Use multiprocessing get_context and python 3.12

.github/workflows/ci.yml

@@ -31,7 +31,8 @@ jobs:
         - linux: py39-oldestdeps-cov-xdist
         - linux: py39-xdist
         - linux: py310-xdist
-        - linux: py311-cov-xdist
+        - linux: py311-xdist
+        - linux: py312-cov-xdist
           coverage: codecov
         - macos: py311-xdist
   test_downstream:

CHANGES.rst

@@ -4,6 +4,16 @@
 Changes to API
 --------------
 
+jump
+~~~~
+
+- Switch multiprocessing method to ``fork_server``. [#249]
+
+ramp_fitting
+~~~~~~~~~~~~
+
+- Switch multiprocessing method to ``fork_server``. [#249]
+
 Bug Fixes
 ---------
 

src/stcal/jump/jump.py

@@ -391,9 +391,10 @@ def detect_jumps(
             ),
         )
         log.info("Creating %d processes for jump detection ", n_slices)
-        pool = multiprocessing.Pool(processes=n_slices)
-######### JUST FOR DEBUGGING #########################
-#        pool = multiprocessing.Pool(processes=1)
+        ctx = multiprocessing.get_context("forkserver")
+        pool = ctx.Pool(processes=n_slices)
+        ######### JUST FOR DEBUGGING #########################
+        # pool = ctx.Pool(processes=1)
         # Starts each slice in its own process. Starmap allows more than one
         # parameter to be passed.
         real_result = pool.starmap(twopt.find_crs, slices)

src/stcal/ramp_fitting/ols_fit.py

@@ -1,10 +1,10 @@
 #! /usr/bin/env python
 
 import logging
+import multiprocessing
 import time
 import warnings
 from multiprocessing import cpu_count
-from multiprocessing.pool import Pool
 
 import numpy as np
 
@@ -160,7 +160,8 @@ def ols_ramp_fit_multiprocessing(
         ramp_data, buffsize, save_opt, readnoise_2d, gain_2d, weighting, number_slices
     )
 
-    pool = Pool(processes=number_slices)
+    ctx = multiprocessing.get_context("forkserver")
+    pool = ctx.Pool(processes=number_slices)
     pool_results = pool.starmap(ols_ramp_fit_single, slices)
     pool.close()
     pool.join()