first version of save_to_remote for HF from FarmReader

docs/_src/api/api/reader.md

@@ -333,6 +333,26 @@ 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(model_name: str, hf_organization: Optional[str] = None, 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**:
+
+- `model_name`: Repository name of the model you want to save to Hugging Face
+- `hf_organization`: The name of the organization you want to save the model to (you must be a member of this organization)
+- `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 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 +692,65 @@ def save(self, directory: Path):
         self.inferencer.model.save(directory)
         self.inferencer.processor.save(directory)
 
+    def save_to_remote(
+        self,
+        model_name: str,
+        hf_organization: Optional[str] = None,
+        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 model_name: Repository name of the model you want to save to Hugging Face
+        :param hf_organization: The name of the organization you want to save the model to (you must be a member of this organization)
+        :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, model_name, organization=hf_organization, 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],