Koina

Accessing a public server

cURL

Here is an example HTTP request using only cURL sending a POST request to with a JSON body. You can find examples for all available models at https://koina.wilhelmlab.org/.

curl "https://koina.wilhelmlab.org/v2/models/Prosit_2019_intensity/infer" \
 --data-raw '
{
  "id": "LGGNEQVTR_GAGSSEPVTGLDAK",
  "inputs": [
    {"name": "peptide_sequences",   "shape": [2,1], "datatype": "BYTES", "data": ["LGGNEQVTR","GAGSSEPVTGLDAK"]},
    {"name": "collision_energies",  "shape": [2,1], "datatype": "FP32",  "data": [25,25]},
    {"name": "precursor_charges",    "shape": [2,1], "datatype": "INT32", "data": [1,2]}
  ]
}'

The output of an HTTP request is always a JSON object. The outputs key contains the outputs the model provides. In this case, there are three outputs: annotation, mz, and intensities. For other models, the keys change.

{
    "id": "LGGNEQVTR_GAGSSEPVTGLDAK",
    "model_name": "Prosit_2019_intensity",
    "model_version": "1",
    "parameters": {
        "sequence_id": 0,
        "sequence_start": false,
        "sequence_end": false
    },
    "outputs": [
        {
            "name": "annotation",
            "datatype": "BYTES",
            "shape": [
                2,
                174
            ],
            "data": [
                "y1+1",
                "y1+2",
                "y1+3",
                "b1+1",
                ...
                "y29+3",
                "b29+1",
                "b29+2",
                "b29+3"
            ]
        },
        {
            "name": "mz",
            "datatype": "FP32",
            "shape": [
                2,
                174
            ],
            "data": [
                175.11895751953125,
                -1.0,
                -1.0,
                114.09133911132812,
                ...
                -1.0,
                -1.0,
                -1.0,
                -1.0
            ]
        },
        {
            "name": "intensities",
            "datatype": "FP32",
            "shape": [
                2,
                174
            ],
            "data": [
                0.2463880330324173,
                -1.0,
                -1.0,
                0.006869315169751644,
                ...
                -1.0,
                -1.0,
                -1.0,
                -1.0
            ]
        }
    ]
}

Python

For examples of how to access models using Python, you can check out our OpenAPI documentation .

Hosting your own server

Dependencies

Koina depends on docker and nvidia-container-toolkit. It has only been tested on Linux (Debian/Ubuntu) with Nvidia GPUs.

You can find an ansible script that installs all dependencies here.

How to run it

After installing the dependencies, you can pull the docker image and run it. If you have multiple GPUs installed on your server, you can choose which one is used by modifying --gpus '"device=0"'. The time it takes to pull the image depends on your connection speed. The first time, it might take up to 5 min. Due to the layered design of Docker images, updating to the latest version will likely (depending on the amount of changes) only take seconds. When the server is first started, Model files are downloaded from Zenodo. The duration of this also depends on connection speed but might take ~10 min as well. Once models are downloaded, the server startup takes ~2 minutes.

When using this docker image, you need to accept the terms in the NVIDIA Deep Learning Container License

docker run \
    --gpus '"device=0"' \
    --shm-size 8G \
    --name koina \
    -p 8500-8502:8500-8502 \
    -d \
    --restart unless-stopped \
    ghcr.io/wilhelm-lab/koina:latest

If you want to stay up to date with the latest version of Koina we suggest you also deploy containrrr/watchtower.

docker run \
  -d \
  --name watchtower \
  -v /var/run/docker.sock:/var/run/docker.sock \
  --restart unless-stopped \
  containrrr/watchtower -i 30 --rolling-restart

Adding your own model

You can find a detailed description under docs/. If you run into any issues please open an issue on GitHub.