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Democratizing ML in proteomics

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# 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/.

```bash
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.

```json
{
    "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 ](https://koina.wilhelmlab.org/docs/#overview).

## Hosting your own server

### Dependencies
Koina depends on [docker](https://docs.docker.com/engine/install/) and [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/overview.html).
It has only been tested on Linux (Debian/Ubuntu) with Nvidia GPUs.

You can find an Ansible playbook that installs all dependencies and sets up the Koina server [here](docs/server/deployment/ansible/).

### 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](NVIDIA_Deep_Learning_Container_License.pdf)
```bash
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.

```bash
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/](docs/README.md). If you run into any issues please open an issue on GitHub.

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