Deprecate Beam API and download from HF GCS bucket

docs/source/beam.mdx

@@ -1,5 +1,11 @@
 # Beam Datasets
 
+<Tip warning={true}>
+
+The Beam API is deprecated and will be removed in the next major release. 
+
+</Tip>
+
 Some datasets are too large to be processed on a single machine. Instead, you can process them with [Apache Beam](https://beam.apache.org/), a library for parallel data processing. The processing pipeline is executed on a distributed processing backend such as [Apache Flink](https://flink.apache.org/), [Apache Spark](https://spark.apache.org/), or [Google Cloud Dataflow](https://cloud.google.com/dataflow).
 
 We have already created Beam pipelines for some of the larger datasets like [wikipedia](https://huggingface.co/datasets/wikipedia), and [wiki40b](https://huggingface.co/datasets/wiki40b). You can load these normally with [`load_dataset`]. But if you want to run your own Beam pipeline with Dataflow, here is how:

src/datasets/arrow_reader.py

@@ -34,6 +34,7 @@
 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables
 from .utils import logging
 from .utils import tqdm as hf_tqdm
+from .utils.deprecation_utils import deprecated
 from .utils.file_utils import cached_path
 
 
@@ -284,6 +285,7 @@ def read_files(
         dataset_kwargs = {"arrow_table": pa_table, "info": self._info, "split": split}
         return dataset_kwargs
 
+    @deprecated()
     def download_from_hf_gcs(self, download_config: DownloadConfig, relative_data_dir):
         """
         Download the dataset files from the Hf GCS

src/datasets/builder.py

@@ -70,6 +70,7 @@
 from .utils import logging
 from .utils import tqdm as hf_tqdm
 from .utils._filelock import FileLock
+from .utils.deprecation_utils import deprecated
 from .utils.file_utils import cached_path, is_remote_url
 from .utils.info_utils import VerificationMode, get_size_checksum_dict, verify_checksums, verify_splits
 from .utils.py_utils import (
@@ -748,7 +749,7 @@ def download_and_prepare(
         download_mode: Optional[Union[DownloadMode, str]] = None,
         verification_mode: Optional[Union[VerificationMode, str]] = None,
         ignore_verifications="deprecated",
-        try_from_hf_gcs: bool = True,
+        try_from_hf_gcs="deprecated",
         dl_manager: Optional[DownloadManager] = None,
         base_path: Optional[str] = None,
         use_auth_token="deprecated",
@@ -785,6 +786,13 @@ def download_and_prepare(
                 </Deprecated>
             try_from_hf_gcs (`bool`):
                 If `True`, it will try to download the already prepared dataset from the HF Google cloud storage.
+
+                <Deprecated version="2.16.0">
+
+                `try_from_hf_gcs` was deprecated in version 2.16.0 and will be removed in 3.0.0.
+                Host the processed files on the Hugging Face Hub instead.
+
+                </Deprecated>
             dl_manager (`DownloadManager`, *optional*):
                 Specific `DownloadManger` to use.
             base_path (`str`, *optional*):
@@ -865,6 +873,14 @@ def download_and_prepare(
         else:
             token = self.token
 
+        if try_from_hf_gcs != "deprecated":
+            warnings.warn(
+                "'try_from_hf_gcs' was deprecated in version 2.16.0 and will be removed in 3.0.0.",
+                FutureWarning,
+            )
+        else:
+            try_from_hf_gcs = False
+
         output_dir = output_dir if output_dir is not None else self._cache_dir
         # output_dir can be a remote bucket on GCS or S3 (when using BeamBasedBuilder for distributed data processing)
         fs, _, [output_dir] = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
@@ -2025,6 +2041,7 @@ class MissingBeamOptions(ValueError):
     pass
 
 
+@deprecated("Use `GeneratorBasedBuilder` or `ArrowBasedBuilder` instead.")
 class BeamBasedBuilder(DatasetBuilder):
     """Beam-based Builder."""
 

src/datasets/commands/run_beam.py

@@ -10,6 +10,7 @@
 from datasets.download.download_config import DownloadConfig
 from datasets.download.download_manager import DownloadMode
 from datasets.load import dataset_module_factory, import_main_class
+from datasets.utils.deprecation_utils import deprecated
 from datasets.utils.info_utils import VerificationMode
 
 
@@ -28,6 +29,9 @@ def run_beam_command_factory(args, **kwargs):
     )
 
 
+@deprecated(
+    "`BeamBasedBuilder` and `datasets-cli run_beam` are deprecated and will be removed in v3.0.0. Please use `GeneratorBasedBuilder` or `ArrowBasedBuilder` instead."
+)
 class RunBeamCommand(BaseDatasetsCLICommand):
     @staticmethod
     def register_subcommand(parser: ArgumentParser):
@@ -135,7 +139,6 @@ def run(self):
                 verification_mode=VerificationMode.NO_CHECKS
                 if self._ignore_verifications
                 else VerificationMode.ALL_CHECKS,
-                try_from_hf_gcs=False,
             )
             if self._save_infos:
                 builder._save_infos()

src/datasets/io/csv.py

@@ -57,7 +57,6 @@ def read(self):
                 download_config=download_config,
                 download_mode=download_mode,
                 verification_mode=verification_mode,
-                # try_from_hf_gcs=try_from_hf_gcs,
                 base_path=base_path,
                 num_proc=self.num_proc,
             )

src/datasets/io/generator.py

@@ -48,7 +48,6 @@ def read(self):
                 download_config=download_config,
                 download_mode=download_mode,
                 verification_mode=verification_mode,
-                try_from_hf_gcs=False,
                 base_path=base_path,
                 num_proc=self.num_proc,
             )

src/datasets/io/parquet.py

@@ -97,7 +97,6 @@ def read(self):
                 download_config=download_config,
                 download_mode=download_mode,
                 verification_mode=verification_mode,
-                # try_from_hf_gcs=try_from_hf_gcs,
                 base_path=base_path,
                 num_proc=self.num_proc,
             )

src/datasets/io/sql.py

@@ -43,7 +43,6 @@ def read(self):
             download_config=download_config,
             download_mode=download_mode,
             verification_mode=verification_mode,
-            # try_from_hf_gcs=try_from_hf_gcs,
             base_path=base_path,
         )
 

src/datasets/io/text.py

@@ -51,7 +51,6 @@ def read(self):
                 download_config=download_config,
                 download_mode=download_mode,
                 verification_mode=verification_mode,
-                # try_from_hf_gcs=try_from_hf_gcs,
                 base_path=base_path,
                 num_proc=self.num_proc,
             )

src/datasets/load.py

@@ -2594,16 +2594,11 @@ def load_dataset(
     if streaming:
         return builder_instance.as_streaming_dataset(split=split)
 
-    # Some datasets are already processed on the HF google storage
-    # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas
-    try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
-
     # Download and prepare data
     builder_instance.download_and_prepare(
         download_config=download_config,
         download_mode=download_mode,
         verification_mode=verification_mode,
-        try_from_hf_gcs=try_from_hf_gcs,
         num_proc=num_proc,
         storage_options=storage_options,
     )

tests/test_builder.py

@@ -243,7 +243,7 @@ def _generate_examples(self, filepaths, dummy_kwarg_with_different_length):
 
 def _run_concurrent_download_and_prepare(tmp_dir):
     builder = DummyBuilder(cache_dir=tmp_dir)
-    builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
+    builder.download_and_prepare(download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
     return builder
 
 
@@ -257,7 +257,7 @@ class BuilderTest(TestCase):
     def test_download_and_prepare(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
             builder = DummyBuilder(cache_dir=tmp_dir)
-            builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+            builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
             self.assertTrue(
                 os.path.exists(
                     os.path.join(
@@ -274,15 +274,12 @@ def test_download_and_prepare(self):
     def test_download_and_prepare_checksum_computation(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
             builder_no_verification = DummyBuilder(cache_dir=tmp_dir)
-            builder_no_verification.download_and_prepare(
-                try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD
-            )
+            builder_no_verification.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
             self.assertTrue(
                 all(v["checksum"] is not None for _, v in builder_no_verification.info.download_checksums.items())
             )
             builder_with_verification = DummyBuilder(cache_dir=tmp_dir)
             builder_with_verification.download_and_prepare(
-                try_from_hf_gcs=False,
                 download_mode=DownloadMode.FORCE_REDOWNLOAD,
                 verification_mode=VerificationMode.ALL_CHECKS,
             )
@@ -326,22 +323,16 @@ def test_download_and_prepare_with_base_path(self):
             # test relative path is missing
             builder = DummyBuilderWithDownload(cache_dir=tmp_dir, rel_path=rel_path)
             with self.assertRaises(FileNotFoundError):
-                builder.download_and_prepare(
-                    try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD, base_path=tmp_dir
-                )
+                builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD, base_path=tmp_dir)
             # test absolute path is missing
             builder = DummyBuilderWithDownload(cache_dir=tmp_dir, abs_path=abs_path)
             with self.assertRaises(FileNotFoundError):
-                builder.download_and_prepare(
-                    try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD, base_path=tmp_dir
-                )
+                builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD, base_path=tmp_dir)
             # test that they are both properly loaded when they exist
             open(os.path.join(tmp_dir, rel_path), "w")
             open(abs_path, "w")
             builder = DummyBuilderWithDownload(cache_dir=tmp_dir, rel_path=rel_path, abs_path=abs_path)
-            builder.download_and_prepare(
-                try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD, base_path=tmp_dir
-            )
+            builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD, base_path=tmp_dir)
             self.assertTrue(
                 os.path.exists(
                     os.path.join(
@@ -580,7 +571,7 @@ def _post_processing_resources(self, split):
             )
             builder._post_process = types.MethodType(_post_process, builder)
             builder._post_processing_resources = types.MethodType(_post_processing_resources, builder)
-            builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+            builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
             self.assertTrue(
                 os.path.exists(
                     os.path.join(
@@ -604,7 +595,7 @@ def _post_process(self, dataset, resources_paths):
         with tempfile.TemporaryDirectory() as tmp_dir:
             builder = DummyBuilder(cache_dir=tmp_dir)
             builder._post_process = types.MethodType(_post_process, builder)
-            builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+            builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
             self.assertTrue(
                 os.path.exists(
                     os.path.join(
@@ -637,7 +628,7 @@ def _post_processing_resources(self, split):
             builder = DummyBuilder(cache_dir=tmp_dir)
             builder._post_process = types.MethodType(_post_process, builder)
             builder._post_processing_resources = types.MethodType(_post_processing_resources, builder)
-            builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+            builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
             self.assertTrue(
                 os.path.exists(
                     os.path.join(
@@ -662,15 +653,14 @@ def _prepare_split(self, split_generator, **kwargs):
             self.assertRaises(
                 ValueError,
                 builder.download_and_prepare,
-                try_from_hf_gcs=False,
                 download_mode=DownloadMode.FORCE_REDOWNLOAD,
             )
             self.assertRaises(FileNotFoundError, builder.as_dataset)
 
     def test_generator_based_download_and_prepare(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
             builder = DummyGeneratorBasedBuilder(cache_dir=tmp_dir)
-            builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+            builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
             self.assertTrue(
                 os.path.exists(
                     os.path.join(
@@ -940,7 +930,7 @@ def test_generator_based_builder_as_dataset(in_memory, tmp_path):
     cache_dir.mkdir()
     cache_dir = str(cache_dir)
     builder = DummyGeneratorBasedBuilder(cache_dir=cache_dir)
-    builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+    builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
         dataset = builder.as_dataset("train", in_memory=in_memory)
     assert dataset.data.to_pydict() == {"text": ["foo"] * 100}
@@ -955,7 +945,7 @@ def test_custom_writer_batch_size(tmp_path, writer_batch_size, default_writer_ba
         DummyGeneratorBasedBuilder.DEFAULT_WRITER_BATCH_SIZE = default_writer_batch_size
     builder = DummyGeneratorBasedBuilder(cache_dir=cache_dir, writer_batch_size=writer_batch_size)
     assert builder._writer_batch_size == (writer_batch_size or default_writer_batch_size)
-    builder.download_and_prepare(try_from_hf_gcs=False, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+    builder.download_and_prepare(download_mode=DownloadMode.FORCE_REDOWNLOAD)
     dataset = builder.as_dataset("train")
     assert len(dataset.data[0].chunks) == expected_chunks