dusty-nv/jetson-containers

Features

• Edge AI Container Orchestrators - Orchestrates GPU-accelerated Docker containers for machine learning workloads on ARM64 edge hardware.
• Edge AI Runtimes - Provides pre-built Docker containers with GPU-accelerated AI frameworks for Jetson platforms.
• Edge AI Model Deployment - Deploys GPU-accelerated deep learning frameworks and inference engines on edge hardware.
• Edge Deployment Platforms - Provides optimized containers for executing quantized LLMs and RAG pipelines on edge devices.
• Quantized LLM Deployments - Executes quantized large language models and RAG pipelines optimized for edge GPUs.
• Multimodal Processing - Accept and process images alongside text in a single conversation turn for visual reasoning.
• Edge Device Installations - Installs PyTorch with CUDA acceleration for AI/ML workloads on edge devices.
• GPU-Optimized Source Compilations - Compiles AI/ML frameworks from source to optimize them for specific edge GPU architectures and CUDA versions.
• CUDA Runtime Detections - Detects the host's CUDA toolkit version and GPU capabilities to ensure container compatibility at runtime.
• CUDA-Aware Container Builders - Builds containers with automated CUDA detection and source-based compilation for edge GPUs.
• Host-Version-Based Image Selectors - Select a container image that matches the host's JetPack or L4T version for automatic compatibility.
• Platform-Version-Matched Container Launchers - Launch a container image that matches the host platform version, pulling or building it automatically.
• Edge Device Image Builders - Ships a build system that assembles Docker images with pre-configured AI/ML packages for edge devices.
• GPU-Accelerated Containers - Runs machine learning models with GPU acceleration on ARM64 edge hardware using pre-built Docker containers.
• CUDA Compatibility Resolution - Automatically detects host CUDA version and GPU capabilities to select compatible container images.
• System-Aware Image Resolution - Selects the correct container image by matching the host's JetPack or L4T version at launch time.
• Edge AI Integrations - Ships integrated ROS and AI framework containers for deploying autonomous agents and multimodal processing on edge hardware.
• Generative AI Edge Stacks - Delivers pre-configured containers for quantized LLMs and RAG pipelines tailored to low-power ARM64 devices.
• AI Package Build Systems - Assembles custom Docker images by combining pre-built GPU-accelerated AI/ML packages with source compilation.
• Reusable Build Layers - Assembles Docker images from reusable, pre-built layers to minimize rebuild time and maximize composability.
• Streaming Chat Responses - Send generated text output incrementally as it is produced for real-time user interaction.
• ARM SBSA CUDA Runtimes - Run the same server-class CUDA toolkit on embedded edge devices starting with platform version 7.2.
• Audio Processing - Convert and analyze audio data for speech and sound processing pipelines.
• JAX Runtimes - Runs JAX machine learning workloads with CUDA support inside containers on edge devices.
• Triton - Ships pre-built containers for running NVIDIA Triton Inference Server on edge devices.
• xLSTM Models - Run xLSTM-based language models on edge hardware using pre-built containers with GPU acceleration.
• GPU-Accelerated Training - Provides cuML-based GPU-accelerated machine learning training within containers.
• Package Installers - Install Mamba-based AI model packages for GPU-accelerated inference on edge devices.
• ONNX Runtime Inference - Run ONNX Runtime inference containers pre-built for edge devices with GPU acceleration.
• Neural Machine Translation - Run optimized transformer models for fast, GPU-accelerated translation on edge devices.
• Edge Speech Model Runtimes - Load and execute speech recognition and text-to-speech models optimized for edge AI devices.
• Vision-Language Models - Load and execute multimodal vision-language models that process both images and text on edge hardware.
• Whisper-Based Engines - Run Whisper speech-to-text models efficiently on edge hardware using a CTranslate2 backend.
• Conversational Agents - Build interactive agents that combine chat, voice, and web interfaces for multimodal conversations.
• Conversation History Stores - Store and retrieve conversation context across multiple turns for coherent dialogue.
• CUDA Version Rebuilders - Recompile the entire dependency chain for a specified CUDA toolkit version, caching wheels to speed up subsequent builds.
• TensorFlow Container Builds - Build a Docker container with a specific TensorFlow version and its GPU-accelerated dependencies for Jetson platforms.
• Transformer Engine Containers - Provides pre-built containers for running NVIDIA Transformer Engine on edge devices.
• TensorFlow Container Runs - Launch a pre-built or auto-built TensorFlow container with GPU support, device detection, and data volume mounts.
• Tool Use and Function Calling - Invoke predefined functions during text generation to automate tool use and external actions.
• Image Processing and Manipulation - Read, convert, and prepare image data for model inference on GPU.
• Containerized Robotics Stacks - Deploys ROS integrated with AI frameworks in containers for autonomous agents on edge hardware.
• RAPIDS Libraries - Delivers RAPIDS libraries integrated into containers for GPU-accelerated data science on edge devices.
• الأنظمة المدمجة - Machine learning container images for embedded hardware.

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