There is an answer in the message board that someone linked to in a comment to the question, however, it seems better to have the answer on this page, as well as some explanation.

The short answer: inside a pod, run grep -c ^processor /proc/cpuinfo - this number should agree with multiprocessing.cpu_count(). If it does, you can trust multiprocessing.cpu_count().

However, AFAICT, this identifies all the cores on the node and completely ignores the resource limits set in your Kubernetes deployment YAML. For example, your deployment file might contain:

spec:
  containers:
  - image: IMAGENAME
    name: LABEL
    ports:
    - containerPort: 5000
    resources:
      limits:
        cpu: 100m
        memory: 400M
      requests:
        cpu: 50m
        memory: 200M

In this article, the author gives the following function, which respects the resource limits (not requests):

import math
from pathlib import Path


def get_cpu_quota_within_docker():
    cpu_cores = None

    cfs_period = Path("/sys/fs/cgroup/cpu/cpu.cfs_period_us")
    cfs_quota = Path("/sys/fs/cgroup/cpu/cpu.cfs_quota_us")

    if cfs_period.exists() and cfs_quota.exists():
        # we are in a linux container with cpu quotas!
        with cfs_period.open('rb') as p, cfs_quota.open('rb') as q:
            p, q = int(p.read()), int(q.read())

            # get the cores allocated by dividing the quota
            # in microseconds by the period in microseconds
            cpu_cores = math.ceil(q / p) if q > 0 and p > 0 else None

    return cpu_cores

So, for the example YAML, the division yields 0.1, but b/c of the call to ceil, it returns 1.0. So what you may be looking for is something like the following (assuming that you have the above-defined function get_cpu_quota_within_docker defined):

import multiprocessing

from somewhere import get_cpu_quota_within_docker

docker_cpus = get_cpu_quota_within_docker()
cpu_count = docker_cpus if docker_cpus else multiprocessing.cpu_count()