Add num_proc= to .push_to_hub() (Dataset and IterableDataset)

src/datasets/arrow_dataset.py

@@ -5397,17 +5397,76 @@ def to_iterable_dataset(self, num_shards: Optional[int] = 1) -> "IterableDataset
             ds = ds.with_format(self._format_type)
         return ds
 
+    def _push_parquet_shards_to_hub_single(
+        self,
+        job_id: int,
+        num_jobs: int,
+        repo_id: str,
+        data_dir: str,
+        split: str,
+        token: Optional[str],
+        revision: Optional[str],
+        create_pr: Optional[bool],
+        num_shards: int,
+        embed_external_files: bool,
+    ):
+        div = num_shards // num_jobs
+        mod = num_shards % num_jobs
+        start = div * job_id + min(job_id, mod)
+        end = start + div + (1 if job_id < mod else 0)
+
+        index_shards = (
+            (start + i, self.shard(num_shards=end - start, index=i, contiguous=True)) for i in range(end - start)
+        )
+
+        api = HfApi(endpoint=config.HF_ENDPOINT, token=token)
+
+        uploaded_size = 0
+        additions: list[CommitOperationAdd] = []
+        for index, shard in index_shards:
+            if embed_external_files:
+                from .io.parquet import get_writer_batch_size
+
+                format = shard.format
+                shard = shard.with_format("arrow")
+                shard = shard.map(
+                    embed_table_storage,
+                    batched=True,
+                    batch_size=get_writer_batch_size(shard.features),
+                    keep_in_memory=True,
+                )
+                shard = shard.with_format(**format)
+            shard_path_in_repo = f"{data_dir}/{split}-{index:05d}-of-{num_shards:05d}.parquet"
+            buffer = BytesIO()
+            shard.to_parquet(buffer)
+            parquet_content = buffer.getvalue()
+            uploaded_size += len(parquet_content)
+            del buffer
+            shard_addition = CommitOperationAdd(path_in_repo=shard_path_in_repo, path_or_fileobj=parquet_content)
+            api.preupload_lfs_files(
+                repo_id=repo_id,
+                additions=[shard_addition],
+                repo_type="dataset",
+                revision=revision,
+                create_pr=create_pr,
+            )
+            additions.append(shard_addition)
+            yield job_id, False, 1
+
+        yield job_id, True, additions
+
     def _push_parquet_shards_to_hub(
         self,
         repo_id: str,
-        data_dir: str = "data",
-        split: Optional[str] = None,
-        token: Optional[str] = None,
-        revision: Optional[str] = None,
-        create_pr: Optional[bool] = False,
-        max_shard_size: Optional[Union[int, str]] = None,
-        num_shards: Optional[int] = None,
-        embed_external_files: bool = True,
+        data_dir: str,
+        split: str,
+        token: Optional[str],
+        revision: Optional[str],
+        create_pr: Optional[bool],
+        max_shard_size: Optional[Union[int, str]],
+        num_shards: Optional[int],
+        embed_external_files: bool,
+        num_proc: Optional[int],
     ) -> tuple[list[CommitOperationAdd], int, int]:
         """Pushes the dataset shards as Parquet files to the hub.
 
@@ -5416,66 +5475,65 @@ def _push_parquet_shards_to_hub(
             uploaded_size (`int`): number of uploaded bytes to the repository
             dataset_nbytes (`int`): approximate size in bytes of the uploaded dataset after uncompression
         """
+        dataset_nbytes = self._estimate_nbytes()
+
         # Find decodable columns, because if there are any, we need to:
         # embed the bytes from the files in the shards
         decodable_columns = (
             [k for k, v in self._info.features.items() if require_decoding(v, ignore_decode_attribute=True)]
             if embed_external_files
             else []
         )
-
-        dataset_nbytes = self._estimate_nbytes()
+        embed_external_files = embed_external_files and bool(decodable_columns)
 
         if num_shards is None:
             max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
             num_shards = int(dataset_nbytes / max_shard_size) + 1
-            num_shards = max(num_shards, 1)
-
-        shards = (self.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards))
-
-        if decodable_columns:
-            from .io.parquet import get_writer_batch_size
-
-            def shards_with_embedded_external_files(shards: Iterator[Dataset]) -> Iterator[Dataset]:
-                for shard in shards:
-                    format = shard.format
-                    shard = shard.with_format("arrow")
-                    shard = shard.map(
-                        embed_table_storage,
-                        batched=True,
-                        batch_size=get_writer_batch_size(shard.features),
-                        keep_in_memory=True,
-                    )
-                    shard = shard.with_format(**format)
-                    yield shard
-
-            shards = shards_with_embedded_external_files(shards)
-
-        api = HfApi(endpoint=config.HF_ENDPOINT, token=token)
+            num_shards = max(num_shards, num_proc or 1)
 
-        uploaded_size = 0
         additions: list[CommitOperationAdd] = []
-        for index, shard in hf_tqdm(
-            enumerate(shards),
-            desc="Uploading the dataset shards",
+
+        num_jobs = num_proc or 1
+        kwargs_iterable = [
+            {
+                "self": self.shard(num_shards=num_jobs, index=job_id, contiguous=True),
+                "job_id": job_id,
+                "num_jobs": num_jobs,
+                "repo_id": repo_id,
+                "data_dir": data_dir,
+                "split": split,
+                "token": token,
+                "revision": revision,
+                "create_pr": create_pr,
+                "num_shards": num_shards,
+                "embed_external_files": embed_external_files,
+            }
+            for job_id in range(num_jobs)
+        ]
+        desc = "Uploading the dataset shards"
+        desc += f" (num_proc={num_proc})" if num_proc is not None and num_proc > 1 else ""
+        pbar = hf_tqdm(
+            unit=" shards",
             total=num_shards,
-        ):
-            shard_path_in_repo = f"{data_dir}/{split}-{index:05d}-of-{num_shards:05d}.parquet"
-            buffer = BytesIO()
-            shard.to_parquet(buffer)
-            parquet_content = buffer.getvalue()
-            uploaded_size += len(parquet_content)
-            del buffer
-            shard_addition = CommitOperationAdd(path_in_repo=shard_path_in_repo, path_or_fileobj=parquet_content)
-            api.preupload_lfs_files(
-                repo_id=repo_id,
-                additions=[shard_addition],
-                repo_type="dataset",
-                revision=revision,
-                create_pr=create_pr,
+            desc=desc,
+        )
+        with contextlib.nullcontext() if num_proc is None and num_proc > 1 else Pool(num_proc) as pool:
+            update_stream = (
+                Dataset._push_parquet_shards_to_hub_single(**kwargs_iterable[0])
+                if pool is None
+                else iflatmap_unordered(
+                    pool,
+                    Dataset._push_parquet_shards_to_hub_single,
+                    kwargs_iterable=kwargs_iterable,
+                )
             )
-            additions.append(shard_addition)
+            for job_id, done, content in update_stream:
+                if not done:
+                    pbar.update(content)
+                else:
+                    additions += content
 
+        uploaded_size = sum(addition.upload_info.size for addition in additions)
         return additions, uploaded_size, dataset_nbytes
 
     def push_to_hub(
@@ -5494,6 +5552,7 @@ def push_to_hub(
         max_shard_size: Optional[Union[int, str]] = None,
         num_shards: Optional[int] = None,
         embed_external_files: bool = True,
+        num_proc: Optional[int] = None,
     ) -> CommitInfo:
         """Pushes the dataset to the hub as a Parquet dataset.
         The dataset is pushed using HTTP requests and does not need to have neither git or git-lfs installed.
@@ -5553,6 +5612,12 @@ def push_to_hub(
                 In particular, this will do the following before the push for the fields of type:
 
                 - [`Audio`] and [`Image`]: remove local path information and embed file content in the Parquet files.
+            num_proc (`int`, *optional*, defaults to `None`):
+                Number of processes when preparing and uploading the dataset.
+                This is helpful if the dataset is made of many samples or media files to embed.
+                Multiprocessing is disabled by default.
+
+                <Added version="4.0.0"/>
 
         Return:
             huggingface_hub.CommitInfo
@@ -5636,6 +5701,7 @@ def push_to_hub(
             num_shards=num_shards,
             create_pr=create_pr,
             embed_external_files=embed_external_files,
+            num_proc=num_proc,
         )
 
         # Check if the repo already has a README.md and/or a dataset_infos.json to update them with the new split info (size and pattern)