Troubleshooting

Common issues and solutions for Clore.ai GPU rentals

Common issues and solutions when renting GPU servers on CLORE.AI marketplace.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

This guide is based on CLORE.AI platform technical documentation.

Table of Contents

• Order Creation Issues

• Connection Issues

• Container Issues

• GPU Issues

• Payment Issues

• Platform Limits

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Order Creation Issues

Order fails: "Insufficient balance"

Cause: Not enough funds to cover creation fee and minimum deposit.

Solution:

• Check your balance in the selected currency (CLORE, BTC, or USDT/USDC)

• Creation fee is charged when order is created

• Top up your balance with enough for several hours of rental

Order fails: "Server not available"

Cause: Server is already rented or offline.

Solution:

• Refresh the marketplace page

• Check server status (online/offline indicator)

• For Spot rentals - you may have been outbid

Order stuck in "Creating" status

Cause: Container is deploying or an error occurred.

Solution:

1. Wait 2-5 minutes (Docker image is being pulled)

2. Check logs in My Orders

3. Large images (10GB+) take longer to download

4. If stuck for more than 10 minutes - cancel and retry

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Connection Issues

Cannot connect via SSH

Cause: Port not configured or container not ready.

Checklist:

1. Port 22 must be set as TCP (not HTTP)

2. Container status must be Active (not Creating)

3. Use the correct mapped port from My Orders

Correct SSH command:

ssh -p <MAPPED_PORT> root@<PROXY_ADDRESS>

Where <MAPPED_PORT> is the public port (e.g., 45678), NOT port 22.

SSH works but web interface doesn't open

Cause: Port set as TCP instead of HTTP, or service not running.

Solution:

1. Web interface ports must be set as HTTP (not TCP)

2. Service must listen on 0.0.0.0, not localhost

3. Check logs - service may have crashed on startup

Correct port configuration:

22/tcp      - SSH access
7860/http   - Gradio/WebUI interface
8000/http   - API server

"Connection refused" error

Cause: Service inside container is not running or listening on wrong address.

Solution:

1. SSH into container and check service status:

   ps aux | grep python
   netstat -tlnp

2. Service must listen on 0.0.0.0, not 127.0.0.1:

   # Wrong:
   python app.py --host 127.0.0.1
   
   # Correct:
   python app.py --host 0.0.0.0

"Connection timed out" error

Cause: Wrong address/port or network issues.

Checklist:

1. Use Proxy address from My Orders (not server IP!)

2. Use Mapped port (public port, not container port)

3. Use correct protocol (http:// for HTTP ports)

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Container Issues

Container keeps restarting

Cause: Error in startup command or insufficient resources.

Solution:

1. Check logs in My Orders

2. Simplify startup command:

   # Bad - long command may fail:
   apt update && apt install -y ... && pip install ... && python ...
   
   # Better - start with simple command:
   sleep infinity

3. Then SSH in and configure manually

Cannot reset container

Cause: Cooldown period between resets.

Fact: Reset container has a 120 second cooldown.

Solution: Wait 2 minutes between reset attempts.

Data lost after restart

Cause: Data not in persistent storage.

Important:

• Data inside container is preserved on Reset Container

• Data is lost when order is cancelled or expires

• Always download results before ending rental:

  scp -P <port> root@<proxy>:/workspace/results.tar.gz ./

Startup command not executing

Cause: Syntax error or image issue.

Common mistakes:

# Error: extra space after \
apt update && \
apt install -y git   # <-- space before next line

# Correct:
apt update && \
apt install -y git && \
python app.py

Solution:

1. Use simple startup: bash or sleep infinity

2. Configure everything via SSH

3. Or create custom Docker image with pre-installed software

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GPU Issues

GPU not visible in container

Check:

nvidia-smi

If command not found:

• Docker image must support CUDA

• Use CUDA-enabled images: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime

If GPU not displayed:

• Verify server has GPU (check marketplace listing)

• Contact server provider

CUDA version mismatch

Error: CUDA driver version is insufficient for CUDA runtime version

Cause: CUDA version in image incompatible with server driver.

Solution:

• Check driver version: nvidia-smi (top right corner)

• Use image with compatible CUDA version

• Safe choice: CUDA 12.8 (works on every card from Turing to Blackwell) — see CUDA & PyTorch Compatibility

Out of GPU memory

Error: CUDA out of memory

Solutions:

1. Use smaller model or quantization

2. Add memory optimization flags:

   • Stable Diffusion: --medvram or --lowvram

   • LLMs: load_in_4bit=True or load_in_8bit=True

3. Clear memory: torch.cuda.empty_cache()

4. Rent server with more VRAM

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Payment Issues

Supported currencies

CLORE.AI supports three currencies:

• CLORE - platform's native token

• BTC - Bitcoin

• USD - stablecoins (if enabled by provider)

Order cancelled: "Outbid"

Cause: Someone offered higher price on Spot market.

Solution:

• Use On-Demand for guaranteed rental

• Or increase your Spot bid price

Balance charged but order not created

Cause: Creation fee is charged even if order fails.

Solution:

• Creation fee is usually minimal

• Check cancellation reason in history

• Contact support for recurring issues

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Platform Limits

Verified from CLORE.AI codebase:

Parameter	Limit
Ports per order	5
Total environment variables	12,288 characters (12KB)
Single env var name	128 characters
Single env var value	1,536 characters
SSH key	3,072 characters
SSH password	32 characters
Jupyter token	32 characters
Container reset cooldown	120 seconds
Port range	1-65535
Port protocols	TCP or HTTP only

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Environment Variables

Use environment variables for SSH and Jupyter access:

Variable	Purpose	Max Length
SSH_KEY	Your public SSH key	3,072 chars
SSH_PASSWORD	SSH password	32 chars
JUPYTER_TOKEN	Jupyter notebook token	32 chars

Example configuration:

SSH_PASSWORD=mypassword123
JUPYTER_TOKEN=mysecrettoken

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Diagnostic Commands

# Check GPU
nvidia-smi

# Check memory usage
free -h

# Check disk space
df -h

# Check running processes
ps aux | grep python

# Check open ports
netstat -tlnp

# Check recent error logs
dmesg | tail -50

# Clear GPU memory (Python)
import torch
torch.cuda.empty_cache()

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Getting Help

If issue persists:

1. Check CLORE.AI Documentation

2. Describe issue with logs and screenshots

3. Include order ID and server ID