Launching a Virtual Machine
Overview
YottaLabs provides an intuitive interface for launching virtual machines with powerful GPU resources. Whether you're training machine learning models, running scientific simulations, or performing data analysis, this platform makes it easy to configure the exact resources you need.
1. Region Selector
What it does: Selects the geographic location where your virtual machine will be hosted.
Why it matters:
Latency: Choose a region close to you for faster response times
Availability: Different regions may have different GPU availability
2. Network Storage
What it does: Configures persistent network-attached storage for your virtual machine.
Storage types typically include:
Standard SSD: Good for general purpose workloads
High-performance SSD: For I/O intensive applications
Archive storage: For infrequently accessed data
Why it matters:Your instance's local storage is ephemeral (lost when instance stops)!
3. VRAM Slider
What it does: Filters instance types based on GPU memory (VRAM) requirements.
Why it matters:
Different models require different amounts of GPU memory
Training large language models needs 40GB+ VRAM
Small computer vision models might only need 8-16GB
Example use cases:
1-8 GB: Small datasets, inference, light training
16-24 GB: Medium neural networks, most computer vision tasks
40-80 GB: Large language models, high-resolution image processing
80+ GB: Multi-GPU training, extremely large models
4. RAM Slider
What it does: Filters instance types based on system RAM .
VRAM vs RAM
RAM is the CPU's general-purpose workspace for running the OS and apps, while VRAM is the GPU's high-speed dedicated memory used exclusively for rendering textures, 3D graphics, and video frames. Essentially, RAM manages the "logic" of your computer, whereas VRAM handles the "visuals."
For deep learning, a good rule of thumb is 2-4x your VRAM in system RAM.
Why it matters:
Data preprocessing often happens in system RAM
Large datasets need to fit in memory
Some applications are RAM-intensive even without GPUs
Provider Selection
Choose different GPU providers:
GPU Configuration
Multi-GPU Selection Buttons
Not all code can utilize multiple GPUs without modification
You'll need to implement data parallelism or model parallelism
PyTorch: Use
DataParallelorDistributedDataParallelTensorFlow: Use distribution strategies
Monitor GPU utilization - don't pay for GPUs you're not using!
Operating System Selection
What it does: Selects the operating system for your virtual machine.
Ubuntu 22.04 LTS: Long-term support, most stabled configurations
Ubuntu 22.04 LTS (shown above):
LTS = Long Term Support (5 years of updates)
Well-supported by AI/ML tools
Most documentation assumes Ubuntu
Native NVIDIA CUDA support
Purchase Option
On-Demand instances are like traditional cloud computing - you get exactly what you pay for, when you need it, for as long as you need it.
Spot instances are spare capacity that cloud providers sell at a discount. The catch? They can be interrupted against your will.
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