The mlserver package comes with inference runtime implementations for scikit-learn and xgboost models.
However, some times we may also need to roll out our own inference server, with custom logic to perform inference.
To support this scenario, MLServer makes it really easy to create your own extensions, which can then be containerised and deployed in a production environment.
In this example, we will train a numpyro model.
The numpyro library streamlines the implementation of probabilistic models, abstracting away advanced inference and training algorithms.
Out of the box, mlserver doesn't provide an inference runtime for numpyro.
However, through this example we will see how easy is to develop our own.
The first step will be to train our model.
This will be a very simple bayesian regression model, based on an example provided in the numpyro docs.
Since this is a probabilistic model, during training we will compute an approximation to the posterior distribution of our model using MCMC.
# Original source code and more details can be found in:
# https://nbviewer.jupyter.org/github/pyro-ppl/numpyro/blob/master/notebooks/source/bayesian_regression.ipynb
import numpyro
import numpy as np
import pandas as pd
from numpyro import distributions as dist
from jax import random
from numpyro.infer import MCMC, NUTS
DATASET_URL = "https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv"
dset = pd.read_csv(DATASET_URL, sep=";")
standardize = lambda x: (x - x.mean()) / x.std()
dset["AgeScaled"] = dset.MedianAgeMarriage.pipe(standardize)
dset["MarriageScaled"] = dset.Marriage.pipe(standardize)
dset["DivorceScaled"] = dset.Divorce.pipe(standardize)
def model(marriage=None, age=None, divorce=None):
a = numpyro.sample("a", dist.Normal(0.0, 0.2))
M, A = 0.0, 0.0
if marriage is not None:
bM = numpyro.sample("bM", dist.Normal(0.0, 0.5))
M = bM * marriage
if age is not None:
bA = numpyro.sample("bA", dist.Normal(0.0, 0.5))
A = bA * age
sigma = numpyro.sample("sigma", dist.Exponential(1.0))
mu = a + M + A
numpyro.sample("obs", dist.Normal(mu, sigma), obs=divorce)
# Start from this source of randomness. We will split keys for subsequent operations.
rng_key = random.PRNGKey(0)
rng_key, rng_key_ = random.split(rng_key)
num_warmup, num_samples = 1000, 2000
# Run NUTS.
kernel = NUTS(model)
mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples)
mcmc.run(
rng_key_, marriage=dset.MarriageScaled.values, divorce=dset.DivorceScaled.values
)
mcmc.print_summary()Now that we have trained our model, the next step will be to save it so that it can be loaded afterwards at serving-time. Note that, since this is a probabilistic model, we will only need to save the traces that approximate the posterior distribution over latent parameters.
This will get saved in a numpyro-divorce.json file.
import json
samples = mcmc.get_samples()
serialisable = {}
for k, v in samples.items():
serialisable[k] = np.asarray(v).tolist()
model_file_name = "numpyro-divorce.json"
with open(model_file_name, "w") as model_file:
json.dump(serialisable, model_file)The next step will be to serve our model using mlserver.
For that, we will first implement an extension which serve as the runtime to perform inference using our custom numpyro model.
Our custom inference wrapper should be responsible of:
- Loading the model from the set samples we saved previously.
- Running inference using our model structure, and the posterior approximated from the samples.
# %load models.py
import json
import numpyro
import numpy as np
from jax import random
from mlserver import MLModel
from mlserver.codecs import decode_args
from mlserver.utils import get_model_uri
from numpyro.infer import Predictive
from numpyro import distributions as dist
from typing import Optional
class NumpyroModel(MLModel):
async def load(self) -> bool:
model_uri = await get_model_uri(self._settings)
with open(model_uri) as model_file:
raw_samples = json.load(model_file)
self._samples = {}
for k, v in raw_samples.items():
self._samples[k] = np.array(v)
self._predictive = Predictive(self._model, self._samples)
return True
@decode_args
async def predict(
self,
marriage: Optional[np.ndarray] = None,
age: Optional[np.ndarray] = None,
divorce: Optional[np.ndarray] = None,
) -> np.ndarray:
predictions = self._predictive(
rng_key=random.PRNGKey(0), marriage=marriage, age=age, divorce=divorce
)
obs = predictions["obs"]
obs_mean = obs.mean()
return np.asarray(obs_mean)
def _model(self, marriage=None, age=None, divorce=None):
a = numpyro.sample("a", dist.Normal(0.0, 0.2))
M, A = 0.0, 0.0
if marriage is not None:
bM = numpyro.sample("bM", dist.Normal(0.0, 0.5))
M = bM * marriage
if age is not None:
bA = numpyro.sample("bA", dist.Normal(0.0, 0.5))
A = bA * age
sigma = numpyro.sample("sigma", dist.Exponential(1.0))
mu = a + M + A
numpyro.sample("obs", dist.Normal(mu, sigma), obs=divorce)The next step will be to create 2 configuration files:
settings.json: holds the configuration of our server (e.g. ports, log level, etc.).model-settings.json: holds the configuration of our model (e.g. input type, runtime to use, etc.).
# %load settings.json
{
"debug": "true"
}# %load model-settings.json
{
"name": "numpyro-divorce",
"implementation": "models.NumpyroModel",
"parameters": {
"uri": "./numpyro-divorce.json"
}
}Now that we have our config in-place, we can start the server by running mlserver start .. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.
mlserver start .Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal.
We now have our model being served by mlserver.
To make sure that everything is working as expected, let's send a request from our test set.
For that, we can use the Python types that mlserver provides out of box, or we can build our request manually.
import requests
import numpy as np
from mlserver.types import InferenceRequest
from mlserver.codecs import NumpyCodec
x_0 = np.array([28.0])
inference_request = InferenceRequest(
inputs=[
NumpyCodec.encode_input(name="marriage", payload=x_0)
]
)
endpoint = "http://localhost:8080/v2/models/numpyro-divorce/infer"
response = requests.post(endpoint, json=inference_request.model_dump())
response.json()Now that we have written and tested our custom model, the next step is to deploy it. With that goal in mind, the rough outline of steps will be to first build a custom image containing our code, and then deploy it.
MLServer will automatically find your requirements.txt file and install necessary python packages
# %load requirements.txt
numpy==1.22.4
numpyro==0.8.0
jax==0.2.24
jaxlib==0.3.7This section expects that Docker is available and running in the background.
MLServer offers helpers to build a custom Docker image containing your code.
In this example, we will use the mlserver build subcommand to create an image, which we'll be able to deploy later.
Note that this section expects that Docker is available and running in the background, as well as a functional cluster with Seldon Core installed and some familiarity with kubectl.
%%bash
mlserver build . -t 'my-custom-numpyro-server:0.1.0'To ensure that the image is fully functional, we can spin up a container and then send a test request. To start the container, you can run something along the following lines in a separate terminal:
docker run -it --rm -p 8080:8080 my-custom-numpyro-server:0.1.0import numpy as np
from mlserver.types import InferenceRequest
from mlserver.codecs import NumpyCodec
x_0 = np.array([28.0])
inference_request = InferenceRequest(
inputs=[
NumpyCodec.encode_input(name="marriage", payload=x_0)
]
)
endpoint = "http://localhost:8080/v2/models/numpyro-divorce/infer"
response = requests.post(endpoint, json=inference_request.model_dump())
response.json()As we should be able to see, the server running within our Docker image responds as expected.
This section expects access to a functional Kubernetes cluster with Seldon Core installed and some familiarity with `kubectl`.
Now that we've built a custom image and verified that it works as expected, we can move to the next step and deploy it. There is a large number of tools out there to deploy images. However, for our example, we will focus on deploying it to a cluster running Seldon Core.
Also consider that depending on your Kubernetes installation Seldon Core might expect to get the container image from a public container registry like [Docker hub](https://hub.docker.com/) or [Google Container Registry](https://cloud.google.com/container-registry). For that you need to do an extra step of pushing the container to the registry using `docker tag <image name> <container registry>/<image name>` and `docker push <container registry>/<image name>` and also updating the `image` section of the yaml file to `<container registry>/<image name>`.
For that, we will need to create a SeldonDeployment resource which instructs Seldon Core to deploy a model embedded within our custom image and compliant with the V2 Inference Protocol.
This can be achieved by applying (i.e. kubectl apply) a SeldonDeployment manifest to the cluster, similar to the one below:
%%writefile seldondeployment.yaml
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
name: numpyro-model
spec:
protocol: v2
predictors:
- name: default
graph:
name: numpyro-divorce
type: MODEL
componentSpecs:
- spec:
containers:
- name: numpyro-divorce
image: my-custom-numpyro-server:0.1.0