cockpit VS pytea

Compare cockpit vs pytea and see what are their differences.

cockpit

Cockpit: A Practical Debugging Tool for Training Deep Neural Networks (by f-dangel)
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cockpit pytea
3 3
468 310
- 0.3%
0.0 1.8
almost 2 years ago about 2 years ago
Python TypeScript
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.

cockpit

Posts with mentions or reviews of cockpit. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

pytea

Posts with mentions or reviews of pytea. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing cockpit and pytea you can also consider the following projects:

TorchDrift - Drift Detection for your PyTorch Models

examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both

backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.

uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

vivit - [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps

Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.