| Task fee | No. Images | Image time | Task Priority |
|---|---|---|---|
| 10 CNX | 1 | 20s | 0.5 CNX/s |
| 15 CNX | 2 | 40s | 0.375 CNX/s |
The task score of a node by its submission order and status
| Hardware | Requirements |
|---|---|
| Model | Mac M1, M2, M3 or newer |
| Memory | 16GB |
| Disk Space | 60GB |
| Network | Public network access to Huggingface and Civitai |
| Blockchain | Platform | Requirements | Download Link |
|---|---|---|---|
| Base | Windows | Nvidia GPU with 8GB VRAM | https://drive.google.com/uc?id=1aFsaakzQJK0LFcbp16P207ZNmz2Aw5li&export=download |
| Base | Mac | M1/M2/M3 and later | https://github.com/crynux-network/crynux-node/releases/download/v3.2.0/crynux-node-lithium-v3.2.0-base-mac-arm64-unsigned.dmg |
| Near | Windows | Nvidia GPU with 8GB VRAM | |
| Near | Mac | M1/M2/M3 and later |
deploy:
resources:
reservations:
devices:
- driver: nvidia
capabilities: \[gpu\]
device\_ids: \["0"\]
deploy:
resources:
reservations:
devices:
- driver: nvidia
capabilities: \[gpu\]
device\_ids: \["0"\]
Task Fee B -->> A: Transaction confirmed deactivate B activate A A ->> R: Send task parameters activate R note over A,R: Task Parameters deactivate A R -->> A: Task parameters saved deactivate R B ->> A: Event: TaskSuccess activate A A ->> R: Get task result deactivate A activate R R -->> A: Task result deactivate R \`\`\` The application initiates the workflow by calling the \`CreateTask\` method of the smart contract. This method receives task parameters related to the task criteria, such as the task type and VRAM requirements, which the network uses to select suitable nodes. The application transfers the task fee to the contract address by specifying it in the transaction's \`value\` field. Upon task completion, tokens are sent to the nodes. If the task fails, the fee is refunded to the application's wallet. After the transaction is confirmed on-chain, the application should then send the task parameters to the Relay. > Selected nodes will retrieve task parameters from the Relay and then execute the tasks locally. > > When images or texts are generated, nodes will create proofs and send them to the blockchain. The blockchain will verify the correctness of these proofs and transfer tokens to the nodes upon successful verification. > > The nodes will upload the result images/texts to the Relay, which will compare the results with the on-chain proofs to verify their accuracy. After sending the task parameters to the Relay, the application should wait for the \`TaskSuccess\` event from the blockchain. Once the event is received, the application can retrieve the images or texts from the Relay, marking the completion of the task workflow. The results have already been verified by the Relay, so no further verification by the application is necessary. For a detailed workflow involving all network participants, please refer to the task lifecycle document: {% content-ref url="/pages/iPukPMh2AXLB0TPkE1Wt" %} \[Task Lifecycle\](/system-design/task-lifecycle.md) {% endcontent-ref %} ## The Reference Applications The workflow has been fully implemented in the showcase applications: the Image Generator and the AI Chatbot. Which can be accessed at: The Image Generator:
| Method | Arguments |
|---|---|
| canny | high\_threshold, low\_threshold |
| scribble\_hed | |
| scribble\_hedsafe | |
| softedge\_hed | |
| softedge\_hedsafe | |
| depth\_midas | |
| mlsd | thr\_v, thr\_d |
| openpose | |
| openpose\_face | |
| openpose\_faceonly | |
| openpose\_full | |
| openpose\_hand | |
| dwpose | |
| scribble\_pidinet | apply\_filter |
| softedge\_pidinet | apply\_filter |
| scribble\_pidisafe | apply\_filter |
| softedge\_pidisafe | apply\_filter |
| normal\_bae | |
| lineart\_coarse | |
| lineart\_realistic | |
| lineart\_anime | |
| depth\_zoe | gamma\_corrected |
| depth\_leres | thr\_a, thr\_b |
| depth\_leres++ | thr\_a, thr\_b |
| shuffle | h, w, f |
| mediapipe\_face | max\_faces, min\_confidence |
| Hardware | Requirements |
|---|---|
| GPU | NVIDIA GPU with 8GB VRAM |
| Memory | 16GB |
| Disk Space | 60GB |
| Network | Public network access to Huggingface and Civitai |
flowchart BT
MM\_BASE\["Wallets"\] --> BASE\_CHAIN\["Crynux on Base (L2)<br/>(Arbitrum Orbit Chain)"\]
NODE\_BASE\["Crynux Nodes"\] --> BASE\_CHAIN
BASE\_CHAIN --> BASE\["Base (L1)"\]
MM2\_BASE\["Wallets"\] --> BASE
STAKERS\["Stakers"\] --> BASE\_CHAIN
(Virtual Chain by Aurora)"\] NODE\_NEAR\["Crynux Nodes"\] --> VC VC --> NEAR\["Near (L1)"\] MM2\_NEAR\["Wallets"\] --> NEAR STAKERS\["Stakers"\] --> VC \`\`\` You can choose your preferred blockchain and connect using MetaMask or any other EVM-compatible wallets. You can also use the Crynux Portal at \[portal.crynux.io\](https://portal.crynux.io) to add networks easily: open the site, connect your wallet, choose the network you want, and the portal will automatically add the corresponding network to MetaMask. ## Crynux Layer 2 Blockchains ### Crynux on Base | Item | Value | | -------------- | --------------------------------- | | JSON RPC |
(OptimismMintableERC20)"\] <-- Standard Optimism Token Bridge --> ETH\_CNX\["CNX on Ethereum
(ERC20)"\] CNX\_PORTAL(("Crynux Portal")) <-- Deposit/Withdraw --> BASE\_CNX CNX\_ON\_BASE\["CNX on 'Crynux on Base'
(Native Token)"\] <-- Standard Aribitrum Token Bridge --> BASE\_CNX CNX\_PORTAL <-- Deposit/Withdraw --> CNX\_ON\_BASE \`\`\` {% hint style="warning" %} Crynux Portal does NOT support direct deposits and withdrawals to Ethereum Network. To move CNX between Base and Ethereum, use their standard ERC20 bridge contracts. {% endhint %}
| Network | Crynux Token CA |
|---|---|
| Base | 0x9557DD9E241bc9636732623B672B4090AF519396 |
| Ethereum | 0xa97998Bf97f5A6A96393b85B4e02A0440AE220F2 |
Where is the faucet? / Where to get the test CNX tokens?
The test CNX tokens can be acquired using the slash command in the Discord of Crynux, follow the tutorial below: \[Get the Test CNX Tokens\](broken://pages/7BF1BqejWnxoI8XfziZA)Can I start multiple node instances on a single GPU?
\*\*TLDR: you may get even less rewards by starting multiple nodes on a single device\*\* No one can stop you doing that. If your GPU is powerful enough, the bottleneck becomes the consensus process (you will be waiting for other nodes to submit results), in such cases you could start multiple nodes to fully utilize the power of the GPU. However, if your nodes are executing too many tasks simultaneously, the task execution will become slower (due to the bottleneck on GPU or network bandwidth). And if you are slower than the other 2 nodes in a task, \* You will get a smaller portion from the task fee. \* Your chance of receiving tasks will decrease, and you will get less tasks. \* Your node could be kicking out of the network. It is not a slashing though, the staked tokens are still safe. The details can be found in the doc: \[Quality of Service (QoS)\](/system-design/quality-of-service-qos.md) Meanwhile, we are developing the new feature to support the concurrent task execution on powerful GPUs and multiple GPUs, which will fully utilize the local capabilities.Can I start a node on multiple GPUs?
No. The node can execute one task on one GPU at the same time. If you have Multiple GPUs, you can start multiple nodes on the device, and assign each GPU to a different node. The tutorial can be found at: \[Assign GPU to the Node\](/node-hosting/assign-gpu-to-the-node.md)Can I use the same wallet on multiple node instances?
No you can't do it. The same wallet can only get one task from the network at the same time. If multiple nodes are started with the same wallet, they will be executing the same task at the same time, and the nodes who submit the result later will just fail. After the hot/cold wallet architecture is implemented, \[as described in this doc\](/node-hosting/private-key-security.md), it can also be used to easily collect funds from multiple nodes to a single cold wallet.Can I use AMD Radeon cards to run a node?
Nope. The AMD GPUs are not supported at this moment. Only Nvidia GPU and Apple M1/M2/M3 are supported. We will add support for AMD GPUs in a future release.Can I start a node without GPU?
No. GPU is required to execute the AI tasks from the applications, which is the fundamental requirement of a Crynux Node.Can I start a node on VPS?
If you mean VPS without GPUs, the answer is no. GPU is required to execute the AI tasks from the applications, which is the fundamental requirement of a Crynux Node.Node manager init error: Failed to download models due to network issue
#### If you are using the Windows binary release please find the log file according to this document: \[Locate the Error Message\](/troubleshooting/locate-the-error-message.md) If there are error messages similar to: \`\`\` FileNotFoundError: \[Errno 2\] No such file or directory: 'C:\\\\Users\\\\...\\\\crynux-node-helium-v2.0.7-windows-x64\\\\crynux-node-helium-v2.0.7-windows-x64\\\\data\\\\huggingface\\\\models--stabilityai--stable-diffusion-xl-base-1.0\\\\snapshots\\\\462165984030d82259a11f4367a4eed129e94a7b\\\\unet\\\\diffusion\_pytorch\_model.fp16.safetensors' \`\`\` It is due to the long path limitation on Windows. Please enable the long path support according to this guide, and then restart the computer: \*\*Enable Long Path Support on Windows\*\* Open the Windows Registry Editor by pressing \`Win + R\` and typing \`regedit\`. Navigate to \`HKEY\_LOCAL\_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\FileSystem\`. Find the \`LongPathsEnabled\` DWORD (create it if not exist) and set its value to \`1\`. #### Otherwise Make sure you could connect to Huggingface on the device running the node. If you are using a proxy, please provide the proxy config to the node according to the doc: \[Proxy Settings\](/node-hosting/proxy-settings.md)Node manager init error: The initial inference task exceeded the timeout limit(5 min)
Your computer is too slow to run a Crynux Node. If the time required for your node to finish a task exceeds the timeout period, other nodes will abort the task since they do not want to waste more time on the waiting. And your node will get no reward at all. Besides, more timeout on the tasks will decrease the QoS score of your node, which will eventually cause your node being kicked out of the network. Please use a more powerful device to run the node instead. To understand the details, please refer to: \[Quality of Service (QoS)\](/system-design/quality-of-service-qos.md)The node status shows Stopped after running for a while
If there is no other error messages shown, the node is probably kicked out of the network due to frequent timeout on tasks.
\* You may be running more nodes than your GPU could handle
\* Your device may not be powerful enough to run a node
If the node has a slow GPU, or poor network, the task submission will be slow. If the time required to finish a task exceeds the timeout period, other nodes will abort the task since they do not want to waste more time on the waiting.
More timeout on the tasks will decrease the QoS score of the timeout node, which will eventually cause the node being kicked out of the network. It is not a slashing though, the staked tokens are still safe. The details can be found in the doc:
\[Quality of Service (QoS)\](/system-design/quality-of-service-qos.md)
Failed to execute script 'main' ... 5 validation errors for Config
If the following popup shows when starting the node on Windows:| Hardware | Requirements |
|---|---|
| GPU | NVIDIA GPU with 8GB VRAM |
| Memory | 16GB |
| Disk Space | 60GB |
| Network | Public network access to Huggingface and Civitai |
| Hardware | Requirements |
|---|---|
| GPU | NVIDIA GPU with 8GB VRAM |
| Memory | 16GB |
| Disk Space | 60GB |
| Network | Public network access to Huggingface and Civitai |
If you have 16GB of memory and use Docker with WSL2 on Windows
The memory limit for WSL is default to 8GB, which is not enough to run the Node. You will have to change the default settings using a \[\`.wslconfig\`\](https://learn.microsoft.com/en-us/answers/questions/1296124/how-to-increase-memory-and-cpu-limits-for-wsl2-win) file to allow WSL to use 16GB memory.If you are running on Linux (Ubuntu/Fedora/CentOS/...)
Install the latest version of NVIDIA Container Toolkit:$ sudo docker run --rm --gpus all ubuntu nvidia-smi