Does The Random Library Python Work With Multiprocessing Reliably?

2025-09-03 00:56:32 274

5 Answers

Quincy
Quincy
2025-09-04 21:11:18
I used to get bitten by identical RNG sequences in child processes until I made a small habit change: always initialize a per-process generator. When you use the global 'random' after fork, the state is inherited, so two workers will produce the same stream. My go-to is to create rng = random.Random() at the start of a worker and seed it with something unique (like a base_seed + worker_id or os.getpid()). If reproducibility matters, plan seeds up front (master_seed -> derived_worker_seeds). If you need cryptographic randomness, don't use 'random' at all — use 'secrets' or os.urandom. Also, if you can, choose the start method: multiprocessing.set_start_method('spawn') or 'forkserver' can avoid fork-inherit problems. For scientific workloads I often prefer 'numpy.random.Generator' with a SeedSequence so I can spawn non-overlapping streams for each process; it's faster and more robust than the stdlib random for large arrays. Bottom line: multiprocessing plus 'random' is fine but requires explicit seeding and a little planning.
Tristan
Tristan
2025-09-07 06:53:06
My short take: be cautious with forks. On POSIX systems a fork duplicates the RNG state, so unless you reseed each worker you'll get identical sequences. That can silently ruin experiments or simulations.

If you want reproducibility across runs, explicitly derive worker seeds from a master seed (e.g., master_seed + worker_index) so each process is deterministic but different. For cryptographic needs, switch to 'secrets' or OS-level randomness. For threaded code, remember global 'random' is not designed for heavy concurrent use—give each thread or process its own RNG. Simple habit: always seed or construct a new random.Random() inside the child process at startup, and you’ll avoid most problems.
Sophia
Sophia
2025-09-08 00:25:33
Practical checklist from my toolbox: (1) Never rely on the global 'random' after a fork without reseeding. I always call random.seed(None) or create rng = random.Random(os.getpid() ^ int(time.time_ns())) in each worker. (2) If you want reproducibility, derive per-worker seeds from a base seed so you can rerun deterministically. (3) For scientific parallelism I prefer 'numpy' SeedSequence + Generator to get independent streams. (4) For anything security-related, use 'secrets' or os.urandom.

I tend to set multiprocessing.set_start_method('spawn') on platforms where I need to avoid fork-inheritance bugs, but on Linux I sometimes use 'forkserver' too. Small habit changes like seeding per process and logging seeds have saved me from subtle bugs more than once, so give those a try and see how it clears up your runs.
Gideon
Gideon
2025-09-08 01:16:37
On data-heavy projects I run into two main concerns: independence of streams and reproducibility. I solved both by shifting from the stdlib 'random' to 'numpy' generators with SeedSequence and explicit child seeds. The flow that works for me is: choose a master_seed, create a SeedSequence(master_seed), then spawn child seeds with ss.spawn(n) and initialize each worker's Generator with PCG64(child_seed). That guarantees statistically independent streams and keeps runs reproducible if I reuse the same master seed.

I also sometimes change the multiprocessing start method to 'forkserver' or 'spawn' depending on the platform. Using 'fork' without reseeding leaves you with duplicated states, while 'spawn' on Windows avoids that by creating fresh interpreters. If you need very fast large-array sampling, 'numpy.random.Generator' is also much faster and more parallel-friendly than looping with stdlib 'random'. Finally, for auditing or testing, log the seeds you used — it saves hours of debugging when results look odd or when you want to reproduce a particular worker's behavior.
Ian
Ian
2025-09-08 23:21:35
If you spawn a handful of worker processes and just call functions that use the global 'random' module without thinking, you can get surprising behavior. My practical experience with Unix-style forks taught me the core rule: when a process is forked, it inherits the entire memory, including the internal state of the global random generator. That means two children can produce identical random sequences unless you reseed them after the fork.

So what do I do now? On Linux I either call random.seed(None) or better, create a fresh instance with random.Random() in each child and seed it with some unique entropy like os.getpid() ^ time.time_ns(). If I want reproducible, controlled streams across workers, I explicitly compute per-worker seeds from a master seed. On Windows (spawn), Python starts fresh interpreters so you’re less likely to accidentally duplicate states, but you should still manage seeding intentionally. For heavy numeric work I lean on 'numpy' generators or 'secrets' for crypto-level randomness. In short: yes, it works reliably if you handle seeding and start methods carefully; otherwise you can get nasty duplicates or non-reproducible runs that bite you later.
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