diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py
index ad5457439f..50a6603d24 100644
--- a/src/spikeinterface/sorters/internal/spyking_circus2.py
+++ b/src/spikeinterface/sorters/internal/spyking_circus2.py
@@ -75,8 +75,9 @@ class Spykingcircus2Sorter(ComponentsBasedSorter):
         "matched_filtering": "Boolean to specify whether circus 2 should detect peaks via matched filtering (slightly slower)",
         "cache_preprocessing": "How to cache the preprocessed recording. Mode can be memory, file, zarr, with extra arguments. In case of memory (default), \
                          memory_limit will control how much RAM can be used. In case of folder or zarr, delete_cache controls if cache is cleaned after sorting",
-        "chunk_preprocessing": "How much RAM (approximately) should be devoted to load data chunks. memory_limit will control how much RAM can be used\
-                         as a fraction of available memory. Otherwise, use total_memory to fix a hard limit",
+        "chunk_preprocessing": "How much RAM (approximately) should be devoted to load all data chunks (given n_jobs).\
+                memory_limit will control how much RAM can be used as a fraction of available memory. Otherwise, use total_memory to fix a hard limit, with\
+                a string syntax  (e.g. '1G', '500M')",
         "multi_units_only": "Boolean to get only multi units activity (i.e. one template per electrode)",
         "job_kwargs": "A dictionary to specify how many jobs and which parameters they should used",
         "seed": "An int to control how chunks are shuffled while detecting peaks",
diff --git a/src/spikeinterface/sortingcomponents/tools.py b/src/spikeinterface/sortingcomponents/tools.py
index e55af1edfb..56b128d028 100644
--- a/src/spikeinterface/sortingcomponents/tools.py
+++ b/src/spikeinterface/sortingcomponents/tools.py
@@ -283,7 +283,9 @@ def set_optimal_chunk_size(recording, job_kwargs, memory_limit=0.5, total_memory
             chunk_duration = chunk_size / recording.get_sampling_frequency()
             job_kwargs = fix_job_kwargs(dict(chunk_duration=f"{chunk_duration}s"))
         else:
-            print("psutil is required to use only a fraction of available memory")
+            import warnings
+
+            warnings.warn("psutil is required to use only a fraction of available memory")
     else:
         from spikeinterface.core.job_tools import convert_string_to_bytes
 
@@ -305,7 +307,7 @@ def get_optimal_n_jobs(job_kwargs, ram_requested, memory_limit=0.25):
 
     recording: Recording
         The recording object
-    ram_requested: dict
+    ram_requested: int
         The amount of RAM (in bytes) requested for the job
     memory_limit: float
         The memory limit in fraction of available memory
@@ -324,7 +326,9 @@ def get_optimal_n_jobs(job_kwargs, ram_requested, memory_limit=0.25):
         n_jobs = int(min(n_jobs, memory_usage // ram_requested))
         job_kwargs.update(dict(n_jobs=n_jobs))
     else:
-        print("psutil is required to use only a fraction of available memory")
+        import warnings
+
+        warnings.warn("psutil is required to use only a fraction of available memory")
     return job_kwargs
 
 
@@ -367,14 +371,20 @@ def cache_preprocessing(
                 if recording.get_total_memory_size() < memory_usage:
                     recording = recording.save_to_memory(format="memory", shared=True, **job_kwargs)
                 else:
-                    print("Recording too large to be preloaded in RAM...")
+                    import warnings
+
+                    warnings.warn("Recording too large to be preloaded in RAM...")
             else:
-                print("psutil is required to preload in memory given only a fraction of available memory")
+                import warnings
+
+                warnings.warn("psutil is required to preload in memory given only a fraction of available memory")
         else:
             if recording.get_total_memory_size() < total_memory:
                 recording = recording.save_to_memory(format="memory", shared=True, **job_kwargs)
             else:
-                print("Recording too large to be preloaded in RAM...")
+                import warnings
+
+                warnings.warn("Recording too large to be preloaded in RAM...")
     elif mode == "folder":
         recording = recording.save_to_folder(**extra_kwargs)
     elif mode == "zarr":