AI/ML Frameworks
Run leading frameworks on scalable GPU infrastructure. From single-GPU experiments to multi-node distributed training, NexGPU provides a flexible and powerful compute foundation for your ML workloads.
FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime
# Install additional dependencies
RUN pip install wandb tensorboard
# Add your custom requirements
COPY requirements.txt .
RUN pip install -r requirements.txtfrom torch.cuda.amp import autocast
with autocast():
outputs = model(inputs)Purpose-Built for ML Workflows
Full Framework Support
Run TensorFlow, PyTorch, JAX and other leading ML frameworks on hardware you choose. Pre-built images ready to use, no manual configuration needed.
Distributed Training
Support for distributed training across single or multiple nodes. Compatible with DeepSpeed, Horovod, PyTorch DDP and other distributed strategies.
Precise Version Control
Pin the exact CUDA version and NVIDIA driver version your code requires. Avoid environment inconsistencies that cause training issues.
Advanced Performance Tuning
Leverage hardware counters for advanced tuning to accelerate GPU performance. Support for mixed precision training, gradient accumulation and more.
Related Guides
Get Started: AI/ML Framework Templates
Use pre-built templates to quickly launch your machine learning workflow.
PyTorch
Deep learning framework, flexible and easy to use with a rich community ecosystem. Supports dynamic computation graphs and GPU acceleration.
TensorFlow
End-to-end machine learning platform. From research to production, providing a complete ML ecosystem.
JAX
Google's high-performance numerical computing library with automatic differentiation and XLA compilation acceleration.
NVIDIA CUDA Toolkit
GPU computing development toolkit including compilers, debuggers, and performance analysis tools.
Build Your ML Workflow on NexGPU
Whether it's academic model experiments or enterprise production training pipelines, NexGPU provides flexible, cost-effective, high-performance GPU computing support.