diff --git a/README.md b/README.md
index f57937e..645583f 100644
--- a/README.md
+++ b/README.md
@@ -280,3 +280,55 @@ run_training(
     torchrun_args=torchrun_args,
     training_args=training_args,
 )
+
+```
+
+## Example training with separate data pre-processing
+
+If the machines in the example above have shared storage, users can pre-process the training dataset a single time so that it can then be distributed to each machine by making the following updates.
+
+```python
+from instructlab.training import (
+    run_training,
+    TorchrunArgs,
+    TrainingArgs,
+    DeepSpeedOptions,
+    DataProcessArgs,
+    data_process as dp
+)
+
+training_args = TrainingArgs(
+    # define data-specific arguments
+    model_path = "ibm-granite/granite-7b-base",
+    data_path = "path/to/dataset.jsonl",
+    ckpt_output_dir = "data/saved_checkpoints",
+    data_output_dir = "data/outputs",
+
+    # define model-trianing parameters
+    max_seq_len = 4096,
+    max_batch_len = 60000,
+    num_epochs = 10,
+    effective_batch_size = 3840,
+    save_samples = 250000,
+    learning_rate = 2e-6,
+    warmup_steps = 800,
+    is_padding_free = True, # set this to true when using Granite-based models
+    random_seed = 42,
+    process_data = True,
+)
+...
+
+data_process_args = DataProcessArgs(
+    data_output_path = training_args.data_output_dir,
+    model_path = training_args.model_path,
+    data_path = training_args.data_path,
+    max_seq_len = training_args.max_seq_len,
+    chat_tmpl_path =  training_args.chat_tmpl_path
+)
+
+dp.main(data_process_args)
+run_training(
+    torch_args=torchrun_args,
+    train_args=training_args,
+)
+```
diff --git a/src/instructlab/training/config.py b/src/instructlab/training/config.py
index 03e963f..05fe479 100644
--- a/src/instructlab/training/config.py
+++ b/src/instructlab/training/config.py
@@ -199,3 +199,6 @@ class TrainingArgs(BaseModel):
     # https://github.com/instructlab/training/issues/28
     # quantize_dtype: QuantizeDataType = QuantizeDataType.NONE
     lora: LoraOptions | None = None
+
+    # This field defines whether or not data processing will occur inside of `run_training()`
+    process_data: Optional[bool] = True
diff --git a/src/instructlab/training/data_process.py b/src/instructlab/training/data_process.py
index 2e6cd39..4bd7c78 100644
--- a/src/instructlab/training/data_process.py
+++ b/src/instructlab/training/data_process.py
@@ -221,6 +221,8 @@ def get_masked_and_orig_text(sample):
 
 
 def main(args: DataProcessArgs):
+    if not os.path.exists(args.data_output_path):
+        os.makedirs(args.data_output_path, exist_ok=True)
     print("\033[92m data arguments are:\033[0m")
     print("\033[36m" + args.model_dump_json() + "\033[0m")
     NUM_PROC = args.num_cpu_procs
diff --git a/src/instructlab/training/main_ds.py b/src/instructlab/training/main_ds.py
index 07684a9..c5cdb2b 100644
--- a/src/instructlab/training/main_ds.py
+++ b/src/instructlab/training/main_ds.py
@@ -645,24 +645,22 @@ def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs) -> None:
             f"the `max_batch_len` cannot be less than `max_seq_len`: {train_args.max_batch_len=} < {train_args.max_seq_len=}"
         )
 
-    # process the training data
-    if not os.path.exists(train_args.data_output_dir):
-        os.makedirs(train_args.data_output_dir, exist_ok=True)
-    dp.main(
-        DataProcessArgs(
-            # XXX(osilkin): make a decision here, either:
-            #   1. the CLI is fully responsible for managing where the data is written
-            #   2. we never cache it and simply write it to a tmp file every time.
-            #
-            # An important reason for why #1 would be preferable is in the case of OpenShift/SELinux
-            # where the user has a defined place for new temporary data to be written.
-            data_output_path=train_args.data_output_dir,
-            model_path=train_args.model_path,
-            data_path=train_args.data_path,
-            max_seq_len=train_args.max_seq_len,
-            chat_tmpl_path=train_args.chat_tmpl_path,
+    if train_args.process_data:
+        dp.main(
+            DataProcessArgs(
+                # XXX(osilkin): make a decision here, either:
+                #   1. the CLI is fully responsible for managing where the data is written
+                #   2. we never cache it and simply write it to a tmp file every time.
+                #
+                # An important reason for why #1 would be preferable is in the case of OpenShift/SELinux
+                # where the user has a defined place for new temporary data to be written.
+                data_output_path=train_args.data_output_dir,
+                model_path=train_args.model_path,
+                data_path=train_args.data_path,
+                max_seq_len=train_args.max_seq_len,
+                chat_tmpl_path=train_args.chat_tmpl_path,
+            )
         )
-    )
 
     if not os.path.exists(train_args.ckpt_output_dir):
         os.makedirs(train_args.ckpt_output_dir, exist_ok=True)