Supported Software
AI, Deep Learning, Machine Learning
EMLI: Exxact Machine Learning Images
A customizable production ready, open source machine learning environment for accelerating AI research prototyping and production deployment. Coupled with friendly, easy to use development environments and frameworks like PyTorch, NVIDIA cuDNN, Caffe2, TensorFlow, and more.
Exxact Machine Learning Images
Preconfigured for Your AI Workload
Exxact AI Solutions are purpose-built to handle demanding training workloads and high-throughput inference deployment. Whether you're training large language models, computer vision networks, or deploying production AI, Exxact GPU Solutions deliver the computational power and reliability needed at every stage of your AI pipeline.
- Multi-GPU Architecture: Scale from single GPU configurations to multi-GPU systems for parallel processing and faster model training
- Flexible Configuration Options: Customize CPU, memory, and storage to match your specific AI workload requirements
- Pre-Validated Software Stack: Ships with AI frameworks and drivers pre-installed and tested for immediate deployment
Pre-Installed Frameworks & Toolchain
Data Annotation Tools
Data annotation is the process of categorizing and labeling data for an AI/ML model to understand specific information, which in turn acts like a human to make decisions and take action. Whether it's bounding boxes, semantic segmentation, keypoint annotation, or other image labeling, Exxact provides an EMLI environment for every developer.
An EMLI Environment for Every Developer
Conda EMLI
For developers who want pre-installed deep learning frameworks and their dependencies in separate Python environments installed natively on the system.
Container EMLI
For developers who want pre-installed frameworks utilizing the latest NGC containers, GPU drivers, and libraries in ready to deploy DL environments with the flexibility of containerization.
DIY EMLI
For experienced developers who want a minimalist install to set up their own private deep learning repositories or custom builds of deep learning frameworks.
