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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To limit a Linux multiprocessing job as a whole, run it inside a cgroup boundary—typically a systemd scope on a systemd-managed host or a container with Docker resource limits. Set an aggregate CPU quota and a hard memory maximum, then choose a worker count that fits both budgets. A per-process limit or CPU pinning alone does not provide the same job-wide control.
Choose a boundary that covers the whole job
A multiprocessing program can create workers and other descendant processes. Put the launcher and its descendants in one resource-control boundary so the limits apply to the job as a group, rather than trying to configure every worker individually. On a host-managed job, use a systemd scope or service; if the workload already runs in Docker, use the container’s resource controls. Both configure Linux resource-control mechanisms, but the effective limits can also be narrowed by parent cgroups. Check the host’s cgroup version and the settings that actually apply to the job.
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For a job launched directly on a systemd host
This illustrative command starts a transient scope with a CPU quota and memory maximum:
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systemd-run --scope -p CPUQuota=200% -p MemoryMax=4G python job.pyCPUQuota=200% represents a maximum CPU bandwidth equivalent to two CPUs’ worth of runtime. MemoryMax=4G requests a 4 GiB hard memory setting. Actual behavior depends on the systemd version, cgroup configuration, parent limits, and how the unit properties are parsed on the host; verify the resulting unit settings rather than assuming the requested values are the effective ones. See the systemd resource-control documentation.
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For a job already running in Docker
Use container limits at launch, for example:
docker run --cpus=2 --memory=4g IMAGE COMMAND--cpus sets a CPU access cap, while --memory sets a memory constraint. Do not substitute --cpu-shares for a hard CPU cap: shares are a relative weight that matters when CPU is contested. Check the Docker version and host/runtime configuration. The Docker resource constraints documentation describes these controls.
Does a CPU quota limit cores or CPU time?
A quota limits CPU-time bandwidth over the scheduler’s quota period; it does not pin a process to a particular set of cores. In systemd, CPUQuota=200% means a maximum equivalent to two CPUs’ worth of runtime. By contrast, AllowedCPUs= restricts where tasks may run. The parent cgroup can narrow that CPU list, and EffectiveCPUs= shows the resulting configuration. Affinity can help with locality or keep a job off selected CPUs, but affinity by itself is not an aggregate CPU-time cap. See the systemd resource-control documentation.
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What happens when a cgroup memory limit is reached?
With cgroup v2, memory.high is a pressure and throttling boundary: crossing it causes heavy reclaim pressure, but does not itself invoke the OOM killer. memory.max is the principal hard memory limit. The Linux Kernel Documentation describes it as: “Memory usage hard limit. This is the main mechanism to limit memory usage of a cgroup.” If usage reaches the limit and cannot be reduced, the kernel invokes the OOM killer within the cgroup; usage can temporarily exceed the limit. A hard limit can therefore cause allocations to fail or processes to be terminated. See the Linux kernel cgroup v2 documentation.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesLeave headroom in the memory budget for the parent process, workers, shared-memory objects, libraries, and other processes in the job. A memory limit that matches only the expected worker allocations can still be too tight once the full job is counted.
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How do I choose a Python multiprocessing worker count?
Set the pool size explicitly when you need a predictable starting point, for example Pool(processes=n). For CPU-bound work, a reasonable initial rule is to keep the number of workers within the usable CPU budget. That is only a CPU-oriented starting point, not a memory-sizing formula.
In Python 3.13 and later, Pool(processes=None) uses os.process_cpu_count() by default instead of os.cpu_count(). The former reports the logical CPUs usable by the calling thread and can be lower than the machine-wide count. It can account for CPU affinity, but it does not universally convert a cgroup CPU quota into an ideal worker count. See the Python multiprocessing documentation.
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Size for memory as well as CPU
Estimate the parent’s and each worker’s memory footprint under representative workload conditions. Include both per-process and shared allocations, then choose a worker count that stays below the job’s memory budget with headroom. CPU count alone cannot tell you how many workers fit in RAM, and there is no universal workers-per-GiB ratio that applies across workloads.
Clean up pools and account for worker resources
Use a Pool as a context manager or explicitly close or terminate it so workers do not outlive the work that needs them. The maxtasksperchild option replaces a worker after a selected number of tasks, which can help release resources accumulated by long-lived workers. On POSIX systems, the spawn and forkserver start methods also launch a resource tracker for named resources such as semaphores and SharedMemory; include those resources when troubleshooting unexpected usage. See the Python multiprocessing documentation.
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Why per-process limits are not enough
Python’s Unix resource module exposes limits such as RLIMIT_CPU, which limits processor time for an individual process and sends SIGXCPU when crossed, and RLIMIT_AS, which limits an individual process’s address space. These are not a simple aggregate CPU-and-memory ceiling for a multiprocessing tree. Use a cgroup boundary for the whole job; per-process limits can be supplementary controls where appropriate. See the Python resource module documentation.
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