nannyml
Activeloop Hub
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nannyml | Activeloop Hub | |
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7 | 31 | |
1,746 | 4,807 | |
1.8% | - | |
8.8 | 9.9 | |
7 days ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | Mozilla Public License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nannyml
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Introduction to NannyML: Model Evaluation without labels
In order to try to solve this issue, NannyML was created. NannyML is an open-source Python library designed in order to make it easy to monitor drift in the distributions of our model input variables and estimate our model performance (even without labels!) thanks to the Confidence-Based Performance Estimation algorithm they developed. But first of all, why do models need to be monitored and why their performance might vary over time?
- Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.
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[D] Data drift is not a good indicator of model performance degradation
But I may have it haha. What we propose in the blog post instead of relying solely on data drift is using performance estimation methods (eg: https://github.com/NannyML) with them you can estimate the performance of the ml model without having access to ground truth.
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[HIRING][Full Time, Part Time, Temporary, Internship, Freelance] Data Science Intern (Remote)
Description NannyML - creators of an Open Source Python library, are looking for multiple Data Science interns to help across research, prototyping, and product. Github: https://github.com/NannyML/nannyml About Us NannyML is an Open Source Python lib …
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What do you think about Detecting Silent ML Failure with an Open Source Python library?
If you think this could add value to your daily life, check it out here: https://github.com/NannyML/nannyml.
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Can I estimate the impact of data drift on performance?
I found it implemented here: https://github.com/NannyML/nannyml
- Show HN: OSS Python library for detecting silent ML model failure
Activeloop Hub
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[Q] where to host 50GB dataset (for free?)
Hey u/platoTheSloth, as u/gopietz mentioned (thanks a lot for the shout-out!!!), you can share them with the general public through uploading to Activeloop Platform (for researchers, we offer special terms, but even as a general public member you get up to 300GBs of free storage!). Thanks to our open source dataset format for AI, Hub, anyone can load the dataset in under 3seconds with one line of code, and stream it while training in PyTorch/TensorFlow.
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[D] NLP has HuggingFace, what does Computer Vision have?
u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :)
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[N] [P] Access 100+ image, video & audio datasets in seconds with one line of code & stream them while training ML models with Activeloop Hub (more at docs.activeloop.ai, description & links in the comments below)
u/gopietz good question. htype="class_label" will work, but querying doesn't support multi-dimensional labels yet. Would you mind opening an issue requesting that feature?
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Easy way to load, create, version, query and visualize computer vision datasets
Hi HN,
In machine learning, we are faced with tensor-based computations (that's the language that ML models think in). I've recently discovered a project that helps you make it much easier to set up and conduct machine learning projects, and enables you to create and store datasets in deep learning-native format.
Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as TensorFlow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai). The real benefit for me is that, I can stream my datasets without the need to store them on my machine (my datasets can be up to 10GB+ big, but it works just as well with 100GB+ datasets like ImageNet (https://docs.activeloop.ai/datasets/imagenet-dataset), for instance).
Hub allows us to store images, audio, video data in a way that can be accessed at lightning speed. The data can be stored on GCS/S3 buckets, local storage, or on Activeloop cloud. The data can directly be used in the training TensorFlow/ PyTorch models so that you don't need to set up data pipelines. The package also comes with data version control, dataset search queries, and distributed workloads.
For me, personally the simplicity of the API stands out, for instance:
Loading datasets in seconds
import hub ds = hub.load("hub://activeloop/cifar10-train")
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Easy way to load, create, version, query & visualize machine learning datasets
Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as Tensorflow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai/3)
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Datasets and model creation flow
Consider this
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[P] Database for AI: Visualize, version-control & explore image, video and audio datasets
Please take a look at our open-source dataset format https://github.com/activeloopai/hub and a tutorial on htypes https://docs.activeloop.ai/how-hub-works/visualization-and-htype
I'm Davit from Activeloop (activeloop.ai).
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The hand-picked selection of the best Python libraries released in 2021
Hub.
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What are good alternatives to zip files when working with large online image datasets?
What solution have you used that you like as a data scientist when working with large datasets? Any standard python API to access the data? Other solution? If anyone has used https://github.com/activeloopai/Hub or other similar API I'd be interested to hear your experience working with it!
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
dvc - 🦉 ML Experiments and Data Management with Git
cuttle-cli - Cuttle automates the transformation of your Python notebook into deployment-ready projects (API, ML pipeline, or just a Python script)
petastorm - 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-significance - Enabling easy statistical significance testing for deep neural networks.
CKAN - CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
barfi - Python Flow Based Programming environment that provides a graphical programming environment.
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
TileDB - The Universal Storage Engine
eurybia - ⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
caer - High-performance Vision library in Python. Scale your research, not boilerplate.