BentoML: Service with sklearn model fails on my EKS cluster

I have created a simple service:

model_runner = bentoml.sklearn.load_runner("mymodel:latest")
svc = bentoml.Service("myservice", runners=[model_runner])

@svc.api(input=NumpyNdarray(), output=NumpyNdarray())
def classify(input_series: np.ndarray) -> np.ndarray:
    return model_runner.run(input_series)

When I run it on my laptop (MacBook Pro M1), using

bentoml serve ./service.py:svc --reload

everything works fine when I invoke the generated classify API.

Now when I push this service to my Yatai server as a bento and deploy it to my K8s cluster (EKS), I get the following error when I invoke the API:

Looking at the code, the problem lies in https://github.com/bentoml/BentoML/blob/119b103e2417291b18127d64d38f092893c8de4f/bentoml/_internal/frameworks/sklearn.py#L163 In my case, _num_threads answers 0. Digging a bit further, resource_quota.cpu is computed here: https://github.com/bentoml/BentoML/blob/119b103e2417291b18127d64d38f092893c8de4f/bentoml/_internal/runner/utils.py#L208. Here are the values I get on the pod running the API:

source	value
file /sys/fs/cgroup/cpu/cpu.cfs_quota_us	-1
file /sys/fs/cgroup/cpu/cpu.cfs_period_us	100000
file /sys/fs/cgroup/cpu/cpu.shares	2
call to os.cpu_count()	2

Given those values, query_cgroup_cpu_count() will return 0.001953125, which once rounded will end up as 0, meaning n_jobs will alway be 0. So the call will always fail on my pods.