Stylization | ['anime', 'gans', 'image-translation'] | ['anime', 'gans', 'image-translation'] | 2022-02-05 | [('tencentarc/gfpgan', 0.5290706753730774, 'ml', 0), ('williamyang1991/vtoonify', 0.515015184879303, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5051544308662415, 'ml-dl', 0)] | 3 | 1 | null | 0 | 1 | 0 | 25 | 24 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 29 |
562 | gis | https://github.com/gboeing/osmnx-examples | [] | null | [] | [] | null | null | null | gboeing/osmnx-examples | osmnx-examples | 1,386 | 493 | 59 | Jupyter Notebook | https://osmnx.readthedocs.io | Gallery of OSMnx tutorials, usage examples, and feature demonstations. | gboeing | 2024-01-10 | 2017-07-22 | 340 | 4.071339 | null | Gallery of OSMnx tutorials, usage examples, and feature demonstations. | ['accessibility', 'binder', 'cities', 'city', 'jupyter-notebook', 'network-analysis', 'notebooks', 'openstreetmap', 'public-transport', 'street-networks', 'transit', 'transport', 'transportation', 'urban-analytics', 'urban-data-science', 'urban-design', 'urban-planning'] | ['accessibility', 'binder', 'cities', 'city', 'jupyter-notebook', 'network-analysis', 'notebooks', 'openstreetmap', 'public-transport', 'street-networks', 'transit', 'transport', 'transportation', 'urban-analytics', 'urban-data-science', 'urban-design', 'urban-planning'] | 2023-12-31 | [('gboeing/osmnx', 0.7930247187614441, 'gis', 5), ('marceloprates/prettymaps', 0.562412440776825, 'viz', 2)] | 1 | 1 | null | 1.1 | 4 | 4 | 79 | 0 | 0 | 3 | 3 | 4 | 0 | 90 | 0 | 29 |
1,225 | perf | https://github.com/nschloe/perfplot | [] | null | [] | [] | null | null | null | nschloe/perfplot | perfplot | 1,261 | 63 | 18 | Python | null | :chart_with_upwards_trend: Performance analysis for Python snippets | nschloe | 2024-01-12 | 2017-02-21 | 362 | 3.483425 | null | :chart_with_upwards_trend: Performance analysis for Python snippets | ['performance-analysis'] | ['performance-analysis'] | 2022-06-06 | [('altair-viz/altair', 0.5681192278862, 'viz', 0), ('pyutils/line_profiler', 0.535541832447052, 'profiling', 0), ('gaogaotiantian/viztracer', 0.528630793094635, 'profiling', 0), ('has2k1/plotnine', 0.5270527005195618, 'viz', 0), ('alexmojaki/heartrate', 0.5038774013519287, 'debug', 0), ('vizzuhq/ipyvizzu', 0.5008931756019592, 'jupyter', 0)] | 13 | 4 | null | 0 | 5 | 1 | 84 | 20 | 0 | 10 | 10 | 5 | 1 | 90 | 0.2 | 29 |
192 | ml | https://github.com/awslabs/dgl-ke | [] | null | [] | [] | null | null | null | awslabs/dgl-ke | dgl-ke | 1,202 | 197 | 27 | Python | https://dglke.dgl.ai/doc/ | High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. | awslabs | 2024-01-11 | 2020-03-03 | 204 | 5.892157 | https://avatars.githubusercontent.com/u/3299148?v=4 | High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. | ['dgl', 'graph-learning', 'knowledge-graph', 'knowledge-graphs-embeddings', 'machine-learning'] | ['dgl', 'graph-learning', 'knowledge-graph', 'knowledge-graphs-embeddings', 'machine-learning'] | 2023-03-20 | [('accenture/ampligraph', 0.7346105575561523, 'data', 2), ('dylanhogg/llmgraph', 0.6202223300933838, 'ml', 1), ('facebookresearch/pytorch-biggraph', 0.6120842099189758, 'ml-dl', 0), ('zjunlp/deepke', 0.5722960233688354, 'ml', 1), ('neuml/txtai', 0.5560591220855713, 'nlp', 1), ('deepgraphlearning/ultra', 0.5505498647689819, 'ml', 1), ('dmlc/dgl', 0.5391981601715088, 'ml-dl', 0), ('koaning/embetter', 0.5223193764686584, 'data', 0), ('stellargraph/stellargraph', 0.5202558040618896, 'graph', 1), ('plasticityai/magnitude', 0.5072051286697388, 'nlp', 1), ('qdrant/fastembed', 0.5057823061943054, 'ml', 0), ('benedekrozemberczki/tigerlily', 0.50465327501297, 'ml-dl', 2)] | 26 | 4 | null | 0.02 | 1 | 0 | 47 | 10 | 0 | 1 | 1 | 1 | 0 | 90 | 0 | 29 |
616 | util | https://github.com/pytoolz/cytoolz | [] | null | [] | [] | null | null | null | pytoolz/cytoolz | cytoolz | 954 | 67 | 25 | Python | null | Cython implementation of Toolz: High performance functional utilities | pytoolz | 2024-01-13 | 2014-04-04 | 512 | 1.861204 | https://avatars.githubusercontent.com/u/5448828?v=4 | Cython implementation of Toolz: High performance functional utilities | [] | [] | 2023-07-21 | [('scikit-build/scikit-build', 0.5701683759689331, 'ml', 0), ('suor/funcy', 0.5295758247375488, 'util', 0), ('cython/cython', 0.5252465009689331, 'util', 0)] | 21 | 5 | null | 0.08 | 3 | 1 | 119 | 6 | 1 | 2 | 1 | 3 | 4 | 90 | 1.3 | 29 |
789 | graph | https://github.com/westhealth/pyvis | [] | null | [] | [] | null | null | null | westhealth/pyvis | pyvis | 850 | 145 | 19 | HTML | http://pyvis.readthedocs.io/en/latest/ | Python package for creating and visualizing interactive network graphs. | westhealth | 2024-01-11 | 2018-05-10 | 298 | 2.845528 | https://avatars.githubusercontent.com/u/22085795?v=4 | Python package for creating and visualizing interactive network graphs. | ['network-visualization', 'networkx'] | ['network-visualization', 'networkx'] | 2023-02-10 | [('pygraphviz/pygraphviz', 0.7577512264251709, 'viz', 0), ('graphistry/pygraphistry', 0.6478259563446045, 'data', 2), ('networkx/networkx', 0.6360735297203064, 'graph', 0), ('plotly/plotly.py', 0.6326491832733154, 'viz', 0), ('h4kor/graph-force', 0.6132168173789978, 'graph', 0), ('holoviz/hvplot', 0.5998131036758423, 'pandas', 0), ('artelys/geonetworkx', 0.5923200249671936, 'gis', 0), ('altair-viz/altair', 0.5836617946624756, 'viz', 0), ('matplotlib/matplotlib', 0.5532649159431458, 'viz', 0), ('bokeh/bokeh', 0.55198734998703, 'viz', 0), ('vizzuhq/ipyvizzu', 0.5483621954917908, 'jupyter', 0), ('has2k1/plotnine', 0.5456839203834534, 'viz', 0), ('pydot/pydot', 0.542072594165802, 'viz', 0), ('mwaskom/seaborn', 0.5389178395271301, 'viz', 0), ('holoviz/holoviz', 0.5358419418334961, 'viz', 0), ('secdev/scapy', 0.5347074866294861, 'util', 1), ('enthought/mayavi', 0.5338135361671448, 'viz', 0), ('gboeing/osmnx', 0.5170513987541199, 'gis', 1), ('cuemacro/chartpy', 0.5167423486709595, 'viz', 0), ('dmlc/dgl', 0.5159277319908142, 'ml-dl', 0), ('kuanb/peartree', 0.5152437090873718, 'gis', 0), ('graphql-python/graphene', 0.5054649114608765, 'web', 0), ('pyqtgraph/pyqtgraph', 0.5053408741950989, 'viz', 0), ('holoviz/panel', 0.5053226947784424, 'viz', 0), ('scitools/iris', 0.503166139125824, 'gis', 0), ('comfyanonymous/comfyui', 0.5001736283302307, 'diffusion', 0)] | 32 | 3 | null | 0.06 | 23 | 3 | 69 | 11 | 0 | 1 | 1 | 23 | 21 | 90 | 0.9 | 29 |
1,582 | nlp | https://github.com/paddlepaddle/rocketqa | ['question-answering'] | null | [] | [] | null | null | null | paddlepaddle/rocketqa | RocketQA | 713 | 124 | 19 | Python | null | 🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models. | paddlepaddle | 2024-01-12 | 2021-09-07 | 125 | 5.704 | https://avatars.githubusercontent.com/u/23534030?v=4 | 🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models. | ['dense-retrieval', 'information-retrieval', 'nlp', 'question-answering'] | ['dense-retrieval', 'information-retrieval', 'nlp', 'question-answering'] | 2022-12-03 | [('intellabs/fastrag', 0.6380553841590881, 'nlp', 3), ('facebookresearch/dpr-scale', 0.6294921636581421, 'nlp', 0), ('ai21labs/in-context-ralm', 0.5692198872566223, 'llm', 0), ('srush/minichain', 0.5608699321746826, 'llm', 1), ('paddlepaddle/paddlenlp', 0.560157835483551, 'llm', 2), ('muennighoff/sgpt', 0.5352213978767395, 'llm', 1), ('lianjiatech/belle', 0.5283323526382446, 'llm', 0), ('freedomintelligence/llmzoo', 0.5235476493835449, 'llm', 0), ('castorini/pyserini', 0.5224674940109253, 'ml', 1), ('deepset-ai/farm', 0.5212914347648621, 'nlp', 2), ('neuml/txtai', 0.5149143934249878, 'nlp', 2), ('llmware-ai/llmware', 0.510935366153717, 'llm', 3), ('baichuan-inc/baichuan-13b', 0.5105500817298889, 'llm', 0), ('night-chen/toolqa', 0.5089247226715088, 'llm', 1), ('jina-ai/clip-as-service', 0.506847620010376, 'nlp', 0), ('explosion/spacy-models', 0.5023024678230286, 'nlp', 1)] | 12 | 3 | null | 0 | 4 | 0 | 29 | 14 | 0 | 0 | 0 | 4 | 5 | 90 | 1.2 | 29 |
1,105 | study | https://github.com/davidadsp/generative_deep_learning_2nd_edition | [] | null | [] | [] | null | null | null | davidadsp/generative_deep_learning_2nd_edition | Generative_Deep_Learning_2nd_Edition | 663 | 223 | 18 | Jupyter Notebook | https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ | The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play. | davidadsp | 2024-01-14 | 2022-03-25 | 96 | 6.865385 | null | The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play. | ['chatgpt', 'dalle2', 'data-science', 'deep-learning', 'diffusion-models', 'generative-adversarial-network', 'gpt-3', 'machine-learning', 'stable-diffusion', 'tensorflow'] | ['chatgpt', 'dalle2', 'data-science', 'deep-learning', 'diffusion-models', 'generative-adversarial-network', 'gpt-3', 'machine-learning', 'stable-diffusion', 'tensorflow'] | 2023-07-18 | [('openai/image-gpt', 0.6263450980186462, 'llm', 0), ('mrdbourke/pytorch-deep-learning', 0.5829065442085266, 'study', 2), ('rasbt/machine-learning-book', 0.5600119233131409, 'study', 2), ('d2l-ai/d2l-en', 0.5406649708747864, 'study', 4), ('tensorlayer/tensorlayer', 0.5377876162528992, 'ml-rl', 2), ('open-mmlab/mmediting', 0.5358924269676208, 'ml', 3), ('microsoft/generative-ai-for-beginners', 0.5330820679664612, 'study', 1), ('lupantech/chameleon-llm', 0.5299697518348694, 'llm', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5259917974472046, 'study', 2), ('lucidrains/imagen-pytorch', 0.5147601962089539, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.5120260119438171, 'ml-dl', 2), ('sharonzhou/long_stable_diffusion', 0.5068796277046204, 'diffusion', 0), ('automatic1111/stable-diffusion-webui', 0.5017077922821045, 'diffusion', 2)] | 4 | 1 | null | 1.35 | 9 | 4 | 22 | 6 | 0 | 0 | 0 | 9 | 8 | 90 | 0.9 | 29 |
435 | pandas | https://github.com/polyaxon/datatile | [] | null | [] | [] | null | null | null | polyaxon/datatile | traceml | 488 | 43 | 14 | Python | null | Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon. | polyaxon | 2024-01-12 | 2016-03-25 | 409 | 1.191489 | https://avatars.githubusercontent.com/u/24544827?v=4 | Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon. | ['dask', 'data-exploration', 'data-profiling', 'data-quality', 'data-quality-checks', 'data-science', 'data-visualization', 'dataframes', 'dataops', 'explainable-ai', 'matplotlib', 'mlops', 'pandas', 'pandas-summary', 'plotly', 'pytorch', 'spark', 'statistics', 'tensorflow', 'tracking'] | ['dask', 'data-exploration', 'data-profiling', 'data-quality', 'data-quality-checks', 'data-science', 'data-visualization', 'dataframes', 'dataops', 'explainable-ai', 'matplotlib', 'mlops', 'pandas', 'pandas-summary', 'plotly', 'pytorch', 'spark', 'statistics', 'tensorflow', 'tracking'] | 2024-01-04 | [('plotly/dash', 0.6874310970306396, 'viz', 3), ('wandb/client', 0.6632310748100281, 'ml', 4), ('krzjoa/awesome-python-data-science', 0.6403499245643616, 'study', 3), ('aimhubio/aim', 0.6386370062828064, 'ml-ops', 5), ('huggingface/datasets', 0.6301923990249634, 'nlp', 3), ('dagworks-inc/hamilton', 0.6286333203315735, 'ml-ops', 3), ('holoviz/panel', 0.6254085302352905, 'viz', 2), ('pandas-dev/pandas', 0.6224325299263, 'pandas', 2), ('gradio-app/gradio', 0.6204770803451538, 'viz', 2), ('mlflow/mlflow', 0.6103001236915588, 'ml-ops', 0), ('ydataai/ydata-profiling', 0.6102992296218872, 'pandas', 6), ('ranaroussi/quantstats', 0.6079445481300354, 'finance', 0), ('dylanhogg/awesome-python', 0.6012967228889465, 'study', 2), ('whylabs/whylogs', 0.5969440937042236, 'util', 4), ('man-group/dtale', 0.5954803824424744, 'viz', 3), ('oegedijk/explainerdashboard', 0.5954424142837524, 'ml-interpretability', 1), 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1,416 | jupyter | https://github.com/xiaohk/stickyland | [] | null | [] | [] | null | null | null | xiaohk/stickyland | stickyland | 470 | 30 | 9 | TypeScript | https://xiaohk.github.io/stickyland/ | Break the linear presentation of Jupyter Notebooks with sticky cells! | xiaohk | 2024-01-12 | 2021-11-02 | 117 | 4.017094 | null | Break the linear presentation of Jupyter Notebooks with sticky cells! | ['dashboard', 'jupyter', 'jupyterlab', 'jupyterlab-extension', 'notebook'] | ['dashboard', 'jupyter', 'jupyterlab', 'jupyterlab-extension', 'notebook'] | 2023-12-24 | [('jupyter-widgets/ipywidgets', 0.6346691250801086, 'jupyter', 1), ('jupyter/notebook', 0.6330485939979553, 'jupyter', 2), ('voila-dashboards/voila', 0.5852877497673035, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.5726215243339539, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.5476192235946655, 'jupyter', 2), ('jupyter/nbformat', 0.5371445417404175, 'jupyter', 0), ('jupyter/nbconvert', 0.536399781703949, 'jupyter', 0), ('bloomberg/ipydatagrid', 0.5169107913970947, 'jupyter', 1), ('mwouts/jupytext', 0.5160862803459167, 'jupyter', 2), ('quantopian/qgrid', 0.5084087252616882, 'jupyter', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5069721937179565, 'jupyter', 0)] | 2 | 1 | null | 0.19 | 2 | 2 | 27 | 1 | 1 | 4 | 1 | 2 | 4 | 90 | 2 | 29 |
1,378 | diffusion | https://github.com/nvlabs/gcvit | [] | null | [] | [] | null | null | null | nvlabs/gcvit | GCVit | 412 | 49 | 10 | Python | https://arxiv.org/abs/2206.09959 | [ICML 2023] Official PyTorch implementation of Global Context Vision Transformers | nvlabs | 2024-01-12 | 2022-06-18 | 84 | 4.879865 | https://avatars.githubusercontent.com/u/2695301?v=4 | [ICML 2023] Official PyTorch implementation of Global Context Vision Transformers | ['ade20k', 'backbone', 'coco', 'deep-learning', 'imagenet', 'imagenet-classification', 'object-detection', 'pre-train', 'pre-trained-model', 'self-attention', 'semantic-segmentation', 'vision-transformer', 'visual-recognition'] | ['ade20k', 'backbone', 'coco', 'deep-learning', 'imagenet', 'imagenet-classification', 'object-detection', 'pre-train', 'pre-trained-model', 'self-attention', 'semantic-segmentation', 'vision-transformer', 'visual-recognition'] | 2023-12-22 | [('microsoft/swin-transformer', 0.6548908352851868, 'ml', 4), ('lucidrains/vit-pytorch', 0.6527162790298462, 'ml-dl', 0), ('roboflow/supervision', 0.6431651711463928, 'ml', 3), ('huggingface/transformers', 0.6319802403450012, 'nlp', 1), ('rwightman/pytorch-image-models', 0.6296912431716919, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.6284846067428589, 'ml-dl', 1), ('deci-ai/super-gradients', 0.6253355145454407, 'ml-dl', 4), ('google-research/maxvit', 0.6166060566902161, 'ml', 2), ('lucidrains/imagen-pytorch', 0.6131131052970886, 'ml-dl', 1), ('salesforce/blip', 0.5959486365318298, 'diffusion', 0), ('intel/intel-extension-for-pytorch', 0.5956222414970398, 'perf', 1), ('open-mmlab/mmsegmentation', 0.5941908359527588, 'ml', 1), ('nielsrogge/transformers-tutorials', 0.5923160910606384, 'study', 1), ('mcahny/deep-video-inpainting', 0.5919110774993896, 'ml-dl', 0), ('pytorch/ignite', 0.5840114951133728, 'ml-dl', 1), ('karpathy/mingpt', 0.5820482969284058, 'llm', 0), ('roboflow/notebooks', 0.5799825191497803, 'study', 2), ('microsoft/focal-transformer', 0.5749183893203735, 'ml', 0), ('idea-research/groundingdino', 0.5644093751907349, 'diffusion', 1), ('microsoft/torchgeo', 0.5610058307647705, 'gis', 1), ('open-mmlab/mmdetection', 0.559407114982605, 'ml', 2), ('nyandwi/modernconvnets', 0.5547017455101013, 'ml-dl', 0), ('blakeblackshear/frigate', 0.5532398223876953, 'util', 1), ('open-mmlab/mmediting', 0.5518020987510681, 'ml', 1), ('google/automl', 0.5431532859802246, 'ml', 1), ('lutzroeder/netron', 0.5401598811149597, 'ml', 1), ('lightly-ai/lightly', 0.5394826531410217, 'ml', 1), ('kornia/kornia', 0.5384776592254639, 'ml-dl', 1), ('huggingface/exporters', 0.5337070822715759, 'ml', 1), ('huggingface/optimum', 0.5319724678993225, 'ml', 0), ('skorch-dev/skorch', 0.531648576259613, 'ml-dl', 0), ('matterport/mask_rcnn', 0.5284178256988525, 'ml-dl', 1), ('nvlabs/prismer', 0.5274479389190674, 'diffusion', 0), ('google-research/deeplab2', 0.5264610648155212, 'ml', 0), ('rasbt/machine-learning-book', 0.5256971716880798, 'study', 1), ('pyg-team/pytorch_geometric', 0.5232054591178894, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.520950198173523, 'study', 1), ('albumentations-team/albumentations', 0.5185796618461609, 'ml-dl', 2), ('nicolas-chaulet/torch-points3d', 0.5170150995254517, 'ml', 0), ('facebookresearch/detr', 0.5166937112808228, 'ml-dl', 0), ('mdbloice/augmentor', 0.5139862895011902, 'ml', 1), ('kshitij12345/torchnnprofiler', 0.5133840441703796, 'profiling', 0), ('facebookresearch/pytorch3d', 0.5093544125556946, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5083329081535339, 'ml', 1), ('azavea/raster-vision', 0.5082259178161621, 'gis', 3), ('huggingface/datasets', 0.5082133412361145, 'nlp', 1), ('graykode/nlp-tutorial', 0.5081471800804138, 'study', 0), ('huggingface/huggingface_hub', 0.5078686475753784, 'ml', 1), ('lucidrains/dalle2-pytorch', 0.5059126019477844, 'diffusion', 1), ('facebookresearch/detectron2', 0.504278838634491, 'ml-dl', 0)] | 6 | 1 | null | 0.63 | 2 | 1 | 19 | 1 | 0 | 1 | 1 | 2 | 2 | 90 | 1 | 29 |
1,732 | testing | https://github.com/kiwicom/pytest-recording | [] | null | [] | [] | null | null | null | kiwicom/pytest-recording | pytest-recording | 347 | 31 | 4 | Python | null | A pytest plugin that allows recording network interactions via VCR.py | kiwicom | 2024-01-11 | 2019-07-16 | 237 | 1.464135 | https://avatars.githubusercontent.com/u/25227300?v=4 | A pytest plugin that allows recording network interactions via VCR.py | ['cassettes', 'pytest', 'testing', 'vcr'] | ['cassettes', 'pytest', 'testing', 'vcr'] | 2023-12-06 | [('pytest-dev/pytest-xdist', 0.5940394401550293, 'testing', 1), ('irmen/pyminiaudio', 0.5641629099845886, 'util', 0), ('samuelcolvin/pytest-pretty', 0.544182538986206, 'testing', 1), ('computationalmodelling/nbval', 0.535578191280365, 'jupyter', 2), ('ionelmc/pytest-benchmark', 0.5216888785362244, 'testing', 1), ('teemu/pytest-sugar', 0.5215980410575867, 'testing', 2), ('pytest-dev/pytest-cov', 0.52159583568573, 'testing', 1)] | 13 | 3 | null | 0.71 | 14 | 10 | 55 | 1 | 3 | 5 | 3 | 14 | 16 | 90 | 1.1 | 29 |
1,404 | llm | https://github.com/approximatelabs/datadm | ['conversational'] | null | [] | [] | null | null | null | approximatelabs/datadm | datadm | 315 | 25 | 8 | Python | null | DataDM is your private data assistant. Slide into your data's DMs | approximatelabs | 2024-01-04 | 2023-05-25 | 35 | 8.82 | https://avatars.githubusercontent.com/u/106505054?v=4 | DataDM is your private data assistant. Slide into your data's DMs | [] | ['conversational'] | 2023-09-11 | [] | 3 | 1 | null | 0.98 | 0 | 0 | 8 | 4 | 0 | 21 | 21 | 0 | 0 | 90 | 0 | 29 |
664 | gis | https://github.com/cgal/cgal-swig-bindings | [] | null | [] | [] | null | null | null | cgal/cgal-swig-bindings | cgal-swig-bindings | 305 | 91 | 28 | C++ | null | CGAL bindings using SWIG | cgal | 2024-01-05 | 2015-03-14 | 463 | 0.658138 | https://avatars.githubusercontent.com/u/5746664?v=4 | CGAL bindings using SWIG | [] | [] | 2023-12-20 | [] | 22 | 3 | null | 0.75 | 15 | 6 | 108 | 1 | 7 | 1 | 7 | 15 | 19 | 90 | 1.3 | 29 |
478 | pandas | https://github.com/holoviz/spatialpandas | [] | null | [] | [] | null | null | null | holoviz/spatialpandas | spatialpandas | 293 | 24 | 23 | Python | null | Pandas extension arrays for spatial/geometric operations | holoviz | 2024-01-04 | 2019-10-28 | 222 | 1.318971 | https://avatars.githubusercontent.com/u/51678735?v=4 | Pandas extension arrays for spatial/geometric operations | ['geographic-data', 'geopandas', 'holoviz', 'pandas', 'spatialpandas'] | ['geographic-data', 'geopandas', 'holoviz', 'pandas', 'spatialpandas'] | 2024-01-11 | [('geopandas/geopandas', 0.6860671043395996, 'gis', 2), ('residentmario/geoplot', 0.6135755777359009, 'gis', 1), ('anitagraser/movingpandas', 0.5773379802703857, 'gis', 1), ('jmcarpenter2/swifter', 0.562759518623352, 'pandas', 1), ('nalepae/pandarallel', 0.5524942874908447, 'pandas', 1), ('makepath/xarray-spatial', 0.5339615941047668, 'gis', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5225083231925964, 'pandas', 0), ('man-group/dtale', 0.5217031240463257, 'viz', 1), ('blaze/blaze', 0.5175127387046814, 'pandas', 0), ('rapidsai/cudf', 0.5126527547836304, 'pandas', 1), ('earthlab/earthpy', 0.5123425722122192, 'gis', 0), ('mwaskom/seaborn', 0.5058495402336121, 'viz', 1), ('adamerose/pandasgui', 0.5028727054595947, 'pandas', 1), ('holoviz/hvplot', 0.5009598135948181, 'pandas', 1)] | 12 | 5 | null | 0.46 | 6 | 5 | 51 | 0 | 4 | 10 | 4 | 6 | 3 | 90 | 0.5 | 29 |
1,713 | diffusion | https://github.com/bentoml/onediffusion | [] | null | [] | [] | null | null | null | bentoml/onediffusion | OneDiffusion | 285 | 17 | 12 | Python | https://bentoml.com | OneDiffusion: Run any Stable Diffusion models and fine-tuned weights with ease | bentoml | 2024-01-05 | 2023-06-12 | 33 | 8.599138 | https://avatars.githubusercontent.com/u/49176046?v=4 | OneDiffusion: Run any Stable Diffusion models and fine-tuned weights with ease | ['ai', 'diffusion-models', 'fine-tuning', 'kubernetes', 'lora', 'model-serving', 'stable-diffusion'] | ['ai', 'diffusion-models', 'fine-tuning', 'kubernetes', 'lora', 'model-serving', 'stable-diffusion'] | 2023-12-08 | [('carson-katri/dream-textures', 0.6899959444999695, 'diffusion', 2), ('stability-ai/stability-sdk', 0.6665179133415222, 'diffusion', 1), ('divamgupta/stable-diffusion-tensorflow', 0.6373262405395508, 'diffusion', 0), ('lllyasviel/controlnet', 0.6226494908332825, 'diffusion', 0), ('mlc-ai/web-stable-diffusion', 0.6144503355026245, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6026777625083923, 'diffusion', 2), ('comfyanonymous/comfyui', 0.5863965749740601, 'diffusion', 1), ('divamgupta/diffusionbee-stable-diffusion-ui', 0.5153639316558838, 'diffusion', 1), ('tanelp/tiny-diffusion', 0.5116053223609924, 'diffusion', 0), ('civitai/sd_civitai_extension', 0.5044161081314087, 'llm', 0), ('thereforegames/unprompted', 0.5029755234718323, 'diffusion', 1)] | 5 | 1 | null | 0.87 | 7 | 3 | 7 | 1 | 0 | 0 | 0 | 7 | 2 | 90 | 0.3 | 29 |
124 | util | https://github.com/mgedmin/check-manifest | [] | null | [] | [] | null | null | null | mgedmin/check-manifest | check-manifest | 283 | 38 | 7 | Python | https://pypi.org/p/check-manifest | Tool to check the completeness of MANIFEST.in for Python packages | mgedmin | 2024-01-04 | 2013-03-05 | 569 | 0.497364 | null | Tool to check the completeness of MANIFEST.in for Python packages | [] | [] | 2023-12-18 | [('pypi/warehouse', 0.5615488886833191, 'util', 0), ('mkdocstrings/griffe', 0.537682056427002, 'util', 0), ('nedbat/coveragepy', 0.5218181610107422, 'testing', 0), ('indygreg/pyoxidizer', 0.5157642364501953, 'util', 0), ('mitsuhiko/rye', 0.5007199645042419, 'util', 0)] | 22 | 6 | null | 0.12 | 1 | 1 | 132 | 1 | 0 | 5 | 5 | 1 | 2 | 90 | 2 | 29 |
273 | data | https://github.com/amzn/ion-python | [] | null | [] | [] | null | null | null | amzn/ion-python | ion-python | 246 | 52 | 25 | Python | https://amazon-ion.github.io/ion-docs/ | A Python implementation of Amazon Ion. | amzn | 2024-01-06 | 2016-04-07 | 407 | 0.603364 | https://avatars.githubusercontent.com/u/105071691?v=4 | A Python implementation of Amazon Ion. | [] | [] | 2024-01-10 | [('pynamodb/pynamodb', 0.6932819485664368, 'data', 0), ('geeogi/async-python-lambda-template', 0.6108747720718384, 'template', 0), ('primal100/pybitcointools', 0.5686578154563904, 'crypto', 0), ('nficano/python-lambda', 0.5418636798858643, 'util', 0), ('falconry/falcon', 0.5367324352264404, 'web', 0), ('ethereum/py-evm', 0.5310432314872742, 'crypto', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.524419903755188, 'util', 0), ('pytables/pytables', 0.5214452147483826, 'data', 0), ('awslabs/python-deequ', 0.5171683430671692, 'ml', 0), ('ethereum/web3.py', 0.5136985778808594, 'crypto', 0), ('boto/boto3', 0.5133451223373413, 'util', 0), ('oracle/graalpython', 0.5121100544929504, 'util', 0), ('pyston/pyston', 0.5089343786239624, 'util', 0), ('encode/httpx', 0.5086729526519775, 'web', 0), ('aws/aws-sdk-pandas', 0.5067648887634277, 'pandas', 0), ('aws/chalice', 0.5045038461685181, 'web', 0)] | 28 | 3 | null | 1.04 | 48 | 35 | 95 | 0 | 4 | 2 | 4 | 48 | 30 | 90 | 0.6 | 29 |
1,459 | util | https://github.com/mamba-org/boa | [] | null | [] | [] | null | null | null | mamba-org/boa | boa | 245 | 54 | 9 | Python | https://boa-build.readthedocs.io/en/latest/ | The fast conda package builder, based on mamba | mamba-org | 2024-01-04 | 2020-05-27 | 191 | 1.276992 | https://avatars.githubusercontent.com/u/66118895?v=4 | The fast conda package builder, based on mamba | ['conda', 'conda-packages', 'mamba'] | ['conda', 'conda-packages', 'mamba'] | 2023-11-19 | [('conda/conda-build', 0.7872036695480347, 'util', 1), ('mamba-org/quetz', 0.7533841729164124, 'util', 1), ('mamba-org/mamba', 0.7310133576393127, 'util', 1), ('conda/constructor', 0.7149392366409302, 'util', 1), ('conda/conda-pack', 0.7062498927116394, 'util', 1), ('mamba-org/micromamba-docker', 0.6690220236778259, 'util', 2), ('conda/conda', 0.5487179756164551, 'util', 1), ('mamba-org/gator', 0.5324650406837463, 'jupyter', 1), ('conda-forge/miniforge', 0.5230752825737, 'util', 0), ('conda-forge/feedstocks', 0.5122072696685791, 'util', 1), ('pomponchik/instld', 0.5095345377922058, 'util', 0), ('spack/spack', 0.5025382041931152, 'util', 0)] | 32 | 4 | null | 0.46 | 20 | 8 | 44 | 2 | 3 | 11 | 3 | 20 | 19 | 90 | 0.9 | 29 |
1,838 | finance | https://github.com/hydrosquall/tiingo-python | [] | null | [] | [] | null | null | null | hydrosquall/tiingo-python | tiingo-python | 227 | 51 | 8 | Python | https://pypi.org/project/tiingo/ | Python client for interacting with the Tiingo Financial Data API (stock ticker and news data) | hydrosquall | 2024-01-12 | 2017-08-25 | 335 | 0.676458 | null | Python client for interacting with the Tiingo Financial Data API (stock ticker and news data) | ['finance', 'stock-market', 'stock-prices', 'stocks', 'ticker-data'] | ['finance', 'stock-market', 'stock-prices', 'stocks', 'ticker-data'] | 2023-12-13 | [('cuemacro/findatapy', 0.6793490052223206, 'finance', 0), ('plotly/dash', 0.5758013725280762, 'viz', 1), ('ranaroussi/yfinance', 0.5750361084938049, 'finance', 0), ('matplotlib/mplfinance', 0.5679528713226318, 'finance', 1), ('nasdaq/data-link-python', 0.5673314332962036, 'finance', 0), ('pmorissette/ffn', 0.5629584789276123, 'finance', 0), ('gbeced/pyalgotrade', 0.5620060563087463, 'finance', 0), ('ethereum/web3.py', 0.5618115067481995, 'crypto', 0), ('gbeced/basana', 0.5487340688705444, 'finance', 0), ('ta-lib/ta-lib-python', 0.5465307831764221, 'finance', 1), ('goldmansachs/gs-quant', 0.5450910329818726, 'finance', 0), ('simple-salesforce/simple-salesforce', 0.5396576523780823, 'data', 0), ('holoviz/panel', 0.5386637449264526, 'viz', 0), ('quantconnect/lean', 0.5375146865844727, 'finance', 1), ('cuemacro/finmarketpy', 0.5366682410240173, 'finance', 0), ('stefmolin/stock-analysis', 0.5315351486206055, 'finance', 2), ('pmaji/crypto-whale-watching-app', 0.5264788269996643, 'crypto', 0), ('ccxt/ccxt', 0.5252950191497803, 'crypto', 0), ('googleapis/google-api-python-client', 0.5202198624610901, 'util', 0), ('ranaroussi/quantstats', 0.5174421072006226, 'finance', 1), ('robcarver17/pysystemtrade', 0.5165610313415527, 'finance', 0), ('hugapi/hug', 0.5074604749679565, 'util', 0), ('quantopian/zipline', 0.5056824684143066, 'finance', 0), ('firmai/atspy', 0.5044682025909424, 'time-series', 1), ('snyk-labs/pysnyk', 0.5022001266479492, 'security', 0), ('encode/httpx', 0.5019758939743042, 'web', 0), ('qdrant/qdrant-client', 0.501908540725708, 'util', 0)] | 13 | 5 | null | 0.83 | 26 | 19 | 78 | 1 | 0 | 3 | 3 | 26 | 28 | 90 | 1.1 | 29 |
1,495 | math | https://github.com/deepmind/synjax | ['probability', 'distributions', 'jax'] | SynJax is a neural network library for JAX structured probability distributions | [] | [] | null | null | null | deepmind/synjax | synjax | 220 | 14 | 12 | Python | null | null | deepmind | 2024-01-04 | 2023-08-04 | 25 | 8.603352 | https://avatars.githubusercontent.com/u/8596759?v=4 | SynJax is a neural network library for JAX structured probability distributions | [] | ['distributions', 'jax', 'probability'] | 2024-01-08 | [('deepmind/dm-haiku', 0.7001689076423645, 'ml-dl', 1), ('google/flax', 0.6082916259765625, 'ml-dl', 1), ('google/evojax', 0.5408310890197754, 'sim', 1), ('deepmind/chex', 0.5291113257408142, 'ml-dl', 1)] | 5 | 3 | null | 0.4 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 29 |
406 | data | https://github.com/google/weather-tools | [] | null | [] | [] | null | null | null | google/weather-tools | weather-tools | 186 | 35 | 15 | Python | https://weather-tools.readthedocs.io/ | Apache Beam pipelines to make weather data accessible and useful. | google | 2024-01-11 | 2021-11-22 | 114 | 1.629537 | https://avatars.githubusercontent.com/u/1342004?v=4 | Apache Beam pipelines to make weather data accessible and useful. | ['apache-beam', 'weather'] | ['apache-beam', 'weather'] | 2024-01-10 | [] | 31 | 2 | null | 1.25 | 32 | 27 | 26 | 0 | 0 | 5 | 5 | 32 | 6 | 90 | 0.2 | 29 |
1,502 | math | https://github.com/deepmind/kfac-jax | ['jax'] | null | [] | [] | null | null | null | deepmind/kfac-jax | kfac-jax | 177 | 14 | 8 | Python | null | Second Order Optimization and Curvature Estimation with K-FAC in JAX. | deepmind | 2024-01-04 | 2022-03-18 | 97 | 1.814056 | https://avatars.githubusercontent.com/u/8596759?v=4 | Second Order Optimization and Curvature Estimation with K-FAC in JAX. | ['bayesian-deep-learning', 'machine-learning', 'optimization'] | ['bayesian-deep-learning', 'jax', 'machine-learning', 'optimization'] | 2024-01-04 | [('deepmind/dm-haiku', 0.5966999530792236, 'ml-dl', 2), ('pytorch/botorch', 0.55311119556427, 'ml-dl', 0)] | 11 | 4 | null | 1.67 | 19 | 16 | 22 | 0 | 2 | 2 | 2 | 19 | 6 | 90 | 0.3 | 29 |
861 | util | https://github.com/hugovk/pypistats | [] | null | [] | [] | null | null | null | hugovk/pypistats | pypistats | 174 | 30 | 5 | Python | https://pypistats.org/api/ | Command-line interface to PyPI Stats API to get download stats for Python packages | hugovk | 2024-01-10 | 2018-09-22 | 279 | 0.622699 | null | Command-line interface to PyPI Stats API to get download stats for Python packages | ['api', 'cli', 'command-line', 'command-line-tool', 'downloads', 'statistics', 'stats'] | ['api', 'cli', 'command-line', 'command-line-tool', 'downloads', 'statistics', 'stats'] | 2024-01-01 | [('ofek/pypinfo', 0.7068412899971008, 'util', 1), ('pypi/warehouse', 0.6038178205490112, 'util', 0), ('cuemacro/findatapy', 0.562679648399353, 'finance', 0), ('urwid/urwid', 0.5486549735069275, 'term', 0), ('google/python-fire', 0.5451176166534424, 'term', 1), ('tox-dev/pipdeptree', 0.5279873609542847, 'util', 1), ('wolph/python-progressbar', 0.5268552303314209, 'util', 1), ('jquast/blessed', 0.5230196118354797, 'term', 1), ('pyodide/micropip', 0.5070849657058716, 'util', 0), ('samuelcolvin/pytest-pretty', 0.5067400932312012, 'testing', 0), ('pypa/gh-action-pypi-publish', 0.5029650926589966, 'util', 0)] | 13 | 4 | null | 0.92 | 11 | 9 | 65 | 0 | 3 | 5 | 3 | 11 | 18 | 90 | 1.6 | 29 |
767 | sim | https://github.com/openfisca/openfisca-core | [] | null | [] | [] | null | null | null | openfisca/openfisca-core | openfisca-core | 157 | 74 | 26 | Python | https://openfisca.org | OpenFisca core engine. See other repositories for countries-specific code & data. | openfisca | 2023-12-26 | 2013-12-29 | 526 | 0.298317 | https://avatars.githubusercontent.com/u/1794404?v=4 | OpenFisca core engine. See other repositories for countries-specific code & data. | ['better-rules', 'legislation-as-code', 'microsimulation', 'rules-as-code'] | ['better-rules', 'legislation-as-code', 'microsimulation', 'rules-as-code'] | 2023-12-18 | [] | 61 | 2 | null | 1.88 | 6 | 3 | 122 | 1 | 0 | 39 | 39 | 6 | 10 | 90 | 1.7 | 29 |
1,399 | llm | https://github.com/openbioml/chemnlp | ['chemistry'] | null | [] | [] | null | null | null | openbioml/chemnlp | chemnlp | 120 | 43 | 3 | Python | null | ChemNLP project | openbioml | 2024-01-12 | 2023-02-13 | 50 | 2.393162 | https://avatars.githubusercontent.com/u/106522429?v=4 | ChemNLP project | [] | ['chemistry'] | 2023-12-09 | [] | 26 | 2 | null | 5.56 | 113 | 92 | 11 | 1 | 0 | 0 | 0 | 113 | 71 | 90 | 0.6 | 29 |
326 | security | https://github.com/sonatype-nexus-community/jake | [] | null | [] | [] | null | null | null | sonatype-nexus-community/jake | jake | 95 | 28 | 8 | Python | https://jake.readthedocs.io/ | Check your Python environments for vulnerable Open Source packages with OSS Index or Sonatype Nexus Lifecycle. | sonatype-nexus-community | 2023-12-08 | 2019-10-10 | 224 | 0.422759 | https://avatars.githubusercontent.com/u/33330803?v=4 | Check your Python environments for vulnerable Open Source packages with OSS Index or Sonatype Nexus Lifecycle. | ['nexus-iq', 'ossindex', 'sonatype-iq', 'vulnerabilities', 'vulnerability-scanners'] | ['nexus-iq', 'ossindex', 'sonatype-iq', 'vulnerabilities', 'vulnerability-scanners'] | 2023-12-08 | [('pyupio/safety', 0.607435405254364, 'security', 1)] | 17 | 4 | null | 1.23 | 7 | 3 | 52 | 1 | 7 | 32 | 7 | 7 | 19 | 90 | 2.7 | 29 |
1,645 | util | https://github.com/danielnoord/pydocstringformatter | ['pep257', 'pep8', 'docstrings'] | null | [] | [] | null | null | null | danielnoord/pydocstringformatter | pydocstringformatter | 62 | 8 | 2 | Python | null | Automatically format your Python docstrings to conform with PEP 8 and PEP 257 | danielnoord | 2023-12-18 | 2022-01-01 | 108 | 0.571805 | null | Automatically format your Python docstrings to conform with PEP 8 and PEP 257 | ['docstrings', 'formatter'] | ['docstrings', 'formatter', 'pep257', 'pep8'] | 2024-01-08 | [('pycqa/docformatter', 0.8163774013519287, 'util', 2), ('hhatto/autopep8', 0.7380919456481934, 'util', 2), ('google/yapf', 0.6070597171783447, 'util', 1), ('mkdocstrings/python', 0.563347578048706, 'util', 0), ('pdoc3/pdoc', 0.5444629192352295, 'util', 1), ('grantjenks/blue', 0.5090311169624329, 'util', 1), ('mitmproxy/pdoc', 0.5013178586959839, 'util', 1)] | 7 | 2 | null | 1.85 | 24 | 20 | 25 | 0 | 0 | 7 | 7 | 23 | 55 | 90 | 2.4 | 29 |
78 | jupyter | https://github.com/quantopian/qgrid | [] | null | [] | [] | null | null | null | quantopian/qgrid | qgrid | 3,007 | 433 | 89 | Python | null | An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks | quantopian | 2024-01-13 | 2014-09-30 | 487 | 6.174538 | https://avatars.githubusercontent.com/u/1393215?v=4 | An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks | [] | [] | 2020-04-07 | [('tkrabel/bamboolib', 0.7011144161224365, 'pandas', 0), ('jakevdp/pythondatasciencehandbook', 0.6440531611442566, 'study', 0), ('bloomberg/ipydatagrid', 0.6440353989601135, 'jupyter', 0), ('jupyter/nbformat', 0.6320311427116394, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.6274958848953247, 'jupyter', 0), ('jupyter/notebook', 0.613193154335022, 'jupyter', 0), ('ipython/ipyparallel', 0.611356794834137, 'perf', 0), ('vizzuhq/ipyvizzu', 0.608697235584259, 'jupyter', 0), ('jupyter/nbdime', 0.5866443514823914, 'jupyter', 0), ('holoviz/panel', 0.5826691389083862, 'viz', 0), ('opengeos/leafmap', 0.5799870491027832, 'gis', 0), ('cmudig/autoprofiler', 0.5721290111541748, 'jupyter', 0), ('aws/graph-notebook', 0.571941614151001, 'jupyter', 0), ('mwouts/jupytext', 0.5685391426086426, 'jupyter', 0), ('adamerose/pandasgui', 0.5642687678337097, 'pandas', 0), ('jupyterlab/jupyterlab', 0.5634688138961792, 'jupyter', 0), ('man-group/dtale', 0.560211718082428, 'viz', 0), ('cohere-ai/notebooks', 0.5572461485862732, 'llm', 0), ('jupyterlab/jupyterlab-desktop', 0.5566320419311523, 'jupyter', 0), ('lux-org/lux', 0.5451768040657043, 'viz', 0), ('kanaries/pygwalker', 0.5393330454826355, 'pandas', 0), ('jupyter/nbconvert', 0.538368284702301, 'jupyter', 0), ('voila-dashboards/voila', 0.5343106985092163, 'jupyter', 0), ('koaning/drawdata', 0.5323299765586853, 'jupyter', 0), ('jupyter/nbgrader', 0.5307871103286743, 'jupyter', 0), ('wesm/pydata-book', 0.5240903496742249, 'study', 0), ('jazzband/tablib', 0.5186475515365601, 'data', 0), ('nteract/papermill', 0.516223669052124, 'jupyter', 0), ('vaexio/vaex', 0.5162068009376526, 'perf', 0), ('maartenbreddels/ipyvolume', 0.5143440961837769, 'jupyter', 0), ('ipython/ipykernel', 0.514000654220581, 'util', 0), ('jupyter-widgets/ipyleaflet', 0.5120397806167603, 'gis', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5115215182304382, 'study', 0), ('ageron/handson-ml2', 0.5087634921073914, 'ml', 0), ('xiaohk/stickyland', 0.5084087252616882, 'jupyter', 0), ('pyqtgraph/pyqtgraph', 0.5077592730522156, 'viz', 0), ('saulpw/visidata', 0.5062557458877563, 'term', 0), ('holoviz/holoviz', 0.5044505596160889, 'viz', 0)] | 30 | 2 | null | 0 | 1 | 1 | 113 | 46 | 0 | 2 | 2 | 1 | 0 | 90 | 0 | 28 |
873 | time-series | https://github.com/rjt1990/pyflux | [] | null | [] | [] | null | null | null | rjt1990/pyflux | pyflux | 2,074 | 243 | 71 | Python | null | Open source time series library for Python | rjt1990 | 2024-01-05 | 2016-02-16 | 415 | 4.99759 | null | Open source time series library for Python | ['statistics', 'time-series'] | ['statistics', 'time-series'] | 2018-12-16 | [('alkaline-ml/pmdarima', 0.7352306842803955, 'time-series', 1), ('tdameritrade/stumpy', 0.6353744268417358, 'time-series', 0), ('awslabs/gluonts', 0.623365581035614, 'time-series', 1), ('firmai/atspy', 0.6143859624862671, 'time-series', 1), ('unit8co/darts', 0.5929312109947205, 'time-series', 1), ('dateutil/dateutil', 0.5882995128631592, 'util', 0), ('google/temporian', 0.5719876289367676, 'time-series', 1), ('pycaret/pycaret', 0.5557315349578857, 'ml', 1), ('pastas/pastas', 0.5493255257606506, 'time-series', 0), ('statsmodels/statsmodels', 0.5408421158790588, 'ml', 1), ('stan-dev/pystan', 0.5393368601799011, 'ml', 0), ('sdispater/pendulum', 0.5265621542930603, 'util', 0), ('pandas-dev/pandas', 0.5234712958335876, 'pandas', 0), ('ta-lib/ta-lib-python', 0.5227634906768799, 'finance', 0), ('mwaskom/seaborn', 0.522447407245636, 'viz', 0), ('altair-viz/altair', 0.5199966430664062, 'viz', 0), ('andgoldschmidt/derivative', 0.5164141654968262, 'math', 0), ('wesm/pydata-book', 0.5128363966941833, 'study', 0), ('stub42/pytz', 0.5127301812171936, 'util', 0), ('pmorissette/ffn', 0.5010930895805359, 'finance', 0)] | 6 | 2 | null | 0 | 1 | 0 | 96 | 62 | 0 | 5 | 5 | 1 | 1 | 90 | 1 | 28 |
1,041 | llm | https://github.com/openai/gpt-2-output-dataset | [] | null | [] | [] | null | null | null | openai/gpt-2-output-dataset | gpt-2-output-dataset | 1,844 | 528 | 76 | Python | null | Dataset of GPT-2 outputs for research in detection, biases, and more | openai | 2024-01-12 | 2019-05-03 | 247 | 7.448355 | https://avatars.githubusercontent.com/u/14957082?v=4 | Dataset of GPT-2 outputs for research in detection, biases, and more | [] | [] | 2023-12-13 | [('karpathy/nanogpt', 0.5051628351211548, 'llm', 0)] | 5 | 1 | null | 0.02 | 1 | 0 | 57 | 1 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 28 |
496 | ml-dl | https://github.com/vt-vl-lab/fgvc | [] | null | [] | [] | null | null | null | vt-vl-lab/fgvc | FGVC | 1,523 | 279 | 70 | Python | null | [ECCV 2020] Flow-edge Guided Video Completion | vt-vl-lab | 2024-01-12 | 2020-09-09 | 176 | 8.61147 | https://avatars.githubusercontent.com/u/31048446?v=4 | [ECCV 2020] Flow-edge Guided Video Completion | [] | [] | 2021-12-14 | [('researchmm/sttn', 0.6461269855499268, 'ml-dl', 0), ('mcahny/deep-video-inpainting', 0.5231187343597412, 'ml-dl', 0)] | 3 | 2 | null | 0 | 1 | 1 | 41 | 25 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 28 |
283 | data | https://github.com/sdispater/orator | [] | null | [] | [] | null | null | null | sdispater/orator | orator | 1,420 | 174 | 45 | Python | https://orator-orm.com | The Orator ORM provides a simple yet beautiful ActiveRecord implementation. | sdispater | 2024-01-04 | 2015-05-24 | 453 | 3.132682 | null | The Orator ORM provides a simple yet beautiful ActiveRecord implementation. | ['database', 'orm'] | ['database', 'orm'] | 2022-03-13 | [('mcfunley/pugsql', 0.5235227346420288, 'data', 1)] | 32 | 4 | null | 0 | 4 | 0 | 105 | 22 | 0 | 3 | 3 | 4 | 2 | 90 | 0.5 | 28 |
732 | pandas | https://github.com/machow/siuba | [] | null | [] | [] | null | null | null | machow/siuba | siuba | 1,074 | 50 | 21 | Python | https://siuba.org | Python library for using dplyr like syntax with pandas and SQL | machow | 2024-01-13 | 2019-02-09 | 259 | 4.139868 | null | Python library for using dplyr like syntax with pandas and SQL | ['data-analysis', 'dplyr', 'pandas', 'sql'] | ['data-analysis', 'dplyr', 'pandas', 'sql'] | 2023-09-19 | [('ibis-project/ibis', 0.6308576464653015, 'data', 2), ('tobymao/sqlglot', 0.6064596176147461, 'data', 1), ('tiangolo/sqlmodel', 0.5724479556083679, 'data', 1), ('andialbrecht/sqlparse', 0.5550650358200073, 'data', 0), ('sqlalchemy/sqlalchemy', 0.5513966679573059, 'data', 1), ('pandas-dev/pandas', 0.5513116717338562, 'pandas', 2), ('malloydata/malloy-py', 0.513921856880188, 'data', 1)] | 10 | 2 | null | 0.71 | 3 | 1 | 60 | 4 | 2 | 8 | 2 | 3 | 0 | 90 | 0 | 28 |
706 | ml | https://github.com/google-research/deeplab2 | [] | null | [] | [] | null | null | null | google-research/deeplab2 | deeplab2 | 965 | 160 | 23 | Python | null | DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks. | google-research | 2024-01-13 | 2021-05-12 | 141 | 6.802618 | https://avatars.githubusercontent.com/u/43830688?v=4 | DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks. | [] | [] | 2023-04-17 | [('open-mmlab/mmsegmentation', 0.5815913081169128, 'ml', 0), ('dmlc/dgl', 0.5739299058914185, 'ml-dl', 0), ('mdbloice/augmentor', 0.5629528760910034, 'ml', 0), ('lightly-ai/lightly', 0.5617728233337402, 'ml', 0), ('microsoft/deepspeed', 0.5402511954307556, 'ml-dl', 0), ('facebookresearch/pytorch3d', 0.5299793481826782, 'ml-dl', 0), ('nvlabs/gcvit', 0.5264610648155212, 'diffusion', 0), ('deepmind/deepmind-research', 0.5203995704650879, 'ml', 0), ('pytorch/ignite', 0.5203951597213745, 'ml-dl', 0), ('lutzroeder/netron', 0.5186880826950073, 'ml', 0), ('azavea/raster-vision', 0.5186462998390198, 'gis', 0), ('open-mmlab/mmdetection', 0.516015350818634, 'ml', 0), ('nvidia/deeplearningexamples', 0.5127051472663879, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.5118420720100403, 'ml-dl', 0), ('albumentations-team/albumentations', 0.5106143355369568, 'ml-dl', 0), ('tensorflow/addons', 0.5076053738594055, 'ml', 0), ('roboflow/supervision', 0.5049787163734436, 'ml', 0), ('mrdbourke/pytorch-deep-learning', 0.5000344514846802, 'study', 0)] | 12 | 4 | null | 0.13 | 2 | 0 | 33 | 9 | 0 | 0 | 0 | 2 | 0 | 90 | 0 | 28 |
226 | sim | https://github.com/facebookresearch/droidlet | [] | null | [] | [] | null | null | null | facebookresearch/droidlet | fairo | 828 | 83 | 39 | Jupyter Notebook | null | A modular embodied agent architecture and platform for building embodied agents | facebookresearch | 2024-01-11 | 2020-11-02 | 169 | 4.89527 | https://avatars.githubusercontent.com/u/16943930?v=4 | A modular embodied agent architecture and platform for building embodied agents | [] | [] | 2023-02-01 | [('minedojo/voyager', 0.6723781228065491, 'llm', 0), ('facebookresearch/habitat-lab', 0.6688793897628784, 'sim', 0), ('operand/agency', 0.5389538407325745, 'llm', 0), ('humanoidagents/humanoidagents', 0.5291570425033569, 'sim', 0)] | 43 | 2 | null | 0.08 | 2 | 0 | 39 | 12 | 0 | 0 | 0 | 2 | 2 | 90 | 1 | 28 |
1,221 | debug | https://github.com/ionelmc/python-hunter | [] | null | [] | [] | null | null | null | ionelmc/python-hunter | python-hunter | 768 | 45 | 14 | Python | https://python-hunter.readthedocs.io/ | Hunter is a flexible code tracing toolkit. | ionelmc | 2024-01-13 | 2015-03-16 | 463 | 1.658236 | null | Hunter is a flexible code tracing toolkit. | ['debugger', 'debugging', 'tracer'] | ['debugger', 'debugging', 'tracer'] | 2023-04-26 | [('gaogaotiantian/viztracer', 0.6034563779830933, 'profiling', 2), ('alexmojaki/snoop', 0.5830564498901367, 'debug', 2), ('alexmojaki/heartrate', 0.5184060335159302, 'debug', 1), ('teamhg-memex/eli5', 0.5018780827522278, 'ml', 0), ('abnamro/repository-scanner', 0.5003989338874817, 'security', 0)] | 9 | 3 | null | 0.37 | 1 | 0 | 108 | 9 | 0 | 6 | 6 | 1 | 2 | 90 | 2 | 28 |
1,805 | sim | https://github.com/google/evojax | ['gpu', 'tpu', 'neuroevolution', 'jax'] | EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit built on the JAX library | [] | [] | null | null | null | google/evojax | evojax | 728 | 64 | 23 | Jupyter Notebook | null | null | google | 2024-01-12 | 2021-12-07 | 112 | 6.5 | https://avatars.githubusercontent.com/u/1342004?v=4 | EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit built on the JAX library | [] | ['gpu', 'jax', 'neuroevolution', 'tpu'] | 2023-08-29 | [('deepmind/dm-haiku', 0.5894790291786194, 'ml-dl', 1), ('deepmind/synjax', 0.5408310890197754, 'math', 1)] | 14 | 3 | null | 0.29 | 0 | 0 | 26 | 5 | 1 | 12 | 1 | 0 | 0 | 90 | 0 | 28 |
357 | data | https://github.com/hyperqueryhq/whale | [] | null | [] | [] | null | null | null | hyperqueryhq/whale | whale | 724 | 39 | 42 | Python | https://rsyi.gitbook.io/whale | 🐳 The stupidly simple CLI workspace for your data warehouse. | hyperqueryhq | 2024-01-04 | 2020-05-27 | 191 | 3.773641 | null | 🐳 The stupidly simple CLI workspace for your data warehouse. | ['data-catalog', 'data-discovery', 'data-documentation'] | ['data-catalog', 'data-discovery', 'data-documentation'] | 2022-10-13 | [('intake/intake', 0.5861302614212036, 'data', 1), ('saulpw/visidata', 0.5835681557655334, 'term', 0), ('databrickslabs/dbx', 0.5740757584571838, 'data', 0), ('google/ml-metadata', 0.5290652513504028, 'ml-ops', 0), ('airbnb/knowledge-repo', 0.520332932472229, 'data', 0), ('airbnb/omniduct', 0.5135779976844788, 'data', 0), ('simonw/datasette', 0.5066918134689331, 'data', 0)] | 17 | 7 | null | 0 | 0 | 0 | 44 | 15 | 0 | 7 | 7 | 0 | 0 | 90 | 0 | 28 |
1,870 | ml | https://github.com/davidmrau/mixture-of-experts | [] | null | [] | [] | null | null | null | davidmrau/mixture-of-experts | mixture-of-experts | 716 | 80 | 4 | Python | null | PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538 | davidmrau | 2024-01-13 | 2019-07-19 | 236 | 3.02657 | null | PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538 | ['mixture-of-experts', 'moe', 'pytorch', 're-implementation', 'sparsely-gated-mixture-of-experts'] | ['mixture-of-experts', 'moe', 'pytorch', 're-implementation', 'sparsely-gated-mixture-of-experts'] | 2023-12-10 | [('laekov/fastmoe', 0.5889419913291931, 'ml', 1), ('nvidia/apex', 0.5525276064872742, 'ml-dl', 0), ('pytorch/ignite', 0.5471916198730469, 'ml-dl', 1), ('pytorch/botorch', 0.5366146564483643, 'ml-dl', 0), ('skorch-dev/skorch', 0.5069704055786133, 'ml-dl', 1)] | 4 | 2 | null | 0.12 | 7 | 5 | 55 | 1 | 0 | 0 | 0 | 7 | 7 | 90 | 1 | 28 |
1,036 | finance | https://github.com/numerai/example-scripts | [] | null | [] | [] | null | null | null | numerai/example-scripts | example-scripts | 703 | 259 | 67 | Jupyter Notebook | https://numer.ai/ | A collection of scripts and notebooks to help you get started quickly. | numerai | 2024-01-13 | 2017-01-06 | 368 | 1.907364 | https://avatars.githubusercontent.com/u/15222762?v=4 | A collection of scripts and notebooks to help you get started quickly. | ['cryptocurrency', 'machine-learning', 'numerai', 'quant-finance'] | ['cryptocurrency', 'machine-learning', 'numerai', 'quant-finance'] | 2024-01-13 | [('ccxt/ccxt', 0.6066434979438782, 'crypto', 1), ('gbeced/basana', 0.6021682024002075, 'finance', 1), ('zvtvz/zvt', 0.5974596738815308, 'finance', 2), ('polakowo/vectorbt', 0.5881688594818115, 'finance', 2), ('ofek/bit', 0.5733424425125122, 'crypto', 0), ('1200wd/bitcoinlib', 0.5599415898323059, 'crypto', 0), ('dylanhogg/crazy-awesome-crypto', 0.5541864633560181, 'crypto', 1), ('goldmansachs/gs-quant', 0.5451831221580505, 'finance', 0), ('openbb-finance/openbbterminal', 0.5369656682014465, 'finance', 2), ('primal100/pybitcointools', 0.5286599397659302, 'crypto', 0), ('ranaroussi/quantstats', 0.5179154872894287, 'finance', 0), ('chancefocus/pixiu', 0.5142377018928528, 'finance', 1), ('microsoft/qlib', 0.5105475187301636, 'finance', 1), ('gbeced/pyalgotrade', 0.5094029307365417, 'finance', 0), ('opentensor/bittensor', 0.5083948969841003, 'ml', 2), ('quantconnect/lean', 0.5045038461685181, 'finance', 0)] | 46 | 2 | null | 0.9 | 14 | 8 | 85 | 0 | 0 | 0 | 0 | 14 | 2 | 90 | 0.1 | 28 |
1,671 | util | https://github.com/erotemic/ubelt | [] | null | [] | [] | null | null | null | erotemic/ubelt | ubelt | 702 | 46 | 18 | Python | null | A Python utility library with a stdlib like feel and extra batteries. Paths, Progress, Dicts, Downloads, Caching, Hashing: ubelt makes it easy! | erotemic | 2024-01-04 | 2017-01-30 | 365 | 1.922535 | null | A Python utility library with a stdlib like feel and extra batteries. Paths, Progress, Dicts, Downloads, Caching, Hashing: ubelt makes it easy! | ['cross-platform', 'utilities', 'utility-library'] | ['cross-platform', 'utilities', 'utility-library'] | 2023-10-27 | [('dgilland/cacheout', 0.6818086504936218, 'perf', 0), ('pytoolz/toolz', 0.6275792717933655, 'util', 0), ('pytables/pytables', 0.615244448184967, 'data', 0), ('pypy/pypy', 0.6026424169540405, 'util', 0), ('pytorch/data', 0.5979729294776917, 'data', 0), ('python-cachier/cachier', 0.5962907671928406, 'perf', 0), ('tqdm/tqdm', 0.5793629884719849, 'term', 1), ('pympler/pympler', 0.574570894241333, 'perf', 0), ('pypa/installer', 0.5646870136260986, 'util', 0), ('grantjenks/python-diskcache', 0.5623111724853516, 'util', 0), ('spotify/annoy', 0.5622727870941162, 'ml', 0), ('agronholm/apscheduler', 0.5611792802810669, 'util', 0), ('1200wd/bitcoinlib', 0.5576600432395935, 'crypto', 0), ('imageio/imageio', 0.5557732582092285, 'util', 0), ('pyston/pyston', 0.5508465766906738, 'util', 0), ('libtcod/python-tcod', 0.5495151877403259, 'gamedev', 0), ('scrapy/scrapy', 0.5488511323928833, 'data', 0), ('hoffstadt/dearpygui', 0.5396130681037903, 'gui', 1), ('rasbt/watermark', 0.5379260182380676, 'util', 0), ('qdrant/fastembed', 0.5371770262718201, 'ml', 0), ('fastai/fastcore', 0.5355119705200195, 'util', 0), ('jovianml/opendatasets', 0.5337167978286743, 'data', 0), ('mkdocstrings/griffe', 0.5309567451477051, 'util', 0), ('spotify/voyager', 0.5305669903755188, 'ml', 0), ('pypa/hatch', 0.5291941165924072, 'util', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.5286123752593994, 'data', 0), ('wxwidgets/phoenix', 0.5278312563896179, 'gui', 1), ('linkedin/shiv', 0.5263099670410156, 'util', 0), ('jquast/blessed', 0.5253154635429382, 'term', 0), ('beeware/toga', 0.5228415727615356, 'gui', 0), ('dlt-hub/dlt', 0.5195381045341492, 'data', 0), ('landscapeio/prospector', 0.518781840801239, 'util', 0), ('platformdirs/platformdirs', 0.5185686945915222, 'util', 1), ('quantopian/zipline', 0.5161089897155762, 'finance', 0), ('faster-cpython/tools', 0.5155495405197144, 'perf', 0), ('ta-lib/ta-lib-python', 0.513969361782074, 'finance', 0), ('dosisod/refurb', 0.513725996017456, 'util', 0), ('joblib/joblib', 0.5093076825141907, 'util', 0), ('samuelcolvin/watchfiles', 0.5087971091270447, 'util', 0), ('micropython/micropython', 0.5087762475013733, 'util', 0), ('eleutherai/pyfra', 0.5078774094581604, 'ml', 0), ('pythonprofilers/memory_profiler', 0.5078474879264832, 'profiling', 0), ('python/cpython', 0.5061374306678772, 'util', 0), ('faster-cpython/ideas', 0.5061014890670776, 'perf', 0), ('rhettbull/osxphotos', 0.5059564709663391, 'util', 0), ('timofurrer/awesome-asyncio', 0.5056865811347961, 'study', 0), ('prompt-toolkit/ptpython', 0.5056498646736145, 'util', 0), ('python-odin/odin', 0.5053083896636963, 'util', 0), ('pyglet/pyglet', 0.5050175786018372, 'gamedev', 0), ('pyodide/micropip', 0.5013461709022522, 'util', 0), ('legrandin/pycryptodome', 0.5003699660301208, 'util', 0)] | 4 | 2 | null | 2.15 | 3 | 2 | 85 | 3 | 5 | 9 | 5 | 3 | 2 | 90 | 0.7 | 28 |
396 | web | https://github.com/klen/muffin | [] | null | [] | [] | null | null | null | klen/muffin | muffin | 659 | 25 | 31 | Python | null | Muffin is a fast, simple and asyncronous web-framework for Python 3 | klen | 2024-01-13 | 2015-02-03 | 469 | 1.405117 | null | Muffin is a fast, simple and asyncronous web-framework for Python 3 | ['asgi', 'asyncio', 'curio', 'muffin', 'trio', 'webframework'] | ['asgi', 'asyncio', 'curio', 'muffin', 'trio', 'webframework'] | 2023-10-11 | [('neoteroi/blacksheep', 0.7668511271476746, 'web', 2), ('masoniteframework/masonite', 0.7306077480316162, 'web', 1), ('pallets/quart', 0.7141019701957703, 'web', 2), ('pallets/flask', 0.6954807639122009, 'web', 0), ('alirn76/panther', 0.6565958857536316, 'web', 0), ('falconry/falcon', 0.6537138819694519, 'web', 1), ('encode/uvicorn', 0.6518058180809021, 'web', 2), ('webpy/webpy', 0.6442074179649353, 'web', 0), ('timofurrer/awesome-asyncio', 0.6332191228866577, 'study', 1), ('pylons/pyramid', 0.6305515766143799, 'web', 0), ('bottlepy/bottle', 0.6195411086082458, 'web', 0), ('encode/httpx', 0.6186836957931519, 'web', 2), ('willmcgugan/textual', 0.6039458513259888, 'term', 0), ('sumerc/yappi', 0.5936639904975891, 'profiling', 2), ('pypy/pypy', 0.5929686427116394, 'util', 0), ('scrapy/scrapy', 0.5914323329925537, 'data', 0), ('eleutherai/pyfra', 0.5880747437477112, 'ml', 0), ('cherrypy/cherrypy', 0.5874270796775818, 'web', 0), ('fastai/fastcore', 0.5848855376243591, 'util', 0), ('reflex-dev/reflex', 0.5825878977775574, 'web', 0), ('holoviz/panel', 0.5814103484153748, 'viz', 0), ('r0x0r/pywebview', 0.5798044800758362, 'gui', 0), ('aio-libs/aiohttp', 0.5731057524681091, 'web', 1), ('pallets/werkzeug', 0.5703849196434021, 'web', 0), ('huge-success/sanic', 0.5701971054077148, 'web', 2), ('flet-dev/flet', 0.5684182643890381, 'web', 0), ('emmett-framework/emmett', 0.56635981798172, 'web', 2), ('klen/py-frameworks-bench', 0.5657544732093811, 'perf', 0), ('ets-labs/python-dependency-injector', 0.5598342418670654, 'util', 1), ('python-trio/trio', 0.5527318716049194, 'perf', 1), ('dylanhogg/awesome-python', 0.5525697469711304, 'study', 0), ('sqlalchemy/mako', 0.5490294694900513, 'template', 0), ('starlite-api/starlite', 0.548690915107727, 'web', 2), ('pyston/pyston', 0.5434106588363647, 'util', 0), ('python-restx/flask-restx', 0.5422064065933228, 'web', 0), ('agronholm/anyio', 0.5409852266311646, 'perf', 3), ('hoffstadt/dearpygui', 0.5390740036964417, 'gui', 0), ('python/cpython', 0.5362712144851685, 'util', 0), ('bokeh/bokeh', 0.5334693193435669, 'viz', 0), ('plotly/dash', 0.5317702889442444, 'viz', 0), ('tiangolo/fastapi', 0.5296502709388733, 'web', 1), ('ipython/ipyparallel', 0.5273743271827698, 'perf', 0), ('eventual-inc/daft', 0.5256049036979675, 'pandas', 0), ('backtick-se/cowait', 0.5236403942108154, 'util', 0), ('encode/starlette', 0.5224829912185669, 'web', 0), ('voila-dashboards/voila', 0.5211663246154785, 'jupyter', 0), ('joblib/joblib', 0.5203728675842285, 'util', 0), ('tornadoweb/tornado', 0.5200356245040894, 'web', 0), ('locustio/locust', 0.5192926526069641, 'testing', 0), ('pytoolz/toolz', 0.5191732048988342, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5174252390861511, 'study', 0), ('roniemartinez/dude', 0.5161595940589905, 'util', 0), ('pyodide/pyodide', 0.5121115446090698, 'util', 0), ('psf/requests', 0.5109789967536926, 'web', 0), ('clips/pattern', 0.5102404356002808, 'nlp', 0), ('s3rius/fastapi-template', 0.5076808333396912, 'web', 1), ('wxwidgets/phoenix', 0.5061802864074707, 'gui', 0), ('pylons/waitress', 0.5056185126304626, 'web', 0), ('maartenbreddels/ipyvolume', 0.50477135181427, 'jupyter', 0), ('indico/indico', 0.5037544369697571, 'web', 0), ('django/django', 0.5025768876075745, 'web', 0), ('pyinfra-dev/pyinfra', 0.5019458532333374, 'util', 0), ('ibis-project/ibis', 0.5011864900588989, 'data', 0), ('google/gin-config', 0.5011733770370483, 'util', 0), ('benoitc/gunicorn', 0.5010045766830444, 'web', 0), ('plotly/plotly.py', 0.5007705092430115, 'viz', 0)] | 13 | 5 | null | 2.56 | 1 | 0 | 109 | 3 | 0 | 43 | 43 | 1 | 0 | 90 | 0 | 28 |
1,161 | jupyter | https://github.com/linealabs/lineapy | [] | null | [] | [] | null | null | null | linealabs/lineapy | lineapy | 641 | 49 | 21 | Jupyter Notebook | https://lineapy.org | Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code. | linealabs | 2024-01-11 | 2021-07-28 | 130 | 4.898472 | https://avatars.githubusercontent.com/u/76981099?v=4 | Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code. | [] | [] | 2023-08-10 | [('ploomber/ploomber', 0.739909827709198, 'ml-ops', 0), ('mage-ai/mage-ai', 0.6465555429458618, 'ml-ops', 0), ('orchest/orchest', 0.6248028874397278, 'ml-ops', 0), ('unstructured-io/pipeline-sec-filings', 0.6072432994842529, 'data', 0), ('meltano/meltano', 0.5844327211380005, 'ml-ops', 0), ('paperswithcode/sota-extractor', 0.5607438087463379, 'data', 0), ('hi-primus/optimus', 0.5603882074356079, 'ml-ops', 0), ('kubeflow-kale/kale', 0.5481640100479126, 'ml-ops', 0), ('nteract/papermill', 0.5371261835098267, 'jupyter', 0), ('airbytehq/airbyte', 0.5353677272796631, 'data', 0), ('saulpw/visidata', 0.5347519516944885, 'term', 0), ('astronomer/astro-sdk', 0.5296847224235535, 'ml-ops', 0), ('koaning/clumper', 0.5260019302368164, 'util', 0), ('intake/intake', 0.5228672623634338, 'data', 0), ('koaning/scikit-lego', 0.5148707032203674, 'ml', 0), ('kestra-io/kestra', 0.5073179602622986, 'ml-ops', 0), ('lean-dojo/leandojo', 0.5060582756996155, 'math', 0), ('dagworks-inc/hamilton', 0.5050464868545532, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5049693584442139, 'ml-ops', 0), ('google/ml-metadata', 0.5038784146308899, 'ml-ops', 0), ('koaning/scikit-partial', 0.5030436515808105, 'data', 0)] | 24 | 2 | null | 0.19 | 0 | 0 | 30 | 5 | 0 | 4 | 4 | 0 | 0 | 90 | 0 | 28 |
1,223 | ml | https://github.com/hpcaitech/energonai | [] | null | [] | [] | null | null | null | hpcaitech/energonai | EnergonAI | 629 | 92 | 23 | Python | null | Large-scale model inference. | hpcaitech | 2024-01-12 | 2022-01-24 | 105 | 5.982337 | https://avatars.githubusercontent.com/u/88699314?v=4 | Large-scale model inference. | [] | [] | 2023-03-08 | [('optimalscale/lmflow', 0.6059041619300842, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5440476536750793, 'llm', 0), ('squeezeailab/squeezellm', 0.5279243588447571, 'llm', 0), ('huggingface/text-embeddings-inference', 0.5272306799888611, 'llm', 0), ('ai21labs/lm-evaluation', 0.5110083818435669, 'llm', 0)] | 13 | 6 | null | 0.13 | 0 | 0 | 24 | 10 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 28 |
1,363 | gamedev | https://github.com/lordmauve/pgzero | [] | null | [] | [] | null | null | null | lordmauve/pgzero | pgzero | 492 | 188 | 29 | Python | https://pygame-zero.readthedocs.io/ | A zero-boilerplate games programming framework for Python 3, based on Pygame. | lordmauve | 2024-01-11 | 2018-02-25 | 309 | 1.590762 | null | A zero-boilerplate games programming framework for Python 3, based on Pygame. | ['education', 'game-framework', 'pygame', 'python-game-development'] | ['education', 'game-framework', 'pygame', 'python-game-development'] | 2022-06-30 | [('pygame/pygame', 0.6985493302345276, 'gamedev', 1), ('pokepetter/ursina', 0.6621728539466858, 'gamedev', 0), ('kitao/pyxel', 0.6230867505073547, 'gamedev', 0), ('pygamelib/pygamelib', 0.6199098229408264, 'gamedev', 0), ('pythonarcade/arcade', 0.6107795238494873, 'gamedev', 0), ('panda3d/panda3d', 0.5944162011146545, 'gamedev', 0), ('pyglet/pyglet', 0.544232964515686, 'gamedev', 0), ('ljvmiranda921/seagull', 0.5329146981239319, 'sim', 0), ('amaargiru/pyroad', 0.5130576491355896, 'study', 0), ('alephalpha/golly', 0.5084664821624756, 'sim', 0), ('renpy/pygame_sdl2', 0.5067479610443115, 'gamedev', 1), ('projectmesa/mesa', 0.5056121349334717, 'sim', 0)] | 45 | 5 | null | 0 | 3 | 1 | 72 | 19 | 0 | 2 | 2 | 3 | 6 | 90 | 2 | 28 |
836 | perf | https://github.com/joblib/loky | [] | null | [] | [] | null | null | null | joblib/loky | loky | 490 | 45 | 12 | Python | http://loky.readthedocs.io/en/stable/ | Robust and reusable Executor for joblib | joblib | 2024-01-07 | 2015-12-25 | 422 | 1.159567 | https://avatars.githubusercontent.com/u/332661?v=4 | Robust and reusable Executor for joblib | ['multiprocessing-library'] | ['multiprocessing-library'] | 2023-06-29 | [('agronholm/apscheduler', 0.5760906934738159, 'util', 0), ('samuelcolvin/arq', 0.5648357272148132, 'data', 0), ('bogdanp/dramatiq', 0.5552358031272888, 'util', 0), ('noxdafox/pebble', 0.5490549802780151, 'perf', 0), ('dask/dask', 0.5317108035087585, 'perf', 0), ('python-trio/trio', 0.5253786444664001, 'perf', 0), ('joblib/joblib', 0.5221757292747498, 'util', 0), ('sumerc/yappi', 0.5158117413520813, 'profiling', 0), ('ipython/ipyparallel', 0.5099025964736938, 'perf', 0)] | 18 | 6 | null | 0.35 | 2 | 1 | 98 | 7 | 0 | 5 | 5 | 2 | 2 | 90 | 1 | 28 |
494 | ml | https://github.com/linkedin/fasttreeshap | [] | null | [] | [] | null | null | null | linkedin/fasttreeshap | FastTreeSHAP | 477 | 29 | 7 | Python | null | Fast SHAP value computation for interpreting tree-based models | linkedin | 2024-01-10 | 2022-01-24 | 105 | 4.536685 | https://avatars.githubusercontent.com/u/357098?v=4 | Fast SHAP value computation for interpreting tree-based models | ['explainable-ai', 'interpretability', 'lightgbm', 'machine-learning', 'random-forest', 'shap', 'xgboost'] | ['explainable-ai', 'interpretability', 'lightgbm', 'machine-learning', 'random-forest', 'shap', 'xgboost'] | 2023-06-26 | [('maif/shapash', 0.6388174295425415, 'ml', 3), ('slundberg/shap', 0.5951489806175232, 'ml-interpretability', 3), ('selfexplainml/piml-toolbox', 0.5518995523452759, 'ml-interpretability', 0), ('teamhg-memex/eli5', 0.542718231678009, 'ml', 3), ('csinva/imodels', 0.5407923460006714, 'ml', 3), ('interpretml/interpret', 0.5312417149543762, 'ml-interpretability', 3), ('marcotcr/lime', 0.5298793315887451, 'ml-interpretability', 0), ('catboost/catboost', 0.5170351266860962, 'ml', 1), ('seldonio/alibi', 0.5031470060348511, 'ml-interpretability', 2)] | 6 | 2 | null | 0.17 | 1 | 0 | 24 | 7 | 3 | 3 | 3 | 1 | 1 | 90 | 1 | 28 |
186 | math | https://github.com/willianfuks/tfcausalimpact | [] | null | [] | [] | null | null | null | willianfuks/tfcausalimpact | tfcausalimpact | 475 | 62 | 12 | Python | null | Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability. | willianfuks | 2024-01-04 | 2020-08-17 | 180 | 2.636796 | null | Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability. | ['causal-inference', 'causalimpact', 'tensorflow-probability'] | ['causal-inference', 'causalimpact', 'tensorflow-probability'] | 2023-11-21 | [('mckinsey/causalnex', 0.6074860692024231, 'math', 1), ('py-why/dowhy', 0.6020914912223816, 'ml', 1)] | 4 | 1 | null | 0.02 | 10 | 5 | 42 | 2 | 1 | 5 | 1 | 10 | 27 | 90 | 2.7 | 28 |
236 | ml-rl | https://github.com/salesforce/warp-drive | [] | null | [] | [] | null | null | null | salesforce/warp-drive | warp-drive | 425 | 77 | 14 | Python | null | Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022) | salesforce | 2024-01-14 | 2021-08-25 | 126 | 3.350225 | https://avatars.githubusercontent.com/u/453694?v=4 | Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022) | ['cuda', 'deep-learning', 'gpu', 'high-throughput', 'multiagent-reinforcement-learning', 'numba', 'pytorch', 'reinforcement-learning'] | ['cuda', 'deep-learning', 'gpu', 'high-throughput', 'multiagent-reinforcement-learning', 'numba', 'pytorch', 'reinforcement-learning'] | 2023-12-20 | [('thu-ml/tianshou', 0.6808977723121643, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.6577045321464539, 'ml-rl', 2), ('denys88/rl_games', 0.6500195264816284, 'ml-rl', 3), ('google/trax', 0.640018880367279, 'ml-dl', 2), ('inspirai/timechamber', 0.6329183578491211, 'sim', 1), ('keras-rl/keras-rl', 0.6099841594696045, 'ml-rl', 1), ('pytorch/rl', 0.6080675721168518, 'ml-rl', 2), ('openai/baselines', 0.5866561532020569, 'ml-rl', 0), ('tensorflow/tensor2tensor', 0.5827850699424744, 'ml', 2), ('tensorlayer/tensorlayer', 0.5802738666534424, 'ml-rl', 2), ('pytorchlightning/pytorch-lightning', 0.5696126222610474, 'ml-dl', 2), ('deepmind/dm_control', 0.5524762272834778, 'ml-rl', 2), ('microsoft/deepspeed', 0.5514455437660217, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5466130971908569, 'ml-dl', 1), ('d2l-ai/d2l-en', 0.5405166745185852, 'study', 3), ('determined-ai/determined', 0.5399549603462219, 'ml-ops', 2), ('facebookresearch/habitat-lab', 0.5386927127838135, 'sim', 2), ('apache/incubator-mxnet', 0.5272499322891235, 'ml-dl', 0), ('huggingface/accelerate', 0.5263185501098633, 'ml', 0), ('pettingzoo-team/pettingzoo', 0.5245906710624695, 'ml-rl', 2), ('ai4finance-foundation/finrl', 0.5230139493942261, 'finance', 1), ('microsoft/onnxruntime', 0.5185619592666626, 'ml', 2), ('openai/spinningup', 0.5154350399971008, 'study', 0), ('nvidia-omniverse/orbit', 0.513428270816803, 'sim', 0), ('ray-project/ray', 0.5116983652114868, 'ml-ops', 3), ('pytorch/pytorch', 0.5105277895927429, 'ml-dl', 2), ('aiqc/aiqc', 0.509090006351471, 'ml-ops', 0), ('horovod/horovod', 0.5066569447517395, 'ml-ops', 2), ('farama-foundation/gymnasium', 0.5065247416496277, 'ml-rl', 1), ('google/tf-quant-finance', 0.5037093162536621, 'finance', 1), ('google/dopamine', 0.5014780759811401, 'ml-rl', 0), ('deepmind/acme', 0.5006597638130188, 'ml-rl', 1), ('sail-sg/envpool', 0.5001153945922852, 'sim', 1)] | 7 | 2 | null | 0.67 | 7 | 6 | 29 | 1 | 4 | 3 | 4 | 7 | 0 | 90 | 0 | 28 |
Subsets and Splits