okhomeco / runpodollama

Running Ollama with Runpod

Running Ollama with Runpod Serverless

How to use it

1. Clone the repo
2. Run poetry install --all-extras
3. You can interact with app through the cli: runpod-ollama
4. Create a .env file and add your Runpod's API_KEY there RUNPOD_API_TOKEN=...

Commands

1. Create a template and endpoint

To create a template and endpoint for the phi model with 10GBs of disk size:

runpod-ollama create-model phi 10

You can check the options with:

runpod-ollama create-model --help

Once the endpoint is created, you need to

1. Go to the endpoint URL and manually change the GPU type, by editing the endpoint.
2. Run the local-proxy server to forward the request to runpod.

Alternatively, you can create the template and endpoint separately with the CLI or with the Runpod's website (check the Blog).

2. Run the local-proxy server

Once the endpoint is created you can run runpod-ollama start-proxy:

❯ runpod-ollama start-proxy
[?] Select an endpoint::
   orca-2 -fb
   llava -fb
   mixtral:8x7b-instruct-v0.1-q5_K_S -fb
 > phi-fb
   mistral-fb

This will start the local proxy, and outputs an example to use the endpoint:

import litellm

response = litellm.completion(
    "ollama/phi-fb",
    messages=[
        {"content": "why the sky is blue?"},
    ],
    base_url="http://127.0.0.1:5001/dtaybcvyltprsx",
    stream=False,
)

print(response.choices[0].message["content"])

How it works

sequenceDiagram
    box Client
    participant Client (e.g. litellm)
    participant Local Proxy
    end
    box Server
    participant Runpod
    participant Ollama
    end
    Client (e.g. litellm)->>Local Proxy: Calls Ollama API
    Local Proxy->>Runpod: Forwards Ollama API call
    Runpod->>Ollama: Forwards Ollama API call
    loop Check every second
        Local Proxy --> Runpod: Check request status

    end
    Ollama-->>Runpod: Ollama responds
    Runpod-->>Local Proxy: Forwards Ollama response
    Local Proxy-->>Client (e.g. litellm): Receives Ollama response

Loading

1. Ollama

You can communicate with Ollama through the rest API. The API is documented here

curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt": "Why is the sky blue?",
"stream": false
}'

Responds:

{
  "model": "llama2",
  "created_at": "2023-08-04T19:22:45.499127Z",
  "response": "The sky is blue because it is the color of the sky.",
  "done": true,
  "context": [1, 2, 3],
  "total_duration": 5043500667,
  "load_duration": 5025959,
  "prompt_eval_count": 26,
  "prompt_eval_duration": 325953000,
  "eval_count": 290,
  "eval_duration": 4709213000
}

2. Runpod

With Runpod's serverless, you can create an endpoint that calls a method you defined. To do that you need:

1. Create a template: The Runpod's template needs a Dockerfile.
   1. The Docker container is executed once the endpoint is created.
   2. In the Docker container, you can call setup a method which is called when your endpoint is called.
2. Create an endpoint: Once the template is created, you can define an endpoint. You should select a template and your GPU type here.

In this project, on the server we:

1. Run Ollama server.
2. Define the endpoint (runpod_wrapper) to forward the request directly to the Ollama server.

3. Local Proxy

You can call your Runpod endpoint with the following URL formats:

• Run the handler: https://api.runpod.ai/v2/<pod-id>/run -> Return job-id
• Check a request status: https://api.runpod.ai/v2/<pod-id>/status/:id

To make the client agnostic to the Runpod API, runpod-ollama start-proxy runs a local proxy server that forwards the request to runpod, and waits until they are resolved.

4. Client

With the Local Proxy running, you can call the change the Ollama base-url to the local-proxy server, and interact with the Ollama on the server.

Blog

Check the blog here

Examples

To run the examples, first install the examples dependencies:

$ poetry install --all-extras

Limitations

• Currently stream option is not enabled
• Error messages are not readable. In case you encountered error, make sure:
  • The local proxy is running
  • The model name you provided is right
• The docker image should be updated. Some models are not working with this version of the Ollama.