Welcome to the ML Cookbook repository! This is your one-stop guide for common use cases in training and inference of machine learning models on the Nebius.ai cloud platform. Whether you're a seasoned data scientist or just starting your ML journey, this repository provides practical examples, best practices, and ready-to-use code snippets to help you get the most out of your ML workflows.
This repository is organized into recipes that cover a variety of ML tasks, leveraging popular tools and technologies such as:
- Kubernetes (K8s): Scalable and efficient orchestration of ML workloads.
- NVIDIA GPUs: Accelerate training and inference with CUDA-enabled hardware.
- Linux: Optimized environments for ML development.
- Open Source Tools: Leverage the power of open-source libraries and frameworks.
Each recipe includes:
- Step-by-step instructions for setup and execution.
- Code examples for training and inference.
- Tips and tricks to optimize performance and avoid common pitfalls.
Here’s a sneak peek of the recipes available in this cookbook:
- Deploy a distributed training job using Kubernetes.
- Leverage NVIDIA GPUs for accelerated training.
- Monitor and scale your training workload.
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