diff --git a/src/gretel_synthetics/batch.py b/src/gretel_synthetics/batch.py
index 2895f494..d3832603 100644
--- a/src/gretel_synthetics/batch.py
+++ b/src/gretel_synthetics/batch.py
@@ -1027,6 +1027,7 @@ def __init__(
         tokenizer: BaseTokenizerTrainer = None,
         mode: str = WRITE,
         checkpoint_dir: str = None,
+        validate_model: bool = True,
     ):
 
         if mode not in (WRITE, READ):  # pragma: no cover
@@ -1115,13 +1116,14 @@ def __init__(
             except FileNotFoundError:
                 self.original_headers = None
 
-            logger.info("Validating underlying models exist via generation test...")
-            try:
-                self.generate_all_batch_lines(parallelism=1, num_lines=1)
-            except Exception as err:
-                raise RuntimeError(
-                    "Error testing generation during model load"
-                ) from err
+            if validate_model:
+                logger.info("Validating underlying models exist via generation test...")
+                try:
+                    self.generate_all_batch_lines(parallelism=1, num_lines=1)
+                except Exception as err:
+                    raise RuntimeError(
+                        "Error testing generation during model load"
+                    ) from err
 
     def _create_header_batches(self):
         num_batches = ceil(len(self._source_df.columns) / self.batch_size)
diff --git a/src/gretel_synthetics/generate_parallel.py b/src/gretel_synthetics/generate_parallel.py
index 0f0311e5..60d775c9 100644
--- a/src/gretel_synthetics/generate_parallel.py
+++ b/src/gretel_synthetics/generate_parallel.py
@@ -3,6 +3,7 @@
 not have to be used directly. They are used automatically by the ``generate.py`` module based
 on the parallelization settings configured for text generation.
 """
+import logging
 import os
 import sys
 
@@ -20,6 +21,8 @@
 
 from gretel_synthetics.errors import TooManyInvalidError
 
+logger = logging.getLogger(__name__)
+
 
 def get_num_workers(
     parallelism: Union[int, float], total_lines: int, chunk_size: int = 5
@@ -180,32 +183,55 @@ def _loky_init_worker(settings: Settings):
         # Workers should be using CPU only (note, this has no effect on the parent process)
         os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
-        # Suppress stdout and stderr in worker threads. Do so on a best-effort basis only.
-        try:
-            devnull = open(os.devnull, "w")
-            try:
-                os.dup2(devnull.fileno(), sys.stdout.fileno())
-                os.dup2(devnull.fileno(), sys.stderr.fileno())
-            except BaseException:
-                pass
-            # On Windows in a Jupyter notebook, we have observed an
-            # 'OSError [WinError 1]: Incorrect function' when trying to sys.stdout/sys.stderr
-            # after the dup2 invocation. Hence, set the references explicitly to make prevent this
-            # (the native code writing to stdout/stderr directly seems to be unaffected).
-            sys.stdout = devnull
-            sys.stderr = devnull
-        except BaseException:  # pylint: disable=broad-except
-            pass
+        # Re-configure logging for this worker, with a format that includes PID to
+        # identify the worker.
+        logging.basicConfig(
+            format="%(asctime)s [%(process)d] - %(levelname)s - %(name)s - %(message)s",
+            force=True,
+        )
+        logging.getLogger("gretel_synthetics.tensorflow").setLevel(logging.WARNING)
+        logger.info("Initializing new generation worker.")
 
         global _loky_worker_generator
         _loky_worker_generator = settings.generator(settings)
 
+        logger.info("Generation worker is ready.")
+        _suppress_stdout()
+
     except BaseException as e:  # pylint: disable=broad-except
         global _loky_worker_init_exception
         _loky_worker_init_exception = e
         # Simply return without raising, to keep the worker alive.
 
 
+def _suppress_stdout() -> None:
+    """
+    Suppress stdout and stderr in worker threads. Do so on a best-effort basis only.
+
+    We have a separate mechanism for workers to report the progress back to the main
+    process, so logging/printing is not necessary and output can get garbled when
+    logging from multiple processes at once without any synchronization.
+    """
+    try:
+        logger.info(
+            "NOTE: stdout and stderr is suppressed for this process, so nothing will be output."
+        )
+        devnull = open(os.devnull, "w")
+        try:
+            os.dup2(devnull.fileno(), sys.stdout.fileno())
+            os.dup2(devnull.fileno(), sys.stderr.fileno())
+        except BaseException:
+            pass
+        # On Windows in a Jupyter notebook, we have observed an
+        # 'OSError [WinError 1]: Incorrect function' when trying to sys.stdout/sys.stderr
+        # after the dup2 invocation. Hence, set the references explicitly to make prevent this
+        # (the native code writing to stdout/stderr directly seems to be unaffected).
+        sys.stdout = devnull
+        sys.stderr = devnull
+    except BaseException:  # pylint: disable=broad-except
+        pass
+
+
 def _loky_worker_process_chunk(
     chunk_size: int, hard_limit: Optional[int] = None
 ) -> Tuple[int, List[GenText], int]:
diff --git a/src/gretel_synthetics/tensorflow/model.py b/src/gretel_synthetics/tensorflow/model.py
index 9d1b5d9b..07c7dc7b 100644
--- a/src/gretel_synthetics/tensorflow/model.py
+++ b/src/gretel_synthetics/tensorflow/model.py
@@ -1,6 +1,8 @@
 """
 Tensorflow - Keras Sequential RNN (GRU)
 """
+import logging
+
 from typing import TYPE_CHECKING
 
 import tensorflow as tf
@@ -16,6 +18,8 @@
     BaseTokenizer = None
 from keras import backend as k
 
+logger = logging.getLogger(__name__)
+
 
 def build_model(
     vocab_size: int, batch_size: int, store: BaseConfig
@@ -30,7 +34,7 @@ def build_model(
     else:
         model = build_default_model(store, batch_size, vocab_size)
 
-    print(model.summary())
+    _print_model_summary(model)
     return model
 
 
@@ -53,11 +57,22 @@ def _prepare_model(
     model.load_weights(tf.train.latest_checkpoint(load_dir)).expect_partial()
 
     model.build(tf.TensorShape([1, None]))
-    model.summary()
 
+    _print_model_summary(model)
     return model
 
 
+def _print_model_summary(model: tf.keras.Model) -> None:
+    model_summary = ""
+
+    def print_fn(line: str) -> None:
+        nonlocal model_summary
+        model_summary += "\t" + line + "\n"
+
+    model.summary(print_fn=print_fn)
+    logger.info("Model summary: \n%s", model_summary)
+
+
 def load_model(
     store: BaseConfig,
     tokenizer: BaseTokenizer,
diff --git a/src/gretel_synthetics/tokenizers.py b/src/gretel_synthetics/tokenizers.py
index aebe4f8a..76f777fc 100644
--- a/src/gretel_synthetics/tokenizers.py
+++ b/src/gretel_synthetics/tokenizers.py
@@ -62,6 +62,7 @@
     format="%(asctime)s : %(threadName)s : %(levelname)s : %(message)s",
     level=logging.INFO,
 )
+logger = logging.getLogger(__name__)
 
 spm_logger = logging.getLogger("sentencepiece")
 spm_logger.setLevel(logging.INFO)
@@ -474,7 +475,7 @@ def _train(self, extra_symbols: Optional[List[str]] = None):
             self.newline_str,
             self.config.field_delimiter_token,
         ] + extra_symbols
-        logging.info("Training SentencePiece tokenizer")
+        logger.info("Training SentencePiece tokenizer")
         spm.SentencePieceTrainer.Train(
             input=self.config.training_data_path,
             model_prefix=const.MODEL_PREFIX,
@@ -569,22 +570,24 @@ def _train(self):
 def _log_sample_data(model_dir: str, sp: spm.SentencePieceProcessor):
     training_data_path = Path(model_dir) / const.TRAINING_DATA
     if not training_data_path.is_file():
-        logging.info("Training data not found for SP sampling")
+        logger.info("Training data not found for SP sampling")
         return
 
     with open(training_data_path) as fin:
         sample = fin.readline().strip()
 
-    logging.info(f"Tokenizer model vocabulary size: {len(sp)} tokens")
-    logging.info(
+    logger.info(f"Tokenizer model vocabulary size: {len(sp)} tokens")
+    logger.info(
         "Mapping first line of training data\n\n{}\n ---- sample tokens mapped to pieces ---- > \n{}\n".format(
             repr(sample), ", ".join(sp.SampleEncodeAsPieces(sample, -1, 0.1))
-        )
+        ),
+        extra={"maybe_sensitive": True},
     )
-    logging.info(
+    logger.info(
         "Mapping first line of training data\n\n{}\n ---- sample tokens mapped to int ---- > \n{}\n".format(
             repr(sample), ", ".join([str(idx) for idx in sp.EncodeAsIds(sample)])
-        )
+        ),
+        extra={"maybe_sensitive": True},
     )
 
 
@@ -605,7 +608,7 @@ def load(cls, model_dir: str):
         """
         sp = spm.SentencePieceProcessor()
         model_fname = f"{const.MODEL_PREFIX}.model"
-        logging.info("Loading tokenizer from: %s", model_fname)
+        logger.info("Loading tokenizer from: %s", model_fname)
         model_path = Path(model_dir) / model_fname
         sp.Load(str(model_path))
         _log_sample_data(model_dir, sp)