diff --git a/quacc/evaluation/comp.py b/quacc/evaluation/comp.py
index 0ea0eef..956c77b 100644
--- a/quacc/evaluation/comp.py
+++ b/quacc/evaluation/comp.py
@@ -5,6 +5,7 @@ from traceback import print_exception as traceback
 
 import pandas as pd
 import quapy as qp
+from joblib import Parallel, delayed
 
 from quacc.dataset import Dataset
 from quacc.environment import env
@@ -21,49 +22,53 @@ def evaluate_comparison(dataset: Dataset, estimators=None) -> DatasetReport:
     log = Logger.logger()
     # with multiprocessing.Pool(1) as pool:
     __pool_size = round(os.cpu_count() * 0.8)
-    with multiprocessing.Pool(__pool_size) as pool:
-        dr = DatasetReport(dataset.name)
-        log.info(f"dataset {dataset.name} [pool size: {__pool_size}]")
-        for d in dataset():
-            log.info(
-                f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} started"
+    # with multiprocessing.Pool(__pool_size) as pool:
+    dr = DatasetReport(dataset.name)
+    log.info(f"dataset {dataset.name} [pool size: {__pool_size}]")
+    for d in dataset():
+        log.info(
+            f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} started"
+        )
+        tasks = [
+            WorkerArgs(
+                _estimate=estim,
+                train=d.train,
+                validation=d.validation,
+                test=d.test,
+                _env=env,
+                q=Logger.queue(),
             )
-            tasks = [
-                WorkerArgs(
-                    _estimate=estim,
-                    train=d.train,
-                    validation=d.validation,
-                    test=d.test,
-                    _env=env,
-                    q=Logger.queue(),
-                )
-                for estim in CE.func[estimators]
-            ]
-            try:
-                tstart = time.time()
-                results = [
-                    r for r in pool.imap(estimate_worker, tasks) if r is not None
-                ]
+            for estim in CE.func[estimators]
+        ]
+        try:
+            tstart = time.time()
+            results = Parallel(n_jobs=1)(delayed(estimate_worker)(t) for t in tasks)
+            results = [r for r in results if r is not None]
+            #     # r for r in pool.imap(estimate_worker, tasks) if r is not None
+            #     r
+            #     for r in map(estimate_worker, tasks)
+            #     if r is not None
+            # ]
 
-                g_time = time.time() - tstart
-                log.info(
-                    f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} finished "
-                    f"[took {g_time:.4f}s]"
-                )
+            g_time = time.time() - tstart
+            log.info(
+                f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} finished "
+                f"[took {g_time:.4f}s]"
+            )
 
-                cr = CompReport(
-                    results,
-                    name=dataset.name,
-                    train_prev=d.train_prev,
-                    valid_prev=d.validation_prev,
-                    g_time=g_time,
-                )
-                dr += cr
+            cr = CompReport(
+                results,
+                name=dataset.name,
+                train_prev=d.train_prev,
+                valid_prev=d.validation_prev,
+                g_time=g_time,
+            )
+            dr += cr
 
-            except Exception as e:
-                log.warning(
-                    f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. "
-                    f"Exception: {e}"
-                )
-                traceback(e)
+        except Exception as e:
+            log.warning(
+                f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. "
+                f"Exception: {e}"
+            )
+            traceback(e)
     return dr