NVIDIA/DIGITSArchived
DIGITS
DIGITS este o platformă de antrenare deep learning pe GPU și un manager de modele utilizat pentru a antrena, ajusta (fine-tune) și gestiona modele de rețele neuronale pe hardware NVIDIA. Funcționează ca un pipeline de machine learning controlat prin REST care se integrează cu stocarea cloud S3 pentru ingestia și organizarea seturilor de date.
Platforma suportă fluxuri de lucru de clasificare a imaginilor, permițând utilizatorilor să antreneze diverse arhitecturi de modele și să exporte clasificatori de imagini antrenați pentru utilizare în medii externe. Include capabilități pentru fine-tuning-ul modelelor pentru a adapta ponderile pre-antrenate la sarcini specifice.
Sistemul oferă o interfață API bazată pe REST pentru automatizarea fluxurilor de lucru de antrenare și gestionarea clusterelor GPU. Utilizează un backend de model bazat pe Caffe cu un pipeline de calcul accelerat pe GPU și gestionează parametrii de antrenare prin configurații JSON structurate.
Features
- Deep Network Training - Trains deep neural network architectures on NVIDIA GPUs to recognize complex patterns in data.
- Caffe Framework Implementations - Utilizes the Caffe deep learning framework as the backend for neural network layers and weight optimization.
- Tensor Operation Accelerators - Implements CUDA-enabled GPU acceleration to offload heavy tensor operations and reduce training time.
- Deep Learning Training Toolsets - Provides a complete software suite for training, fine-tuning, and iterating on deep neural networks on GPU hardware.
- Neural Network Training - Trains various neural network architectures, including autoencoders and object detection networks, by adjusting internal weights.
- ML Lifecycle Pipelines - Implements automated workflows spanning data preparation and training, controlled via a REST API.
- GPU Cluster Management Platforms - Provides a platform for orchestrating and managing GPU clusters to trigger training functions programmatically.
- REST API Interfaces - Exposes system functions through RESTful HTTP endpoints to allow programmatic control of training workflows.
- Model Managers - Functions as a comprehensive manager for organizing datasets and managing trained image classifiers.
- Classification Training - Implements full workflows for preparing datasets and training models to categorize images.
- Image Classifiers - Builds and trains neural networks that recognize and categorize images, including exporting them for external use.
- Model Exporting - Serializes trained weights and network definitions into portable files for external deployment.
- S3 Integrated Training Systems - Integrates S3 cloud storage for automated dataset ingestion and organization within the training pipeline.
- Training Dataset Preparation - Provides tools for organizing large-scale training data imported from cloud storage.
- Model Fine-Tuning - Provides processes for optimizing pretrained models on task-specific datasets to adapt general knowledge.
- Hybrid Local-S3 Storage - Combines local disk access with S3-compatible object storage for high-speed GPU access to training data.
- ML Dataset Imports - Imports and organizes training data from cloud storage endpoints specifically for deep learning workflows.
- JSON-Driven Configurations - Uses structured JSON files to store training parameters and hyperparameters for reproducible experiments.
- Automation Job APIs - Provides RESTful interfaces for programmatically triggering and managing deep learning training jobs.
- Deep Learning Frameworks - Web-based interface for training deep learning models.
- General Machine Learning - Web application for training deep learning models.
- MLOps and Infrastructure - Deep learning GPU training system.
- Research Implementations - Interactive deep learning training system for image classification and segmentation.
- Specialized Segmentation - Deep learning training system for medical images.
