Modal's Compute and Storage Resources
Last updated August 26, 2024
Modal provides a comprehensive set of compute and storage resources to support your machine learning workflows, empowering you to scale your projects efficiently and achieve optimal performance.
Compute Resources
- CPU Options: Choose from a variety of CPU-based instances to meet your specific computing requirements. These options are suitable for general-purpose workloads and training models that don't require specialized hardware.
- GPU Acceleration: If your machine learning tasks benefit from GPU acceleration, Modal offers powerful GPU instances powered by NVIDIA GPUs. These instances are ideal for deep learning models and demanding training workloads.
- TPU for Maximum Performance: For exceptionally demanding models or large-scale training, leverage Google's Tensor Processing Units (TPUs) through Modal. TPUs are designed specifically for machine learning workloads and can deliver significant performance gains.
- Elastic Scaling: Scale your compute resources up or down on demand to match the needs of your project. This allows you to optimize costs and only use the resources you need.
Storage Resources
- Persistent Storage: Store your model files, data, and other project artifacts reliably using Modal's persistent storage options. This guarantees data availability even when your instances are shut down.
- Cloud Integration: Modal seamlessly integrates with popular cloud storage services like AWS S3 and Google Cloud Storage, allowing you to leverage your existing data storage infrastructure.
- Data Access Optimization: Modal optimizes data access for your training and inference jobs, ensuring efficient data loading and transfer between your compute instances and storage locations.
Choosing the Right Resources
- Model Complexity and Size: Consider the complexity of your model and the size of your datasets when selecting compute resources. Models requiring high-performance computing or large datasets benefit from GPU or TPU instances.
- Training Time and Budget: Balance the tradeoff between performance and cost when choosing your compute resources. More powerful machines generally provide faster training times but come at higher costs.
- Data Storage Needs: Determine the storage requirements for your model files, data, and other project assets. Consider the frequency of access, data size, and durability requirements.
Modal's flexible and scalable compute and storage resources provide the foundation for building and deploying efficient machine learning solutions. By carefully selecting and configuring these resources, you can optimize your workflows, reduce costs, and achieve superior performance.
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