Production release (#109)

* using ThreadPoolExecutor instead of ThreadPool to avoid semlock

* first attempt using multiprocessing

* added comments to clarify purpose

---------

Co-authored-by: ilkin <ilkin@nygen.io>

scarf/utils.py

@@ -173,10 +173,20 @@ def controlled_compute(arr, nthreads):
     Returns:
         Result of computation.
     """
-    from multiprocessing.pool import ThreadPool
     import dask
 
-    with dask.config.set(schedular="threads", pool=ThreadPool(nthreads)):  # type: ignore
+    try:
+        # Multiprocessing may be faster, but it throws exception if SemLock is not implemented.
+        # For example, multiprocessing won't work on AWS Lambda, in those scenarios we switch ThreadPoolExecutor
+        from multiprocessing.pool import ThreadPool
+
+        pool = ThreadPool(nthreads)
+    except Exception:
+        from concurrent.futures import ThreadPoolExecutor
+
+        pool = ThreadPoolExecutor(nthreads)
+
+    with dask.config.set(schedular="threads", pool=pool):  # type: ignore
         res = arr.compute()
     return res