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323 | security | https://github.com/pyupio/safety | [] | null | [] | [] | null | null | null | pyupio/safety | safety | 1,571 | 138 | 32 | Python | https://pyup.io/safety/ | Safety checks Python dependencies for known security vulnerabilities and suggests the proper remediations for vulnerabilities detected. | pyupio | 2024-01-12 | 2016-10-19 | 379 | 4.135765 | https://avatars.githubusercontent.com/u/16113910?v=4 | Safety checks Python dependencies for known security vulnerabilities and suggests the proper remediations for vulnerabilities detected. | ['security', 'security-vulnerability', 'travis', 'vulnerability-detection', 'vulnerability-scanners'] | ['security', 'security-vulnerability', 'travis', 'vulnerability-detection', 'vulnerability-scanners'] | 2023-11-15 | [('trailofbits/pip-audit', 0.7114713788032532, 'security', 1), ('aswinnnn/pyscan', 0.6276425123214722, 'security', 2), ('sonatype-nexus-community/jake', 0.607435405254364, 'security', 1), ('jazzband/pip-tools', 0.5792787671089172, 'util', 0), ('facebookincubator/bowler', 0.5631865859031677, 'util', 0), ('facebook/pyre-check', 0.5607929229736328, 'typing', 1), ('legrandin/pycryptodome', 0.5586642622947693, 'util', 1), ('pdm-project/pdm', 0.5419641733169556, 'util', 0), ('pyca/cryptography', 0.5316013097763062, 'util', 0), ('fsspec/filesystem_spec', 0.5129398107528687, 'util', 0)] | 41 | 4 | null | 0.44 | 12 | 1 | 88 | 2 | 0 | 8 | 8 | 12 | 11 | 90 | 0.9 | 38 |
1,261 | llm | https://github.com/ist-daslab/gptq | [] | null | [] | [] | null | null | null | ist-daslab/gptq | gptq | 1,533 | 118 | 28 | Python | https://arxiv.org/abs/2210.17323 | Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers". | ist-daslab | 2024-01-13 | 2022-10-19 | 66 | 22.929487 | https://avatars.githubusercontent.com/u/35098403?v=4 | Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers". | [] | [] | 2023-07-11 | [('karpathy/mingpt', 0.7072771787643433, 'llm', 0), ('huggingface/optimum', 0.6178866624832153, 'ml', 0), ('openai/image-gpt', 0.6169224381446838, 'llm', 0), ('alignmentresearch/tuned-lens', 0.5439256429672241, 'ml-interpretability', 0), ('eleutherai/knowledge-neurons', 0.53592449426651, 'ml-interpretability', 0), ('nielsrogge/transformers-tutorials', 0.5349066257476807, 'study', 0), ('huggingface/transformers', 0.5263904929161072, 'nlp', 0), ('bigscience-workshop/megatron-deepspeed', 0.5179560780525208, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5179560780525208, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5007389187812805, 'study', 0)] | 5 | 3 | null | 0.29 | 8 | 2 | 15 | 6 | 0 | 0 | 0 | 8 | 5 | 90 | 0.6 | 38 |
1,330 | llm | https://github.com/jina-ai/thinkgpt | ['chain-of-thought', 'language-model'] | null | [] | [] | null | null | null | jina-ai/thinkgpt | thinkgpt | 1,420 | 119 | 25 | Python | null | Agent techniques to augment your LLM and push it beyong its limits | jina-ai | 2024-01-13 | 2023-04-14 | 41 | 34.158076 | https://avatars.githubusercontent.com/u/60539444?v=4 | Agent techniques to augment your LLM and push it beyong its limits | [] | ['chain-of-thought', 'language-model'] | 2023-05-16 | [('aiwaves-cn/agents', 0.6464569568634033, 'nlp', 1), ('ibm/dromedary', 0.6295039057731628, 'llm', 1), ('microsoft/autogen', 0.5731350779533386, 'llm', 0), ('minedojo/voyager', 0.5710037350654602, 'llm', 0), ('hwchase17/langchain', 0.5536667704582214, 'llm', 1), ('nomic-ai/gpt4all', 0.551749587059021, 'llm', 1), ('young-geng/easylm', 0.5500134825706482, 'llm', 1), ('langchain-ai/langgraph', 0.5343421697616577, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5296755433082581, 'study', 0), ('operand/agency', 0.5273327827453613, 'llm', 0), ('deepset-ai/haystack', 0.527265191078186, 'llm', 1), ('nebuly-ai/nebullvm', 0.5268137454986572, 'perf', 0), ('explosion/spacy-llm', 0.5256912708282471, 'llm', 0), ('noahshinn/reflexion', 0.5254539847373962, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5156129002571106, 'llm', 1), ('mlc-ai/mlc-llm', 0.5101083517074585, 'llm', 1), ('microsoft/lmops', 0.5094317197799683, 'llm', 1), ('ray-project/ray-llm', 0.5078703165054321, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5059940814971924, 'llm', 0), ('geekan/metagpt', 0.5057692527770996, 'llm', 0), ('oliveirabruno01/babyagi-asi', 0.5025618076324463, 'llm', 1), ('deep-diver/pingpong', 0.5024981498718262, 'llm', 0)] | 3 | 2 | null | 1.19 | 2 | 0 | 9 | 8 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 38 |
1,678 | util | https://github.com/pycqa/pyflakes | [] | null | [] | [] | null | null | null | pycqa/pyflakes | pyflakes | 1,317 | 182 | 29 | Python | https://pypi.org/project/pyflakes | A simple program which checks Python source files for errors | pycqa | 2024-01-12 | 2014-04-07 | 512 | 2.571548 | https://avatars.githubusercontent.com/u/8749848?v=4 | A simple program which checks Python source files for errors | ['linter'] | ['linter'] | 2024-01-05 | [('klen/pylama', 0.6928360462188721, 'util', 1), ('nedbat/coveragepy', 0.5710163116455078, 'testing', 0), ('instagram/fixit', 0.5664411783218384, 'util', 1), ('landscapeio/prospector', 0.5659628510475159, 'util', 0), ('pycqa/pycodestyle', 0.5580477118492126, 'util', 0), ('google/yapf', 0.5379751920700073, 'util', 0), ('microsoft/pyright', 0.511178195476532, 'typing', 0), ('google/pytype', 0.5093093514442444, 'typing', 1), ('python/mypy', 0.5037577748298645, 'typing', 1)] | 84 | 4 | null | 0.19 | 11 | 10 | 119 | 0 | 0 | 3 | 3 | 11 | 14 | 90 | 1.3 | 38 |
445 | ml | https://github.com/csinva/imodels | [] | null | [] | [] | null | null | null | csinva/imodels | imodels | 1,237 | 110 | 26 | Jupyter Notebook | https://csinva.io/imodels | Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). | csinva | 2024-01-11 | 2019-07-04 | 238 | 5.181927 | null | Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). | ['ai', 'artificial-intelligence', 'bayesian-rule-list', 'data-science', 'explainable-ai', 'explainable-ml', 'imodels', 'interpretability', 'machine-learning', 'ml', 'optimal-classification-tree', 'rule-learning', 'rulefit', 'rules', 'scikit-learn', 'statistics', 'supervised-learning'] | ['ai', 'artificial-intelligence', 'bayesian-rule-list', 'data-science', 'explainable-ai', 'explainable-ml', 'imodels', 'interpretability', 'machine-learning', 'ml', 'optimal-classification-tree', 'rule-learning', 'rulefit', 'rules', 'scikit-learn', 'statistics', 'supervised-learning'] | 2023-12-30 | [('interpretml/interpret', 0.7508851289749146, 'ml-interpretability', 7), ('pair-code/lit', 0.6702791452407837, 'ml-interpretability', 1), ('tensorflow/lucid', 0.6663603782653809, 'ml-interpretability', 2), ('maif/shapash', 0.6528401374816895, 'ml', 3), ('selfexplainml/piml-toolbox', 0.6522828936576843, 'ml-interpretability', 0), ('marcotcr/lime', 0.6466513276100159, 'ml-interpretability', 0), ('pytorch/captum', 0.6309248208999634, 'ml-interpretability', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.6214880347251892, 'study', 2), ('xplainable/xplainable', 0.6195234656333923, 'ml-interpretability', 5), ('seldonio/alibi', 0.6186242699623108, 'ml-interpretability', 2), ('teamhg-memex/eli5', 0.6143868565559387, 'ml', 3), ('mlflow/mlflow', 0.5987911224365234, 'ml-ops', 3), ('huggingface/evaluate', 0.5934129357337952, 'ml', 1), ('eleutherai/pythia', 0.5875741243362427, 'ml-interpretability', 1), ('polyaxon/datatile', 0.5763207674026489, 'pandas', 3), ('huggingface/datasets', 0.5633199214935303, 'nlp', 1), ('tensorflow/tensorflow', 0.5597484707832336, 'ml-dl', 2), ('oegedijk/explainerdashboard', 0.5566130876541138, 'ml-interpretability', 0), ('cleanlab/cleanlab', 0.5562312602996826, 'ml', 1), ('tensorflow/data-validation', 0.5509118437767029, 'ml-ops', 0), ('skops-dev/skops', 0.5491555333137512, 'ml-ops', 2), ('ddbourgin/numpy-ml', 0.5491467118263245, 'ml', 1), ('giskard-ai/giskard', 0.5474556088447571, 'data', 3), ('firmai/industry-machine-learning', 0.5446041226387024, 'study', 2), ('slundberg/shap', 0.5444343090057373, 'ml-interpretability', 2), ('whylabs/whylogs', 0.5419551730155945, 'util', 2), ('linkedin/fasttreeshap', 0.5407923460006714, 'ml', 3), ('wandb/client', 0.5401941537857056, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5383384227752686, 'ml', 2), ('automl/auto-sklearn', 0.5375404953956604, 'ml', 1), ('nccr-itmo/fedot', 0.5283494591712952, 'ml-ops', 1), ('mosaicml/composer', 0.5268099904060364, 'ml-dl', 1), ('scikit-learn/scikit-learn', 0.525899350643158, 'ml', 3), ('microsoft/nni', 0.524517297744751, 'ml', 2), ('explosion/thinc', 0.5241812467575073, 'ml-dl', 3), ('rasbt/machine-learning-book', 0.5234281420707703, 'study', 2), ('patchy631/machine-learning', 0.5233355760574341, 'ml', 0), ('koaning/scikit-lego', 0.5232925415039062, 'ml', 2), ('rafiqhasan/auto-tensorflow', 0.5232248902320862, 'ml-dl', 1), ('carla-recourse/carla', 0.5205970406532288, 'ml', 4), ('alirezadir/machine-learning-interview-enlightener', 0.5202349424362183, 'study', 2), ('onnx/onnx', 0.5179754495620728, 'ml', 3), ('bentoml/bentoml', 0.5169817209243774, 'ml-ops', 2), ('rasbt/mlxtend', 0.5165061354637146, 'ml', 3), ('determined-ai/determined', 0.5160141587257385, 'ml-ops', 2), ('aimhubio/aim', 0.5152265429496765, 'ml-ops', 4), ('polyaxon/polyaxon', 0.5118154883384705, 'ml-ops', 4), ('gradio-app/gradio', 0.5105863809585571, 'viz', 2), ('featurelabs/featuretools', 0.5102759599685669, 'ml', 3), ('kubeflow/fairing', 0.5098203420639038, 'ml-ops', 0), ('tensorflow/tensor2tensor', 0.5070091485977173, 'ml', 1), ('ml-tooling/opyrator', 0.5064631104469299, 'viz', 1), ('arize-ai/phoenix', 0.5046104192733765, 'ml-interpretability', 0), ('cdpierse/transformers-interpret', 0.500318169593811, 'ml-interpretability', 3), ('tensorlayer/tensorlayer', 0.5001160502433777, 'ml-rl', 1)] | 22 | 5 | null | 2.12 | 8 | 4 | 55 | 0 | 5 | 8 | 5 | 8 | 1 | 90 | 0.1 | 38 |
1,010 | time-series | https://github.com/bashtage/arch | [] | null | [] | [] | null | null | null | bashtage/arch | arch | 1,215 | 279 | 44 | Python | null | ARCH models in Python | bashtage | 2024-01-13 | 2014-08-29 | 491 | 2.471665 | null | ARCH models in Python | ['adf', 'arch', 'bootstrap', 'df-gls', 'dickey-fuller', 'finance', 'financial-econometrics', 'forecasting', 'model-confidence-set', 'multiple-comparison-procedures', 'phillips-perron', 'reality-check', 'risk', 'spa', 'time-series', 'unit-root', 'variance', 'volatility'] | ['adf', 'arch', 'bootstrap', 'df-gls', 'dickey-fuller', 'finance', 'financial-econometrics', 'forecasting', 'model-confidence-set', 'multiple-comparison-procedures', 'phillips-perron', 'reality-check', 'risk', 'spa', 'time-series', 'unit-root', 'variance', 'volatility'] | 2024-01-05 | [('firmai/atspy', 0.6273349523544312, 'time-series', 3), ('alkaline-ml/pmdarima', 0.6099317669868469, 'time-series', 2), ('statsmodels/statsmodels', 0.5926198363304138, 'ml', 1), ('goldmansachs/gs-quant', 0.5530153512954712, 'finance', 0), ('kernc/backtesting.py', 0.5312038064002991, 'finance', 1), ('awslabs/gluonts', 0.5267676115036011, 'time-series', 2), ('domokane/financepy', 0.5076860785484314, 'finance', 2), ('pmorissette/ffn', 0.5025431513786316, 'finance', 0), ('ranaroussi/quantstats', 0.5018336772918701, 'finance', 1), ('cuemacro/finmarketpy', 0.5008066892623901, 'finance', 0), ('crflynn/stochastic', 0.5002689957618713, 'sim', 0)] | 35 | 3 | null | 1.29 | 25 | 24 | 114 | 0 | 8 | 5 | 8 | 25 | 25 | 90 | 1 | 38 |
1,043 | data | https://github.com/ydataai/ydata-synthetic | [] | null | [] | [] | null | null | null | ydataai/ydata-synthetic | ydata-synthetic | 1,195 | 233 | 29 | Jupyter Notebook | https://docs.synthetic.ydata.ai | Synthetic data generators for tabular and time-series data | ydataai | 2024-01-13 | 2020-05-04 | 195 | 6.123719 | https://avatars.githubusercontent.com/u/57689451?v=4 | Synthetic data generators for tabular and time-series data | ['datageneration', 'datagenerator', 'deep-learning', 'gan', 'gan-architectures', 'gans', 'generative-adversarial-network', 'machine-learning', 'pytorch', 'synthetic-data', 'tensorflow2', 'time-series', 'timeseries', 'training-data'] | ['datageneration', 'datagenerator', 'deep-learning', 'gan', 'gan-architectures', 'gans', 'generative-adversarial-network', 'machine-learning', 'pytorch', 'synthetic-data', 'tensorflow2', 'time-series', 'timeseries', 'training-data'] | 2024-01-02 | [('sdv-dev/sdv', 0.9112098217010498, 'data', 7), ('awslabs/autogluon', 0.5872030258178711, 'ml', 4), ('borisbanushev/stockpredictionai', 0.5105303525924683, 'finance', 0), ('vaexio/vaex', 0.5099949836730957, 'perf', 1), ('winedarksea/autots', 0.5098974704742432, 'time-series', 3), ('nicolas-hbt/pygraft', 0.5071893334388733, 'ml', 2), ('mljar/mljar-supervised', 0.5016704797744751, 'ml', 1)] | 22 | 1 | null | 1.23 | 34 | 17 | 45 | 0 | 9 | 9 | 9 | 34 | 16 | 90 | 0.5 | 38 |
966 | gis | https://github.com/microsoft/globalmlbuildingfootprints | [] | null | [] | [] | null | null | null | microsoft/globalmlbuildingfootprints | GlobalMLBuildingFootprints | 1,186 | 175 | 60 | Python | null | Worldwide building footprints derived from satellite imagery | microsoft | 2024-01-12 | 2022-04-22 | 92 | 12.811728 | https://avatars.githubusercontent.com/u/6154722?v=4 | Worldwide building footprints derived from satellite imagery | [] | [] | 2024-01-03 | [('zorzi-s/polyworldpretrainednetwork', 0.600134015083313, 'gis', 0), ('lydorn/polygonization-by-frame-field-learning', 0.5449085831642151, 'gis', 0)] | 7 | 2 | null | 0.35 | 17 | 8 | 21 | 0 | 0 | 0 | 0 | 17 | 11 | 90 | 0.6 | 38 |
625 | util | https://github.com/pyca/bcrypt | [] | null | [] | [] | null | null | null | pyca/bcrypt | bcrypt | 1,083 | 195 | 28 | Python | null | Modern(-ish) password hashing for your software and your servers | pyca | 2024-01-13 | 2013-05-11 | 559 | 1.935904 | https://avatars.githubusercontent.com/u/5615737?v=4 | Modern(-ish) password hashing for your software and your servers | [] | [] | 2024-01-12 | [] | 32 | 5 | null | 3.48 | 86 | 81 | 130 | 0 | 0 | 2 | 2 | 86 | 90 | 90 | 1 | 38 |
811 | ml | https://github.com/automl/tabpfn | [] | null | [] | [] | null | null | null | automl/tabpfn | TabPFN | 1,028 | 85 | 14 | Python | http://priorlabs.ai | Official implementation of the TabPFN paper (https://arxiv.org/abs/2207.01848) and the tabpfn package. | automl | 2024-01-13 | 2022-07-01 | 82 | 12.449827 | https://avatars.githubusercontent.com/u/6469053?v=4 | Official implementation of the TabPFN paper (https://arxiv.org/abs/2207.01848) and the tabpfn package. | [] | [] | 2023-10-22 | [] | 7 | 3 | null | 0.67 | 26 | 14 | 19 | 3 | 0 | 0 | 0 | 26 | 23 | 90 | 0.9 | 38 |
1,150 | data | https://github.com/aio-libs/aiocache | [] | null | [] | [] | null | null | null | aio-libs/aiocache | aiocache | 971 | 139 | 22 | Python | http://aiocache.readthedocs.io | Asyncio cache manager for redis, memcached and memory | aio-libs | 2024-01-11 | 2016-09-30 | 382 | 2.538088 | https://avatars.githubusercontent.com/u/7049303?v=4 | Asyncio cache manager for redis, memcached and memory | ['asyncio', 'cache', 'cachemanager', 'memcached', 'redis'] | ['asyncio', 'cache', 'cachemanager', 'memcached', 'redis'] | 2024-01-10 | [('long2ice/fastapi-cache', 0.6432105898857117, 'web', 3), ('grantjenks/python-diskcache', 0.6346278786659241, 'util', 1), ('samuelcolvin/arq', 0.554317057132721, 'data', 2), ('dgilland/cacheout', 0.5477664470672607, 'perf', 0), ('python-cachier/cachier', 0.5451957583427429, 'perf', 2)] | 43 | 5 | null | 2.15 | 36 | 33 | 89 | 0 | 3 | 3 | 3 | 36 | 28 | 90 | 0.8 | 38 |
89 | testing | https://github.com/taverntesting/tavern | [] | null | [] | [] | null | null | null | taverntesting/tavern | tavern | 969 | 186 | 27 | Python | https://taverntesting.github.io/ | A command-line tool and Python library and Pytest plugin for automated testing of RESTful APIs, with a simple, concise and flexible YAML-based syntax | taverntesting | 2024-01-12 | 2017-11-01 | 325 | 2.973696 | https://avatars.githubusercontent.com/u/33286481?v=4 | A command-line tool and Python library and Pytest plugin for automated testing of RESTful APIs, with a simple, concise and flexible YAML-based syntax | ['http', 'mqtt', 'pytest', 'test-automation', 'testing'] | ['http', 'mqtt', 'pytest', 'test-automation', 'testing'] | 2023-12-26 | [('pytest-dev/pytest-xdist', 0.6020364165306091, 'testing', 1), ('simple-salesforce/simple-salesforce', 0.6006324291229248, 'data', 0), ('ionelmc/pytest-benchmark', 0.5957930684089661, 'testing', 1), ('getsentry/responses', 0.5727768540382385, 'testing', 0), ('wolever/parameterized', 0.5629963874816895, 'testing', 0), ('lundberg/respx', 0.5600868463516235, 'testing', 2), ('seleniumbase/seleniumbase', 0.5573855042457581, 'testing', 1), ('hugapi/hug', 0.5526415705680847, 'util', 1), ('falconry/falcon', 0.5523936152458191, 'web', 1), ('flipkart-incubator/astra', 0.552314817905426, 'web', 0), ('requests/toolbelt', 0.545853853225708, 'util', 1), ('python-restx/flask-restx', 0.5409488081932068, 'web', 0), ('nedbat/coveragepy', 0.5402073264122009, 'testing', 0), ('pytest-dev/pytest', 0.5295860767364502, 'testing', 1), ('buildbot/buildbot', 0.5253430604934692, 'util', 0), ('computationalmodelling/nbval', 0.5216328501701355, 'jupyter', 2), ('pmorissette/bt', 0.5144594311714172, 'finance', 0), ('pytest-dev/pytest-cov', 0.511971116065979, 'testing', 1), ('samuelcolvin/pytest-pretty', 0.5094279050827026, 'testing', 1), ('pytest-dev/pytest-testinfra', 0.5087285041809082, 'testing', 1), ('pytest-dev/pytest-mock', 0.5072631239891052, 'testing', 1), ('teemu/pytest-sugar', 0.5043500661849976, 'testing', 2), ('nasdaq/data-link-python', 0.5025106072425842, 'finance', 0)] | 61 | 4 | null | 1.06 | 27 | 19 | 76 | 1 | 0 | 33 | 33 | 27 | 16 | 90 | 0.6 | 38 |
147 | util | https://github.com/zenodo/zenodo | [] | null | [] | [] | null | null | null | zenodo/zenodo | zenodo | 862 | 252 | 45 | Python | https://zenodo.org | Research. Shared. | zenodo | 2024-01-06 | 2013-02-11 | 572 | 1.506617 | https://avatars.githubusercontent.com/u/2675345?v=4 | Research. Shared. | ['digital-library', 'elasticsearch', 'flask', 'invenio', 'inveniosoftware', 'library-management', 'open-access', 'open-science', 'postgresql', 'research-data-management', 'research-data-repository', 'scientific-publications', 'zenodo'] | ['digital-library', 'elasticsearch', 'flask', 'invenio', 'inveniosoftware', 'library-management', 'open-access', 'open-science', 'postgresql', 'research-data-management', 'research-data-repository', 'scientific-publications', 'zenodo'] | 2023-12-11 | [('simonw/datasette', 0.6021542549133301, 'data', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5640177726745605, 'template', 1), ('piccolo-orm/piccolo_admin', 0.5565163493156433, 'data', 1), ('airbytehq/airbyte', 0.5533841252326965, 'data', 1), ('airbnb/knowledge-repo', 0.5470719933509827, 'data', 0), ('cerlymarco/medium_notebook', 0.5398790240287781, 'study', 0), ('firmai/industry-machine-learning', 0.5377620458602905, 'study', 0), ('plotly/dash', 0.5337997078895569, 'viz', 1), ('krzjoa/awesome-python-data-science', 0.5312089323997498, 'study', 0), ('aws/aws-sdk-pandas', 0.5311623811721802, 'pandas', 0), ('saulpw/visidata', 0.5197573900222778, 'term', 0), ('netflix/metaflow', 0.513927161693573, 'ml-ops', 0), ('eleutherai/pyfra', 0.5133705139160156, 'ml', 0), ('brettkromkamp/contextualise', 0.5124539136886597, 'data', 0), ('alphasecio/langchain-examples', 0.511971652507782, 'llm', 0), ('coleifer/peewee', 0.5084249377250671, 'data', 0), ('github/innovationgraph', 0.5064103007316589, 'data', 0), ('polyaxon/datatile', 0.5025171041488647, 'pandas', 0)] | 67 | 5 | null | 0.48 | 65 | 12 | 133 | 1 | 0 | 39 | 39 | 64 | 111 | 90 | 1.7 | 38 |
803 | data | https://github.com/neo4j/neo4j-python-driver | [] | null | [] | [] | null | null | null | neo4j/neo4j-python-driver | neo4j-python-driver | 850 | 213 | 98 | Python | https://neo4j.com/docs/api/python-driver/current/ | Neo4j Bolt driver for Python | neo4j | 2024-01-08 | 2015-05-05 | 456 | 1.864035 | https://avatars.githubusercontent.com/u/201120?v=4 | Neo4j Bolt driver for Python | ['binary-protocol', 'cypher', 'database-driver', 'driver', 'graph-database', 'neo4j', 'protocol', 'query-language'] | ['binary-protocol', 'cypher', 'database-driver', 'driver', 'graph-database', 'neo4j', 'protocol', 'query-language'] | 2024-01-09 | [('accenture/cymple', 0.6172598600387573, 'data', 2), ('datastax/python-driver', 0.5756858587265015, 'data', 0), ('scylladb/python-driver', 0.5569538474082947, 'data', 0), ('pydot/pydot', 0.5061096549034119, 'viz', 0)] | 43 | 4 | null | 1.9 | 36 | 36 | 106 | 0 | 15 | 14 | 15 | 36 | 25 | 90 | 0.7 | 38 |
790 | ml-interpretability | https://github.com/selfexplainml/piml-toolbox | [] | null | [] | [] | null | null | null | selfexplainml/piml-toolbox | PiML-Toolbox | 791 | 96 | 21 | Jupyter Notebook | https://selfexplainml.github.io/PiML-Toolbox | PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics | selfexplainml | 2024-01-13 | 2022-04-29 | 91 | 8.638066 | https://avatars.githubusercontent.com/u/74489521?v=4 | PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics | ['interpretable-machine-learning', 'low-code', 'ml-workflow', 'model-diagnostics'] | ['interpretable-machine-learning', 'low-code', 'ml-workflow', 'model-diagnostics'] | 2024-01-08 | [('csinva/imodels', 0.6522828936576843, 'ml', 0), ('kubeflow/fairing', 0.6489830613136292, 'ml-ops', 0), ('pair-code/lit', 0.6214944124221802, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.6020028591156006, 'ml', 0), ('huggingface/evaluate', 0.5961371660232544, 'ml', 0), ('wandb/client', 0.5873650908470154, 'ml', 0), ('teamhg-memex/eli5', 0.5864848494529724, 'ml', 0), ('pan-ml/panml', 0.5762995481491089, 'llm', 0), ('featurelabs/featuretools', 0.5691878795623779, 'ml', 0), ('ml-tooling/opyrator', 0.5633277297019958, 'viz', 0), ('stan-dev/pystan', 0.5616912245750427, 'ml', 0), ('microsoft/nni', 0.5612987279891968, 'ml', 0), ('apple/coremltools', 0.558883786201477, 'ml', 0), ('gradio-app/gradio', 0.5576595664024353, 'viz', 0), ('evidentlyai/evidently', 0.5544941425323486, 'ml-ops', 0), ('linkedin/fasttreeshap', 0.5518995523452759, 'ml', 0), ('tensorflow/lucid', 0.5517100095748901, 'ml-interpretability', 0), ('huggingface/datasets', 0.5501903891563416, 'nlp', 0), ('mlflow/mlflow', 0.5481675863265991, 'ml-ops', 0), ('scikit-learn/scikit-learn', 0.5468170642852783, 'ml', 0), ('rafiqhasan/auto-tensorflow', 0.5389483571052551, 'ml-dl', 0), ('polyaxon/datatile', 0.5384085178375244, 'pandas', 0), ('rasbt/mlxtend', 0.5372369885444641, 'ml', 0), ('epistasislab/tpot', 0.5343623161315918, 'ml', 0), ('tensorflow/data-validation', 0.5335401892662048, 'ml-ops', 0), ('nccr-itmo/fedot', 0.53252112865448, 'ml-ops', 0), ('pytorch/captum', 0.5324576497077942, 'ml-interpretability', 0), ('goldmansachs/gs-quant', 0.5287847518920898, 'finance', 0), ('pycaret/pycaret', 0.5286346077919006, 'ml', 0), ('maif/shapash', 0.527449905872345, 'ml', 0), ('zenml-io/zenml', 0.5244457125663757, 'ml-ops', 0), ('dagworks-inc/hamilton', 0.5237611532211304, 'ml-ops', 0), ('interpretml/interpret', 0.5234452486038208, 'ml-interpretability', 1), ('eleutherai/pyfra', 0.5199788808822632, 'ml', 0), ('huggingface/huggingface_hub', 0.5193759202957153, 'ml', 0), ('brokenloop/jsontopydantic', 0.515333354473114, 'util', 0), ('skops-dev/skops', 0.5126495957374573, 'ml-ops', 0), ('probml/pyprobml', 0.512611985206604, 'ml', 0), ('pytoolz/toolz', 0.5094278454780579, 'util', 0), ('polyaxon/polyaxon', 0.5091049075126648, 'ml-ops', 0), ('whylabs/whylogs', 0.5081930756568909, 'util', 0), ('huggingface/transformers', 0.5078108310699463, 'nlp', 0), ('titanml/takeoff', 0.5073431134223938, 'llm', 0), ('seldonio/alibi', 0.5072975158691406, 'ml-interpretability', 0), ('mljar/mljar-supervised', 0.5052536129951477, 'ml', 0), ('microsoft/flaml', 0.5052233338356018, 'ml', 0), ('lucidrains/toolformer-pytorch', 0.5051028728485107, 'llm', 0), ('pymc-devs/pymc3', 0.5050045847892761, 'ml', 0), ('amaargiru/pyroad', 0.5047166347503662, 'study', 0), ('firmai/atspy', 0.5030118823051453, 'time-series', 0), ('conceptofmind/toolformer', 0.5030021071434021, 'llm', 0), ('fmind/mlops-python-package', 0.5029227137565613, 'template', 0), ('anthropics/evals', 0.5009229183197021, 'llm', 0), ('shankarpandala/lazypredict', 0.500099241733551, 'ml', 0)] | 6 | 2 | null | 2.46 | 4 | 1 | 21 | 0 | 3 | 2 | 3 | 4 | 3 | 90 | 0.8 | 38 |
824 | typing | https://github.com/python-attrs/cattrs | [] | null | [] | [] | null | null | null | python-attrs/cattrs | cattrs | 725 | 101 | 20 | Python | https://catt.rs | Composable custom class converters for attrs. | python-attrs | 2024-01-13 | 2016-08-28 | 387 | 1.872003 | https://avatars.githubusercontent.com/u/25880274?v=4 | Composable custom class converters for attrs. | ['attrs', 'deserialization', 'serialization'] | ['attrs', 'deserialization', 'serialization'] | 2024-01-13 | [('lidatong/dataclasses-json', 0.5296611189842224, 'util', 0)] | 62 | 3 | null | 2.48 | 78 | 63 | 90 | 0 | 4 | 4 | 4 | 78 | 121 | 90 | 1.6 | 38 |
1,754 | ml | https://github.com/criteo/autofaiss | ['knn', 'similarity', 'embeddings', 'vector-search'] | null | [] | [] | 1 | null | null | criteo/autofaiss | autofaiss | 684 | 65 | 18 | Python | https://criteo.github.io/autofaiss/ | Automatically create Faiss knn indices with the most optimal similarity search parameters. | criteo | 2024-01-13 | 2021-04-28 | 143 | 4.754717 | https://avatars.githubusercontent.com/u/1713646?v=4 | Automatically create Faiss knn indices with the most optimal similarity search parameters. | [] | ['embeddings', 'knn', 'similarity', 'vector-search'] | 2024-01-13 | [('facebookresearch/faiss', 0.629601776599884, 'ml', 3), ('qdrant/quaterion', 0.6242879629135132, 'ml', 1), ('lmcinnes/pynndescent', 0.5459467172622681, 'ml', 0), ('qdrant/qdrant', 0.5368368625640869, 'data', 1)] | 15 | 4 | null | 0.25 | 14 | 7 | 33 | 0 | 4 | 25 | 4 | 14 | 15 | 90 | 1.1 | 38 |
1,752 | jupyter | https://github.com/aws/graph-notebook | [] | null | [] | [] | null | null | null | aws/graph-notebook | graph-notebook | 652 | 157 | 35 | Jupyter Notebook | https://github.com/aws/graph-notebook | Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL. | aws | 2024-01-13 | 2020-10-01 | 173 | 3.753289 | https://avatars.githubusercontent.com/u/2232217?v=4 | Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL. | ['apache', 'cypher', 'graph', 'gremlin', 'jupyter', 'jupyter-notebook', 'jupyter-widgets', 'neptune', 'opencypher', 'rdf', 'sparql', 'tinkerpop'] | ['apache', 'cypher', 'graph', 'gremlin', 'jupyter', 'jupyter-notebook', 'jupyter-widgets', 'neptune', 'opencypher', 'rdf', 'sparql', 'tinkerpop'] | 2023-12-21 | [('jupyter-widgets/ipywidgets', 0.687077522277832, 'jupyter', 0), ('voila-dashboards/voila', 0.6807038187980652, 'jupyter', 2), ('cohere-ai/notebooks', 0.6492716073989868, 'llm', 0), ('mwouts/jupytext', 0.6454940438270569, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.6357448697090149, 'jupyter', 2), ('jupyter/nbformat', 0.6213883757591248, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5957169532775879, 'jupyter', 2), ('jupyter/notebook', 0.5949639081954956, 'jupyter', 2), ('bloomberg/ipydatagrid', 0.5870513916015625, 'jupyter', 0), ('quantopian/qgrid', 0.571941614151001, 'jupyter', 0), ('ipython/ipykernel', 0.568101167678833, 'util', 2), ('jupyter-lsp/jupyterlab-lsp', 0.5679754018783569, 'jupyter', 2), ('jakevdp/pythondatasciencehandbook', 0.559874951839447, 'study', 1), ('jupyter/nbdime', 0.5575152039527893, 'jupyter', 2), ('holoviz/panel', 0.5436458587646484, 'viz', 1), ('nteract/papermill', 0.5402319431304932, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.539257287979126, 'jupyter', 2), ('jupyter-widgets/ipyleaflet', 0.5392546653747559, 'gis', 1), ('opengeos/leafmap', 0.5389112830162048, 'gis', 2), ('mamba-org/gator', 0.5385718941688538, 'jupyter', 1), ('tkrabel/bamboolib', 0.5372664928436279, 'pandas', 1), ('alphasecio/langchain-examples', 0.5361274480819702, 'llm', 1), ('ipython/ipyparallel', 0.5359687805175781, 'perf', 1), ('plotly/plotly.py', 0.5306247472763062, 'viz', 1), ('fchollet/deep-learning-with-python-notebooks', 0.525762140750885, 'study', 0), ('jupyterlab/jupyterlab', 0.5233699083328247, 'jupyter', 1), ('jupyter/nbconvert', 0.522909939289093, 'jupyter', 0), ('giswqs/mapwidget', 0.5196599364280701, 'gis', 1), ('ageron/handson-ml2', 0.519343912601471, 'ml', 0), ('accenture/cymple', 0.5142837166786194, 'data', 1), ('strawberry-graphql/strawberry', 0.5056154131889343, 'web', 0), ('aws/aws-sdk-pandas', 0.5009594559669495, 'pandas', 0)] | 30 | 4 | null | 1.63 | 20 | 13 | 40 | 1 | 10 | 13 | 10 | 20 | 18 | 90 | 0.9 | 38 |
619 | gis | https://github.com/scitools/iris | [] | null | [] | [] | null | null | null | scitools/iris | iris | 587 | 279 | 45 | Python | https://scitools-iris.readthedocs.io/en/stable/ | A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data | scitools | 2024-01-05 | 2012-08-06 | 599 | 0.979733 | https://avatars.githubusercontent.com/u/1391487?v=4 | A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data | ['data-analysis', 'earth-science', 'grib', 'iris', 'meteorology', 'netcdf', 'oceanography', 'spaceweather', 'visualisation'] | ['data-analysis', 'earth-science', 'grib', 'iris', 'meteorology', 'netcdf', 'oceanography', 'spaceweather', 'visualisation'] | 2024-01-12 | [('enthought/mayavi', 0.7232843041419983, 'viz', 0), ('giswqs/geemap', 0.6783716082572937, 'gis', 0), ('residentmario/geoplot', 0.6692157983779907, 'gis', 0), ('holoviz/holoviz', 0.6550614833831787, 'viz', 0), ('mwaskom/seaborn', 0.6527572870254517, 'viz', 0), ('contextlab/hypertools', 0.6321009993553162, 'ml', 0), ('pyqtgraph/pyqtgraph', 0.6289077401161194, 'viz', 0), ('altair-viz/altair', 0.6287659406661987, 'viz', 0), ('sentinel-hub/eo-learn', 0.623789370059967, 'gis', 0), ('marcomusy/vedo', 0.6136019825935364, 'viz', 0), ('pytroll/satpy', 0.6135034561157227, 'gis', 0), ('cloudsen12/easystac', 0.6042935252189636, 'gis', 0), ('gregorhd/mapcompare', 0.6005646586418152, 'gis', 0), ('roban/cosmolopy', 0.5999904274940491, 'sim', 0), ('lux-org/lux', 0.5947362184524536, 'viz', 0), ('holoviz/hvplot', 0.5860223770141602, 'pandas', 0), ('man-group/dtale', 0.5857634544372559, 'viz', 1), ('holoviz/panel', 0.5823258757591248, 'viz', 0), ('opengeos/leafmap', 0.5820508003234863, 'gis', 0), ('earthlab/earthpy', 0.5802125930786133, 'gis', 0), ('has2k1/plotnine', 0.5705260634422302, 'viz', 1), ('holoviz/geoviews', 0.5698901414871216, 'gis', 0), ('matplotlib/matplotlib', 0.5653036236763, 'viz', 0), ('krzjoa/awesome-python-data-science', 0.5579238533973694, 'study', 1), ('numpy/numpy', 0.5556809306144714, 'math', 0), ('artelys/geonetworkx', 0.5512532591819763, 'gis', 0), ('raphaelquast/eomaps', 0.5512466430664062, 'gis', 0), ('kanaries/pygwalker', 0.5480643510818481, 'pandas', 1), ('geopandas/geopandas', 0.5416747331619263, 'gis', 0), ('pandas-dev/pandas', 0.5412265062332153, 'pandas', 1), ('graphistry/pygraphistry', 0.5314244627952576, 'data', 0), ('pysal/pysal', 0.5261117815971375, 'gis', 0), ('makepath/xarray-spatial', 0.523902952671051, 'gis', 0), ('plotly/plotly.py', 0.5217393040657043, 'viz', 0), ('fatiando/verde', 0.5199081301689148, 'gis', 1), ('imageio/imageio', 0.5107640027999878, 'util', 0), ('albahnsen/pycircular', 0.5095266103744507, 'math', 0), ('bokeh/bokeh', 0.5092189908027649, 'viz', 1), ('bmabey/pyldavis', 0.5086809992790222, 'ml', 0), ('plotly/dash', 0.5075518488883972, 'viz', 0), ('mito-ds/monorepo', 0.5061976909637451, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5051771402359009, 'study', 0), ('westhealth/pyvis', 0.503166139125824, 'graph', 0), ('rasbt/mlxtend', 0.5022916793823242, 'ml', 0)] | 107 | 2 | null | 5.21 | 276 | 209 | 139 | 0 | 8 | 7 | 8 | 276 | 410 | 90 | 1.5 | 38 |
216 | ml-ops | https://github.com/nccr-itmo/fedot | [] | null | [] | [] | null | null | null | nccr-itmo/fedot | FEDOT | 579 | 80 | 9 | Python | https://fedot.readthedocs.io | Automated modeling and machine learning framework FEDOT | nccr-itmo | 2024-01-13 | 2020-01-13 | 211 | 2.742219 | https://avatars.githubusercontent.com/u/65946329?v=4 | Automated modeling and machine learning framework FEDOT | ['automated-machine-learning', 'automation', 'automl', 'evolutionary-algorithms', 'fedot', 'genetic-programming', 'hyperparameter-optimization', 'machine-learning', 'multimodality', 'parameter-tuning', 'structural-learning'] | ['automated-machine-learning', 'automation', 'automl', 'evolutionary-algorithms', 'fedot', 'genetic-programming', 'hyperparameter-optimization', 'machine-learning', 'multimodality', 'parameter-tuning', 'structural-learning'] | 2024-01-10 | [('automl/auto-sklearn', 0.7373944520950317, 'ml', 3), ('microsoft/nni', 0.6999444365501404, 'ml', 4), ('winedarksea/autots', 0.6496773958206177, 'time-series', 2), ('microsoft/flaml', 0.6372272372245789, 'ml', 4), ('keras-team/autokeras', 0.6287457346916199, 'ml-dl', 3), ('awslabs/autogluon', 0.6268003582954407, 'ml', 4), ('adap/flower', 0.618629515171051, 'ml-ops', 1), ('districtdatalabs/yellowbrick', 0.6075289249420166, 'ml', 1), ('epistasislab/tpot', 0.6070546507835388, 'ml', 6), ('huggingface/autotrain-advanced', 0.5916618704795837, 'ml', 1), ('xplainable/xplainable', 0.5892693996429443, 'ml-interpretability', 1), ('nevronai/metisfl', 0.5833522081375122, 'ml', 1), ('mlflow/mlflow', 0.5817329287528992, 'ml-ops', 1), ('featurelabs/featuretools', 0.5784884095191956, 'ml', 3), ('mosaicml/composer', 0.5770966410636902, 'ml-dl', 1), ('ml-tooling/opyrator', 0.5759302377700806, 'viz', 1), ('mljar/mljar-supervised', 0.5752649307250977, 'ml', 4), ('alpa-projects/alpa', 0.5670198202133179, 'ml-dl', 1), ('operand/agency', 0.5655133128166199, 'llm', 1), ('tensorflow/tensorflow', 0.5654616355895996, 'ml-dl', 1), ('bentoml/bentoml', 0.5645349621772766, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5636411905288696, 'ml-ops', 2), ('huggingface/datasets', 0.5636368989944458, 'nlp', 1), ('google/pyglove', 0.5598992109298706, 'util', 2), ('determined-ai/determined', 0.5581679940223694, 'ml-ops', 2), ('shankarpandala/lazypredict', 0.5503329038619995, 'ml', 2), ('ludwig-ai/ludwig', 0.5483391284942627, 'ml-ops', 1), ('rafiqhasan/auto-tensorflow', 0.5481418371200562, 'ml-dl', 2), ('giskard-ai/giskard', 0.5448036789894104, 'data', 1), ('onnx/onnx', 0.5438551306724548, 'ml', 1), ('ai4finance-foundation/finrl', 0.5424655675888062, 'finance', 0), ('ray-project/ray', 0.541328489780426, 'ml-ops', 3), ('explosion/thinc', 0.5396167635917664, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.5393419861793518, 'ml', 1), ('microsoft/lmops', 0.5380227565765381, 'llm', 0), ('ourownstory/neural_prophet', 0.5363118648529053, 'ml', 1), ('firmai/industry-machine-learning', 0.5340647101402283, 'study', 1), ('apple/coremltools', 0.5334883332252502, 'ml', 1), ('firmai/atspy', 0.5334661602973938, 'time-series', 0), ('selfexplainml/piml-toolbox', 0.53252112865448, 'ml-interpretability', 0), ('lucidrains/toolformer-pytorch', 0.5307614803314209, 'llm', 0), ('csinva/imodels', 0.5283494591712952, 'ml', 1), ('scikit-learn/scikit-learn', 0.5258017778396606, 'ml', 1), ('gradio-app/gradio', 0.5231483578681946, 'viz', 1), ('polyaxon/datatile', 0.521264910697937, 'pandas', 0), ('sktime/sktime', 0.520081639289856, 'time-series', 1), ('mlc-ai/mlc-llm', 0.5192214250564575, 'llm', 0), ('patchy631/machine-learning', 0.517076313495636, 'ml', 0), ('online-ml/river', 0.5161853432655334, 'ml', 1), ('eugeneyan/testing-ml', 0.5155410766601562, 'testing', 1), ('horovod/horovod', 0.5143430829048157, 'ml-ops', 1), ('ggerganov/ggml', 0.5140013098716736, 'ml', 1), ('hpcaitech/colossalai', 0.5125412344932556, 'llm', 0), ('interpretml/interpret', 0.5118159651756287, 'ml-interpretability', 1), ('huggingface/transformers', 0.5109488368034363, 'nlp', 1), ('neuralmagic/sparseml', 0.508357048034668, 'ml-dl', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5080487728118896, 'study', 1), ('kubeflow/pipelines', 0.5040023922920227, 'ml-ops', 1), ('tensorly/tensorly', 0.5016763806343079, 'ml-dl', 1), ('feast-dev/feast', 0.5015140175819397, 'ml-ops', 1)] | 32 | 3 | null | 2.06 | 102 | 58 | 49 | 0 | 4 | 3 | 4 | 102 | 146 | 90 | 1.4 | 38 |
905 | util | https://github.com/methexis-inc/terminal-copilot | [] | null | [] | [] | null | null | null | methexis-inc/terminal-copilot | terminal-copilot | 539 | 41 | 7 | Python | null | A smart terminal assistant that helps you find the right command. | methexis-inc | 2024-01-10 | 2022-12-11 | 59 | 9.091566 | https://avatars.githubusercontent.com/u/110575158?v=4 | A smart terminal assistant that helps you find the right command. | [] | [] | 2023-12-09 | [('tiangolo/typer', 0.5327745079994202, 'term', 0), ('tconbeer/harlequin', 0.5151594877243042, 'term', 0)] | 10 | 3 | null | 0.35 | 4 | 4 | 13 | 1 | 3 | 5 | 3 | 4 | 6 | 90 | 1.5 | 38 |
1,533 | data | https://github.com/dbt-labs/dbt-spark | ['spark', 'dbt'] | null | [] | [] | null | null | null | dbt-labs/dbt-spark | dbt-spark | 339 | 199 | 18 | Python | https://getdbt.com | dbt-spark contains all of the code enabling dbt to work with Apache Spark and Databricks | dbt-labs | 2024-01-06 | 2019-03-21 | 253 | 1.336149 | https://avatars.githubusercontent.com/u/18339788?v=4 | dbt-spark contains all of the code enabling dbt to work with Apache Spark and Databricks | [] | ['dbt', 'spark'] | 2024-01-11 | [('databricks/dbt-databricks', 0.6950157284736633, 'data', 1)] | 73 | 4 | null | 2.98 | 94 | 63 | 59 | 0 | 28 | 18 | 28 | 94 | 103 | 90 | 1.1 | 38 |
1,837 | llm | https://github.com/bobazooba/xllm | [] | null | [] | [] | null | null | null | bobazooba/xllm | xllm | 308 | 17 | 3 | Python | https://t.me/talequestbot | 🦖 X—LLM: Cutting Edge & Easy LLM Finetuning | bobazooba | 2024-01-14 | 2023-11-10 | 11 | 26.617284 | null | 🦖 X—LLM: Cutting Edge & Easy LLM Finetuning | ['alpaca', 'bitsandbytes', 'cerebras', 'chatgpt', 'deep-learning', 'deep-neural-networks', 'gpt', 'gpt-4', 'gptq', 'large-language-models', 'llama', 'llama2', 'llm', 'mistral', 'openai', 'pytorch', 'torch', 'vicuna', 'zephyr'] | ['alpaca', 'bitsandbytes', 'cerebras', 'chatgpt', 'deep-learning', 'deep-neural-networks', 'gpt', 'gpt-4', 'gptq', 'large-language-models', 'llama', 'llama2', 'llm', 'mistral', 'openai', 'pytorch', 'torch', 'vicuna', 'zephyr'] | 2023-12-07 | [('hiyouga/llama-efficient-tuning', 0.7037465572357178, 'llm', 5), ('hiyouga/llama-factory', 0.7037465572357178, 'llm', 5), ('lianjiatech/belle', 0.6800654530525208, 'llm', 1), 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1,575 | data | https://github.com/scikit-hep/uproot5 | ['science'] | null | [] | [] | null | null | null | scikit-hep/uproot5 | uproot5 | 208 | 63 | 19 | Python | https://uproot.readthedocs.io | ROOT I/O in pure Python and NumPy. | scikit-hep | 2024-01-09 | 2020-05-08 | 194 | 1.069016 | https://avatars.githubusercontent.com/u/23454624?v=4 | ROOT I/O in pure Python and NumPy. | ['analysis', 'big-data', 'bigdata', 'file-format', 'hep', 'hep-ex', 'hep-py', 'numpy', 'root', 'root-cern', 'scikit-hep'] | ['analysis', 'big-data', 'bigdata', 'file-format', 'hep', 'hep-ex', 'hep-py', 'numpy', 'root', 'root-cern', 'science', 'scikit-hep'] | 2024-01-12 | [('numpy/numpy', 0.5980868935585022, 'math', 1), ('scipy/scipy', 0.5634987354278564, 'math', 0), ('blaze/blaze', 0.5468524098396301, 'pandas', 0), ('cython/cython', 0.5226044058799744, 'util', 1), ('fredrik-johansson/mpmath', 0.516451895236969, 'math', 0), ('pypy/pypy', 0.5114945769309998, 'util', 0), ('fsspec/filesystem_spec', 0.5076752305030823, 'util', 0)] | 44 | 4 | null | 3.35 | 87 | 72 | 45 | 0 | 26 | 29 | 26 | 87 | 180 | 90 | 2.1 | 38 |
1,532 | data | https://github.com/databricks/dbt-databricks | ['databricks', 'dbt'] | null | [] | [] | null | null | null | databricks/dbt-databricks | dbt-databricks | 165 | 91 | 19 | Python | https://databricks.com | A dbt adapter for Databricks. | databricks | 2024-01-06 | 2021-10-19 | 119 | 1.386555 | https://avatars.githubusercontent.com/u/4998052?v=4 | A dbt adapter for Databricks. | ['databricks', 'dbt', 'etl', 'sql'] | ['databricks', 'dbt', 'etl', 'sql'] | 2024-01-12 | [('dbt-labs/dbt-spark', 0.6950157284736633, 'data', 1), ('databrickslabs/dbx', 0.6466124057769775, 'data', 1), ('duckdb/dbt-duckdb', 0.5661155581474304, 'data', 1), ('airbnb/omniduct', 0.5590947866439819, 'data', 0), ('airbytehq/airbyte', 0.5521032214164734, 'data', 1), ('dbt-labs/dbt-core', 0.5302814841270447, 'ml-ops', 0), ('dlt-hub/dlt', 0.5166671276092529, 'data', 0), ('tobymao/sqlglot', 0.5069236755371094, 'data', 2), ('tconbeer/sqlfmt', 0.5048282146453857, 'data', 2)] | 69 | 3 | null | 5.67 | 107 | 88 | 27 | 0 | 29 | 38 | 29 | 107 | 168 | 90 | 1.6 | 38 |
1,746 | util | https://github.com/callowayproject/bump-my-version | ['code-quality'] | null | [] | [] | null | null | null | callowayproject/bump-my-version | bump-my-version | 115 | 14 | 7 | Python | https://callowayproject.github.io/bump-my-version/ | A small command line tool to simplify releasing software by updating all version strings in your source code by the correct increment and optionally commit and tag the changes. | callowayproject | 2024-01-11 | 2023-04-12 | 41 | 2.74744 | https://avatars.githubusercontent.com/u/305772?v=4 | A small command line tool to simplify releasing software by updating all version strings in your source code by the correct increment and optionally commit and tag the changes. | ['bumpversion', 'version', 'versioning'] | ['bumpversion', 'code-quality', 'version', 'versioning'] | 2024-01-13 | [('c4urself/bump2version', 0.7459608316421509, 'util', 1), ('pypa/setuptools_scm', 0.6197443604469299, 'util', 1), ('mtkennerly/dunamai', 0.6012407541275024, 'util', 1), ('asottile/pyupgrade', 0.5788130760192871, 'util', 1), ('mtkennerly/poetry-dynamic-versioning', 0.5595990419387817, 'util', 1), ('python-versioneer/python-versioneer', 0.5228790640830994, 'util', 0)] | 12 | 5 | null | 4.5 | 58 | 50 | 9 | 0 | 24 | 38 | 24 | 58 | 92 | 90 | 1.6 | 38 |
747 | study | https://github.com/karpathy/micrograd | [] | null | [] | [] | null | null | null | karpathy/micrograd | micrograd | 7,103 | 917 | 131 | Jupyter Notebook | null | A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API | karpathy | 2024-01-14 | 2020-04-13 | 198 | 35.847873 | null | A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API | [] | [] | 2020-04-18 | [('pytorch/ignite', 0.7160765528678894, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.6794243454933167, 'perf', 0), ('skorch-dev/skorch', 0.644400417804718, 'ml-dl', 0), ('nvidia/apex', 0.6441670060157776, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.634564220905304, 'ml-dl', 0), ('denys88/rl_games', 0.6286336779594421, 'ml-rl', 0), ('pytorch/glow', 0.6073175668716431, 'ml', 0), ('ggerganov/ggml', 0.6055065393447876, 'ml', 0), ('huggingface/transformers', 0.6045575737953186, 'nlp', 0), ('mrdbourke/pytorch-deep-learning', 0.602993369102478, 'study', 0), ('microsoft/nni', 0.6010633111000061, 'ml', 0), ('arogozhnikov/einops', 0.600235104560852, 'ml-dl', 0), ('neuralmagic/sparseml', 0.597611129283905, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.597282350063324, 'study', 0), ('pytorch/rl', 0.5971662402153015, 'ml-rl', 0), ('microsoft/flaml', 0.5962915420532227, 'ml', 0), ('thu-ml/tianshou', 0.5894965529441833, 'ml-rl', 0), ('nvidia/deeplearningexamples', 0.583264946937561, 'ml-dl', 0), ('explosion/thinc', 0.581674337387085, 'ml-dl', 0), ('keras-team/autokeras', 0.5800349712371826, 'ml-dl', 0), ('pytorch/pytorch', 0.5792595744132996, 'ml-dl', 0), ('pytorch/data', 0.5738345980644226, 'data', 0), ('ray-project/ray', 0.5609050393104553, 'ml-ops', 0), ('alpa-projects/alpa', 0.5604699850082397, 'ml-dl', 0), ('ashleve/lightning-hydra-template', 0.5562566518783569, 'util', 0), ('uber/petastorm', 0.5534528493881226, 'data', 0), ('horovod/horovod', 0.552807092666626, 'ml-ops', 0), ('microsoft/onnxruntime', 0.5451004505157471, 'ml', 0), ('rentruewang/koila', 0.5448654890060425, 'ml', 0), ('tensorlayer/tensorlayer', 0.544183075428009, 'ml-rl', 0), ('lucidrains/imagen-pytorch', 0.5438899993896484, 'ml-dl', 0), ('nicolas-chaulet/torch-points3d', 0.5409857034683228, 'ml', 0), ('deepmind/dm-haiku', 0.5368869304656982, 'ml-dl', 0), ('xl0/lovely-tensors', 0.5362251400947571, 'ml-dl', 0), ('determined-ai/determined', 0.5347241163253784, 'ml-ops', 0), ('pyg-team/pytorch_geometric', 0.5318362712860107, 'ml-dl', 0), ('huggingface/optimum', 0.5316488146781921, 'ml', 0), ('google/trax', 0.5311139822006226, 'ml-dl', 0), ('aws/sagemaker-python-sdk', 0.5261572003364563, 'ml', 0), ('facebookresearch/pytorch3d', 0.5227355360984802, 'ml-dl', 0), ('lightly-ai/lightly', 0.5221474766731262, 'ml', 0), ('aiqc/aiqc', 0.5214496850967407, 'ml-ops', 0), ('intellabs/bayesian-torch', 0.5212419629096985, 'ml', 0), ('huggingface/accelerate', 0.5209835767745972, 'ml', 0), ('mosaicml/composer', 0.517072856426239, 'ml-dl', 0), ('allenai/allennlp', 0.5140243768692017, 'nlp', 0), ('google/automl', 0.5122846364974976, 'ml', 0), ('ludwig-ai/ludwig', 0.5106154680252075, 'ml-ops', 0), ('koaning/human-learn', 0.509779691696167, 'data', 0), ('pyro-ppl/pyro', 0.5077245831489563, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5075839757919312, 'ml', 0), ('activeloopai/deeplake', 0.5072367191314697, 'ml-ops', 0), ('epistasislab/tpot', 0.5062547922134399, 'ml', 0), ('plasma-umass/scalene', 0.5055549740791321, 'profiling', 0), ('rasbt/deeplearning-models', 0.5046243667602539, 'ml-dl', 0), ('huggingface/peft', 0.5045297145843506, 'llm', 0), ('kubeflow/fairing', 0.5037677884101868, 'ml-ops', 0), ('microsoft/deepspeed', 0.5035680532455444, 'ml-dl', 0), ('lucidrains/dalle2-pytorch', 0.5032515525817871, 'diffusion', 0), ('salesforce/deeptime', 0.5016177892684937, 'time-series', 0)] | 2 | 1 | null | 0 | 7 | 4 | 46 | 46 | 0 | 0 | 0 | 7 | 3 | 90 | 0.4 | 37 |
817 | study | https://github.com/firmai/industry-machine-learning | [] | null | [] | [] | null | null | null | firmai/industry-machine-learning | industry-machine-learning | 6,946 | 1,151 | 389 | Jupyter Notebook | https://www.linkedin.com/company/firmai | A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai) | firmai | 2024-01-13 | 2019-05-03 | 247 | 28.056549 | null | A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai) | ['data-science', 'datascience', 'example', 'firmai', 'jupyter-notebook', 'machine-learning', 'practical-machine-learning'] | ['data-science', 'datascience', 'example', 'firmai', 'jupyter-notebook', 'machine-learning', 'practical-machine-learning'] | 2021-12-18 | [('cerlymarco/medium_notebook', 0.6431946158409119, 'study', 2), ('ageron/handson-ml2', 0.6424956321716309, 'ml', 0), ('feast-dev/feast', 0.6406149864196777, 'ml-ops', 2), ('krzjoa/awesome-python-data-science', 0.6359909772872925, 'study', 2), ('tensorflow/data-validation', 0.6276463866233826, 'ml-ops', 0), ('gradio-app/gradio', 0.6118634939193726, 'viz', 2), ('mlflow/mlflow', 0.6037585735321045, 'ml-ops', 1), ('tensorflow/tensorflow', 0.6019365191459656, 'ml-dl', 1), ('kubeflow-kale/kale', 0.5946695804595947, 'ml-ops', 2), ('scikit-learn/scikit-learn', 0.5884523987770081, 'ml', 2), ('huggingface/datasets', 0.5870203375816345, 'nlp', 1), ('mrdbourke/zero-to-mastery-ml', 0.5863468647003174, 'study', 2), ('onnx/onnx', 0.5830479264259338, 'ml', 1), ('tensorflow/tensor2tensor', 0.5822129249572754, 'ml', 1), ('polyaxon/polyaxon', 0.5819257497787476, 'ml-ops', 2), ('patchy631/machine-learning', 0.5763393044471741, 'ml', 0), ('rasbt/mlxtend', 0.5761663913726807, 'ml', 2), ('determined-ai/determined', 0.574253261089325, 'ml-ops', 2), ('polyaxon/datatile', 0.5717259049415588, 'pandas', 1), ('googlecloudplatform/vertex-ai-samples', 0.5715494751930237, 'ml', 1), ('dylanhogg/awesome-python', 0.5685208439826965, 'study', 2), ('jovianml/opendatasets', 0.5650241374969482, 'data', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5614524483680725, 'study', 1), ('rasbt/machine-learning-book', 0.5610123872756958, 'study', 1), ('huggingface/evaluate', 0.5610095858573914, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5604248642921448, 'ml', 2), ('xplainable/xplainable', 0.5596107840538025, 'ml-interpretability', 2), ('tensorlayer/tensorlayer', 0.5479680299758911, 'ml-rl', 0), ('automl/auto-sklearn', 0.5466862320899963, 'ml', 0), ('csinva/imodels', 0.5446041226387024, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5426095128059387, 'ml', 1), ('aws/sagemaker-python-sdk', 0.5424572229385376, 'ml', 1), ('sktime/sktime', 0.541723370552063, 'time-series', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5408152937889099, 'study', 0), ('teamhg-memex/eli5', 0.5391788482666016, 'ml', 2), ('zenodo/zenodo', 0.5377620458602905, 'util', 0), ('uber/petastorm', 0.5362139940261841, 'data', 1), ('nccr-itmo/fedot', 0.5340647101402283, 'ml-ops', 1), ('airbnb/knowledge-repo', 0.5338939428329468, 'data', 1), ('google-research/google-research', 0.5328598618507385, 'ml', 1), ('ddbourgin/numpy-ml', 0.5301154255867004, 'ml', 1), ('microsoft/nni', 0.528578519821167, 'ml', 2), ('explosion/thinc', 0.5279266834259033, 'ml-dl', 1), ('online-ml/river', 0.5276996493339539, 'ml', 2), ('rasbt/stat451-machine-learning-fs20', 0.5267770886421204, 'study', 0), ('wandb/client', 0.5243285894393921, 'ml', 2), ('dagworks-inc/hamilton', 0.5236888527870178, 'ml-ops', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5232796669006348, 'study', 1), ('probml/pyprobml', 0.5219587087631226, 'ml', 1), ('keras-team/keras', 0.5215654969215393, 'ml-dl', 2), ('kubeflow/pipelines', 0.5197041034698486, 'ml-ops', 2), ('fatiando/verde', 0.5189895629882812, 'gis', 1), ('d2l-ai/d2l-en', 0.5169808268547058, 'study', 2), ('scikit-learn-contrib/imbalanced-learn', 0.5154035687446594, 'ml', 2), ('google/tf-quant-finance', 0.5136438012123108, 'finance', 0), ('hazyresearch/meerkat', 0.5096346735954285, 'viz', 2), ('drivendata/cookiecutter-data-science', 0.5075307488441467, 'template', 2), ('wesm/pydata-book', 0.5071250200271606, 'study', 0), ('netflix/metaflow', 0.5058495402336121, 'ml-ops', 3), ('adap/flower', 0.5053890347480774, 'ml-ops', 1), ('doccano/doccano', 0.50450199842453, 'nlp', 1), ('milvus-io/bootcamp', 0.5041669011116028, 'data', 0), ('pycaret/pycaret', 0.5026911497116089, 'ml', 2), ('ploomber/ploomber', 0.5007092952728271, 'ml-ops', 2)] | 6 | 4 | null | 0 | 0 | 0 | 57 | 25 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 37 |
1,022 | finance | https://github.com/google/tf-quant-finance | [] | null | [] | [] | null | null | null | google/tf-quant-finance | tf-quant-finance | 4,160 | 545 | 168 | Python | null | High-performance TensorFlow library for quantitative finance. | google | 2024-01-14 | 2019-07-24 | 235 | 17.637795 | https://avatars.githubusercontent.com/u/1342004?v=4 | High-performance TensorFlow library for quantitative finance. | ['finance', 'gpu', 'gpu-computing', 'high-performance', 'high-performance-computing', 'numerical-integration', 'numerical-methods', 'numerical-optimization', 'quantitative-finance', 'quantlib', 'tensorflow'] | ['finance', 'gpu', 'gpu-computing', 'high-performance', 'high-performance-computing', 'numerical-integration', 'numerical-methods', 'numerical-optimization', 'quantitative-finance', 'quantlib', 'tensorflow'] | 2023-08-15 | [('tensorly/tensorly', 0.636419951915741, 'ml-dl', 1), ('pytorch/pytorch', 0.6339718103408813, 'ml-dl', 1), ('goldmansachs/gs-quant', 0.6332827806472778, 'finance', 0), ('intel/intel-extension-for-pytorch', 0.6181202530860901, 'perf', 0), ('ggerganov/ggml', 0.6140791773796082, 'ml', 0), ('arogozhnikov/einops', 0.6102033853530884, 'ml-dl', 1), ('nvidia/tensorrt-llm', 0.6080841422080994, 'viz', 1), ('horovod/horovod', 0.6027436852455139, 'ml-ops', 1), ('tensorlayer/tensorlayer', 0.5923408269882202, 'ml-rl', 1), ('xl0/lovely-tensors', 0.5893839597702026, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5870956182479858, 'ml', 1), ('tlkh/tf-metal-experiments', 0.5859279036521912, 'perf', 2), ('tensorflow/tensorflow', 0.5797778367996216, 'ml-dl', 1), ('catboost/catboost', 0.5715440511703491, 'ml', 2), ('ray-project/ray', 0.5673449039459229, 'ml-ops', 1), ('tensorflow/addons', 0.5670149326324463, 'ml', 1), ('microsoft/deepspeed', 0.5623762011528015, 'ml-dl', 1), ('google/gin-config', 0.5610681772232056, 'util', 1), ('rafiqhasan/auto-tensorflow', 0.5538135766983032, 'ml-dl', 1), ('tensorflow/similarity', 0.552481472492218, 'ml-dl', 1), ('ranaroussi/quantstats', 0.5515581369400024, 'finance', 2), ('pytorch/ignite', 0.5492528676986694, 'ml-dl', 0), ('ta-lib/ta-lib-python', 0.5487057566642761, 'finance', 2), ('huggingface/datasets', 0.5474328994750977, 'nlp', 1), ('determined-ai/determined', 0.5471197366714478, 'ml-ops', 1), ('aws/sagemaker-python-sdk', 0.5465176105499268, 'ml', 1), ('zvtvz/zvt', 0.5456732511520386, 'finance', 1), ('polyaxon/datatile', 0.5451685190200806, 'pandas', 1), ('microsoft/qlib', 0.5447477698326111, 'finance', 2), ('ai4finance-foundation/finrl', 0.54345703125, 'finance', 1), ('explosion/thinc', 0.5434539318084717, 'ml-dl', 1), ('keras-team/keras', 0.5417336821556091, 'ml-dl', 1), ('blackhc/toma', 0.5406495928764343, 'ml-dl', 1), ('nvidia/warp', 0.5373433828353882, 'sim', 1), ('pytorchlightning/pytorch-lightning', 0.534396767616272, 'ml-dl', 0), ('fastai/fastcore', 0.5327866077423096, 'util', 0), ('eventual-inc/daft', 0.5302774906158447, 'pandas', 0), ('dmlc/xgboost', 0.5295057892799377, 'ml', 0), ('d2l-ai/d2l-en', 0.5287189483642578, 'study', 1), ('mrdbourke/m1-machine-learning-test', 0.5282031893730164, 'ml', 1), ('intel/scikit-learn-intelex', 0.5270321369171143, 'perf', 1), ('activeloopai/deeplake', 0.5263845324516296, 'ml-ops', 1), ('rapidsai/cudf', 0.5263254046440125, 'pandas', 1), ('pytorch/torchrec', 0.5250528454780579, 'ml-dl', 1), ('cupy/cupy', 0.5244703888893127, 'math', 1), ('quantconnect/lean', 0.5243059992790222, 'finance', 1), ('rasbt/machine-learning-book', 0.523298442363739, 'study', 0), ('cython/cython', 0.5225537419319153, 'util', 0), ('isl-org/open3d', 0.5206282138824463, 'sim', 2), ('huggingface/transformers', 0.5193233489990234, 'nlp', 1), ('polakowo/vectorbt', 0.5182610154151917, 'finance', 2), ('googlecloudplatform/vertex-ai-samples', 0.5174439549446106, 'ml', 0), ('plasma-umass/scalene', 0.5155574083328247, 'profiling', 1), ('gradio-app/gradio', 0.5152886509895325, 'viz', 0), ('dylanhogg/awesome-python', 0.5142082571983337, 'study', 0), ('firmai/industry-machine-learning', 0.5136438012123108, 'study', 0), ('ashleve/lightning-hydra-template', 0.5111380219459534, 'util', 0), ('gbeced/pyalgotrade', 0.5105053186416626, 'finance', 0), ('ddbourgin/numpy-ml', 0.509579598903656, 'ml', 0), ('huggingface/accelerate', 0.5086351633071899, 'ml', 0), ('exaloop/codon', 0.5077102780342102, 'perf', 1), ('pytorch/glow', 0.5075867772102356, 'ml', 0), ('keras-team/autokeras', 0.5068373680114746, 'ml-dl', 1), ('merantix-momentum/squirrel-core', 0.5065999031066895, 'ml', 1), ('pytorch/rl', 0.5055859088897705, 'ml-rl', 0), ('pycaret/pycaret', 0.5044597387313843, 'ml', 1), ('pypy/pypy', 0.5041675567626953, 'util', 0), ('salesforce/warp-drive', 0.5037093162536621, 'ml-rl', 1), ('mrdbourke/tensorflow-deep-learning', 0.5032038688659668, 'study', 1), ('uber/petastorm', 0.5018066167831421, 'data', 1), ('nyandwi/modernconvnets', 0.501559853553772, 'ml-dl', 1)] | 47 | 2 | null | 0.29 | 0 | 0 | 54 | 5 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 37 |
460 | util | https://github.com/rspeer/python-ftfy | [] | null | [] | [] | null | null | null | rspeer/python-ftfy | python-ftfy | 3,647 | 153 | 76 | Python | http://ftfy.readthedocs.org | Fixes mojibake and other glitches in Unicode text, after the fact. | rspeer | 2024-01-12 | 2012-08-24 | 596 | 6.113266 | null | Fixes mojibake and other glitches in Unicode text, after the fact. | [] | [] | 2023-11-21 | [] | 18 | 6 | null | 0.1 | 1 | 0 | 139 | 2 | 0 | 12 | 12 | 1 | 0 | 90 | 0 | 37 |
86 | graph | https://github.com/stellargraph/stellargraph | [] | null | [] | [] | null | null | null | stellargraph/stellargraph | stellargraph | 2,836 | 418 | 64 | Python | https://stellargraph.readthedocs.io/ | StellarGraph - Machine Learning on Graphs | stellargraph | 2024-01-13 | 2018-04-13 | 302 | 9.372993 | https://avatars.githubusercontent.com/u/36725857?v=4 | StellarGraph - Machine Learning on Graphs | ['data-science', 'deep-learning', 'gcn', 'geometric-deep-learning', 'graph-analysis', 'graph-convolutional-networks', 'graph-data', 'graph-machine-learning', 'graph-neural-networks', 'graphs', 'heterogeneous-networks', 'interpretability', 'link-prediction', 'machine-learning', 'machine-learning-algorithms', 'networkx', 'saliency-map', 'stellargraph-library'] | ['data-science', 'deep-learning', 'gcn', 'geometric-deep-learning', 'graph-analysis', 'graph-convolutional-networks', 'graph-data', 'graph-machine-learning', 'graph-neural-networks', 'graphs', 'heterogeneous-networks', 'interpretability', 'link-prediction', 'machine-learning', 'machine-learning-algorithms', 'networkx', 'saliency-map', 'stellargraph-library'] | 2021-10-29 | [('chandlerbang/awesome-self-supervised-gnn', 0.6943688988685608, 'study', 3), ('danielegrattarola/spektral', 0.6824637055397034, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.6726529002189636, 'ml-dl', 4), ('dmlc/dgl', 0.6574554443359375, 'ml-dl', 2), ('google-deepmind/materials_discovery', 0.6555060148239136, 'sim', 0), ('benedekrozemberczki/tigerlily', 0.6446079015731812, 'ml-dl', 3), ('a-r-j/graphein', 0.6164317727088928, 'sim', 3), ('graphistry/pygraphistry', 0.5959926247596741, 'data', 1), ('rampasek/graphgps', 0.5865841507911682, 'graph', 0), ('accenture/ampligraph', 0.5474543571472168, 'data', 1), ('networkx/networkx', 0.5425050258636475, 'graph', 1), ('googlecloudplatform/vertex-ai-samples', 0.5413510799407959, 'ml', 1), ('ddbourgin/numpy-ml', 0.5346408486366272, 'ml', 1), ('onnx/onnx', 0.5222296714782715, 'ml', 2), ('awslabs/dgl-ke', 0.5202558040618896, 'ml', 1), ('lutzroeder/netron', 0.514754056930542, 'ml', 2), ('tensorflow/tensorflow', 0.5133888125419617, 'ml-dl', 2), ('pygraphviz/pygraphviz', 0.5024316310882568, 'viz', 0), ('hazyresearch/hgcn', 0.500386118888855, 'ml', 0)] | 36 | 6 | null | 0 | 5 | 1 | 70 | 27 | 0 | 5 | 5 | 5 | 2 | 90 | 0.4 | 37 |
818 | study | https://github.com/alirezadir/machine-learning-interview-enlightener | [] | null | [] | [] | null | null | null | alirezadir/machine-learning-interview-enlightener | Machine-Learning-Interviews | 2,645 | 494 | 53 | Jupyter Notebook | null | This repo is meant to serve as a guide for Machine Learning/AI technical interviews. | alirezadir | 2024-01-14 | 2021-01-31 | 156 | 16.924132 | null | This repo is meant to serve as a guide for Machine Learning/AI technical interviews. | ['ai', 'deep-learning', 'interview', 'interview-practice', 'interview-preparation', 'interviews', 'machine-learning', 'machine-learning-algorithms', 'scalable-applications', 'system-design'] | ['ai', 'deep-learning', 'interview', 'interview-practice', 'interview-preparation', 'interviews', 'machine-learning', 'machine-learning-algorithms', 'scalable-applications', 'system-design'] | 2023-10-26 | [('bentoml/bentoml', 0.657772958278656, 'ml-ops', 3), ('google-research/google-research', 0.6368386745452881, 'ml', 2), ('google-research/language', 0.6070800423622131, 'nlp', 1), ('amanchadha/coursera-deep-learning-specialization', 0.6021793484687805, 'study', 1), ('patchy631/machine-learning', 0.5960847735404968, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.5835177302360535, 'ml', 1), ('oegedijk/explainerdashboard', 0.5830463171005249, 'ml-interpretability', 0), ('xplainable/xplainable', 0.5782299637794495, 'ml-interpretability', 2), ('tensorflow/tensorflow', 0.5763082504272461, 'ml-dl', 2), ('microsoft/nni', 0.5761905312538147, 'ml', 3), ('onnx/onnx', 0.5712395906448364, 'ml', 2), ('tensorlayer/tensorlayer', 0.5669152140617371, 'ml-rl', 1), ('mlflow/mlflow', 0.5668816566467285, 'ml-ops', 2), ('tensorflow/tensor2tensor', 0.5649195909500122, 'ml', 2), ('polyaxon/polyaxon', 0.564633309841156, 'ml-ops', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5615020394325256, 'study', 2), ('firmai/industry-machine-learning', 0.5614524483680725, 'study', 1), ('wandb/client', 0.5583623051643372, 'ml', 2), ('explosion/thinc', 0.5558744668960571, 'ml-dl', 3), ('mindsdb/mindsdb', 0.5548688173294067, 'data', 2), ('feast-dev/feast', 0.5510007739067078, 'ml-ops', 1), ('netflix/metaflow', 0.5488802194595337, 'ml-ops', 2), ('aimhubio/aim', 0.5477184653282166, 'ml-ops', 2), ('doccano/doccano', 0.5449758768081665, 'nlp', 1), ('deepmind/dm_control', 0.5414921641349792, 'ml-rl', 2), ('nvidia/nemo', 0.5406086444854736, 'nlp', 1), ('lastmile-ai/aiconfig', 0.5404054522514343, 'util', 1), ('cheshire-cat-ai/core', 0.539746105670929, 'llm', 1), ('activeloopai/deeplake', 0.5346347689628601, 'ml-ops', 3), ('keras-team/keras', 0.5340282917022705, 'ml-dl', 2), ('winedarksea/autots', 0.5339345335960388, 'time-series', 2), ('cleanlab/cleanlab', 0.5334048867225647, 'ml', 0), ('determined-ai/determined', 0.5306956768035889, 'ml-ops', 2), ('ml-tooling/opyrator', 0.5304664969444275, 'viz', 1), ('antonosika/gpt-engineer', 0.527275800704956, 'llm', 1), ('avaiga/taipy', 0.5267717242240906, 'data', 0), ('gradio-app/gradio', 0.5261391401290894, 'viz', 2), ('keras-rl/keras-rl', 0.5244634747505188, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.5230525732040405, 'ml-rl', 2), ('cerlymarco/medium_notebook', 0.5219733119010925, 'study', 2), ('polyaxon/datatile', 0.521611213684082, 'pandas', 0), ('csinva/imodels', 0.5202349424362183, 'ml', 2), ('sweepai/sweep', 0.5161364078521729, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5155435800552368, 'nlp', 0), ('iterative/dvc', 0.5138351917266846, 'ml-ops', 2), ('hpcaitech/colossalai', 0.5127544403076172, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5122131109237671, 'ml-dl', 3), ('qdrant/qdrant', 0.5117506384849548, 'data', 1), ('automl/auto-sklearn', 0.5101978778839111, 'ml', 0), ('interpretml/interpret', 0.5096079707145691, 'ml-interpretability', 2), ('salesforce/logai', 0.5083866119384766, 'util', 2), ('nccr-itmo/fedot', 0.5080487728118896, 'ml-ops', 1), ('seldonio/alibi', 0.5047518014907837, 'ml-interpretability', 1), ('microsoft/onnxruntime', 0.5041807293891907, 'ml', 2), ('ourownstory/neural_prophet', 0.503267228603363, 'ml', 2), ('ddbourgin/numpy-ml', 0.5029667019844055, 'ml', 1), ('microsoft/generative-ai-for-beginners', 0.5020521283149719, 'study', 1), ('jindongwang/transferlearning', 0.5019001364707947, 'ml', 2), ('marqo-ai/marqo', 0.5009713768959045, 'ml', 2), ('oneil512/insight', 0.5009233355522156, 'ml', 1), ('kubeflow/pipelines', 0.5008544921875, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5003179907798767, 'ml-dl', 1)] | 7 | 4 | null | 1.33 | 1 | 0 | 36 | 3 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 37 |
704 | pandas | https://github.com/jmcarpenter2/swifter | [] | null | [] | [] | null | null | null | jmcarpenter2/swifter | swifter | 2,402 | 101 | 31 | Python | null | A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner | jmcarpenter2 | 2024-01-12 | 2018-04-07 | 303 | 7.916196 | null | A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner | ['dask', 'modin', 'pandas', 'pandas-dataframe', 'parallel-computing', 'parallelization'] | ['dask', 'modin', 'pandas', 'pandas-dataframe', 'parallel-computing', 'parallelization'] | 2023-07-31 | [('nalepae/pandarallel', 0.7605847716331482, 'pandas', 1), ('ddelange/mapply', 0.6554047465324402, 'pandas', 0), ('modin-project/modin', 0.6068665385246277, 'perf', 2), ('dask/dask', 0.60169517993927, 'perf', 2), ('rapidsai/cudf', 0.5688180923461914, 'pandas', 2), ('mementum/bta-lib', 0.5637902021408081, 'finance', 0), ('blaze/blaze', 0.5631571412086487, 'pandas', 0), ('holoviz/spatialpandas', 0.562759518623352, 'pandas', 1), ('lux-org/lux', 0.5564085841178894, 'viz', 1), ('joblib/joblib', 0.5563712120056152, 'util', 1), ('eventual-inc/daft', 0.55370032787323, 'pandas', 0), ('adamerose/pandasgui', 0.5478309988975525, 'pandas', 1), ('vaexio/vaex', 0.5449737906455994, 'perf', 0), ('pandas-dev/pandas', 0.5424719452857971, 'pandas', 1), ('tkrabel/bamboolib', 0.5419985055923462, 'pandas', 1), ('twopirllc/pandas-ta', 0.539636492729187, 'finance', 1), ('fugue-project/fugue', 0.5381412506103516, 'pandas', 2), ('sfu-db/connector-x', 0.5345582962036133, 'data', 0), ('pola-rs/polars', 0.5324315428733826, 'pandas', 0), ('pytoolz/toolz', 0.5265946388244629, 'util', 0), ('klen/py-frameworks-bench', 0.519839346408844, 'perf', 0), ('pytables/pytables', 0.5137337446212769, 'data', 0), ('fastai/fastcore', 0.5132153034210205, 'util', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5050438046455383, 'pandas', 0)] | 17 | 4 | null | 0.33 | 5 | 0 | 70 | 6 | 0 | 14 | 14 | 5 | 2 | 90 | 0.4 | 37 |
956 | ml-dl | https://github.com/danielegrattarola/spektral | [] | null | ['2006.12138'] | [] | null | null | null | danielegrattarola/spektral | spektral | 2,314 | 337 | 44 | Python | https://graphneural.network | Graph Neural Networks with Keras and Tensorflow 2. | danielegrattarola | 2024-01-13 | 2019-01-17 | 262 | 8.808048 | null | Graph Neural Networks with Keras and Tensorflow 2. | ['deep-learning', 'graph-deep-learning', 'graph-neural-networks', 'keras', 'tensorflow', 'tensorflow2'] | ['deep-learning', 'graph-deep-learning', 'graph-neural-networks', 'keras', 'tensorflow', 'tensorflow2'] | 2023-06-01 | [('pyg-team/pytorch_geometric', 0.7439136505126953, 'ml-dl', 2), ('dmlc/dgl', 0.6934017539024353, 'ml-dl', 2), ('stellargraph/stellargraph', 0.6824637055397034, 'graph', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.6770707964897156, 'study', 2), ('nyandwi/modernconvnets', 0.6024564504623413, 'ml-dl', 2), ('rampasek/graphgps', 0.5940183997154236, 'graph', 0), ('keras-rl/keras-rl', 0.5715480446815491, 'ml-rl', 2), ('keras-team/keras', 0.5592799186706543, 'ml-dl', 2), ('benedekrozemberczki/tigerlily', 0.5574583411216736, 'ml-dl', 1), ('tensorflow/addons', 0.5564872026443481, 'ml', 2), ('hazyresearch/hgcn', 0.5537418723106384, 'ml', 0), ('a-r-j/graphein', 0.551216721534729, 'sim', 2), ('googlecloudplatform/vertex-ai-samples', 0.545731782913208, 'ml', 0), ('lutzroeder/netron', 0.5387458205223083, 'ml', 3), ('google-deepmind/materials_discovery', 0.5381258130073547, 'sim', 0), ('graphistry/pygraphistry', 0.5304725170135498, 'data', 0), ('onnx/onnx', 0.5261642932891846, 'ml', 3), ('tensorflow/tensorflow', 0.5244050025939941, 'ml-dl', 2), ('cvxgrp/pymde', 0.520326554775238, 'ml', 0), ('tensorlayer/tensorlayer', 0.5144057869911194, 'ml-rl', 2), ('keras-team/keras-nlp', 0.5112001299858093, 'nlp', 3), ('horovod/horovod', 0.5111513733863831, 'ml-ops', 3), ('ddbourgin/numpy-ml', 0.5083205699920654, 'ml', 0), ('xl0/lovely-tensors', 0.5056509375572205, 'ml-dl', 1), ('accenture/ampligraph', 0.5038337707519531, 'data', 0), ('tensorly/tensorly', 0.5032951831817627, 'ml-dl', 1)] | 27 | 3 | null | 0.33 | 3 | 1 | 61 | 8 | 1 | 1 | 1 | 3 | 5 | 90 | 1.7 | 37 |
255 | crypto | https://github.com/bmoscon/cryptofeed | [] | null | [] | [] | null | null | null | bmoscon/cryptofeed | cryptofeed | 1,973 | 712 | 79 | Python | null | Cryptocurrency Exchange Websocket Data Feed Handler | bmoscon | 2024-01-13 | 2017-12-16 | 319 | 6.176655 | null | Cryptocurrency Exchange Websocket Data Feed Handler | ['asyncio', 'binance', 'bitcoin', 'btc', 'coinbase', 'coinbase-api', 'crypto', 'cryptocurrencies', 'cryptocurrency', 'ethereum', 'exchange', 'ftx-exchange', 'influxdb', 'market-data', 'trading', 'trading-platform', 'websocket', 'websockets'] | ['asyncio', 'binance', 'bitcoin', 'btc', 'coinbase', 'coinbase-api', 'crypto', 'cryptocurrencies', 'cryptocurrency', 'ethereum', 'exchange', 'ftx-exchange', 'influxdb', 'market-data', 'trading', 'trading-platform', 'websocket', 'websockets'] | 2024-01-07 | [('ccxt/ccxt', 0.6092362999916077, 'crypto', 9), ('miguelgrinberg/python-socketio', 0.595906138420105, 'util', 2), ('freqtrade/freqtrade', 0.5561859011650085, 'crypto', 3), ('websocket-client/websocket-client', 0.5356486439704895, 'web', 2), ('pmaji/crypto-whale-watching-app', 0.5131617188453674, 'crypto', 3), ('gbeced/basana', 0.5091555118560791, 'finance', 3)] | 112 | 0 | null | 0.71 | 16 | 11 | 74 | 0 | 2 | 12 | 2 | 16 | 13 | 90 | 0.8 | 37 |
1,684 | util | https://github.com/landscapeio/prospector | ['linting', 'styling'] | null | [] | [] | null | null | null | landscapeio/prospector | prospector | 1,882 | 176 | 35 | Python | null | Inspects Python source files and provides information about type and location of classes, methods etc | landscapeio | 2024-01-12 | 2013-08-05 | 547 | 3.439687 | https://avatars.githubusercontent.com/u/4759094?v=4 | Inspects Python source files and provides information about type and location of classes, methods etc | [] | ['linting', 'styling'] | 2023-10-18 | [('eugeneyan/python-collab-template', 0.6496816873550415, 'template', 1), ('google/pytype', 0.6452606916427612, 'typing', 0), ('pympler/pympler', 0.6098625659942627, 'perf', 0), ('hadialqattan/pycln', 0.6040316224098206, 'util', 0), ('nedbat/coveragepy', 0.6040084362030029, 'testing', 0), ('gaogaotiantian/viztracer', 0.6018545627593994, 'profiling', 0), ('pyutils/line_profiler', 0.5992322564125061, 'profiling', 0), ('mkdocstrings/griffe', 0.5961143970489502, 'util', 0), ('facebook/pyre-check', 0.5935577154159546, 'typing', 0), ('klen/pylama', 0.5915239453315735, 'util', 0), ('pytoolz/toolz', 0.5871560573577881, 'util', 0), ('urwid/urwid', 0.586963951587677, 'term', 0), ('python/cpython', 0.5828319787979126, 'util', 0), ('jiffyclub/snakeviz', 0.582079291343689, 'profiling', 0), ('hhatto/autopep8', 0.5784581303596497, 'util', 0), ('astral-sh/ruff', 0.5776445269584656, 'util', 0), ('eleutherai/pyfra', 0.5767074227333069, 'ml', 0), ('brandon-rhodes/python-patterns', 0.5751134157180786, 'util', 0), ('pycqa/isort', 0.574863851070404, 'util', 0), ('instagram/monkeytype', 0.5743587017059326, 'typing', 0), ('rubik/radon', 0.5695351362228394, 'util', 0), ('google/yapf', 0.5676681995391846, 'util', 0), ('python-rope/rope', 0.5660980939865112, 'util', 0), ('pycqa/pyflakes', 0.5659628510475159, 'util', 0), ('mitmproxy/pdoc', 0.5606246590614319, 'util', 0), ('tiangolo/typer', 0.558975100517273, 'term', 0), ('xrudelis/pytrait', 0.5579221248626709, 'util', 0), ('wesm/pydata-book', 0.5570473670959473, 'study', 0), ('grantjenks/blue', 0.5565185546875, 'util', 0), ('requests/toolbelt', 0.5488244891166687, 'util', 0), ('amaargiru/pyroad', 0.5477283596992493, 'study', 0), ('alexmojaki/snoop', 0.5469942688941956, 'debug', 0), ('pypi/warehouse', 0.545502245426178, 'util', 0), ('psf/black', 0.5413801670074463, 'util', 0), ('hoffstadt/dearpygui', 0.5395547747612, 'gui', 0), ('ta-lib/ta-lib-python', 0.5358104109764099, 'finance', 0), ('python/mypy', 0.5345390439033508, 'typing', 0), ('pypa/hatch', 0.5343112945556641, 'util', 0), ('samuelcolvin/python-devtools', 0.5334382057189941, 'debug', 0), ('microsoft/pyright', 0.5327200293540955, 'typing', 0), ('pypy/pypy', 0.5321429371833801, 'util', 0), ('pycqa/pycodestyle', 0.531697690486908, 'util', 0), ('pycqa/eradicate', 0.5313110947608948, 'util', 2), ('pythonprofilers/memory_profiler', 0.5307861566543579, 'profiling', 0), ('sourcery-ai/sourcery', 0.5299766659736633, 'util', 0), ('agronholm/typeguard', 0.528834879398346, 'typing', 0), ('pygments/pygments', 0.5254445672035217, 'util', 0), ('pycqa/flake8', 0.5223195552825928, 'util', 0), ('instagram/fixit', 0.5204662084579468, 'util', 0), ('erotemic/ubelt', 0.518781840801239, 'util', 0), ('beeware/toga', 0.517236590385437, 'gui', 0), ('google/python-fire', 0.5145845413208008, 'term', 0), ('googleapis/google-api-python-client', 0.5145336389541626, 'util', 0), ('python-attrs/attrs', 0.5136226415634155, 'typing', 0), ('facebookincubator/bowler', 0.5116630792617798, 'util', 0), ('python/typeshed', 0.509270191192627, 'typing', 0), ('dosisod/refurb', 0.5087876915931702, 'util', 0), ('pycqa/pylint-django', 0.5079518556594849, 'util', 0), ('roniemartinez/dude', 0.5066967606544495, 'util', 0), ('willmcgugan/textual', 0.5064331889152527, 'term', 0), ('pyglet/pyglet', 0.5031312108039856, 'gamedev', 0)] | 90 | 3 | null | 0.88 | 15 | 2 | 127 | 3 | 4 | 10 | 4 | 15 | 23 | 90 | 1.5 | 37 |
298 | util | https://github.com/julienpalard/pipe | [] | null | [] | [] | null | null | null | julienpalard/pipe | Pipe | 1,797 | 111 | 26 | Python | null | A Python library to use infix notation in Python | julienpalard | 2024-01-13 | 2010-04-08 | 720 | 2.49336 | null | A Python library to use infix notation in Python | [] | [] | 2024-01-07 | [('google/latexify_py', 0.5925378799438477, 'util', 0), ('pytoolz/toolz', 0.5835241079330444, 'util', 0), ('connorferster/handcalcs', 0.5392968058586121, 'jupyter', 0), ('pyston/pyston', 0.5197369456291199, 'util', 0), ('sympy/sympy', 0.5134634971618652, 'math', 0), ('pmorissette/ffn', 0.5104817748069763, 'finance', 0), ('geospatialpython/pyshp', 0.5082536935806274, 'gis', 0)] | 29 | 4 | null | 0.17 | 9 | 6 | 168 | 0 | 0 | 1 | 1 | 9 | 22 | 90 | 2.4 | 37 |
591 | data | https://github.com/uber/petastorm | [] | null | [] | [] | null | null | null | uber/petastorm | petastorm | 1,711 | 280 | 41 | Python | null | Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. | uber | 2024-01-12 | 2018-06-15 | 293 | 5.828224 | https://avatars.githubusercontent.com/u/538264?v=4 | Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. | ['deep-learning', 'machine-learning', 'parquet', 'parquet-files', 'pyarrow', 'pyspark', 'pytorch', 'sysml', 'tensorflow'] | ['deep-learning', 'machine-learning', 'parquet', 'parquet-files', 'pyarrow', 'pyspark', 'pytorch', 'sysml', 'tensorflow'] | 2023-12-02 | [('microsoft/deepspeed', 0.6462931632995605, 'ml-dl', 3), ('horovod/horovod', 0.6362690329551697, 'ml-ops', 4), ('determined-ai/determined', 0.6062763929367065, 'ml-ops', 4), ('ageron/handson-ml2', 0.5999022126197815, 'ml', 0), ('gradio-app/gradio', 0.5904715061187744, 'viz', 2), ('pytorch/ignite', 0.5855341553688049, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5829108953475952, 'ml-dl', 3), ('merantix-momentum/squirrel-core', 0.5779027938842773, 'ml', 4), ('tensorflow/tensor2tensor', 0.5750295519828796, 'ml', 2), ('ashleve/lightning-hydra-template', 0.5747993588447571, 'util', 2), ('aws/sagemaker-python-sdk', 0.5683594346046448, 'ml', 3), ('rasbt/machine-learning-book', 0.5682684183120728, 'study', 3), ('paddlepaddle/paddle', 0.5659868717193604, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.5632723569869995, 'perf', 3), ('fchollet/deep-learning-with-python-notebooks', 0.5607595443725586, 'study', 0), ('mlflow/mlflow', 0.559465765953064, 'ml-ops', 1), ('huggingface/huggingface_hub', 0.5579898357391357, 'ml', 3), ('nvidia/deeplearningexamples', 0.5578290820121765, 'ml-dl', 3), ('eventual-inc/daft', 0.557521641254425, 'pandas', 2), ('deepmind/dm-haiku', 0.557473361492157, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5541288256645203, 'ml-dl', 2), ('karpathy/micrograd', 0.5534528493881226, 'study', 0), ('huggingface/transformers', 0.5520153641700745, 'nlp', 4), ('oml-team/open-metric-learning', 0.5485543012619019, 'ml', 2), ('skorch-dev/skorch', 0.5473853945732117, 'ml-dl', 2), ('ggerganov/ggml', 0.5469512939453125, 'ml', 1), ('aiqc/aiqc', 0.5461896061897278, 'ml-ops', 0), ('dmlc/xgboost', 0.5380932688713074, 'ml', 1), ('firmai/industry-machine-learning', 0.5362139940261841, 'study', 1), ('microsoft/flaml', 0.5358170866966248, 'ml', 2), ('huggingface/datasets', 0.5324203372001648, 'nlp', 4), ('keras-team/autokeras', 0.5320855975151062, 'ml-dl', 3), ('deepmodeling/deepmd-kit', 0.5317656397819519, 'sim', 2), ('alpa-projects/alpa', 0.5310642719268799, 'ml-dl', 2), ('ray-project/ray', 0.5304347276687622, 'ml-ops', 4), ('kubeflow/fairing', 0.529427170753479, 'ml-ops', 0), ('aistream-peelout/flow-forecast', 0.5273684859275818, 'time-series', 2), ('microsoft/jarvis', 0.5265276432037354, 'llm', 2), ('apache/incubator-mxnet', 0.5250005722045898, 'ml-dl', 0), ('adap/flower', 0.5249413847923279, 'ml-ops', 4), ('towhee-io/towhee', 0.5247065424919128, 'ml-ops', 1), ('mrdbourke/pytorch-deep-learning', 0.5223989486694336, 'study', 3), ('lightly-ai/lightly', 0.5194175243377686, 'ml', 3), ('keras-team/keras', 0.5165746808052063, 'ml-dl', 4), ('explosion/thinc', 0.5144953727722168, 'ml-dl', 4), ('facebookresearch/pytorch3d', 0.5134731531143188, 'ml-dl', 0), ('deepchecks/deepchecks', 0.5133116245269775, 'data', 3), ('pytorch/torchrec', 0.5117344260215759, 'ml-dl', 2), ('google/trax', 0.5106483101844788, 'ml-dl', 2), ('ml-tooling/opyrator', 0.5089090466499329, 'viz', 1), ('dmlc/dgl', 0.5088360905647278, 'ml-dl', 1), ('microsoft/nni', 0.508219301700592, 'ml', 4), ('pytorch/data', 0.5076653361320496, 'data', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5072482824325562, 'ml', 3), ('tensorlayer/tensorlayer', 0.5072025060653687, 'ml-rl', 2), ('arogozhnikov/einops', 0.5071843862533569, 'ml-dl', 3), ('dylanhogg/awesome-python', 0.5067077875137329, 'study', 2), ('titanml/takeoff', 0.5053594708442688, 'llm', 0), ('nvidia/apex', 0.5052030086517334, 'ml-dl', 0), ('mosaicml/composer', 0.5039240121841431, 'ml-dl', 3), ('fastai/fastcore', 0.5022857189178467, 'util', 0), ('google/tf-quant-finance', 0.5018066167831421, 'finance', 1), ('pyg-team/pytorch_geometric', 0.5014254450798035, 'ml-dl', 2), ('catboost/catboost', 0.5008516311645508, 'ml', 1), ('vaexio/vaex', 0.500319242477417, 'perf', 2), ('pycaret/pycaret', 0.5002906322479248, 'ml', 1)] | 50 | 4 | null | 0.06 | 4 | 2 | 68 | 1 | 1 | 20 | 1 | 4 | 4 | 90 | 1 | 37 |
1,004 | finance | https://github.com/pmorissette/ffn | [] | null | [] | [] | null | null | null | pmorissette/ffn | ffn | 1,697 | 283 | 59 | Python | pmorissette.github.io/ffn | ffn - a financial function library for Python | pmorissette | 2024-01-13 | 2014-06-19 | 501 | 3.382403 | null | ffn - a financial function library for Python | [] | [] | 2023-12-31 | [('pytoolz/toolz', 0.6924606561660767, 'util', 0), ('domokane/financepy', 0.6907992959022522, 'finance', 0), ('goldmansachs/gs-quant', 0.6325653195381165, 'finance', 0), ('gbeced/pyalgotrade', 0.628166913986206, 'finance', 0), ('ta-lib/ta-lib-python', 0.5941312909126282, 'finance', 0), ('fredrik-johansson/mpmath', 0.58493971824646, 'math', 0), ('daxm/fmpsdk', 0.5664402842521667, 'finance', 0), ('hydrosquall/tiingo-python', 0.5629584789276123, 'finance', 0), ('quantecon/quantecon.py', 0.559691309928894, 'sim', 0), ('ethtx/ethtx', 0.5535359978675842, 'crypto', 0), ('cuemacro/finmarketpy', 0.5505915284156799, 'finance', 0), ('firmai/atspy', 0.5481270551681519, 'time-series', 0), ('robcarver17/pysystemtrade', 0.5440186262130737, 'finance', 0), ('quantopian/pyfolio', 0.5423455834388733, 'finance', 0), ('connorferster/handcalcs', 0.5378669500350952, 'jupyter', 0), ('eleutherai/pyfra', 0.5368715524673462, 'ml', 0), ('alkaline-ml/pmdarima', 0.5328418612480164, 'time-series', 0), ('pandas-dev/pandas', 0.5297597050666809, 'pandas', 0), ('numpy/numpy', 0.5275481343269348, 'math', 0), ('stan-dev/pystan', 0.5250624418258667, 'ml', 0), ('quantopian/zipline', 0.5246074199676514, 'finance', 0), ('mementum/backtrader', 0.5245431661605835, 'finance', 0), ('primal100/pybitcointools', 0.5242424607276917, 'crypto', 0), ('1200wd/bitcoinlib', 0.5135668516159058, 'crypto', 0), ('cuemacro/findatapy', 0.5108827948570251, 'finance', 0), ('julienpalard/pipe', 0.5104817748069763, 'util', 0), ('google/latexify_py', 0.5060634016990662, 'util', 0), ('scipy/scipy', 0.502606987953186, 'math', 0), ('bashtage/arch', 0.5025431513786316, 'time-series', 0), ('pypy/pypy', 0.5018722414970398, 'util', 0), ('rjt1990/pyflux', 0.5010930895805359, 'time-series', 0)] | 32 | 3 | null | 0.44 | 22 | 18 | 117 | 0 | 4 | 1 | 4 | 22 | 15 | 90 | 0.7 | 37 |
629 | debug | https://github.com/alexmojaki/birdseye | [] | null | [] | [] | null | null | null | alexmojaki/birdseye | birdseye | 1,593 | 75 | 42 | JavaScript | https://birdseye.readthedocs.io | Graphical Python debugger which lets you easily view the values of all evaluated expressions | alexmojaki | 2024-01-13 | 2017-07-22 | 340 | 4.679396 | null | Graphical Python debugger which lets you easily view the values of all evaluated expressions | ['ast', 'birdseye', 'debugger', 'debugging', 'python-debugger'] | ['ast', 'birdseye', 'debugger', 'debugging', 'python-debugger'] | 2023-10-16 | [('alexmojaki/snoop', 0.6003603935241699, 'debug', 2), ('alexmojaki/heartrate', 0.5575025081634521, 'debug', 1), ('gaogaotiantian/viztracer', 0.5461402535438538, 'profiling', 1), ('samuelcolvin/python-devtools', 0.5242282748222351, 'debug', 0), ('google/pytype', 0.5039346218109131, 'typing', 0)] | 10 | 4 | null | 0.02 | 2 | 2 | 79 | 3 | 0 | 1 | 1 | 2 | 9 | 90 | 4.5 | 37 |
179 | nlp | https://github.com/explosion/spacy-models | [] | null | [] | [] | null | null | null | explosion/spacy-models | spacy-models | 1,465 | 301 | 47 | Python | https://spacy.io | 💫 Models for the spaCy Natural Language Processing (NLP) library | explosion | 2024-01-12 | 2017-03-14 | 359 | 4.08078 | https://avatars.githubusercontent.com/u/20011530?v=4 | 💫 Models for the spaCy Natural Language Processing (NLP) library | ['machine-learning', 'machine-learning-models', 'models', 'natural-language-processing', 'nlp', 'spacy', 'spacy-models', 'statistical-models'] | ['machine-learning', 'machine-learning-models', 'models', 'natural-language-processing', 'nlp', 'spacy', 'spacy-models', 'statistical-models'] | 2023-11-22 | [('explosion/spacy-stanza', 0.7454922199249268, 'nlp', 4), ('nltk/nltk', 0.6720556020736694, 'nlp', 3), ('huggingface/neuralcoref', 0.6682751774787903, 'nlp', 3), ('explosion/spacy-transformers', 0.6662450432777405, 'llm', 4), ('explosion/spacy-streamlit', 0.6529020071029663, 'nlp', 4), ('flairnlp/flair', 0.6428545117378235, 'nlp', 3), ('explosion/spacy', 0.6409233212471008, 'nlp', 4), ('allenai/allennlp', 0.6203604340553284, 'nlp', 2), ('iclrandd/blackstone', 0.612856388092041, 'nlp', 2), ('norskregnesentral/skweak', 0.608527660369873, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.5910124182701111, 'llm', 1), ('explosion/spacy-llm', 0.5904589295387268, 'llm', 4), ('sloria/textblob', 0.5832897424697876, 'nlp', 2), ('freedomintelligence/llmzoo', 0.5769272446632385, 'llm', 0), ('lm-sys/fastchat', 0.5767900943756104, 'llm', 0), ('rasahq/rasa', 0.5728164315223694, 'llm', 4), ('lianjiatech/belle', 0.5633299946784973, 'llm', 0), ('llmware-ai/llmware', 0.5522136092185974, 'llm', 2), ('hannibal046/awesome-llm', 0.5515271425247192, 'study', 0), ('deepset-ai/farm', 0.5511075854301453, 'nlp', 1), ('infinitylogesh/mutate', 0.5492627620697021, 'nlp', 0), ('yueyu1030/attrprompt', 0.5483715534210205, 'llm', 1), ('alibaba/easynlp', 0.5481346845626831, 'nlp', 2), ('qanastek/drbert', 0.5431317090988159, 'llm', 2), ('mooler0410/llmspracticalguide', 0.5413009524345398, 'study', 2), ('lexpredict/lexpredict-lexnlp', 0.5394803285598755, 'nlp', 1), ('huggingface/transformers', 0.5389872193336487, 'nlp', 3), ('keras-team/keras-nlp', 0.5345762372016907, 'nlp', 3), ('makcedward/nlpaug', 0.5314129590988159, 'nlp', 3), ('jonasgeiping/cramming', 0.5299116373062134, 'nlp', 1), ('juncongmoo/pyllama', 0.5298112630844116, 'llm', 0), ('graykode/nlp-tutorial', 0.5290482044219971, 'study', 2), ('thilinarajapakse/simpletransformers', 0.5240768790245056, 'nlp', 0), ('eleutherai/lm-evaluation-harness', 0.5173574686050415, 'llm', 0), ('koaning/whatlies', 0.5144206881523132, 'nlp', 1), ('jalammar/ecco', 0.513950765132904, 'ml-interpretability', 2), ('neuralmagic/sparseml', 0.5111375451087952, 'ml-dl', 1), ('explosion/thinc', 0.5104645490646362, 'ml-dl', 4), ('ai21labs/lm-evaluation', 0.5086509585380554, 'llm', 0), ('pemistahl/lingua-py', 0.5069782137870789, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5058228969573975, 'llm', 3), ('gunthercox/chatterbot-corpus', 0.5032789707183838, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5031586289405823, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5027161836624146, 'llm', 1), ('paddlepaddle/rocketqa', 0.5023024678230286, 'nlp', 1), ('openai/gpt-2', 0.5017908215522766, 'llm', 0), ('reasoning-machines/pal', 0.5003161430358887, 'llm', 0)] | 14 | 7 | null | 4.5 | 3 | 3 | 83 | 2 | 199 | 149 | 199 | 3 | 0 | 90 | 0 | 37 |
1,617 | util | https://github.com/samuelcolvin/watchfiles | [] | null | [] | [] | null | null | null | samuelcolvin/watchfiles | watchfiles | 1,435 | 99 | 18 | Python | https://watchfiles.helpmanual.io | Simple, modern and fast file watching and code reload in python. | samuelcolvin | 2024-01-14 | 2017-10-13 | 328 | 4.367391 | null | Simple, modern and fast file watching and code reload in python. | ['asyncio', 'filesystem', 'inotify', 'inotifywatch', 'notify', 'uvicorn'] | ['asyncio', 'filesystem', 'inotify', 'inotifywatch', 'notify', 'uvicorn'] | 2023-11-25 | [('airtai/faststream', 0.5354728698730469, 'perf', 1), ('tox-dev/py-filelock', 0.5285630822181702, 'util', 0), ('magicstack/uvloop', 0.5178032517433167, 'util', 1), ('timofurrer/awesome-asyncio', 0.5153577327728271, 'study', 1), ('erotemic/ubelt', 0.5087971091270447, 'util', 0), ('sumerc/yappi', 0.5043572187423706, 'profiling', 1), ('python-trio/trio', 0.5042653679847717, 'perf', 0), ('grantjenks/python-diskcache', 0.5041387677192688, 'util', 1), ('samuelcolvin/arq', 0.5021094679832458, 'data', 1)] | 40 | 3 | null | 0.25 | 10 | 4 | 76 | 2 | 3 | 5 | 3 | 10 | 15 | 90 | 1.5 | 37 |
645 | profiling | https://github.com/p403n1x87/austin | [] | null | [] | [] | null | null | null | p403n1x87/austin | austin | 1,311 | 40 | 17 | C | https://pypi.org/project/austin-dist/ | Python frame stack sampler for CPython | p403n1x87 | 2024-01-12 | 2018-09-20 | 279 | 4.686925 | null | Python frame stack sampler for CPython | ['debugging-tools', 'performance', 'profiling'] | ['debugging-tools', 'performance', 'profiling'] | 2023-10-04 | [('faster-cpython/tools', 0.6291638612747192, 'perf', 0), ('faster-cpython/ideas', 0.6115438938140869, 'perf', 0), ('benfred/py-spy', 0.5970548987388611, 'profiling', 1), ('brandtbucher/specialist', 0.5802419185638428, 'perf', 0), ('gotcha/ipdb', 0.5680873394012451, 'debug', 0), ('pympler/pympler', 0.5645143389701843, 'perf', 0), ('klen/py-frameworks-bench', 0.5635471343994141, 'perf', 0), ('python/cpython', 0.5600637197494507, 'util', 0), ('markshannon/faster-cpython', 0.5521705150604248, 'perf', 0), ('pyutils/line_profiler', 0.5507018566131592, 'profiling', 0), ('inducer/pudb', 0.5496501326560974, 'debug', 0), ('ipython/ipyparallel', 0.5462062954902649, 'perf', 0), ('alexmojaki/snoop', 0.5450026392936707, 'debug', 1), ('alexmojaki/heartrate', 0.5422681570053101, 'debug', 0), ('ionelmc/pytest-benchmark', 0.540304958820343, 'testing', 1), ('pytorch/data', 0.5269789695739746, 'data', 0), ('samuelcolvin/python-devtools', 0.523003876209259, 'debug', 0), ('sumerc/yappi', 0.5229708552360535, 'profiling', 1), ('pypy/pypy', 0.5207717418670654, 'util', 0), ('cython/cython', 0.5145069360733032, 'util', 1), ('lcompilers/lpython', 0.5144882798194885, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5097584128379822, 'study', 0), ('pythonspeed/filprofiler', 0.5042270421981812, 'profiling', 0), ('facebookincubator/cinder', 0.5041605830192566, 'perf', 0), ('asweigart/pyperclip', 0.5013977885246277, 'util', 0)] | 7 | 4 | null | 1.19 | 2 | 0 | 65 | 3 | 2 | 5 | 2 | 2 | 7 | 90 | 3.5 | 37 |
612 | testing | https://github.com/pytest-dev/pytest-bdd | [] | null | [] | [] | null | null | null | pytest-dev/pytest-bdd | pytest-bdd | 1,239 | 210 | 57 | Python | https://pytest-bdd.readthedocs.io/en/latest/ | BDD library for the py.test runner | pytest-dev | 2024-01-13 | 2013-03-29 | 565 | 2.190705 | https://avatars.githubusercontent.com/u/8897583?v=4 | BDD library for the py.test runner | [] | [] | 2023-12-02 | [('behave/behave', 0.6536642909049988, 'testing', 0), ('nedbat/coveragepy', 0.6206257939338684, 'testing', 0), ('pmorissette/bt', 0.5979208946228027, 'finance', 0), ('ionelmc/pytest-benchmark', 0.5788668394088745, 'testing', 0), ('wolever/parameterized', 0.5707379579544067, 'testing', 0), ('libtcod/python-tcod', 0.5265241265296936, 'gamedev', 0), ('microsoft/playwright-python', 0.5180904865264893, 'testing', 0), ('pytoolz/toolz', 0.5150437355041504, 'util', 0), ('teemu/pytest-sugar', 0.5115352869033813, 'testing', 0), ('pyodide/pyodide', 0.5067077279090881, 'util', 0), ('alexmojaki/snoop', 0.5047500133514404, 'debug', 0)] | 60 | 3 | null | 1.1 | 33 | 14 | 131 | 1 | 0 | 9 | 9 | 33 | 37 | 90 | 1.1 | 37 |
791 | data | https://github.com/jsonpickle/jsonpickle | [] | null | [] | [] | null | null | null | jsonpickle/jsonpickle | jsonpickle | 1,180 | 163 | 34 | Python | https://jsonpickle.readthedocs.io/en/latest/ | Python library for serializing any arbitrary object graph into JSON. It can take almost any Python object and turn the object into JSON. Additionally, it can reconstitute the object back into Python. | jsonpickle | 2024-01-13 | 2009-12-10 | 737 | 1.599535 | https://avatars.githubusercontent.com/u/165337?v=4 | Python library for serializing any arbitrary object graph into JSON. It can take almost any Python object and turn the object into JSON. Additionally, it can reconstitute the object back into Python. | ['bsd-3-clause', 'deserialization', 'json', 'objectstorage', 'pickle', 'serialization'] | ['bsd-3-clause', 'deserialization', 'json', 'objectstorage', 'pickle', 'serialization'] | 2023-12-03 | [('marshmallow-code/marshmallow', 0.6203814744949341, 'util', 2), ('python-odin/odin', 0.6082868576049805, 'util', 1), ('uqfoundation/dill', 0.601097047328949, 'data', 0), ('brokenloop/jsontopydantic', 0.5869114398956299, 'util', 0), ('yukinarit/pyserde', 0.5483598113059998, 'util', 2), ('strawberry-graphql/strawberry', 0.5452963709831238, 'web', 0), ('graphistry/pygraphistry', 0.535290539264679, 'data', 0), ('tiangolo/sqlmodel', 0.5255077481269836, 'data', 1), ('lidatong/dataclasses-json', 0.5186536908149719, 'util', 1), ('plotly/plotly.py', 0.5167441964149475, 'viz', 0), ('lk-geimfari/mimesis', 0.5048171281814575, 'data', 1), ('scikit-hep/awkward-1.0', 0.500355064868927, 'data', 1)] | 73 | 6 | null | 0.67 | 11 | 5 | 172 | 1 | 0 | 3 | 3 | 11 | 24 | 90 | 2.2 | 37 |
1,222 | testing | https://github.com/ionelmc/pytest-benchmark | [] | null | [] | [] | null | null | null | ionelmc/pytest-benchmark | pytest-benchmark | 1,158 | 115 | 19 | Python | null | py.test fixture for benchmarking code | ionelmc | 2024-01-12 | 2014-10-10 | 485 | 2.384819 | null | py.test fixture for benchmarking code | ['benchmark', 'benchmarking', 'performance', 'pytest'] | ['benchmark', 'benchmarking', 'performance', 'pytest'] | 2023-12-15 | [('klen/py-frameworks-bench', 0.6951150298118591, 'perf', 1), ('pytest-dev/pytest', 0.6786613464355469, 'testing', 0), ('samuelcolvin/dirty-equals', 0.6507035493850708, 'util', 1), ('locustio/locust', 0.6405083537101746, 'testing', 2), ('teemu/pytest-sugar', 0.6360356211662292, 'testing', 1), ('pytest-dev/pytest-mock', 0.6237248182296753, 'testing', 1), ('nedbat/coveragepy', 0.6183704733848572, 'testing', 0), ('pytest-dev/pytest-xdist', 0.613385796546936, 'testing', 1), ('pmorissette/bt', 0.6115341186523438, 'finance', 0), ('computationalmodelling/nbval', 0.6038401126861572, 'jupyter', 1), ('wolever/parameterized', 0.6030191779136658, 'testing', 0), ('taverntesting/tavern', 0.5957930684089661, 'testing', 1), ('pytest-dev/pytest-bdd', 0.5788668394088745, 'testing', 0), ('pytest-dev/pytest-asyncio', 0.5777239203453064, 'testing', 0), ('pympler/pympler', 0.5586925148963928, 'perf', 0), ('rubik/radon', 0.554404079914093, 'util', 0), ('pyutils/line_profiler', 0.5505008101463318, 'profiling', 0), ('nteract/testbook', 0.5495516061782837, 'jupyter', 1), ('pypy/pypy', 0.5463877320289612, 'util', 0), ('samuelcolvin/pytest-pretty', 0.5456304550170898, 'testing', 1), ('spulec/freezegun', 0.5428466200828552, 'testing', 0), ('p403n1x87/austin', 0.540304958820343, 'profiling', 1), ('mrdbourke/m1-machine-learning-test', 0.5336135625839233, 'ml', 0), ('kiwicom/pytest-recording', 0.5216888785362244, 'testing', 1), ('alexmojaki/snoop', 0.5151593685150146, 'debug', 0), ('eugeneyan/python-collab-template', 0.5136798024177551, 'template', 0), ('getsentry/responses', 0.5085520148277283, 'testing', 0), ('pytest-dev/pytest-cov', 0.5074380040168762, 'testing', 1), ('benfred/py-spy', 0.5037754774093628, 'profiling', 0)] | 41 | 6 | null | 0.29 | 11 | 6 | 113 | 1 | 0 | 2 | 2 | 11 | 20 | 90 | 1.8 | 37 |
442 | gis | https://github.com/toblerity/fiona | [] | null | [] | [] | null | null | null | toblerity/fiona | Fiona | 1,096 | 209 | 47 | Python | https://fiona.readthedocs.io/ | Fiona reads and writes geographic data files | toblerity | 2024-01-10 | 2011-12-31 | 630 | 1.7385 | https://avatars.githubusercontent.com/u/859968?v=4 | Fiona reads and writes geographic data files | ['cli', 'cython', 'gdal', 'gis', 'ogr', 'vector'] | ['cli', 'cython', 'gdal', 'gis', 'ogr', 'vector'] | 2023-12-17 | [('rasterio/rasterio', 0.5375838279724121, 'gis', 4)] | 72 | 3 | null | 2.04 | 23 | 18 | 147 | 1 | 8 | 10 | 8 | 23 | 33 | 90 | 1.4 | 37 |
1,467 | term | https://github.com/1j01/textual-paint | [] | null | [] | [] | null | null | null | 1j01/textual-paint | textual-paint | 861 | 10 | 4 | Python | https://pypi.org/project/textual-paint/ | :art: MS Paint in your terminal. | 1j01 | 2024-01-12 | 2023-04-10 | 42 | 20.430508 | null | :art: MS Paint in your terminal. | ['ansi-art', 'ansi-editor', 'artscene', 'ascii-art', 'bbs', 'drawing', 'image', 'image-editor', 'irc', 'mirc', 'mspaint', 'paint', 'pixel-art', 'pixel-editor', 'terminal', 'text-art', 'textual', 'tui'] | ['ansi-art', 'ansi-editor', 'artscene', 'ascii-art', 'bbs', 'drawing', 'image', 'image-editor', 'irc', 'mirc', 'mspaint', 'paint', 'pixel-art', 'pixel-editor', 'terminal', 'text-art', 'textual', 'tui'] | 2024-01-12 | [('borisdayma/dalle-mini', 0.510983943939209, 'diffusion', 0)] | 1 | 0 | null | 28.81 | 0 | 0 | 9 | 0 | 0 | 5 | 5 | 0 | 0 | 90 | 0 | 37 |
1,735 | viz | https://github.com/pygraphviz/pygraphviz | [] | null | [] | [] | null | null | null | pygraphviz/pygraphviz | pygraphviz | 717 | 200 | 36 | C | https://pygraphviz.github.io/ | Python interface to Graphviz graph drawing package | pygraphviz | 2024-01-08 | 2013-08-02 | 547 | 1.309418 | https://avatars.githubusercontent.com/u/5148488?v=4 | Python interface to Graphviz graph drawing package | ['complex-networks', 'graph-visualization', 'spec-0'] | ['complex-networks', 'graph-visualization', 'spec-0'] | 2024-01-08 | [('westhealth/pyvis', 0.7577512264251709, 'graph', 0), ('pydot/pydot', 0.7296152114868164, 'viz', 0), ('networkx/networkx', 0.6983128786087036, 'graph', 3), ('graphistry/pygraphistry', 0.678774356842041, 'data', 1), ('plotly/plotly.py', 0.6489458680152893, 'viz', 0), ('artelys/geonetworkx', 0.5921590328216553, 'gis', 0), ('dmlc/dgl', 0.5841652154922485, 'ml-dl', 0), ('h4kor/graph-force', 0.571456253528595, 'graph', 0), ('graphql-python/graphene', 0.5481510162353516, 'web', 0), ('matplotlib/matplotlib', 0.5422382354736328, 'viz', 0), ('holoviz/hvplot', 0.5362616181373596, 'pandas', 0), ('vizzuhq/ipyvizzu', 0.5348131060600281, 'jupyter', 0), ('holoviz/holoviz', 0.5206543207168579, 'viz', 0), ('has2k1/plotnine', 0.5177363753318787, 'viz', 0), ('holoviz/panel', 0.5157747268676758, 'viz', 0), ('cuemacro/chartpy', 0.5139192342758179, 'viz', 0), ('bokeh/bokeh', 0.5085844993591309, 'viz', 0), ('altair-viz/altair', 0.5070845484733582, 'viz', 0), ('pyg-team/pytorch_geometric', 0.502930760383606, 'ml-dl', 0), ('stellargraph/stellargraph', 0.5024316310882568, 'graph', 0), ('kuanb/peartree', 0.5017895698547363, 'gis', 0), ('pyvista/pyvista', 0.5015963315963745, 'viz', 0), ('vmiklos/ged2dot', 0.5002699494361877, 'data', 0)] | 53 | 5 | null | 1.04 | 34 | 23 | 127 | 0 | 3 | 2 | 3 | 34 | 59 | 90 | 1.7 | 37 |
1,704 | util | https://github.com/mtkennerly/poetry-dynamic-versioning | ['poetry'] | null | [] | [] | null | null | null | mtkennerly/poetry-dynamic-versioning | poetry-dynamic-versioning | 530 | 33 | 4 | Python | null | Plugin for Poetry to enable dynamic versioning based on VCS tags | mtkennerly | 2024-01-13 | 2019-06-06 | 242 | 2.183637 | null | Plugin for Poetry to enable dynamic versioning based on VCS tags | ['bazaar', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'plugin', 'poetry', 'semantic-versioning', 'subversion', 'versioning'] | ['bazaar', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'plugin', 'poetry', 'semantic-versioning', 'subversion', 'versioning'] | 2024-01-03 | [('mtkennerly/dunamai', 0.7415024042129517, 'util', 11), ('tiangolo/poetry-version-plugin', 0.6403163075447083, 'util', 0), ('pypa/setuptools_scm', 0.6003091931343079, 'util', 2), ('python-versioneer/python-versioneer', 0.5653854012489319, 'util', 0), ('callowayproject/bump-my-version', 0.5595990419387817, 'util', 1), ('python-poetry/install.python-poetry.org', 0.5113168358802795, 'util', 1)] | 13 | 3 | null | 1.1 | 13 | 12 | 56 | 0 | 11 | 13 | 11 | 13 | 34 | 90 | 2.6 | 37 |
1,861 | sim | https://github.com/nvidia-omniverse/omniisaacgymenvs | ['robot-learning'] | null | [] | [] | null | null | null | nvidia-omniverse/omniisaacgymenvs | OmniIsaacGymEnvs | 518 | 141 | 16 | Python | null | Reinforcement Learning Environments for Omniverse Isaac Gym | nvidia-omniverse | 2024-01-14 | 2022-06-01 | 86 | 5.963816 | https://avatars.githubusercontent.com/u/57824658?v=4 | Reinforcement Learning Environments for Omniverse Isaac Gym | [] | ['robot-learning'] | 2023-12-08 | [('nvidia-omniverse/isaacgymenvs', 0.8064512610435486, 'sim', 0), ('nvidia-omniverse/orbit', 0.6811214685440063, 'sim', 1), ('humancompatibleai/imitation', 0.6052762866020203, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.6047082543373108, 'ml-rl', 0), ('arise-initiative/robosuite', 0.5800082087516785, 'ml-rl', 1), ('openai/baselines', 0.5605931282043457, 'ml-rl', 0), ('inspirai/timechamber', 0.5575793981552124, 'sim', 0), ('pettingzoo-team/pettingzoo', 0.5554696917533875, 'ml-rl', 0), ('pytorch/rl', 0.5446157455444336, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5333084464073181, 'ml-rl', 0), ('kzl/decision-transformer', 0.5326739549636841, 'ml-rl', 0), ('thu-ml/tianshou', 0.5271867513656616, 'ml-rl', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5251854062080383, 'study', 0), ('facebookresearch/habitat-lab', 0.5088497400283813, 'sim', 0), ('google/dopamine', 0.5081252455711365, 'ml-rl', 0)] | 6 | 1 | null | 1.4 | 59 | 17 | 20 | 1 | 0 | 4 | 4 | 59 | 151 | 90 | 2.6 | 37 |
1,527 | llm | https://github.com/vahe1994/spqr | ['falcon', 'llama', 'quantization', 'compression'] | Quantization algorithm and the model evaluation code for SpQR method for LLM compression | [] | [] | null | null | null | vahe1994/spqr | SpQR | 475 | 39 | 22 | Python | null | null | vahe1994 | 2024-01-12 | 2023-06-05 | 34 | 13.912134 | null | Quantization algorithm and the model evaluation code for SpQR method for LLM compression | [] | ['compression', 'falcon', 'llama', 'quantization'] | 2023-11-13 | [('opengvlab/omniquant', 0.6055932641029358, 'llm', 1), ('artidoro/qlora', 0.5260251760482788, 'llm', 0)] | 8 | 5 | null | 0.52 | 8 | 4 | 7 | 2 | 0 | 0 | 0 | 8 | 5 | 90 | 0.6 | 37 |
556 | gis | https://github.com/corteva/rioxarray | [] | null | [] | [] | null | null | null | corteva/rioxarray | rioxarray | 455 | 69 | 16 | Python | https://corteva.github.io/rioxarray | geospatial xarray extension powered by rasterio | corteva | 2024-01-09 | 2019-04-16 | 250 | 1.82 | https://avatars.githubusercontent.com/u/39543515?v=4 | geospatial xarray extension powered by rasterio | ['gdal', 'geospatial', 'gis', 'netcdf', 'raster', 'rasterio', 'xarray'] | ['gdal', 'geospatial', 'gis', 'netcdf', 'raster', 'rasterio', 'xarray'] | 2023-12-29 | [('osgeo/gdal', 0.5278557538986206, 'gis', 1), ('rasterio/rasterio', 0.5225644707679749, 'gis', 3), ('makepath/xarray-spatial', 0.511622428894043, 'gis', 1), ('cogeotiff/rio-tiler', 0.5002418756484985, 'gis', 3)] | 33 | 7 | null | 1 | 30 | 12 | 58 | 0 | 4 | 14 | 4 | 30 | 37 | 90 | 1.2 | 37 |
1,700 | template | https://github.com/asacristani/fastapi-rocket-boilerplate | [] | null | [] | [] | null | null | null | asacristani/fastapi-rocket-boilerplate | fastapi-rocket-boilerplate | 370 | 56 | 6 | Python | null | 🐍💨 FastAPI Rocket Boilerplate to build an API based in Python with its most modern technologies! | asacristani | 2024-01-10 | 2023-09-20 | 18 | 19.621212 | null | 🐍💨 FastAPI Rocket Boilerplate to build an API based in Python with its most modern technologies! | ['boilerplate', 'boilerplate-backend', 'fastapi'] | ['boilerplate', 'boilerplate-backend', 'fastapi'] | 2023-10-16 | [('tiangolo/fastapi', 0.7025260925292969, 'web', 1), ('fastai/fastcore', 0.6752527952194214, 'util', 0), ('rawheel/fastapi-boilerplate', 0.6740272045135498, 'web', 2), ('s3rius/fastapi-template', 0.6634681224822998, 'web', 1), ('vitalik/django-ninja', 0.6605289578437805, 'web', 0), ('dmontagu/fastapi_client', 0.6545476913452148, 'web', 0), ('koxudaxi/fastapi-code-generator', 0.607068657875061, 'web', 1), ('starlite-api/starlite', 0.5981463193893433, 'web', 0), ('hugapi/hug', 0.5935749411582947, 'util', 0), ('python-restx/flask-restx', 0.5854663848876953, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5566263198852539, 'template', 1), ('falconry/falcon', 0.5512214303016663, 'web', 0), ('lk-geimfari/mimesis', 0.5495461225509644, 'data', 0), ('janetech-inc/fast-api-admin-template', 0.5484818816184998, 'template', 0), ('awtkns/fastapi-crudrouter', 0.5432131886482239, 'web', 1), ('fastapi-users/fastapi-users', 0.5379810333251953, 'web', 1), ('willmcgugan/textual', 0.5363441109657288, 'term', 0), ('pyston/pyston', 0.5267325639724731, 'util', 0), ('kubeflow/fairing', 0.5263985991477966, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5223199725151062, 'viz', 1), ('zhanymkanov/fastapi-best-practices', 0.5217467546463013, 'study', 1), ('alirn76/panther', 0.5177244544029236, 'web', 0), ('pypy/pypy', 0.508145809173584, 'util', 0), ('airtai/faststream', 0.5075085759162903, 'perf', 0), ('urwid/urwid', 0.5037996172904968, 'term', 0), ('pdoc3/pdoc', 0.502884566783905, 'util', 0), ('pytoolz/toolz', 0.5019211769104004, 'util', 0), ('martinheinz/python-project-blueprint', 0.5014850497245789, 'template', 1)] | 5 | 2 | null | 0.83 | 4 | 1 | 4 | 3 | 1 | 3 | 1 | 4 | 3 | 90 | 0.8 | 37 |
1,455 | util | https://github.com/conda/conda-build | ['conda'] | null | [] | [] | null | null | null | conda/conda-build | conda-build | 356 | 403 | 51 | Python | https://docs.conda.io/projects/conda-build/ | Commands and tools for building conda packages | conda | 2024-01-14 | 2014-01-17 | 523 | 0.679945 | https://avatars.githubusercontent.com/u/6392739?v=4 | Commands and tools for building conda packages | ['conda', 'conda-build', 'package-management'] | ['conda', 'conda-build', 'package-management'] | 2024-01-10 | [('conda/constructor', 0.8005626201629639, 'util', 1), ('mamba-org/boa', 0.7872036695480347, 'util', 1), ('mamba-org/quetz', 0.7619379758834839, 'util', 1), ('conda/conda-pack', 0.7299324870109558, 'util', 1), ('mamba-org/mamba', 0.6777679920196533, 'util', 1), ('mamba-org/gator', 0.6426661014556885, 'jupyter', 1), ('pypa/hatch', 0.5962553024291992, 'util', 0), ('conda/conda', 0.5913727283477783, 'util', 2), ('pomponchik/instld', 0.5880274772644043, 'util', 0), ('mamba-org/micromamba-docker', 0.5653933882713318, 'util', 1), ('conda-forge/feedstocks', 0.5539048314094543, 'util', 1), ('conda-forge/conda-smithy', 0.5523984432220459, 'util', 0), ('indygreg/pyoxidizer', 0.5000386834144592, 'util', 0)] | 244 | 3 | null | 4.15 | 391 | 335 | 122 | 0 | 9 | 25 | 9 | 390 | 208 | 90 | 0.5 | 37 |
1,761 | data | https://github.com/tconbeer/sqlfmt | ['code-quality'] | null | [] | [] | 1 | null | null | tconbeer/sqlfmt | sqlfmt | 307 | 11 | 3 | Python | https://sqlfmt.com | sqlfmt formats your dbt SQL files so you don't have to | tconbeer | 2024-01-11 | 2021-07-19 | 132 | 2.323243 | null | sqlfmt formats your dbt SQL files so you don't have to | ['dbt', 'formatter', 'sql'] | ['code-quality', 'dbt', 'formatter', 'sql'] | 2024-01-12 | [('tconbeer/harlequin', 0.569164514541626, 'term', 1), ('databricks/dbt-databricks', 0.5048282146453857, 'data', 2)] | 12 | 5 | null | 2.1 | 56 | 41 | 30 | 0 | 16 | 19 | 16 | 56 | 49 | 90 | 0.9 | 37 |
1,357 | gis | https://github.com/raphaelquast/eomaps | [] | null | [] | [] | null | null | null | raphaelquast/eomaps | EOmaps | 284 | 20 | 5 | Python | https://eomaps.readthedocs.io/ | A library to create interactive maps of geographical datasets | raphaelquast | 2024-01-13 | 2021-09-27 | 122 | 2.325146 | null | A library to create interactive maps of geographical datasets | ['cartopy', 'earth-observation', 'geospatial', 'gis', 'interactive-maps', 'interactive-visualization', 'mapping', 'matplotlib', 'plotting', 'visualization'] | ['cartopy', 'earth-observation', 'geospatial', 'gis', 'interactive-maps', 'interactive-visualization', 'mapping', 'matplotlib', 'plotting', 'visualization'] | 2023-12-20 | [('residentmario/geoplot', 0.707546055316925, 'gis', 1), ('scitools/cartopy', 0.6839972138404846, 'gis', 2), ('opengeos/leafmap', 0.6778762936592102, 'gis', 3), ('holoviz/geoviews', 0.6664366126060486, 'gis', 2), ('giswqs/geemap', 0.6554696559906006, 'gis', 3), ('gregorhd/mapcompare', 0.625028669834137, 'gis', 0), ('geopandas/geopandas', 0.6084570288658142, 'gis', 2), ('marceloprates/prettymaps', 0.6013832688331604, 'viz', 1), ('visgl/deck.gl', 0.6003251075744629, 'viz', 1), ('artelys/geonetworkx', 0.585459291934967, 'gis', 0), ('bokeh/bokeh', 0.5836126804351807, 'viz', 2), ('plotly/plotly.py', 0.581186056137085, 'viz', 1), ('earthlab/earthpy', 0.5780810713768005, 'gis', 0), ('pyproj4/pyproj', 0.5670640468597412, 'gis', 1), ('domlysz/blendergis', 0.5599436163902283, 'gis', 2), ('scitools/iris', 0.5512466430664062, 'gis', 0), ('hazyresearch/meerkat', 0.543424129486084, 'viz', 0), ('python-visualization/folium', 0.5424359440803528, 'gis', 0), ('holoviz/holoviz', 0.5355204343795776, 'viz', 0), ('mwaskom/seaborn', 0.531478226184845, 'viz', 1), ('altair-viz/altair', 0.5242673754692078, 'viz', 1), ('nomic-ai/deepscatter', 0.5207083225250244, 'viz', 1), ('man-group/dtale', 0.5189169049263, 'viz', 1), ('imageio/imageio', 0.514751672744751, 'util', 0), ('pandas-dev/pandas', 0.5139701962471008, 'pandas', 0), ('isl-org/open3d', 0.5115019679069519, 'sim', 1), ('holoviz/panel', 0.5107569098472595, 'viz', 1), ('matplotlib/matplotlib', 0.5100708603858948, 'viz', 2), ('darribas/gds_env', 0.5032002329826355, 'gis', 0), ('fatiando/verde', 0.5024335384368896, 'gis', 1), ('holoviz/hvplot', 0.5002750754356384, 'pandas', 1)] | 6 | 3 | null | 22.73 | 37 | 27 | 28 | 1 | 25 | 33 | 25 | 37 | 61 | 90 | 1.6 | 37 |
582 | ml | https://github.com/merantix-momentum/squirrel-core | [] | null | [] | [] | null | null | null | merantix-momentum/squirrel-core | squirrel-core | 271 | 8 | 14 | Python | https://squirrel-core.readthedocs.io/ | A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut: | merantix-momentum | 2024-01-05 | 2022-02-11 | 102 | 2.642061 | https://avatars.githubusercontent.com/u/98414099?v=4 | A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way 🌰 | ['ai', 'cloud-computing', 'collaboration', 'computer-vision', 'cv', 'data-ingestion', 'data-mesh', 'data-science', 'dataops', 'datasets', 'deep-learning', 'distributed', 'jax', 'machine-learning', 'ml', 'natural-language-processing', 'nlp', 'pytorch', 'tensorflow'] | ['ai', 'cloud-computing', 'collaboration', 'computer-vision', 'cv', 'data-ingestion', 'data-mesh', 'data-science', 'dataops', 'datasets', 'deep-learning', 'distributed', 'jax', 'machine-learning', 'ml', 'natural-language-processing', 'nlp', 'pytorch', 'tensorflow'] | 2024-01-04 | [('eventual-inc/daft', 0.6807416081428528, 'pandas', 3), ('huggingface/datasets', 0.6653859615325928, 'nlp', 8), ('gradio-app/gradio', 0.6419711709022522, 'viz', 3), ('tensorflow/tensorflow', 0.6386204957962036, 'ml-dl', 5), ('mlflow/mlflow', 0.63669753074646, 'ml-ops', 3), ('kubeflow/fairing', 0.6223782300949097, 'ml-ops', 0), ('dylanhogg/awesome-python', 0.6165292263031006, 'study', 5), ('ray-project/ray', 0.6162523627281189, 'ml-ops', 6), ('polyaxon/polyaxon', 0.6144108772277832, 'ml-ops', 6), ('horovod/horovod', 0.6060230135917664, 'ml-ops', 4), ('wandb/client', 0.5987028479576111, 'ml', 7), ('featurelabs/featuretools', 0.5986401438713074, 'ml', 2), ('determined-ai/determined', 0.5961888432502747, 'ml-ops', 5), ('aws/sagemaker-python-sdk', 0.5961750149726868, 'ml', 3), ('polyaxon/datatile', 0.5911334156990051, 'pandas', 4), 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1,458 | util | https://github.com/mamba-org/micromamba-docker | [] | null | [] | [] | null | null | null | mamba-org/micromamba-docker | micromamba-docker | 232 | 41 | 11 | Shell | null | Rapid builds of small Conda-based containers using micromamba. | mamba-org | 2024-01-13 | 2021-01-22 | 157 | 1.472348 | https://avatars.githubusercontent.com/u/66118895?v=4 | Rapid builds of small Conda-based containers using micromamba. | ['build', 'ci', 'conda', 'container', 'docker', 'dockerfile', 'environment', 'mamba', 'micromamba'] | ['build', 'ci', 'conda', 'container', 'docker', 'dockerfile', 'environment', 'mamba', 'micromamba'] | 2024-01-11 | [('mamba-org/boa', 0.6690220236778259, 'util', 2), ('conda/conda-build', 0.5653933882713318, 'util', 1), ('darribas/gds_env', 0.5356658697128296, 'gis', 1), ('mamba-org/quetz', 0.5259016752243042, 'util', 1)] | 18 | 7 | null | 2.67 | 34 | 33 | 36 | 0 | 18 | 11 | 18 | 34 | 42 | 90 | 1.2 | 37 |
1,614 | llm | https://github.com/tiger-ai-lab/mammoth | ['instruction-tuning'] | null | [] | [] | null | null | null | tiger-ai-lab/mammoth | MAmmoTH | 221 | 23 | 11 | Jupyter Notebook | null | This repo contains the code and data for "MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning" | tiger-ai-lab | 2024-01-12 | 2023-09-06 | 20 | 10.59589 | https://avatars.githubusercontent.com/u/144196744?v=4 | This repo contains the code and data for "MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning" | [] | ['instruction-tuning'] | 2024-01-13 | [('declare-lab/instruct-eval', 0.6364500522613525, 'llm', 0), ('yizhongw/self-instruct', 0.5941129922866821, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5743323564529419, 'llm', 0), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5739008784294128, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5284292697906494, 'llm', 1), ('hiyouga/llama-factory', 0.5284292697906494, 'llm', 1)] | 4 | 3 | null | 0.98 | 18 | 13 | 4 | 0 | 0 | 0 | 0 | 18 | 25 | 90 | 1.4 | 37 |
1,453 | util | https://github.com/conda-forge/conda-smithy | [] | null | [] | [] | null | null | null | conda-forge/conda-smithy | conda-smithy | 140 | 173 | 25 | Python | https://conda-forge.org/ | The tool for managing conda-forge feedstocks. | conda-forge | 2024-01-05 | 2015-04-11 | 459 | 0.304726 | https://avatars.githubusercontent.com/u/11897326?v=4 | The tool for managing conda-forge feedstocks. | ['continuous-integration'] | ['continuous-integration'] | 2024-01-11 | [('conda-forge/feedstocks', 0.8073468208312988, 'util', 0), ('conda/conda-build', 0.5523984432220459, 'util', 0), ('conda/conda-pack', 0.5372809767723083, 'util', 0), ('mamba-org/quetz', 0.5191918015480042, 'util', 0), ('mamba-org/mamba', 0.5094754695892334, 'util', 0)] | 112 | 3 | null | 6.65 | 61 | 48 | 107 | 0 | 19 | 24 | 19 | 61 | 174 | 90 | 2.9 | 37 |
101 | nlp | https://github.com/clips/pattern | [] | null | [] | [] | null | null | null | clips/pattern | pattern | 8,609 | 1,600 | 544 | Python | https://github.com/clips/pattern/wiki | Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization. | clips | 2024-01-13 | 2011-05-03 | 665 | 12.945865 | https://avatars.githubusercontent.com/u/765924?v=4 | Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization. | ['machine-learning', 'natural-language-processing', 'network-analysis', 'sentiment-analysis', 'web-mining', 'wordnet'] | ['machine-learning', 'natural-language-processing', 'network-analysis', 'sentiment-analysis', 'web-mining', 'wordnet'] | 2020-04-25 | [('alirezamika/autoscraper', 0.7112637758255005, 'data', 1), ('scrapy/scrapy', 0.667265772819519, 'data', 0), ('webpy/webpy', 0.6514905095100403, 'web', 0), ('rasbt/mlxtend', 0.6350256204605103, 'ml', 1), ('roniemartinez/dude', 0.6342862844467163, 'util', 0), ('sloria/textblob', 0.6183449029922485, 'nlp', 1), ('masoniteframework/masonite', 0.6094305515289307, 'web', 0), ('plotly/dash', 0.5705082416534424, 'viz', 0), ('nv7-github/googlesearch', 0.5700188279151917, 'util', 0), ('holoviz/panel', 0.5649107098579407, 'viz', 0), ('gradio-app/gradio', 0.5603600144386292, 'viz', 1), ('pallets/flask', 0.5583645105361938, 'web', 0), ('requests/toolbelt', 0.5543271899223328, 'util', 0), ('eleutherai/pyfra', 0.5541388988494873, 'ml', 0), ('explosion/spacy', 0.5512140989303589, 'nlp', 2), ('dylanhogg/awesome-python', 0.5490561723709106, 'study', 2), ('reflex-dev/reflex', 0.547705352306366, 'web', 0), ('1200wd/bitcoinlib', 0.5464682579040527, 'crypto', 0), ('ranaroussi/quantstats', 0.5454891324043274, 'finance', 0), ('binux/pyspider', 0.5396731495857239, 'data', 0), ('falconry/falcon', 0.5375832915306091, 'web', 0), ('googleapis/google-api-python-client', 0.5339103937149048, 'util', 0), ('online-ml/river', 0.5316668152809143, 'ml', 1), ('seleniumbase/seleniumbase', 0.5308951735496521, 'testing', 0), ('bottlepy/bottle', 0.5306470990180969, 'web', 0), ('eliasdabbas/advertools', 0.5270535349845886, 'data', 0), ('willmcgugan/textual', 0.5270101428031921, 'term', 0), ('scikit-learn/scikit-learn', 0.524055540561676, 'ml', 1), ('pemistahl/lingua-py', 0.5218809247016907, 'nlp', 1), ('krzjoa/awesome-python-data-science', 0.5186637043952942, 'study', 1), ('polyaxon/datatile', 0.5179307460784912, 'pandas', 0), ('gbeced/pyalgotrade', 0.5164464712142944, 'finance', 0), ('uberi/speech_recognition', 0.5162601470947266, 'ml', 0), ('goldmansachs/gs-quant', 0.5158860087394714, 'finance', 0), ('pylons/pyramid', 0.5145288705825806, 'web', 0), ('wesm/pydata-book', 0.5136392116546631, 'study', 0), ('klen/muffin', 0.5102404356002808, 'web', 0), ('quantconnect/lean', 0.5098506808280945, 'finance', 0), ('pytoolz/toolz', 0.5079754590988159, 'util', 0), ('pyodide/pyodide', 0.5079214572906494, 'util', 0), ('ta-lib/ta-lib-python', 0.5062140226364136, 'finance', 0), ('r0x0r/pywebview', 0.5047861933708191, 'gui', 0), ('pallets/werkzeug', 0.5018793344497681, 'web', 0), ('pandas-dev/pandas', 0.5012263655662537, 'pandas', 0), ('cherrypy/cherrypy', 0.500634491443634, 'web', 0), ('probml/pyprobml', 0.5005698204040527, 'ml', 1)] | 30 | 6 | null | 0 | 2 | 0 | 155 | 45 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 36 |
137 | nlp | https://github.com/ddangelov/top2vec | [] | null | [] | [] | null | null | null | ddangelov/top2vec | Top2Vec | 2,768 | 363 | 40 | Python | null | Top2Vec learns jointly embedded topic, document and word vectors. | ddangelov | 2024-01-13 | 2020-03-20 | 201 | 13.732105 | null | Top2Vec learns jointly embedded topic, document and word vectors. | ['bert', 'document-embedding', 'pre-trained-language-models', 'semantic-search', 'sentence-encoder', 'sentence-transformers', 'text-search', 'text-semantic-similarity', 'top2vec', 'topic-modeling', 'topic-modelling', 'topic-search', 'topic-vector', 'word-embeddings'] | ['bert', 'document-embedding', 'pre-trained-language-models', 'semantic-search', 'sentence-encoder', 'sentence-transformers', 'text-search', 'text-semantic-similarity', 'top2vec', 'topic-modeling', 'topic-modelling', 'topic-search', 'topic-vector', 'word-embeddings'] | 2023-11-16 | [('sebischair/lbl2vec', 0.8003798723220825, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.6133404970169067, 'llm', 1), ('neuml/txtai', 0.608359694480896, 'nlp', 1), ('maartengr/bertopic', 0.6046782732009888, 'nlp', 3), ('rare-technologies/gensim', 0.59908527135849, 'nlp', 2), ('muennighoff/sgpt', 0.5922024846076965, 'llm', 1), ('llmware-ai/llmware', 0.5913721323013306, 'llm', 2), ('jina-ai/clip-as-service', 0.5902553796768188, 'nlp', 1), ('jina-ai/finetuner', 0.587577760219574, 'ml', 1), ('plasticityai/magnitude', 0.584179162979126, 'nlp', 1), ('koaning/whatlies', 0.5833550095558167, 'nlp', 0), ('ukplab/sentence-transformers', 0.5732832551002502, 'nlp', 1), ('alibaba/easynlp', 0.5686218738555908, 'nlp', 1), ('amansrivastava17/embedding-as-service', 0.5621562600135803, 'nlp', 1), ('graykode/nlp-tutorial', 0.5482358932495117, 'study', 1), ('jonasgeiping/cramming', 0.5433996915817261, 'nlp', 0), ('extreme-bert/extreme-bert', 0.5432024598121643, 'llm', 1), ('jina-ai/vectordb', 0.5428557395935059, 'data', 0), ('deepset-ai/farm', 0.5399625897407532, 'nlp', 1), ('chroma-core/chroma', 0.5397540330886841, 'data', 0), ('intellabs/fastrag', 0.527250349521637, 'nlp', 2), ('ai21labs/in-context-ralm', 0.5187664031982422, 'llm', 0), ('qdrant/fastembed', 0.5031333565711975, 'ml', 0)] | 2 | 0 | null | 0.54 | 11 | 3 | 46 | 2 | 7 | 7 | 7 | 11 | 7 | 90 | 0.6 | 36 |
685 | ml-dl | https://github.com/nerdyrodent/vqgan-clip | [] | null | [] | [] | null | null | null | nerdyrodent/vqgan-clip | VQGAN-CLIP | 2,537 | 423 | 53 | Python | null | Just playing with getting VQGAN+CLIP running locally, rather than having to use colab. | nerdyrodent | 2024-01-12 | 2021-07-02 | 134 | 18.852442 | null | Just playing with getting VQGAN+CLIP running locally, rather than having to use colab. | ['text-to-image', 'text2image'] | ['text-to-image', 'text2image'] | 2022-10-02 | [] | 7 | 2 | null | 0 | 5 | 2 | 31 | 16 | 0 | 0 | 0 | 5 | 4 | 90 | 0.8 | 36 |
725 | study | https://github.com/amanchadha/coursera-deep-learning-specialization | [] | null | [] | [] | null | null | null | amanchadha/coursera-deep-learning-specialization | coursera-deep-learning-specialization | 2,459 | 1,925 | 25 | Jupyter Notebook | null | Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models | amanchadha | 2024-01-13 | 2020-06-24 | 187 | 13.089734 | null | Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models | ['andrew-ng', 'andrew-ng-course', 'cnns', 'convolutional-neural-network', 'convolutional-neural-networks', 'coursera', 'coursera-assignment', 'coursera-machine-learning', 'coursera-specialization', 'deep-learning', 'hyperparameter-optimization', 'hyperparameter-tuning', 'neural-machine-translation', 'neural-network', 'neural-networks', 'neural-style-transfer', 'recurrent-neural-network', 'recurrent-neural-networks', 'regularization', 'rnns'] | ['andrew-ng', 'andrew-ng-course', 'cnns', 'convolutional-neural-network', 'convolutional-neural-networks', 'coursera', 'coursera-assignment', 'coursera-machine-learning', 'coursera-specialization', 'deep-learning', 'hyperparameter-optimization', 'hyperparameter-tuning', 'neural-machine-translation', 'neural-network', 'neural-networks', 'neural-style-transfer', 'recurrent-neural-network', 'recurrent-neural-networks', 'regularization', 'rnns'] | 2024-01-12 | [('udacity/deep-learning-v2-pytorch', 0.6206496357917786, 'study', 2), ('alirezadir/machine-learning-interview-enlightener', 0.6021793484687805, 'study', 1), ('mrdbourke/tensorflow-deep-learning', 0.592336118221283, 'study', 1), ('mosaicml/composer', 0.5713127255439758, 'ml-dl', 3), ('explosion/thinc', 0.5682108998298645, 'ml-dl', 1), ('onnx/onnx', 0.5587916970252991, 'ml', 2), ('mrdbourke/zero-to-mastery-ml', 0.5553426742553711, 'study', 1), ('jindongwang/transferlearning', 0.5513812899589539, 'ml', 1), ('keras-team/keras', 0.5479432940483093, 'ml-dl', 2), ('rwightman/pytorch-image-models', 0.5459620952606201, 'ml-dl', 0), ('ddbourgin/numpy-ml', 0.5457329154014587, 'ml', 1), ('bentoml/bentoml', 0.540047824382782, 'ml-ops', 1), ('mrdbourke/pytorch-deep-learning', 0.5357545614242554, 'study', 1), ('nyandwi/modernconvnets', 0.5350536704063416, 'ml-dl', 3), ('christoschristofidis/awesome-deep-learning', 0.5327649116516113, 'study', 2), ('tensorlayer/tensorlayer', 0.5279957056045532, 'ml-rl', 2), ('keras-rl/keras-rl', 0.5269455909729004, 'ml-rl', 1), ('nvidia/deeplearningexamples', 0.5228648781776428, 'ml-dl', 1), ('alpa-projects/alpa', 0.5197420120239258, 'ml-dl', 1), ('thilinarajapakse/simpletransformers', 0.5146978497505188, 'nlp', 0), ('milvus-io/bootcamp', 0.5138573050498962, 'data', 1), ('lutzroeder/netron', 0.5117756128311157, 'ml', 2), ('tensorflow/tensorflow', 0.5084401369094849, 'ml-dl', 2), ('tensorflow/tensor2tensor', 0.5060564875602722, 'ml', 1), ('graykode/nlp-tutorial', 0.5051466226577759, 'study', 0), ('keras-team/autokeras', 0.5036728382110596, 'ml-dl', 1), ('huggingface/autotrain-advanced', 0.500561535358429, 'ml', 1)] | 8 | 2 | null | 0.04 | 4 | 1 | 43 | 0 | 0 | 0 | 0 | 4 | 0 | 90 | 0 | 36 |
235 | nlp | https://github.com/salesforce/codet5 | [] | null | [] | [] | null | null | null | salesforce/codet5 | CodeT5 | 2,438 | 381 | 40 | Python | https://arxiv.org/abs/2305.07922 | Home of CodeT5: Open Code LLMs for Code Understanding and Generation | salesforce | 2024-01-14 | 2021-08-16 | 128 | 19.025641 | https://avatars.githubusercontent.com/u/453694?v=4 | Home of CodeT5: Open Code LLMs for Code Understanding and Generation | ['code-generation', 'code-intelligence', 'code-understanding', 'language-model', 'large-language-models'] | ['code-generation', 'code-intelligence', 'code-understanding', 'language-model', 'large-language-models'] | 2023-07-21 | [('thudm/codegeex', 0.6897627115249634, 'llm', 1), ('salesforce/codegen', 0.6593608856201172, 'nlp', 0), ('ludwig-ai/ludwig', 0.6258037686347961, 'ml-ops', 0), ('alpha-vllm/llama2-accessory', 0.6213086843490601, 'llm', 0), ('salesforce/xgen', 0.6172817945480347, 'llm', 2), ('eugeneyan/open-llms', 0.6007983088493347, 'study', 1), ('nomic-ai/gpt4all', 0.5974037051200867, 'llm', 1), ('young-geng/easylm', 0.591428816318512, 'llm', 2), ('bigcode-project/starcoder', 0.5859589576721191, 'llm', 1), ('conceptofmind/toolformer', 0.5832077264785767, 'llm', 1), ('tigerlab-ai/tiger', 0.5818438529968262, 'llm', 1), ('hegelai/prompttools', 0.580586850643158, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5782813429832458, 'study', 1), ('h2oai/h2o-llmstudio', 0.5726329684257507, 'llm', 0), ('argilla-io/argilla', 0.5648234486579895, 'nlp', 0), ('dylanhogg/llmgraph', 0.563266396522522, 'ml', 0), ('hwchase17/langchain', 0.5549836158752441, 'llm', 1), ('hiyouga/llama-factory', 0.5533753633499146, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5533753037452698, 'llm', 2), ('lupantech/chameleon-llm', 0.551190972328186, 'llm', 1), ('citadel-ai/langcheck', 0.5499178171157837, 'llm', 1), ('eth-sri/lmql', 0.5481631755828857, 'llm', 1), ('nat/openplayground', 0.5432443022727966, 'llm', 1), ('microsoft/promptflow', 0.5427641868591309, 'llm', 0), ('bobazooba/xllm', 0.5426592826843262, 'llm', 1), ('agenta-ai/agenta', 0.5426530241966248, 'llm', 1), ('nebuly-ai/nebullvm', 0.5400938987731934, 'perf', 1), ('eleutherai/the-pile', 0.5400211811065674, 'data', 0), ('llmware-ai/llmware', 0.5333616733551025, 'llm', 1), ('shishirpatil/gorilla', 0.5298489332199097, 'llm', 0), ('juncongmoo/pyllama', 0.5293552875518799, 'llm', 0), ('bentoml/openllm', 0.5279268622398376, 'ml-ops', 0), ('next-gpt/next-gpt', 0.5271520614624023, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5260124802589417, 'perf', 0), ('night-chen/toolqa', 0.525848388671875, 'llm', 1), ('thudm/chatglm2-6b', 0.524960994720459, 'llm', 1), ('facebookresearch/codellama', 0.5207399725914001, 'llm', 1), ('lianjiatech/belle', 0.5200687050819397, 'llm', 0), ('ibm/dromedary', 0.5191128253936768, 'llm', 1), ('modularml/mojo', 0.518414318561554, 'util', 0), ('confident-ai/deepeval', 0.5176749229431152, 'testing', 1), ('bigscience-workshop/petals', 0.5150795578956604, 'data', 1), ('embedchain/embedchain', 0.5125962495803833, 'llm', 0), ('ravenscroftj/turbopilot', 0.5119407773017883, 'llm', 1), ('numba/llvmlite', 0.5081905126571655, 'util', 0), ('openbmb/toolbench', 0.5070496201515198, 'llm', 0), ('openai/evals', 0.503447949886322, 'llm', 1), ('run-llama/llama-lab', 0.5031118392944336, 'llm', 1), ('li-plus/chatglm.cpp', 0.5017570853233337, 'llm', 1), ('lastmile-ai/aiconfig', 0.500541627407074, 'util', 0)] | 3 | 1 | null | 0.44 | 14 | 3 | 29 | 6 | 0 | 0 | 0 | 14 | 9 | 90 | 0.6 | 36 |
1,326 | util | https://github.com/scrapinghub/dateparser | ['date', 'datetime', 'parsing'] | null | [] | [] | null | null | null | scrapinghub/dateparser | dateparser | 2,408 | 462 | 134 | Python | null | python parser for human readable dates | scrapinghub | 2024-01-13 | 2014-11-24 | 479 | 5.025641 | https://avatars.githubusercontent.com/u/699596?v=4 | python parser for human readable dates | [] | ['date', 'datetime', 'parsing'] | 2023-12-21 | [('dateutil/dateutil', 0.7123557329177856, 'util', 2), ('sdispater/pendulum', 0.6702179908752441, 'util', 2), ('arrow-py/arrow', 0.5817139744758606, 'util', 2)] | 133 | 0 | null | 0.62 | 30 | 15 | 111 | 1 | 3 | 3 | 3 | 30 | 33 | 90 | 1.1 | 36 |
1,815 | study | https://github.com/mrdbourke/zero-to-mastery-ml | [] | null | [] | [] | null | null | null | mrdbourke/zero-to-mastery-ml | zero-to-mastery-ml | 2,378 | 3,146 | 124 | Jupyter Notebook | https://dbourke.link/ZTMmlcourse | All course materials for the Zero to Mastery Machine Learning and Data Science course. | mrdbourke | 2024-01-13 | 2019-09-23 | 227 | 10.469182 | null | All course materials for the Zero to Mastery Machine Learning and Data Science course. | ['data-science', 'deep-learning', 'machine-learning'] | ['data-science', 'deep-learning', 'machine-learning'] | 2023-11-16 | [('mrdbourke/tensorflow-deep-learning', 0.7862590551376343, 'study', 1), ('mrdbourke/pytorch-deep-learning', 0.676668107509613, 'study', 2), ('firmai/industry-machine-learning', 0.5863468647003174, 'study', 2), ('patchy631/machine-learning', 0.5848323106765747, 'ml', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5553426742553711, 'study', 1), ('udacity/deep-learning-v2-pytorch', 0.5445974469184875, 'study', 1), ('tensorlayer/tensorlayer', 0.5104413628578186, 'ml-rl', 1), ('d2l-ai/d2l-en', 0.5092582702636719, 'study', 3), ('tensorflow/tensorflow', 0.5074943900108337, 'ml-dl', 2), ('onnx/onnx', 0.5012384057044983, 'ml', 2)] | 25 | 1 | null | 1.21 | 6 | 2 | 52 | 2 | 0 | 0 | 0 | 6 | 5 | 90 | 0.8 | 36 |
1,627 | math | https://github.com/mckinsey/causalnex | ['causation'] | null | [] | [] | null | null | null | mckinsey/causalnex | causalnex | 2,070 | 242 | 46 | Python | http://causalnex.readthedocs.io/ | A Python library that helps data scientists to infer causation rather than observing correlation. | mckinsey | 2024-01-12 | 2019-12-12 | 215 | 9.596026 | https://avatars.githubusercontent.com/u/4265350?v=4 | A Python library that helps data scientists to infer causation rather than observing correlation. | ['bayesian-inference', 'bayesian-networks', 'causal-inference', 'causal-models', 'causal-networks', 'causalnex', 'data-science', 'machine-learning'] | ['bayesian-inference', 'bayesian-networks', 'causal-inference', 'causal-models', 'causal-networks', 'causalnex', 'causation', 'data-science', 'machine-learning'] | 2023-07-11 | [('py-why/dowhy', 0.7358757853507996, 'ml', 5), ('willianfuks/tfcausalimpact', 0.6074860692024231, 'math', 1), ('py-why/econml', 0.5422161221504211, 'ml', 2), ('rasbt/mlxtend', 0.5194756984710693, 'ml', 2), ('carla-recourse/carla', 0.5116320252418518, 'ml', 1), ('teamhg-memex/eli5', 0.5101348161697388, 'ml', 2)] | 35 | 3 | null | 0.37 | 7 | 0 | 50 | 6 | 4 | 5 | 4 | 7 | 1 | 90 | 0.1 | 36 |
1,505 | study | https://github.com/cgpotts/cs224u | ['nlp', 'nlu'] | Code for CS224u: Natural Language Understanding | [] | [] | null | null | null | cgpotts/cs224u | cs224u | 2,020 | 861 | 85 | Jupyter Notebook | null | Code for Stanford CS224u | cgpotts | 2024-01-12 | 2015-01-30 | 469 | 4.301795 | null | Code for Stanford CS224u | [] | ['nlp', 'nlu'] | 2023-12-14 | [('tatsu-lab/stanford_alpaca', 0.5506641268730164, 'llm', 0), ('lexpredict/lexpredict-lexnlp', 0.5473800897598267, 'nlp', 1), ('allenai/allennlp', 0.5052735805511475, 'nlp', 1)] | 30 | 6 | null | 0.44 | 2 | 2 | 109 | 1 | 0 | 0 | 0 | 2 | 2 | 90 | 1 | 36 |
301 | template | https://github.com/pyscaffold/pyscaffold | [] | null | [] | [] | null | null | null | pyscaffold/pyscaffold | pyscaffold | 1,941 | 177 | 39 | Python | https://pyscaffold.org | 🛠 Python project template generator with batteries included | pyscaffold | 2024-01-13 | 2014-04-02 | 512 | 3.78468 | https://avatars.githubusercontent.com/u/34571116?v=4 | 🛠 Python project template generator with batteries included | ['distribution', 'git', 'package', 'package-creation', 'project-template', 'release-automation', 'template-project'] | ['distribution', 'git', 'package', 'package-creation', 'project-template', 'release-automation', 'template-project'] | 2023-06-20 | [('eugeneyan/python-collab-template', 0.6021620631217957, 'template', 0), ('martinheinz/python-project-blueprint', 0.5791087746620178, 'template', 0), ('sqlalchemy/mako', 0.5707492828369141, 'template', 0), ('pypa/hatch', 0.5699118971824646, 'util', 0), ('tezromach/python-package-template', 0.553695797920227, 'template', 0), ('pdoc3/pdoc', 0.5431029796600342, 'util', 0), ('python-poetry/poetry', 0.5353856682777405, 'util', 0), ('pdm-project/pdm', 0.531360924243927, 'util', 0), ('pypa/flit', 0.5155603885650635, 'util', 0), ('indygreg/pyoxidizer', 0.5098890662193298, 'util', 0)] | 58 | 6 | null | 1.12 | 6 | 0 | 119 | 7 | 3 | 20 | 3 | 6 | 6 | 90 | 1 | 36 |
382 | llm | https://github.com/minimaxir/aitextgen | [] | null | [] | [] | null | null | null | minimaxir/aitextgen | aitextgen | 1,824 | 220 | 42 | Python | https://docs.aitextgen.io | A robust Python tool for text-based AI training and generation using GPT-2. | minimaxir | 2024-01-14 | 2019-12-29 | 213 | 8.551909 | null | A robust Python tool for text-based AI training and generation using GPT-2. | [] | [] | 2023-05-17 | [('minimaxir/gpt-2-simple', 0.7194006443023682, 'llm', 0), ('microsoft/pycodegpt', 0.6111297011375427, 'llm', 0), ('facebookresearch/parlai', 0.5751350522041321, 'nlp', 0), ('minimaxir/textgenrnn', 0.57369065284729, 'nlp', 0), ('huggingface/text-generation-inference', 0.563347339630127, 'llm', 0), ('databrickslabs/dolly', 0.5475108623504639, 'llm', 0), ('nvidia/nemo', 0.5452271699905396, 'nlp', 0), ('google/sentencepiece', 0.5358452796936035, 'nlp', 0), ('microsoft/generative-ai-for-beginners', 0.5282863974571228, 'study', 0), ('explosion/spacy', 0.5253174901008606, 'nlp', 0), ('nateshmbhat/pyttsx3', 0.5253090858459473, 'util', 0), ('sharonzhou/long_stable_diffusion', 0.5227047801017761, 'diffusion', 0), ('prefecthq/marvin', 0.5176252126693726, 'nlp', 0), ('bytedance/lightseq', 0.5134293437004089, 'nlp', 0), ('openlmlab/moss', 0.5120126008987427, 'llm', 0), ('torantulino/auto-gpt', 0.5113155841827393, 'llm', 0), ('minimaxir/simpleaichat', 0.507713258266449, 'llm', 0), ('google-research/electra', 0.5072848796844482, 'ml-dl', 0), ('killianlucas/open-interpreter', 0.5057147741317749, 'llm', 0), ('kagisearch/vectordb', 0.5047615766525269, 'data', 0), ('krohling/bondai', 0.5016161799430847, 'llm', 0), ('norskregnesentral/skweak', 0.5007104873657227, 'nlp', 0)] | 11 | 4 | null | 0.04 | 3 | 0 | 49 | 8 | 0 | 3 | 3 | 3 | 3 | 90 | 1 | 36 |
364 | ml | https://github.com/rentruewang/koila | [] | null | [] | [] | null | null | null | rentruewang/koila | koila | 1,804 | 64 | 11 | Python | https://rentruewang.github.io/koila/ | Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code. | rentruewang | 2024-01-12 | 2021-11-17 | 114 | 15.706468 | null | Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code. | ['deep-learning', 'gradient-accumulation', 'lazy-evaluation', 'machine-learning', 'memory-management', 'neural-network', 'out-of-memory', 'pytorch'] | ['deep-learning', 'gradient-accumulation', 'lazy-evaluation', 'machine-learning', 'memory-management', 'neural-network', 'out-of-memory', 'pytorch'] | 2024-01-10 | [('blackhc/toma', 0.6982141137123108, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.6254847645759583, 'perf', 4), ('pytorch/ignite', 0.6201809644699097, 'ml-dl', 4), ('nvidia/apex', 0.6033901572227478, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5876379609107971, 'study', 3), ('arogozhnikov/einops', 0.5856212973594666, 'ml-dl', 2), ('skorch-dev/skorch', 0.5766161680221558, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.5745282769203186, 'study', 3), ('pytorch/data', 0.5698388814926147, 'data', 0), ('cvxgrp/pymde', 0.5673205852508545, 'ml', 2), ('karpathy/micrograd', 0.5448654890060425, 'study', 0), ('huggingface/accelerate', 0.5446726083755493, 'ml', 0), ('timdettmers/bitsandbytes', 0.5362823009490967, 'util', 0), ('pyg-team/pytorch_geometric', 0.5282607674598694, 'ml-dl', 2), ('allenai/allennlp', 0.5262885689735413, 'nlp', 2), ('denys88/rl_games', 0.5238789916038513, 'ml-rl', 2), ('ashleve/lightning-hydra-template', 0.5227289199829102, 'util', 2), ('pytorch/pytorch', 0.5214821696281433, 'ml-dl', 3), ('pytorch/torchrec', 0.5190505385398865, 'ml-dl', 2), ('cupy/cupy', 0.5168486833572388, 'math', 0), ('pytorch/captum', 0.5131102800369263, 'ml-interpretability', 0), ('nicolas-chaulet/torch-points3d', 0.5067731738090515, 'ml', 0), ('nvidia/cuda-python', 0.5058760046958923, 'ml', 0)] | 4 | 2 | null | 0.46 | 1 | 0 | 26 | 0 | 0 | 1 | 1 | 1 | 0 | 90 | 0 | 36 |
908 | math | https://github.com/google-research/torchsde | [] | null | [] | [] | null | null | null | google-research/torchsde | torchsde | 1,415 | 178 | 34 | Python | null | Differentiable SDE solvers with GPU support and efficient sensitivity analysis. | google-research | 2024-01-12 | 2020-07-06 | 186 | 7.601688 | https://avatars.githubusercontent.com/u/43830688?v=4 | Differentiable SDE solvers with GPU support and efficient sensitivity analysis. | ['deep-learning', 'deep-neural-networks', 'differential-equations', 'dynamical-systems', 'neural-differential-equations', 'pytorch', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-volatility-models'] | ['deep-learning', 'deep-neural-networks', 'differential-equations', 'dynamical-systems', 'neural-differential-equations', 'pytorch', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-volatility-models'] | 2023-09-26 | [('denys88/rl_games', 0.5168188810348511, 'ml-rl', 2), ('stability-ai/stability-sdk', 0.5064745545387268, 'diffusion', 0)] | 8 | 4 | null | 0.12 | 5 | 4 | 43 | 4 | 1 | 2 | 1 | 5 | 6 | 90 | 1.2 | 36 |
1,214 | crypto | https://github.com/binance/binance-public-data | [] | null | [] | [] | null | null | null | binance/binance-public-data | binance-public-data | 1,231 | 411 | 32 | Python | null | Details on how to get Binance public data | binance | 2024-01-13 | 2020-08-24 | 179 | 6.871611 | https://avatars.githubusercontent.com/u/69836600?v=4 | Details on how to get Binance public data | [] | [] | 2023-11-01 | [] | 20 | 3 | null | 0.15 | 51 | 38 | 41 | 2 | 0 | 0 | 0 | 51 | 48 | 90 | 0.9 | 36 |
949 | web | https://github.com/long2ice/fastapi-cache | [] | null | [] | [] | null | null | null | long2ice/fastapi-cache | fastapi-cache | 974 | 119 | 9 | Python | https://github.com/long2ice/fastapi-cache | fastapi-cache is a tool to cache fastapi response and function result, with backends support redis and memcached. | long2ice | 2024-01-12 | 2020-08-25 | 179 | 5.441341 | null | fastapi-cache is a tool to cache fastapi response and function result, with backends support redis and memcached. | ['cache', 'fastapi', 'memcached', 'redis'] | ['cache', 'fastapi', 'memcached', 'redis'] | 2023-12-07 | [('aio-libs/aiocache', 0.6432105898857117, 'data', 3), ('grantjenks/python-diskcache', 0.6009683609008789, 'util', 1), ('dgilland/cacheout', 0.5193830728530884, 'perf', 0), ('zilliztech/gptcache', 0.506112813949585, 'llm', 1), ('python-cachier/cachier', 0.5056164860725403, 'perf', 1), ('dmontagu/fastapi_client', 0.5032126307487488, 'web', 0)] | 26 | 1 | null | 2.75 | 65 | 38 | 41 | 1 | 1 | 3 | 1 | 65 | 43 | 90 | 0.7 | 36 |
482 | gis | https://github.com/sentinelsat/sentinelsat | [] | null | [] | [] | null | null | null | sentinelsat/sentinelsat | sentinelsat | 943 | 239 | 62 | Python | https://sentinelsat.readthedocs.io | Search and download Copernicus Sentinel satellite images | sentinelsat | 2024-01-10 | 2015-05-22 | 453 | 2.079055 | https://avatars.githubusercontent.com/u/29057552?v=4 | Search and download Copernicus Sentinel satellite images | ['copernicus', 'esa', 'geographic-data', 'open-data', 'remote-sensing', 'satellite-imagery', 'sentinel'] | ['copernicus', 'esa', 'geographic-data', 'open-data', 'remote-sensing', 'satellite-imagery', 'sentinel'] | 2023-11-08 | [('plant99/felicette', 0.6691089272499084, 'gis', 1), ('giswqs/aws-open-data-geo', 0.6044768691062927, 'gis', 2), ('sentinel-hub/sentinelhub-py', 0.5519406199455261, 'gis', 1), ('developmentseed/label-maker', 0.5269395112991333, 'gis', 2), ('azavea/raster-vision', 0.5150529742240906, 'gis', 1), ('developmentseed/landsat-util', 0.5123438239097595, 'gis', 0)] | 43 | 5 | null | 0.23 | 11 | 9 | 105 | 2 | 1 | 3 | 1 | 11 | 37 | 90 | 3.4 | 36 |
1,140 | viz | https://github.com/nomic-ai/deepscatter | [] | null | [] | [] | null | null | null | nomic-ai/deepscatter | deepscatter | 928 | 42 | 16 | TypeScript | null | Zoomable, animated scatterplots in the browser that scales over a billion points | nomic-ai | 2024-01-13 | 2018-10-30 | 274 | 3.386861 | https://avatars.githubusercontent.com/u/102670180?v=4 | Zoomable, animated scatterplots in the browser that scales over a billion points | ['data-visualization', 'visualization', 'webgl'] | ['data-visualization', 'visualization', 'webgl'] | 2024-01-10 | [('visgl/deck.gl', 0.6989966630935669, 'viz', 3), ('holoviz/datashader', 0.6156784296035767, 'gis', 0), ('bokeh/bokeh', 0.594667911529541, 'viz', 1), ('mckinsey/vizro', 0.5346618294715881, 'viz', 2), ('altair-viz/altair', 0.5337156653404236, 'viz', 1), ('plotly/plotly.py', 0.5329233407974243, 'viz', 2), ('residentmario/geoplot', 0.5221992135047913, 'gis', 0), ('raphaelquast/eomaps', 0.5207083225250244, 'gis', 1), ('holoviz/holoviz', 0.5163466930389404, 'viz', 0), ('holoviz/hvplot', 0.511212944984436, 'pandas', 0), ('pyqtgraph/pyqtgraph', 0.5000237822532654, 'viz', 1)] | 16 | 4 | null | 2.17 | 11 | 8 | 63 | 0 | 3 | 1 | 3 | 11 | 4 | 90 | 0.4 | 36 |
874 | time-series | https://github.com/winedarksea/autots | [] | null | [] | [] | null | null | null | winedarksea/autots | AutoTS | 925 | 87 | 18 | Python | null | Automated Time Series Forecasting | winedarksea | 2024-01-13 | 2019-11-26 | 218 | 4.243119 | null | Automated Time Series Forecasting | ['automl', 'autots', 'deep-learning', 'feature-engineering', 'forecasting', 'machine-learning', 'preprocessing', 'time-series'] | ['automl', 'autots', 'deep-learning', 'feature-engineering', 'forecasting', 'machine-learning', 'preprocessing', 'time-series'] | 2024-01-03 | [('awslabs/autogluon', 0.7558371424674988, 'ml', 5), ('sktime/sktime', 0.7021391987800598, 'time-series', 3), ('ourownstory/neural_prophet', 0.6846452355384827, 'ml', 4), ('salesforce/merlion', 0.682404637336731, 'time-series', 4), ('microsoft/nni', 0.6789240837097168, 'ml', 4), ('automl/auto-sklearn', 0.6762388944625854, 'ml', 1), ('firmai/atspy', 0.6719325184822083, 'time-series', 2), ('keras-team/autokeras', 0.6600058078765869, 'ml-dl', 3), ('microsoft/flaml', 0.6591876745223999, 'ml', 3), ('nccr-itmo/fedot', 0.6496773958206177, 'ml-ops', 2), ('xplainable/xplainable', 0.6385115385055542, 'ml-interpretability', 1), ('nixtla/statsforecast', 0.6360719799995422, 'time-series', 4), ('huggingface/autotrain-advanced', 0.6144011616706848, 'ml', 2), ('alkaline-ml/pmdarima', 0.6125960350036621, 'time-series', 3), ('mljar/mljar-supervised', 0.578948438167572, 'ml', 3), ('alpa-projects/alpa', 0.5752678513526917, 'ml-dl', 2), ('shankarpandala/lazypredict', 0.5729676485061646, 'ml', 2), ('salesforce/deeptime', 0.5623159408569336, 'time-series', 3), ('awslabs/gluonts', 0.5618434548377991, 'time-series', 4), ('aistream-peelout/flow-forecast', 0.557109534740448, 'time-series', 3), ('bentoml/bentoml', 0.5495694875717163, 'ml-ops', 2), ('torantulino/auto-gpt', 0.5468161702156067, 'llm', 0), ('autoviml/auto_ts', 0.5450201034545898, 'time-series', 2), ('featurelabs/featuretools', 0.5383171439170837, 'ml', 3), ('feast-dev/feast', 0.5365046262741089, 'ml-ops', 1), ('unit8co/darts', 0.5341631770133972, 'time-series', 4), ('alirezadir/machine-learning-interview-enlightener', 0.5339345335960388, 'study', 2), ('mosaicml/composer', 0.5297396183013916, 'ml-dl', 2), ('mindsdb/mindsdb', 0.5278527736663818, 'data', 2), ('facebook/prophet', 0.5207078456878662, 'time-series', 2), ('blue-yonder/tsfresh', 0.5182939171791077, 'time-series', 1), ('onnx/onnx', 0.516512930393219, 'ml', 2), ('google/temporian', 0.5130333304405212, 'time-series', 2), ('google/pyglove', 0.5115013122558594, 'util', 2), ('ydataai/ydata-synthetic', 0.5098974704742432, 'data', 3), ('uber/orbit', 0.5088127255439758, 'time-series', 3), ('huggingface/datasets', 0.5051737427711487, 'nlp', 2)] | 1 | 0 | null | 5.08 | 21 | 15 | 50 | 0 | 13 | 12 | 13 | 21 | 50 | 90 | 2.4 | 36 |
929 | web | https://github.com/koxudaxi/fastapi-code-generator | [] | null | [] | [] | null | null | null | koxudaxi/fastapi-code-generator | fastapi-code-generator | 862 | 92 | 20 | Python | null | This code generator creates FastAPI app from an openapi file. | koxudaxi | 2024-01-12 | 2020-06-14 | 189 | 4.553962 | null | This code generator creates FastAPI app from an openapi file. | ['fastapi', 'generator', 'openapi', 'pydantic'] | ['fastapi', 'generator', 'openapi', 'pydantic'] | 2023-09-07 | [('dmontagu/fastapi_client', 0.6843157410621643, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.607068657875061, 'template', 1), ('kuimono/openapi-schema-pydantic', 0.6038326025009155, 'util', 1)] | 24 | 3 | null | 1.21 | 16 | 8 | 44 | 4 | 5 | 11 | 5 | 16 | 18 | 90 | 1.1 | 36 |
842 | util | https://github.com/wolph/python-progressbar | [] | null | [] | [] | null | null | null | wolph/python-progressbar | python-progressbar | 831 | 141 | 22 | Python | http://progressbar-2.readthedocs.org/en/latest/ | Progressbar 2 - A progress bar for Python 2 and Python 3 - "pip install progressbar2" | wolph | 2024-01-10 | 2012-02-20 | 623 | 1.333563 | null | Progressbar 2 - A progress bar for Python 2 and Python 3 - "pip install progressbar2" | ['bar', 'cli', 'console', 'eta', 'gui', 'percentage', 'progress', 'progress-bar', 'progressbar', 'rate', 'terminal', 'time'] | ['bar', 'cli', 'console', 'eta', 'gui', 'percentage', 'progress', 'progress-bar', 'progressbar', 'rate', 'terminal', 'time'] | 2024-01-02 | [('tqdm/tqdm', 0.7864949107170105, 'term', 9), ('rockhopper-technologies/enlighten', 0.7673426270484924, 'term', 0), ('rsalmei/alive-progress', 0.6114795207977295, 'util', 7), ('hugovk/pypistats', 0.5268552303314209, 'util', 1)] | 46 | 3 | null | 0.96 | 13 | 9 | 145 | 0 | 3 | 9 | 3 | 13 | 38 | 90 | 2.9 | 36 |
1,500 | ml-dl | https://github.com/deepmind/chex | ['numpy', 'testing', 'autograd', 'jax'] | Chex is a library of utilities for helping to write reliable JAX code | [] | [] | null | null | null | deepmind/chex | chex | 667 | 40 | 18 | Python | https://chex.readthedocs.io | null | deepmind | 2024-01-13 | 2020-08-06 | 181 | 3.670597 | https://avatars.githubusercontent.com/u/8596759?v=4 | Chex is a library of utilities for helping to write reliable JAX code | [] | ['autograd', 'jax', 'numpy', 'testing'] | 2023-12-09 | [('google/flax', 0.5962191820144653, 'ml-dl', 1), ('deepmind/dm-haiku', 0.5875384211540222, 'ml-dl', 1), ('deepmind/synjax', 0.5291113257408142, 'math', 1), ('samuelcolvin/rtoml', 0.5042668581008911, 'data', 0)] | 40 | 4 | null | 1.33 | 18 | 11 | 42 | 1 | 7 | 6 | 7 | 18 | 8 | 90 | 0.4 | 36 |
1,497 | util | https://github.com/instagram/fixit | ['linter'] | null | [] | [] | null | null | null | instagram/fixit | Fixit | 633 | 57 | 26 | Python | https://fixit.rtfd.io/en/latest/ | Advanced Python linting framework with auto-fixes and hierarchical configuration that makes it easy to write custom in-repo lint rules. | instagram | 2024-01-14 | 2020-02-20 | 205 | 3.077083 | https://avatars.githubusercontent.com/u/549085?v=4 | Advanced Python linting framework with auto-fixes and hierarchical configuration that makes it easy to write custom in-repo lint rules. | [] | ['linter'] | 2023-12-21 | [('python-rope/rope', 0.5731984972953796, 'util', 0), ('pycqa/pyflakes', 0.5664411783218384, 'util', 1), ('python/mypy', 0.5660220384597778, 'typing', 1), ('grahamdumpleton/wrapt', 0.5495676398277283, 'util', 0), ('klen/pylama', 0.5459169745445251, 'util', 1), ('landscapeio/prospector', 0.5204662084579468, 'util', 0), ('eugeneyan/python-collab-template', 0.5144226551055908, 'template', 0), ('pytoolz/toolz', 0.5103968977928162, 'util', 0), ('asottile/reorder-python-imports', 0.5014389753341675, 'util', 1)] | 41 | 4 | null | 2.12 | 37 | 22 | 47 | 1 | 0 | 3 | 3 | 37 | 28 | 90 | 0.8 | 36 |
1,144 | util | https://github.com/terrycain/aioboto3 | [] | null | [] | [] | null | null | null | terrycain/aioboto3 | aioboto3 | 608 | 63 | 8 | Python | null | Wrapper to use boto3 resources with the aiobotocore async backend | terrycain | 2024-01-12 | 2017-09-25 | 331 | 1.836066 | null | Wrapper to use boto3 resources with the aiobotocore async backend | ['async', 'aws', 'boto3'] | ['async', 'aws', 'boto3'] | 2023-12-08 | [('aio-libs/aiobotocore', 0.6956399083137512, 'util', 1), ('geeogi/async-python-lambda-template', 0.5498723387718201, 'template', 0), ('samuelcolvin/aioaws', 0.5090085864067078, 'data', 1)] | 27 | 4 | null | 0.37 | 14 | 10 | 77 | 1 | 0 | 10 | 10 | 14 | 29 | 90 | 2.1 | 36 |
358 | ml-ops | https://github.com/google/ml-metadata | [] | null | [] | [] | null | null | null | google/ml-metadata | ml-metadata | 577 | 134 | 29 | C++ | https://www.tensorflow.org/tfx/guide/mlmd | For recording and retrieving metadata associated with ML developer and data scientist workflows. | google | 2024-01-10 | 2019-01-15 | 263 | 2.193916 | https://avatars.githubusercontent.com/u/1342004?v=4 | For recording and retrieving metadata associated with ML developer and data scientist workflows. | [] | [] | 2024-01-12 | [('astronomer/astro-sdk', 0.5482959747314453, 'ml-ops', 0), ('whylabs/whylogs', 0.5445284247398376, 'util', 0), ('ploomber/ploomber', 0.5324745774269104, 'ml-ops', 0), ('airbnb/knowledge-repo', 0.5292649865150452, 'data', 0), ('hyperqueryhq/whale', 0.5290652513504028, 'data', 0), ('dagworks-inc/hamilton', 0.5270819664001465, 'ml-ops', 0), ('mage-ai/mage-ai', 0.524271547794342, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5165597200393677, 'ml-ops', 0), ('simonw/datasette', 0.5161576271057129, 'data', 0), ('netflix/metaflow', 0.5132206082344055, 'ml-ops', 0), ('intake/intake', 0.506055474281311, 'data', 0), ('linealabs/lineapy', 0.5038784146308899, 'jupyter', 0), ('dbt-labs/dbt-core', 0.5018987655639648, 'ml-ops', 0), ('iterative/dvc', 0.5009445548057556, 'ml-ops', 0)] | 19 | 2 | null | 1.1 | 12 | 1 | 61 | 0 | 3 | 7 | 3 | 12 | 53 | 90 | 4.4 | 36 |
1,569 | ml | https://github.com/nicolas-hbt/pygraft | ['knowledge-graph', 'ontology-generation'] | null | [] | [] | 1 | null | null | nicolas-hbt/pygraft | pygraft | 551 | 36 | 12 | Python | https://pygraft.readthedocs.io/en/latest/ | Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips | nicolas-hbt | 2024-01-12 | 2023-09-07 | 20 | 26.6 | null | Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips | ['artificial-intelligence', 'benchmarking', 'contributions-welcome', 'data-generator', 'graph-generator', 'knowledge-base', 'knowledge-graph', 'linked-data', 'machine-learning', 'ontology', 'ontology-generation', 'owl', 'rdf', 'rdfs', 'schema', 'semantic-web', 'semantics', 'synthetic-data', 'synthetic-dataset-generation'] | ['artificial-intelligence', 'benchmarking', 'contributions-welcome', 'data-generator', 'graph-generator', 'knowledge-base', 'knowledge-graph', 'linked-data', 'machine-learning', 'ontology', 'ontology-generation', 'owl', 'rdf', 'rdfs', 'schema', 'semantic-web', 'semantics', 'synthetic-data', 'synthetic-dataset-generation'] | 2023-12-01 | [('sdv-dev/sdv', 0.6006342172622681, 'data', 2), ('mindsdb/mindsdb', 0.5319455862045288, 'data', 2), ('dylanhogg/llmgraph', 0.5217655897140503, 'ml', 1), ('ydataai/ydata-synthetic', 0.5071893334388733, 'data', 2), ('strawberry-graphql/strawberry', 0.5039038062095642, 'web', 0)] | 1 | 1 | null | 0.75 | 1 | 0 | 4 | 1 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 36 |
1,247 | util | https://github.com/steamship-core/steamship-langchain | [] | null | [] | [] | null | null | null | steamship-core/steamship-langchain | steamship-langchain | 499 | 98 | 12 | Python | null | steamship-langchain | steamship-core | 2024-01-12 | 2023-02-04 | 51 | 9.702778 | https://avatars.githubusercontent.com/u/99272373?v=4 | steamship-langchain | [] | [] | 2023-09-12 | [('steamship-core/python-client', 0.5675639510154724, 'util', 0)] | 7 | 2 | null | 2.23 | 1 | 0 | 11 | 4 | 17 | 42 | 17 | 1 | 1 | 90 | 1 | 36 |
1,706 | util | https://github.com/snok/install-poetry | ['github', 'action'] | null | [] | [] | null | null | null | snok/install-poetry | install-poetry | 491 | 46 | 6 | Shell | null | Github action for installing and configuring Poetry | snok | 2024-01-05 | 2020-10-25 | 170 | 2.883389 | https://avatars.githubusercontent.com/u/64945977?v=4 | Github action for installing and configuring Poetry | [] | ['action', 'github'] | 2024-01-11 | [('python-poetry/install.python-poetry.org', 0.6302388310432434, 'util', 0)] | 22 | 5 | null | 0.44 | 15 | 11 | 39 | 0 | 1 | 8 | 1 | 15 | 27 | 90 | 1.8 | 36 |
1,612 | llm | https://github.com/lupantech/scienceqa | ['thought-chain'] | null | [] | [] | null | null | null | lupantech/scienceqa | ScienceQA | 487 | 62 | 9 | Python | null | Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering". | lupantech | 2024-01-13 | 2022-10-17 | 67 | 7.253191 | null | Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering". | [] | ['thought-chain'] | 2023-12-30 | [('kyegomez/tree-of-thoughts', 0.5537173748016357, 'llm', 0), ('noahshinn/reflexion', 0.5062936544418335, 'llm', 0)] | 4 | 2 | null | 0.67 | 5 | 5 | 15 | 0 | 0 | 1 | 1 | 5 | 10 | 90 | 2 | 36 |
490 | gis | https://github.com/cogeotiff/rio-tiler | [] | null | [] | [] | null | null | null | cogeotiff/rio-tiler | rio-tiler | 453 | 96 | 65 | Python | https://cogeotiff.github.io/rio-tiler/ | User friendly Rasterio plugin to read raster datasets. | cogeotiff | 2024-01-11 | 2017-10-06 | 329 | 1.374512 | https://avatars.githubusercontent.com/u/40065466?v=4 | User friendly Rasterio plugin to read raster datasets. | ['cog', 'cogeotiff', 'gdal', 'maptile', 'mercator', 'raster', 'raster-processing', 'rasterio', 'satellite', 'slippy-map', 'tile'] | ['cog', 'cogeotiff', 'gdal', 'maptile', 'mercator', 'raster', 'raster-processing', 'rasterio', 'satellite', 'slippy-map', 'tile'] | 2024-01-12 | [('rasterio/rasterio', 0.7036706209182739, 'gis', 2), ('cogeotiff/rio-cogeo', 0.6072856783866882, 'gis', 4), ('osgeo/gdal', 0.5156446099281311, 'gis', 1), ('corteva/rioxarray', 0.5002418756484985, 'gis', 3)] | 26 | 4 | null | 1.38 | 20 | 15 | 76 | 0 | 0 | 24 | 24 | 20 | 29 | 90 | 1.4 | 36 |
855 | ml-ops | https://github.com/astronomer/astronomer | [] | null | [] | [] | null | null | null | astronomer/astronomer | astronomer | 453 | 83 | 45 | Python | https://www.astronomer.io | Helm Charts for the Astronomer Platform, Apache Airflow as a Service on Kubernetes | astronomer | 2024-01-11 | 2018-01-15 | 315 | 1.437443 | https://avatars.githubusercontent.com/u/12449437?v=4 | Helm Charts for the Astronomer Platform, Apache Airflow as a Service on Kubernetes | ['apache-airflow', 'astronomer-platform', 'docker', 'kubernetes'] | ['apache-airflow', 'astronomer-platform', 'docker', 'kubernetes'] | 2024-01-09 | [('astronomer/airflow-chart', 0.8572540283203125, 'ml-ops', 2), ('anyscale/airflow-provider-ray', 0.5748955011367798, 'ml-ops', 0), ('apache/airflow', 0.5602481961250305, 'ml-ops', 1), ('gefyrahq/gefyra', 0.5323020815849304, 'util', 2)] | 75 | 4 | null | 5.15 | 56 | 53 | 73 | 0 | 15 | 60 | 15 | 56 | 17 | 90 | 0.3 | 36 |
1,457 | util | https://github.com/conda/constructor | ['conda'] | null | [] | [] | null | null | null | conda/constructor | constructor | 430 | 166 | 33 | Python | https://conda.github.io/constructor/ | tool for creating installers from conda packages | conda | 2024-01-04 | 2016-02-12 | 415 | 1.03472 | https://avatars.githubusercontent.com/u/6392739?v=4 | tool for creating installers from conda packages | [] | ['conda'] | 2024-01-13 | [('conda/conda-build', 0.8005626201629639, 'util', 1), ('conda/conda-pack', 0.7213409543037415, 'util', 1), ('mamba-org/boa', 0.7149392366409302, 'util', 1), ('mamba-org/quetz', 0.7079612612724304, 'util', 1), ('mamba-org/gator', 0.5621293783187866, 'jupyter', 1), ('mamba-org/mamba', 0.5558754801750183, 'util', 1), ('pyodide/micropip', 0.5295533537864685, 'util', 0), ('ofek/pyapp', 0.5201569199562073, 'util', 0), ('conda-forge/feedstocks', 0.507718563079834, 'util', 1)] | 70 | 6 | null | 1.56 | 57 | 47 | 96 | 0 | 8 | 6 | 8 | 57 | 33 | 90 | 0.6 | 36 |
713 | gis | https://github.com/weecology/deepforest | [] | null | [] | [] | null | null | null | weecology/deepforest | DeepForest | 411 | 157 | 15 | Python | https://deepforest.readthedocs.io/ | Python Package for Airborne RGB machine learning | weecology | 2024-01-10 | 2018-03-07 | 307 | 1.335035 | https://avatars.githubusercontent.com/u/1156696?v=4 | Python Package for Airborne RGB machine learning | [] | [] | 2023-12-27 | [('sentinel-hub/eo-learn', 0.5667561888694763, 'gis', 0), ('mdbloice/augmentor', 0.5662134885787964, 'ml', 0), ('lightly-ai/lightly', 0.5572022199630737, 'ml', 0), ('pycaret/pycaret', 0.5376171469688416, 'ml', 0), ('earthlab/earthpy', 0.5284902453422546, 'gis', 0), ('radiantearth/radiant-mlhub', 0.5279979705810547, 'gis', 0), ('gradio-app/gradio', 0.525834858417511, 'viz', 0), ('azavea/raster-vision', 0.5204096436500549, 'gis', 0), ('facebookresearch/pytorch3d', 0.5166642069816589, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5097160339355469, 'study', 0), ('featurelabs/featuretools', 0.5066676139831543, 'ml', 0), ('rasbt/mlxtend', 0.5020350217819214, 'ml', 0)] | 14 | 6 | null | 2.17 | 115 | 86 | 71 | 1 | 0 | 12 | 12 | 115 | 104 | 90 | 0.9 | 36 |
1,526 | llm | https://github.com/operand/agency | [] | null | [] | [] | null | null | null | operand/agency | agency | 351 | 18 | 10 | Python | https://createwith.agency | A fast and minimal framework for building agent-integrated systems | operand | 2024-01-12 | 2023-05-23 | 36 | 9.75 | null | A fast and minimal framework for building agent-integrated systems | ['actor', 'actor-model', 'agent', 'agents', 'agi', 'ai', 'api', 'artificial-general-intelligence', 'artificial-intelligence', 'autonomous-agent', 'autonomous-agents', 'framework', 'llm', 'llmops', 'llms', 'machine-learning', 'minimal'] | ['actor', 'actor-model', 'agent', 'agents', 'agi', 'ai', 'api', 'artificial-general-intelligence', 'artificial-intelligence', 'autonomous-agent', 'autonomous-agents', 'framework', 'llm', 'llmops', 'llms', 'machine-learning', 'minimal'] | 2024-01-12 | [('prefecthq/marvin', 0.6231884956359863, 'nlp', 3), ('transformeroptimus/superagi', 0.6139060258865356, 'llm', 8), ('microsoft/lmops', 0.6131894588470459, 'llm', 2), ('geekan/metagpt', 0.6091906428337097, 'llm', 2), ('mlc-ai/mlc-llm', 0.6070036888122559, 'llm', 1), ('unity-technologies/ml-agents', 0.596383810043335, 'ml-rl', 1), ('mindsdb/mindsdb', 0.5936707854270935, 'data', 4), ('ludwig-ai/ludwig', 0.5899900197982788, 'ml-ops', 2), ('zacwellmer/worldmodels', 0.5862823128700256, 'ml-rl', 0), ('aiwaves-cn/agents', 0.5783196687698364, 'nlp', 2), ('antonosika/gpt-engineer', 0.5778323411941528, 'llm', 2), ('cheshire-cat-ai/core', 0.5766038298606873, 'llm', 2), ('projectmesa/mesa', 0.5751315355300903, 'sim', 0), ('bentoml/bentoml', 0.5735385417938232, 'ml-ops', 3), ('nccr-itmo/fedot', 0.5655133128166199, 'ml-ops', 1), ('microsoft/semantic-kernel', 0.5540736317634583, 'llm', 3), ('lastmile-ai/aiconfig', 0.5538642406463623, 'util', 2), ('lucidrains/toolformer-pytorch', 0.5433422923088074, 'llm', 1), ('facebookresearch/habitat-lab', 0.5416178107261658, 'sim', 1), ('pathwaycom/llm-app', 0.5409510135650635, 'llm', 3), ('hpcaitech/colossalai', 0.5398745536804199, 'llm', 1), ('facebookresearch/droidlet', 0.5389538407325745, 'sim', 0), ('deepset-ai/haystack', 0.5344579815864563, 'llm', 2), ('microsoft/promptflow', 0.5344325304031372, 'llm', 2), ('ml-tooling/opyrator', 0.5331600308418274, 'viz', 1), ('krohling/bondai', 0.5317063331604004, 'llm', 2), ('yoheinakajima/babyagi', 0.53130042552948, 'llm', 2), ('pettingzoo-team/pettingzoo', 0.5311383605003357, 'ml-rl', 1), ('smol-ai/developer', 0.5302104949951172, 'llm', 2), ('jina-ai/thinkgpt', 0.5273327827453613, 'llm', 0), ('pytorchlightning/pytorch-lightning', 0.526900053024292, 'ml-dl', 3), ('assafelovic/gpt-researcher', 0.5227841734886169, 'llm', 1), ('chatarena/chatarena', 0.5225850343704224, 'llm', 2), ('google/dopamine', 0.5216368436813354, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5203875303268433, 'ml-rl', 1), ('oliveirabruno01/babyagi-asi', 0.519800066947937, 'llm', 3), ('microsoft/autogen', 0.5193168520927429, 'llm', 2), ('minedojo/voyager', 0.516743540763855, 'llm', 0), ('langchain-ai/langgraph', 0.5159871578216553, 'llm', 1), ('mnotgod96/appagent', 0.51549232006073, 'llm', 2), ('googlecloudplatform/vertex-ai-samples', 0.5143194198608398, 'ml', 1), ('adap/flower', 0.5125753283500671, 'ml-ops', 4), ('linksoul-ai/autoagents', 0.5123705267906189, 'llm', 1), ('uber/fiber', 0.5117772817611694, 'data', 1), ('nebuly-ai/nebullvm', 0.5101591348648071, 'perf', 3), ('inspirai/timechamber', 0.5097473859786987, 'sim', 0), ('microsoft/generative-ai-for-beginners', 0.5075168013572693, 'study', 2), ('pytorch/rl', 0.5073025822639465, 'ml-rl', 2), ('torantulino/auto-gpt', 0.5023353099822998, 'llm', 3), ('modularml/mojo', 0.5005974769592285, 'util', 2), ('onnx/onnx', 0.5004202127456665, 'ml', 1)] | 3 | 1 | null | 5.54 | 11 | 9 | 8 | 0 | 15 | 23 | 15 | 11 | 0 | 90 | 0 | 36 |
857 | ml-ops | https://github.com/astronomer/astro-sdk | [] | null | [] | [] | null | null | null | astronomer/astro-sdk | astro-sdk | 299 | 35 | 13 | Python | https://astro-sdk-python.rtfd.io/ | Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow. | astronomer | 2024-01-12 | 2021-12-06 | 112 | 2.666242 | https://avatars.githubusercontent.com/u/12449437?v=4 | Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow. | ['airflow', 'apache-airflow', 'bigquery', 'dags', 'data-analysis', 'data-science', 'elt', 'etl', 'gcs', 'pandas', 'postgres', 's3', 'snowflake', 'sql', 'sqlite', 'workflows'] | ['airflow', 'apache-airflow', 'bigquery', 'dags', 'data-analysis', 'data-science', 'elt', 'etl', 'gcs', 'pandas', 'postgres', 's3', 'snowflake', 'sql', 'sqlite', 'workflows'] | 2024-01-09 | [('mage-ai/mage-ai', 0.6467173099517822, 'ml-ops', 4), ('apache/airflow', 0.6314418911933899, 'ml-ops', 5), ('kestra-io/kestra', 0.6130484342575073, 'ml-ops', 2), ('flyteorg/flyte', 0.5711103081703186, 'ml-ops', 2), ('hi-primus/optimus', 0.5675498843193054, 'ml-ops', 2), ('ploomber/ploomber', 0.5675156116485596, 'ml-ops', 1), ('google/ml-metadata', 0.5482959747314453, 'ml-ops', 0), ('prefecthq/server', 0.5464804768562317, 'util', 0), ('orchest/orchest', 0.5454973578453064, 'ml-ops', 3), ('getindata/kedro-kubeflow', 0.541987419128418, 'ml-ops', 0), ('dagster-io/dagster', 0.5415989756584167, 'ml-ops', 2), ('kubeflow-kale/kale', 0.5413955450057983, 'ml-ops', 0), ('prefecthq/prefect', 0.5360534191131592, 'ml-ops', 1), ('linealabs/lineapy', 0.5296847224235535, 'jupyter', 0), ('aws/aws-sdk-pandas', 0.525879442691803, 'pandas', 3), ('fugue-project/fugue', 0.5199980735778809, 'pandas', 2), ('tobymao/sqlglot', 0.5131558179855347, 'data', 5), ('fastai/fastcore', 0.5107561945915222, 'util', 0), ('meltano/meltano', 0.5098263621330261, 'ml-ops', 1), ('airbytehq/airbyte', 0.5056763887405396, 'data', 6), ('kubeflow/fairing', 0.5054138898849487, 'ml-ops', 0)] | 39 | 2 | null | 3.85 | 66 | 57 | 26 | 0 | 14 | 40 | 14 | 66 | 29 | 90 | 0.4 | 36 |
1,520 | ml-ops | https://github.com/lithops-cloud/lithops | [] | null | [] | [] | null | null | null | lithops-cloud/lithops | lithops | 293 | 92 | 13 | Python | http://lithops.cloud | A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀 | lithops-cloud | 2024-01-12 | 2018-04-23 | 301 | 0.97296 | https://avatars.githubusercontent.com/u/71205470?v=4 | A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀 | ['big-data', 'big-data-analytics', 'cloud-computing', 'data-processing', 'distributed', 'kubernetes', 'multicloud', 'multiprocessing', 'object-storage', 'parallel', 'serverless', 'serverless-computing', 'serverless-functions'] | ['big-data', 'big-data-analytics', 'cloud-computing', 'data-processing', 'distributed', 'kubernetes', 'multicloud', 'multiprocessing', 'object-storage', 'parallel', 'serverless', 'serverless-computing', 'serverless-functions'] | 2024-01-12 | [('skypilot-org/skypilot', 0.6185680031776428, 'llm', 2), ('apache/spark', 0.5891563296318054, 'data', 1), ('flyteorg/flyte', 0.5794029235839844, 'ml-ops', 1), ('backtick-se/cowait', 0.5676038861274719, 'util', 1), ('jina-ai/jina', 0.5622606873512268, 'ml', 1), ('eventual-inc/daft', 0.5597764849662781, 'pandas', 0), ('airbytehq/airbyte', 0.5396174788475037, 'data', 0), ('fugue-project/fugue', 0.53661048412323, 'pandas', 1), ('aws/chalice', 0.5222293138504028, 'web', 1), ('netflix/metaflow', 0.5198798179626465, 'ml-ops', 1), ('localstack/localstack', 0.514224648475647, 'util', 0), ('dagster-io/dagster', 0.5072967410087585, 'ml-ops', 0), ('googlecloudplatform/vertex-ai-samples', 0.5040023922920227, 'ml', 0)] | 45 | 2 | null | 5.6 | 59 | 58 | 70 | 0 | 5 | 13 | 5 | 59 | 152 | 90 | 2.6 | 36 |
950 | util | https://github.com/cqcl/tket | [] | null | [] | [] | null | null | null | cqcl/tket | tket | 220 | 45 | 17 | C++ | https://tket.quantinuum.com/ | Source code for the TKET quantum compiler, Python bindings and utilities | cqcl | 2024-01-11 | 2021-09-13 | 124 | 1.772152 | https://avatars.githubusercontent.com/u/15688781?v=4 | Source code for the TKET quantum compiler, Python bindings and utilities | ['compiler', 'quantum-computing'] | ['compiler', 'quantum-computing'] | 2024-01-12 | [('pyscf/pyscf', 0.677206814289093, 'sim', 0), ('cqcl/lambeq', 0.6623311638832092, 'nlp', 0), ('quantumlib/cirq', 0.6193458437919617, 'sim', 1), ('jackhidary/quantumcomputingbook', 0.6081982851028442, 'study', 1), ('numba/llvmlite', 0.5800346732139587, 'util', 0), ('qiskit/qiskit', 0.5771373510360718, 'sim', 1)] | 29 | 2 | null | 6.54 | 155 | 134 | 28 | 0 | 50 | 58 | 50 | 155 | 102 | 90 | 0.7 | 36 |
1,867 | util | https://github.com/pypdfium2-team/pypdfium2 | [] | null | [] | [] | null | null | null | pypdfium2-team/pypdfium2 | pypdfium2 | 216 | 12 | 6 | Python | https://pypdfium2.readthedocs.io/ | Python bindings to PDFium | pypdfium2-team | 2024-01-13 | 2021-10-23 | 118 | 1.823884 | https://avatars.githubusercontent.com/u/93039761?v=4 | Python bindings to PDFium | ['pdf', 'pdf-documents', 'pdf-to-image', 'pdfium', 'rasterisation'] | ['pdf', 'pdf-documents', 'pdf-to-image', 'pdfium', 'rasterisation'] | 2024-01-10 | [('py-pdf/pypdf2', 0.6358337998390198, 'util', 2), ('pyfpdf/fpdf2', 0.6115421652793884, 'util', 1), ('camelot-dev/camelot', 0.5336915850639343, 'util', 0), ('jorisschellekens/borb', 0.5246109366416931, 'util', 1)] | 4 | 2 | null | 8.15 | 45 | 44 | 27 | 0 | 37 | 42 | 37 | 45 | 93 | 90 | 2.1 | 36 |
1,768 | data | https://github.com/meltano/sdk | ['data-engineering'] | null | [] | [] | null | null | null | meltano/sdk | sdk | 74 | 53 | 7 | Python | https://sdk.meltano.com | Write 70% less code by using the SDK to build custom extractors and loaders that adhere to the Singer standard: https://sdk.meltano.com | meltano | 2024-01-12 | 2021-06-21 | 136 | 0.543547 | https://avatars.githubusercontent.com/u/43816713?v=4 | Write 70% less code by using the SDK to build custom extractors and loaders that adhere to the Singer standard: https://sdk.meltano.com | ['sdk'] | ['data-engineering', 'sdk'] | 2024-01-11 | [] | 70 | 3 | null | 10.56 | 159 | 134 | 31 | 0 | 28 | 31 | 28 | 157 | 284 | 90 | 1.8 | 36 |
931 | data | https://github.com/scylladb/python-driver | [] | null | [] | [] | null | null | null | scylladb/python-driver | python-driver | 57 | 35 | 9 | Python | https://python-driver.docs.scylladb.com | ScyllaDB Python Driver, originally DataStax Python Driver for Apache Cassandra | scylladb | 2024-01-11 | 2018-11-20 | 271 | 0.210332 | https://avatars.githubusercontent.com/u/14364730?v=4 | ScyllaDB Python Driver, originally DataStax Python Driver for Apache Cassandra | ['scylladb'] | ['scylladb'] | 2024-01-11 | [('datastax/python-driver', 0.8284926414489746, 'data', 0), ('neo4j/neo4j-python-driver', 0.5569538474082947, 'data', 0)] | 211 | 6 | null | 2.17 | 41 | 16 | 63 | 0 | 0 | 23 | 23 | 41 | 105 | 90 | 2.6 | 36 |