在线 GPU 深度学习
使用在线 GPU 资源从任何地方训练深度学习模型。无需硬件。
| Platform | Free Tier | GPU Options | Pricing | Strengths |
|---|---|---|---|---|
| SynpixCloud | Trial credits | RTX 3060 to H100 | $0.30 - $5+/hr | Competitive pricing, good RTX selection |
| Google Colab | Yes (limited) | T4, A100 (Pro) | Free - $50/mo | Free tier, Jupyter integration |
| AWS EC2 | No | T4 to H100 | $0.50 - $40+/hr | Full cloud ecosystem, reliability |
| Lambda Labs | No | A100, H100 | $1.10 - $3+/hr | ML-focused, good A100 pricing |
| Kaggle | Yes (30 hrs/wk) | T4, P100 | Free | Free weekly hours, competition integration |
Choose Platform & GPU
Select based on your budget, GPU needs, and familiarity with the platform.
💡 Start with free tiers to learn, then upgrade for serious work.
Prepare Your Data
Upload datasets to cloud storage or the platform's persistent storage.
💡 Compress datasets before upload. Use platform storage to avoid re-uploading.
Set Up Environment
Install dependencies, frameworks, and configure your training scripts.
💡 Use Docker or save environment snapshots to avoid repeated setup.
Run Training
Launch training with proper checkpointing and monitoring.
💡 Save checkpoints frequently. Use tensorboard or wandb for monitoring.
Download Results
Export trained models, logs, and any generated artifacts.
💡 Automate exports to cloud storage. Keep instance running until download completes.
Jupyter Notebook
Browser-based interactive coding environment
SSH Terminal
Command-line access to remote machine
VS Code Remote
Full IDE experience on remote GPU
Web IDE
Platform-provided development environment
- Always save checkpoints every few epochs
- Use cloud storage (S3, GCS) for large datasets
- Monitor costs with budget alerts
- Stop instances when not actively training
- Use tmux/screen for long-running processes
- Test code locally before scaling up