326 Replace threading with multiprocessing for multiple chunks

docs/notebooks/big_datasets.ipynb

@@ -54,7 +54,7 @@
     "pyopia process config.toml --num-chunks 4\n",
     "```\n",
     "\n",
-    "This will split the list of raw files into 4 chunks to be processed in parallell using threading. This tool will organise the chunks of file names so that the appropriate background files into the correct places (i.e. for moving background, the last `average_window` number of files in the previous chunk are added to the start of the next chunk; and for fixed background the same initial `average_window` number of files are added to the top of each chunk)."
+    "This will split the list of raw files into 4 chunks to be processed in parallell using multiprocessing. This tool will organise the chunks of file names so that the appropriate background files into the correct places (i.e. for moving background, the last `average_window` number of files in the previous chunk are added to the start of the next chunk; and for fixed background the same initial `average_window` number of files are added to the top of each chunk)."
    ]
   }
  ],

pyopia/__init__.py

@@ -1 +1 @@
-__version__ = '2.7.0'
+__version__ = '2.8.0'

pyopia/cli.py

@@ -13,7 +13,7 @@
 from rich.logging import RichHandler
 import rich.progress
 import pandas as pd
-import threading
+import multiprocessing
 
 import pyopia
 import pyopia.background
@@ -187,19 +187,22 @@ def process_file_list(file_list, c):
                     logger.error(e)
                     logger.debug(''.join(traceback.format_tb(e.__traceback__)))
 
-    # With one chunk we keep the non-threaded functionality to ensure backwards compatibility
+    # With one chunk we keep the non-multiprocess functionality to ensure backwards compatibility
     job_list = []
     if num_chunks == 1:
         process_file_list(raw_files, 0)
     else:
         for c, chunk in enumerate(raw_files.chunked_files):
-            job = threading.Thread(target=process_file_list, args=(chunk, c, ))
-            job.start()
+            job = multiprocessing.Process(target=process_file_list, args=(chunk, c))
             job_list.append(job)
 
-    # Calculate and print total processing time
-    # If we are using threads, make sure all jobs have finished
+    # Start all the jobs
+    [job.start() for job in job_list]
+
+    # If we are using multiprocessing, make sure all jobs have finished
     [job.join() for job in job_list]
+
+    # Calculate and print total processing time
     time_total = pd.to_timedelta(time.time() - t1, 'seconds')
     with Progress(transient=True) as progress:
         progress.console.print(f"[blue]PROCESSING COMPLETED IN {time_total}")
@@ -256,7 +259,7 @@ def setup_logging(pipeline_config):
         handlers = [logging.FileHandler(log_file, mode='a')]
 
     # Configure logger
-    log_format = '%(asctime)s %(levelname)s %(threadName)s [%(module)s.%(funcName)s] %(message)s'
+    log_format = '%(asctime)s %(levelname)s %(processName)s [%(module)s.%(funcName)s] %(message)s'
     logging.basicConfig(level=log_level, datefmt='%Y-%m-%d %H:%M:%S', format=log_format, handlers=handlers)
 
 

pyproject.toml

@@ -43,14 +43,15 @@ h5py = "^3.9.0"
 poetry-version-plugin = "^0.2.0"
 tensorflow-macos = {version = "^2.16.2", optional = true, markers = "sys_platform == 'darwin' and platform_machine == 'arm64'"}
 tensorflow-cpu = {version = "^2.16.2", optional = true}
+keras = {version = "3.5.0", optional = true}
 dask = ">=2024.8.1"
 nbconvert = "^7.16.4"
 h5netcdf = ">= 1.3.0"
 scikit-image = "^0.24.0"
 
 [tool.poetry.extras]
-classification-arm64 = ["tensorflow-macos"]
-classification = ["tensorflow-cpu"]
+classification-arm64 = ["tensorflow-macos", "keras"]
+classification = ["tensorflow-cpu", "keras"]
 
 [tool.poetry-version-plugin]
 source = "init"