first version of save_to_remote for HF from FarmReader (#2618)

* first version of save_to_remote for HF from FarmReader

* Update Documentation & Code Style

* Changes based on comments

* Update Documentation & Code Style

* imports order

* making small changes to pydoc

* indent fix

* Update Documentation & Code Style

* keyword arguments instead of positional

* Changing to repo_id

huggingface-hub package would have to be v0.5 or higher - checking how to handle with Thomas

* Update Documentation & Code Style

* adding huggingface-hub dependency 0.5 or above

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Sara Zan <sarazanzo94@gmail.com>

docs/_src/api/api/reader.md

@@ -333,6 +333,25 @@ Saves the Reader model so that it can be reused at a later point in time.
 
 - `directory`: Directory where the Reader model should be saved
 
+<a id="farm.FARMReader.save_to_remote"></a>
+
+#### FARMReader.save\_to\_remote
+
+```python
+def save_to_remote(repo_id: str, private: Optional[bool] = None, commit_message: str = "Add new model to Hugging Face.")
+```
+
+Saves the Reader model to Hugging Face Model Hub with the given model_name. For this to work:
+
+- Be logged in to Hugging Face on your machine via transformers-cli
+- Have git lfs installed (https://packagecloud.io/github/git-lfs/install), you can test it by git lfs --version
+
+**Arguments**:
+
+- `repo_id`: A namespace (user or an organization) and a repo name separated by a '/' of the model you want to save to Hugging Face
+- `private`: Set to true to make the model repository private
+- `commit_message`: Commit message while saving to Hugging Face
+
 <a id="farm.FARMReader.predict_batch"></a>
 
 #### FARMReader.predict\_batch

haystack/nodes/reader/farm.py

@@ -4,8 +4,12 @@ import logging
 import multiprocessing
 from pathlib import Path
 from collections import defaultdict
+import os
+import tempfile
 from time import perf_counter
+
 import torch
+from huggingface_hub import create_repo, HfFolder, Repository
 
 from haystack.errors import HaystackError
 from haystack.modeling.data_handler.data_silo import DataSilo, DistillationDataSilo
@@ -688,6 +692,58 @@ class FARMReader(BaseReader):
         self.inferencer.model.save(directory)
         self.inferencer.processor.save(directory)
 
+    def save_to_remote(
+        self, repo_id: str, private: Optional[bool] = None, commit_message: str = "Add new model to Hugging Face."
+    ):
+        """
+        Saves the Reader model to Hugging Face Model Hub with the given model_name. For this to work:
+        - Be logged in to Hugging Face on your machine via transformers-cli
+        - Have git lfs installed (https://packagecloud.io/github/git-lfs/install), you can test it by git lfs --version
+
+        :param repo_id: A namespace (user or an organization) and a repo name separated by a '/' of the model you want to save to Hugging Face
+        :param private: Set to true to make the model repository private
+        :param commit_message: Commit message while saving to Hugging Face
+        """
+        # Note: This function was inspired by the save_to_hub function in the sentence-transformers repo (https://github.com/UKPLab/sentence-transformers/)
+        # Especially for git-lfs tracking.
+
+        token = HfFolder.get_token()
+        if token is None:
+            raise ValueError(
+                "To save this reader model to Hugging Face, make sure you login to the hub on this computer by typing `transformers-cli login`."
+            )
+
+        repo_url = create_repo(token=token, repo_id=repo_id, private=private, repo_type=None, exist_ok=True)
+
+        transformer_models = self.inferencer.model.convert_to_transformers()
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            repo = Repository(tmp_dir, clone_from=repo_url)
+
+            self.inferencer.processor.tokenizer.save_pretrained(tmp_dir)
+
+            # convert_to_transformers (above) creates one model per prediction head.
+            # As the FarmReader models only have one head (QA) we go with this.
+            transformer_models[0].save_pretrained(tmp_dir)
+
+            large_files = []
+            for root, dirs, files in os.walk(tmp_dir):
+                for filename in files:
+                    file_path = os.path.join(root, filename)
+                    rel_path = os.path.relpath(file_path, tmp_dir)
+
+                    if os.path.getsize(file_path) > (5 * 1024 * 1024):
+                        large_files.append(rel_path)
+
+            if len(large_files) > 0:
+                logger.info("Track files with git lfs: {}".format(", ".join(large_files)))
+                repo.lfs_track(large_files)
+
+            logger.info("Push model to the hub. This might take a while")
+            commit_url = repo.push_to_hub(commit_message=commit_message)
+
+        return commit_url
+
     def predict_batch(
         self,
         queries: List[str],

setup.cfg

@@ -75,6 +75,8 @@ install_requires =
     # azure-core>=1.23 needs typing-extensions>=4.0.1
     # pip unfortunately backtracks into the databind direction ultimately getting lost.
     azure-core<1.23
+    # audio's espnet-model-zoo requires huggingface-hub version <0.8 while we need >=0.5 to be able to use create_repo in FARMReader
+    huggingface-hub<0.8.0,>=0.5.0
 
     # Preprocessing
     more_itertools  # for windowing
@@ -157,7 +159,6 @@ audio =
     espnet
     espnet-model-zoo
     pydub
-    huggingface-hub<0.8.0
 beir =
     beir; platform_system != 'Windows'
 crawler =