2 个仓库
Awesome GitHub RepositoriesConfigurable Model Training
Training routines that are executed based on parameters defined in external configuration files.
Distinct from Model Inference Run Executions: The candidates focus on inference runs or data models, not the use of config files to drive the training process.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Configurable Model Training. Refine with filters or upvote what's useful.
Awesome Configurable Model Training GitHub Repositories
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- nervanasystems/neon
NervanaSystems/neon
3,864在 GitHub 上查看Neon is a deep learning framework and hardware-abstraction machine learning stack used for designing, training, and deploying neural network architectures. It functions as a graph-based computation engine that utilizes just-in-time kernel compilation to optimize machine code for tensors. The platform decouples model definitions from execution kernels, allowing it to support multiple CPU and GPU backends. This architecture enables the distribution of computational workloads across parallelized hardware environments to increase processing speed and overall efficiency. The system covers the ful
Allows execution of model training tasks via configuration files to define parameters and backend settings.
Python - stability-ai/stable-audio-tools
Stability-AI/stable-audio-tools
3,790在 GitHub 上查看Stable-audio-tools is a toolkit for training and deploying latent diffusion models for high-fidelity audio synthesis. It provides a framework for generating audio by iteratively refining noise within a compressed latent space, using specialized encoders to preserve temporal and spectral features of the audio signal. The project features a system for adapting pre-trained audio checkpoints to new datasets through modular initialization and configuration files. It includes utilities for weight extraction and inference model export, which remove training metadata and optimizer states to create li
Uses structured configuration files to define hyperparameters and data pipelines for reproducible audio model training.
Python
