Reverse Dependencies of codecarbon

The following projects have a declared dependency on codecarbon:

• a2perf — Benchmarking suite for evaluating autonomous agents in real-world domains.
• adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• alectio-sdk — Integrate customer side ML application with the Alectio Platform
• alexandra-ai-eval — Evaluation of finetuned models.
• ARLBench — Python Boilerplate that contains all the code you need to create a Python package.
• carbontracking — A carbon tracking library for Python applications
• cody-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• connectomemapper — Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping
• efficientbioai — efficientbioai is a python package for efficient deep learning in bioimaging
• ezDL — A Simple Tool to make Deep Learning projects easier
• fasterbench — A library for benchmarking AI models
• federa — FedERA is a highly dynamic and customizable framework that can accommodate many use cases with flexibility by implementing several functionalities over different federated learning algorithms, and essentially creating a plug-and-play architecture to accommodate different use cases.
• flask-sustainable — Brings sustainability to Flask via HTTP headers
• fmriprep — A robust and easy-to-use pipeline for preprocessing of diverse fMRI data
• GATorch — GATorch is a tool seamlessly integrated with PyTorch that enables ML developers to generate an energy consumption report. By attaching your model, the tool automatically tracks the energy consumption of your model's training and generates graphs and plots to gain in-depth insights into the energy consumption of your model.
• geowatch — no summary
• gradsflow — An open-source AutoML Library based on PyTorch
• HollerithMLTrainTrack — A tool for tracking and analyzing ML model training processes.
• hyperfetch — A tool to optimize and post hyperparameters for your reinforcement learning application. Currently available on Linux and macOS.
• janus-core — Tools for machine learnt interatomic potentials
• moabb — Mother of All BCI Benchmarks
• mteb — Massive Text Embedding Benchmark
• mw-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• nlg-metricverse — An End-to-End Library for Evaluating Natural Language Generation.
• optiml-flow — A simple package to track various computational resources, usage statistics, energy consumption, etc. of any ML experiment
• optimum-benchmark — Optimum-Benchmark is a unified multi-backend utility for benchmarking Transformers, Timm, Diffusers and Sentence-Transformers with full support of Optimum's hardware optimizations & quantization schemes.
• psyki — Python-based implementation of PSyKI, i.e. a Platform for Symbolic Knowledge Injection
• py-experimenter — The PyExperimenter is a tool for the automatic execution of experiments, e.g. for machine learning (ML), capturing corresponding results in a unified manner in a database.
• pymialsrtk — PyMIALSRTK: Nipype pipelines for the MIAL Super Resolution Toolkit
• pyrovision — Datasets and models for wildfire detection in PyTorch
• pytest-codecarbon — Pytest plugin for measuring carbon emissions
• pytorch-bench — torch benchmarking tool
• setfit — Efficient few-shot learning with Sentence Transformers
• simple-generation — A python package to run inference with HuggingFace checkpoints wrapping many convenient features.
• simvue — Simulation tracking and monitoring
• siste-test — HyperFetch. A tool to optimize and fetch hyperparameters for your reinforcement learning application.
• span-marker — Named Entity Recognition using Span Markers
• stanscofi — Package for STANdard drug Screening by COllaborative FIltering. Performs benchmarks against datasets and SotA algorithms, and implements training, validation and testing procedures.
• trailmet — Transmute AI Model Efficiency Toolkit
• transformers — State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow