Use Cases

Common applications and workflows for rented GPU computing power.

AI model training

Machine learning workflows

Deep learning frameworks:

PyTorch with CUDA acceleration
TensorFlow/Keras GPU support
JAX for research workflows
Hugging Face Transformers

Pre-installed libraries:

# Verify GPU availability
import torch
print(torch.cuda.is_available())
print(torch.cuda.get_device_name())

# Common ML stack
import tensorflow as tf
import pandas as pd
import numpy as np
import sklearn

Training optimization:

Mixed precision training for faster performance
Gradient accumulation for large batch sizes
Model checkpointing for long training runs
TensorBoard monitoring for loss tracking

Popular model types

Computer vision:

Image classification (ResNet, EfficientNet)
Object detection (YOLO, R-CNN)
Semantic segmentation (U-Net, DeepLab)
Generative models (StyleGAN, DCGAN)

Natural language processing:

Transformer fine-tuning (BERT, GPT)
Language model training
Text generation and completion
Sentiment analysis and classification

Time series and forecasting:

LSTM and GRU networks
Transformer architectures for sequences
Financial market prediction
Demand forecasting models

Training best practices

Resource management:

# Monitor GPU memory usage
import torch
print(f"Allocated: {torch.cuda.memory_allocated()}")
print(f"Cached: {torch.cuda.memory_reserved()}")

# Clear cache when needed
torch.cuda.empty_cache()

# Use gradient accumulation for large models
for i, batch in enumerate(dataloader):
    loss = model(batch)
    loss = loss / accumulation_steps
    loss.backward()

    if (i + 1) % accumulation_steps == 0:
        optimizer.step()
        optimizer.zero_grad()

Checkpointing:

# Save model checkpoints regularly
if epoch % save_frequency == 0:
    torch.save({
        'epoch': epoch,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'loss': loss,
    }, f'/workspace/checkpoint_epoch_{epoch}.pth')

Stable Diffusion and image generation

Pre-configured environments

Automatic1111 WebUI:

Access via http://10.77.x.2:7860
100+ pre-installed models
Extensions for enhanced functionality
ControlNet for guided generation

ComfyUI:

Node-based workflow interface
Advanced model chaining
Custom node support
Real-time parameter adjustment

Model management

Popular models included:

Stable Diffusion 1.5 and 2.1
SDXL (Stable Diffusion XL)
Realistic Vision
DreamShaper
Anything V3/V4/V5

Custom model installation:

# Download models to appropriate directories
cd /workspace/stable-diffusion-webui/models/Stable-diffusion/
wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt

# For ComfyUI
cd /workspace/ComfyUI/models/checkpoints/
wget https://example.com/custom-model.safetensors

Advanced techniques

ControlNet workflows:

Pose-guided generation
Depth map conditioning
Edge detection guidance
Style transfer applications

LoRA fine-tuning:

Character-specific training
Style adaptation
Concept reinforcement
Efficient model customization

Upscaling and enhancement:

Real-ESRGAN integration
GFPGAN face restoration
CodeFormer enhancement
Custom upscaling models

Cryptocurrency mining

Pre-configured miners

GPU miners included:

T-Rex (NVIDIA optimized)
TeamRedMiner (AMD optimized)
lolMiner (dual mining support)
NBMiner (LHR bypass)

CPU miners:

XMRig (Monero optimized)
CPUMiner (general purpose)
SRBMiner (multi-algorithm)

Mining setup

Auto-configuration:

# Check available miners
ls /opt/miners/

# T-Rex for NVIDIA cards
/opt/miners/t-rex/t-rex -a ethash -o stratum+tcp://pool:4444 -u wallet -w worker

# TeamRedMiner for AMD
/opt/miners/teamredminer/teamredminer -a ethash -o stratum+tcp://pool:4444 -u wallet

Pool configuration:

Pre-configured pool lists
Automatic failover support
Regional pool optimization
SSL/TLS encrypted connections

Profitability optimization

Algorithm switching:

Automatic profit switching
Market condition monitoring
Real-time profitability calculation
Multi-pool support

Performance tuning:

# NVIDIA optimization
nvidia-smi -pm 1  # Enable persistence mode
nvidia-smi -pl 250  # Set power limit

# Memory clock optimization
nvidia-settings -a [gpu:0]/GPUMemoryTransferRate[3]=8000

# Monitor performance
watch -n 1 nvidia-smi

Earnings tracking:

Real-time hashrate monitoring
Pool statistics integration
Profitability calculators
Historical performance data

3D rendering and visualization

Rendering engines

Blender integration:

CUDA/OpenCL acceleration
Cycles and Eevee renderers
GPU-accelerated viewport
OptiX denoising support

Other rendering software:

Cinema 4D with Redshift
Autodesk Maya with Arnold
3ds Max with V-Ray
Houdini with Mantra/Karma

Rendering workflows

Animation rendering:

# Command-line Blender rendering
blender -b scene.blend -o /workspace/render/frame_#### -s 1 -e 250 -a

# GPU-specific optimization
blender -b scene.blend -P gpu_render_script.py

Batch processing:

# Python script for batch rendering
import bpy
import os

# Set GPU rendering
bpy.context.preferences.addons['cycles'].preferences.compute_device_type = 'CUDA'
bpy.context.preferences.addons['cycles'].preferences.get_devices()

# Render multiple scenes
scene_files = ['/workspace/scene1.blend', '/workspace/scene2.blend']
for scene in scene_files:
    bpy.ops.wm.open_mainfile(filepath=scene)
    bpy.context.scene.render.filepath = f'/workspace/output/{os.path.basename(scene)}'
    bpy.ops.render.render(write_still=True)

Optimization techniques

Memory management:

Tile-based rendering for large scenes
Out-of-core geometry handling
Texture streaming optimization
GPU memory monitoring

Performance tuning:

Optimal tile sizes for GPU
Denoising parameter adjustment
Sample count optimization
Light cache configuration

Gaming and streaming

Remote gaming setup

Steam integration:

Steam client pre-installed
GPU-optimized game settings
Cloud save synchronization
Workshop content support

Game streaming:

Parsec client configuration
OBS Studio for recording
Low-latency streaming setup
Hardware encoding optimization

Performance optimization

Graphics settings:

# NVIDIA settings for gaming
nvidia-settings --assign [gpu:0]/GPUPowerMizerMode=1
nvidia-settings --assign [gpu:0]/GPUFanControlState=1

# Display configuration
xrandr --output HDMI-1 --mode 1920x1080 --rate 60

Network optimization:

Low-latency VPN configuration
QoS settings for gaming traffic
Bandwidth allocation optimization
Jitter reduction techniques

Popular gaming scenarios

Cloud gaming:

AAA title streaming
VR content rendering
Multi-monitor gaming setups
Competitive gaming optimization

Content creation:

Game recording and editing
Live streaming production
Highlight compilation
Social media content creation

Scientific computing

Research applications

Computational biology:

Protein folding simulations
Molecular dynamics
Bioinformatics pipelines
Drug discovery workflows

Physics simulations:

Finite element analysis
Computational fluid dynamics
Particle physics calculations
Climate modeling

Data science:

Large-scale data processing
Statistical modeling
Visualization and plotting
Interactive analysis

Software environments

Pre-installed tools:

MATLAB with GPU acceleration
Jupyter Lab with scientific kernels
R with GPU packages
GNU Octave

Custom installations:

# Install domain-specific tools
conda install -c conda-forge gromacs
pip install biopython rdkit-pypi
apt install quantum-espresso

# GPU-accelerated libraries
pip install cupy-cuda11x  # GPU NumPy
pip install rapids-cudf   # GPU pandas

Workflow optimization

Parallel processing:

# Multi-GPU acceleration
import torch.nn as nn
from torch.nn.parallel import DataParallel

model = MyModel()
if torch.cuda.device_count() > 1:
    model = DataParallel(model)
model.to('cuda')

Memory management:

Efficient data loading strategies
Batch processing optimization
Out-of-core computing techniques
Memory mapping for large datasets

Development and testing

Software development

IDE access:

VS Code Server (http://10.77.x.2:8080)
JupyterLab development environment
Git integration and version control
Package manager access

Testing environments:

Isolated dependency testing
Performance benchmarking
GPU compatibility validation
Cross-platform testing

DevOps workflows

Container development:

# Build Docker images with GPU support
docker build --tag myapp:gpu .
docker run --gpus all myapp:gpu

# Kubernetes GPU workload testing
kubectl apply -f gpu-workload.yaml

CI/CD integration:

Automated GPU testing pipelines
Performance regression detection
Model validation workflows
Deployment testing

Cost optimization strategies

Efficient resource usage

Right-sizing rentals:

Match GPU tier to workload requirements
Use performance monitoring to validate sizing
Consider memory requirements vs compute needs
Avoid over-provisioning

Time management:

# Set up automatic completion
echo "sudo shutdown -h +240" | at now  # 4-hour auto-shutdown

# Monitor remaining time
timeout 3600 python long_training.py  # 1-hour timeout

Batch processing

Job scheduling:

Queue multiple tasks for single rental
Optimize task ordering for efficiency
Use containerization for reproducible environments
Implement checkpoint/resume mechanisms

Data preparation:

Pre-download datasets during off-peak hours
Use compression for transfer efficiency
Prepare data locally when possible
Cache frequently used models and datasets

Cost tracking

Usage monitoring:

# Track training costs
import time
start_time = time.time()
# ... training code ...
duration = time.time() - start_time
cost = duration / 3600 * hourly_rate
print(f"Training cost: ${cost:.2f}")

Performance metrics:

Samples per second for ML training
Frames per second for rendering
Hash rate for mining
Throughput for data processing