Merge branch 'main' into print_venv

* fix: let users know to not call preprocess for vision mode

* fix: improve ux for pretraining dataset and skip prepare ds

* feat: add info to doc

* Update src/axolotl/cli/preprocess.py following comment

Co-authored-by: salman <salman.mohammadi@outlook.com>

---------

Co-authored-by: salman <salman.mohammadi@outlook.com>

docker/Dockerfile-base

@@ -22,9 +22,11 @@ RUN apt-get update \
     && mkdir /root/.conda \
     && bash Miniconda3-latest-Linux-x86_64.sh -b \
     && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+    && conda create -n "axolotl-py${PYTHON_VERSION}" python="${PYTHON_VERSION}" \
+    && conda init bash \
+    && echo "conda activate axolotl-py${PYTHON_VERSION}" >> ~/.bashrc
 
-ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+ENV PATH="/root/miniconda3/envs/axolotl-py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 

docker/Dockerfile-base-next

@@ -22,9 +22,11 @@ RUN apt-get update \
     && mkdir /root/.conda \
     && bash Miniconda3-latest-Linux-x86_64.sh -b \
     && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+    && conda create -n "axolotl-py${PYTHON_VERSION}" python="${PYTHON_VERSION}" \
+    && conda init bash \
+    && echo "conda activate axolotl-py${PYTHON_VERSION}" >> ~/.bashrc
 
-ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+ENV PATH="/root/miniconda3/envs/axolotl-py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 

docker/Dockerfile-base-nightly

@@ -22,9 +22,11 @@ RUN apt-get update \
     && mkdir /root/.conda \
     && bash Miniconda3-latest-Linux-x86_64.sh -b \
     && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+    && conda create -n "axolotl-py${PYTHON_VERSION}" python="${PYTHON_VERSION}" \
+    && conda init bash \
+    && echo "conda activate axolotl-py${PYTHON_VERSION}" >> ~/.bashrc
 
-ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+ENV PATH="/root/miniconda3/envs/axolotl-py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 

docs/faq.qmd

@@ -51,6 +51,10 @@ description: Frequently asked questions
 >   pad_token: "..."
 > ```
 
+**Q: `IterableDataset error` or `KeyError: 'input_ids'` when using `preprocess` CLI**
+
+> A: This is because you may be using `preprocess` CLI with `pretraining_dataset:` or `skip_prepare_dataset: true` respectively. Please use `axolotl train` CLI directly instead as these datasets are prepared on demand.
+
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**

src/axolotl/cli/preprocess.py

@@ -35,6 +35,12 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     check_accelerate_default_config()
     check_user_token()
 
+    for key in ["skip_prepare_dataset", "pretraining_dataset"]:
+        if cfg.get("key"):
+            raise ValueError(
+                f"You have set `{key}:`. `preprocess` is not needed. Run the `axolotl train` CLI directly instead."
+            )
+
     if not cfg.dataset_prepared_path:
         msg = (
             Fore.RED

src/axolotl/utils/data/shared.py

@@ -526,8 +526,9 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
     if len(datasets) == 1:
         ds = datasets[0]
 
-        # Do not shuffle if curriculum sampling is enabled
-        if cfg.curriculum_sampling:
+        # Do not shuffle if curriculum sampling is enabled or
+        # shuffle_merged_datasets is disabled
+        if cfg.curriculum_sampling or not cfg.shuffle_merged_datasets:
             return ds
 
         return ds.shuffle(seed=cfg.seed)

src/axolotl/utils/trainer.py

@@ -609,6 +609,9 @@ def prepare_opinionated_env(cfg):
     if cfg.qlora_sharded_model_loading:
         # model loading is forked after the tokenizer
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    if cfg.sample_packing:
+        # multipack parallel packing sampler defaults to using fork
+        os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
 def setup_trainer(