Make GPU dependencies optional (NVIDIA#27)

* Move GPU imports and make them optional

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Move gpu dependencies to a seperate install

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Remove unused import

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Switch to placeholder import that raises on usage

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Remove deprecated utils usage

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Add cuML attribution

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Safe import tests, improve install instruction, update gha workflow

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Fix pytests due to loc bug

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* update install instructions

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Raise on non module-not-found errors, update logging

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

* Update logging to not change root logger

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>

---------

Signed-off-by: Ayush Dattagupta <ayushdg95@gmail.com>
Signed-off-by: Nicole Luo <nluo@nvidia.com>

.github/workflows/test.yml

@@ -40,9 +40,8 @@ jobs:
         # Explicitly install cython: https://github.com/VKCOM/YouTokenToMe/issues/94
         run: |
           pip install wheel cython
-          pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com .
+          pip install --no-cache-dir .
           pip install pytest
       - name: Run tests
-        # TODO: Remove env variable when gpu dependencies are optional
         run: |
-          RAPIDS_NO_INITIALIZE=1 python -m pytest -v --cpu
+          python -m pytest -v --cpu

README.md

@@ -37,12 +37,20 @@ These modules are designed to be flexible and allow for reordering with few exce
 
 ## Installation
 
-NeMo Curator currently requires Python 3.10 and a GPU with CUDA 12 or above installed in order to be used.
+NeMo Curator currently requires Python 3.10 and the GPU accelerated modules require CUDA 12 or above installed in order to be used.
 
-NeMo Curator can be installed manually by cloning the repository and installing as follows:
+NeMo Curator can be installed manually by cloning the repository and installing as follows -
+
+For CPU only modules:
+```
+pip install .
 ```
-pip install --extra-index-url https://pypi.nvidia.com .
+
+For CPU + CUDA accelerated modules
 ```
+pip install --extra-index-url https://pypi.nvidia.com ".[cuda12x]"
+```
+
 ### NeMo Framework Container
 
 NeMo Curator is available in the [NeMo Framework Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo). The NeMo Framework Container provides an end-to-end platform for development of custom generative AI models anywhere. The latest release of NeMo Curator comes preinstalled in the container.

nemo_curator/datasets/doc_dataset.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import dask.dataframe as dd
-import dask_cudf
 
 from nemo_curator.utils.distributed_utils import read_data, write_to_disk
 from nemo_curator.utils.file_utils import get_all_files_paths_under
@@ -182,10 +181,7 @@ def _read_json_or_parquet(
                 )
                 dfs.append(df)
 
-            if backend == "cudf":
-                raw_data = dask_cudf.concat(dfs, ignore_unknown_divisions=True)
-            else:
-                raw_data = dd.concat(dfs, ignore_unknown_divisions=True)
+            raw_data = dd.concat(dfs, ignore_unknown_divisions=True)
 
     elif isinstance(input_files, str):
         # Single file

nemo_curator/gpu_deduplication/utils.py

@@ -13,84 +13,8 @@
 # limitations under the License.
 
 import argparse
-import logging
-import os
-import socket
-from contextlib import nullcontext
 from time import time
 
-import cudf
-from dask_cuda import LocalCUDACluster
-from distributed import Client, performance_report
-
-
-def create_logger(rank, log_file, name="logger", log_level=logging.INFO):
-    # Create the logger
-    logger = logging.getLogger(name)
-    logger.setLevel(log_level)
-
-    myhost = socket.gethostname()
-
-    extra = {"host": myhost, "rank": rank}
-    formatter = logging.Formatter(
-        "%(asctime)s | %(host)s | Rank %(rank)s | %(message)s"
-    )
-
-    # File handler for output
-    file_handler = logging.FileHandler(log_file, mode="a")
-    file_handler.setFormatter(formatter)
-    logger.addHandler(file_handler)
-    logger = logging.LoggerAdapter(logger, extra)
-
-    return logger
-
-
-# TODO: Remove below to use nemo_curator.distributed_utils.get_client
-def get_client(args) -> Client:
-    if args.scheduler_address:
-        if args.scheduler_file:
-            raise ValueError(
-                "Only one of scheduler_address or scheduler_file can be provided"
-            )
-        else:
-            return Client(address=args.scheduler_address, timeout="30s")
-    elif args.scheduler_file:
-        return Client(scheduler_file=args.scheduler_file, timeout="30s")
-    else:
-        extra_kwargs = (
-            {
-                "enable_tcp_over_ucx": True,
-                "enable_nvlink": True,
-                "enable_infiniband": False,
-                "enable_rdmacm": False,
-            }
-            if args.nvlink_only and args.protocol == "ucx"
-            else {}
-        )
-
-        cluster = LocalCUDACluster(
-            rmm_pool_size=args.rmm_pool_size,
-            protocol=args.protocol,
-            rmm_async=True,
-            **extra_kwargs,
-        )
-        return Client(cluster)
-
-
-def performance_report_if(path=None, report_name="dask-profile.html"):
-    if path is not None:
-        return performance_report(os.path.join(path, report_name))
-    else:
-        return nullcontext()
-
-
-# TODO: Remove below to use nemo_curator.distributed_utils._enable_spilling
-def enable_spilling():
-    """
-    Enables spilling to host memory for cudf
-    """
-    cudf.set_option("spill", True)
-
 
 def get_num_workers(client):
     """

nemo_curator/modules/__init__.py

@@ -19,14 +19,19 @@
 # See https://github.com/NVIDIA/NeMo-Curator/issues/31
 os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = "1"
 
+from nemo_curator.utils.import_utils import gpu_only_import_from
+
 from .add_id import AddId
 from .exact_dedup import ExactDuplicates
 from .filter import Filter, Score, ScoreFilter
-from .fuzzy_dedup import LSH, MinHash
 from .meta import Sequential
 from .modify import Modify
 from .task import TaskDecontamination
 
+# GPU packages
+LSH = gpu_only_import_from("nemo_curator.modules.fuzzy_dedup", "LSH")
+MinHash = gpu_only_import_from("nemo_curator.modules.fuzzy_dedup", "MinHash")
+
 # Pytorch related imports must come after all imports that require cugraph,
 # because of context cleanup issues b/w pytorch and cugraph
 # See this issue: https://github.com/rapidsai/cugraph/issues/2718

nemo_curator/modules/exact_dedup.py

@@ -28,7 +28,8 @@
 
 from nemo_curator._compat import DASK_P2P_ERROR
 from nemo_curator.datasets import DocumentDataset
-from nemo_curator.gpu_deduplication.utils import create_logger, performance_report_if
+from nemo_curator.log import create_logger
+from nemo_curator.utils.distributed_utils import performance_report_if
 from nemo_curator.utils.gpu_utils import is_cudf_type
 
 

nemo_curator/modules/fuzzy_dedup.py

@@ -22,12 +22,12 @@
 from typing import List, Tuple, Union
 
 import cudf
-import cugraph
 import cugraph.dask as dcg
 import cugraph.dask.comms.comms as Comms
 import cupy as cp
 import dask_cudf
 import numpy as np
+from cugraph import MultiGraph
 from dask import dataframe as dd
 from dask.dataframe.shuffle import shuffle as dd_shuffle
 from dask.utils import M
@@ -39,12 +39,13 @@
     filter_text_rows_by_bucket_batch,
     merge_left_to_shuffled_right,
 )
-from nemo_curator.gpu_deduplication.utils import create_logger, performance_report_if
-from nemo_curator.utils.distributed_utils import get_current_client, get_num_workers
-from nemo_curator.utils.fuzzy_dedup_utils.id_mapping import (
-    convert_str_id_to_int,
-    int_ids_to_str,
+from nemo_curator.log import create_logger
+from nemo_curator.utils.distributed_utils import (
+    get_current_client,
+    get_num_workers,
+    performance_report_if,
 )
+from nemo_curator.utils.fuzzy_dedup_utils.id_mapping import int_ids_to_str
 from nemo_curator.utils.fuzzy_dedup_utils.io_utils import (
     aggregated_anchor_docs_with_bk_read,
     get_restart_offsets,
@@ -1120,7 +1121,7 @@ def _run_connected_components(
         df = df[[self.left_id, self.right_id]].astype(np.int64)
         df = dask_cudf.concat([df, self_edge_df])
 
-        G = cugraph.MultiGraph(directed=False)
+        G = MultiGraph(directed=False)
         G.from_dask_cudf_edgelist(
             df, source=self.left_id, destination=self.right_id, renumber=False
         )

nemo_curator/scripts/compute_minhashes.py

@@ -18,12 +18,13 @@
 from nemo_curator import MinHash
 from nemo_curator.datasets import DocumentDataset
 from nemo_curator.gpu_deduplication.ioutils import strip_trailing_sep
-from nemo_curator.gpu_deduplication.utils import (
-    create_logger,
-    parse_nc_args,
+from nemo_curator.gpu_deduplication.utils import parse_nc_args
+from nemo_curator.log import create_logger
+from nemo_curator.utils.distributed_utils import (
+    get_client,
     performance_report_if,
+    read_data,
 )
-from nemo_curator.utils.distributed_utils import get_client, read_data
 from nemo_curator.utils.file_utils import get_all_files_paths_under
 
 

nemo_curator/scripts/connected_components.py

@@ -15,7 +15,7 @@
 import os
 import time
 
-from nemo_curator.gpu_deduplication.utils import enable_spilling, parse_nc_args
+from nemo_curator.gpu_deduplication.utils import parse_nc_args
 from nemo_curator.modules.fuzzy_dedup import ConnectedComponents
 from nemo_curator.utils.distributed_utils import get_client
 
@@ -32,9 +32,10 @@ def main(args):
     st = time.time()
     output_path = os.path.join(args.output_dir, "connected_components.parquet")
     args.set_torch_to_use_rmm = False
+    args.enable_spilling = True
+
     client = get_client(args, cluster_type="gpu")
-    enable_spilling()
-    client.run(enable_spilling)
+
     components_stage = ConnectedComponents(
         cache_dir=args.cache_dir,
         jaccard_pairs_path=args.jaccard_pairs_path,

nemo_curator/scripts/find_exact_duplicates.py

@@ -19,7 +19,8 @@
 
 from nemo_curator.datasets import DocumentDataset
 from nemo_curator.gpu_deduplication.ioutils import strip_trailing_sep
-from nemo_curator.gpu_deduplication.utils import create_logger, parse_nc_args
+from nemo_curator.gpu_deduplication.utils import parse_nc_args
+from nemo_curator.log import create_logger
 from nemo_curator.modules import ExactDuplicates
 from nemo_curator.utils.distributed_utils import get_client, read_data
 from nemo_curator.utils.file_utils import get_all_files_paths_under

nemo_curator/scripts/jaccard_compute.py

@@ -15,13 +15,13 @@
 import os
 import time
 
-from nemo_curator.gpu_deduplication.utils import enable_spilling, parse_nc_args
+from nemo_curator.gpu_deduplication.utils import parse_nc_args
 from nemo_curator.modules.fuzzy_dedup import JaccardSimilarity
 from nemo_curator.utils.distributed_utils import get_client, get_num_workers
 
 
 def main(args):
-    description = """Computes the Jaccard similarity between document pairs
+    """Computes the Jaccard similarity between document pairs
     from partitioned parquet dataset. Result is a parquet dataset consiting of
     document id pair along with their Jaccard similarity score.
     """
@@ -30,9 +30,9 @@ def main(args):
     output_final_results_path = os.path.join(
         OUTPUT_PATH, "jaccard_similarity_results.parquet"
     )
+    args.enable_spilling = True
     client = get_client(args, "gpu")
-    enable_spilling()
-    client.run(enable_spilling)
+
     print(f"Num Workers = {get_num_workers(client)}", flush=True)
     print("Connected to dask cluster", flush=True)
     print("Running jaccard compute script", flush=True)

nemo_curator/scripts/jaccard_shuffle.py

@@ -15,12 +15,9 @@
 import os
 import time
 
-from nemo_curator.gpu_deduplication.utils import (
-    get_client,
-    get_num_workers,
-    parse_nc_args,
-)
+from nemo_curator.gpu_deduplication.utils import get_num_workers, parse_nc_args
 from nemo_curator.modules.fuzzy_dedup import _Shuffle
+from nemo_curator.utils.distributed_utils import get_client
 from nemo_curator.utils.fuzzy_dedup_utils.io_utils import (
     get_text_ddf_from_json_path_with_blocksize,
 )
@@ -38,7 +35,7 @@ def main(args):
     OUTPUT_PATH = args.output_dir
     output_shuffled_docs_path = os.path.join(OUTPUT_PATH, "shuffled_docs.parquet")
 
-    client = get_client(args)
+    client = get_client(args, "gpu")
     client.run(func)
     print(f"Num Workers = {get_num_workers(client)}", flush=True)
     print("Connected to dask cluster", flush=True)

nemo_curator/scripts/map_buckets.py

@@ -15,12 +15,9 @@
 import os
 import time
 
-from nemo_curator.gpu_deduplication.utils import (
-    get_client,
-    get_num_workers,
-    parse_nc_args,
-)
+from nemo_curator.gpu_deduplication.utils import get_num_workers, parse_nc_args
 from nemo_curator.modules.fuzzy_dedup import _MapBuckets
+from nemo_curator.utils.distributed_utils import get_client
 from nemo_curator.utils.fuzzy_dedup_utils.io_utils import (
     get_bucket_ddf_from_parquet_path,
     get_text_ddf_from_json_path_with_blocksize,
@@ -157,7 +154,7 @@ def main(args):
     output_anchor_docs_with_bk_path = os.path.join(
         OUTPUT_PATH, "anchor_docs_with_bk.parquet"
     )
-    client = get_client(args)
+    client = get_client(args, "gpu")
     print(f"Num Workers = {get_num_workers(client)}", flush=True)
     print("Connected to dask cluster", flush=True)
     print("Running jaccard map buckets script", flush=True)

nemo_curator/scripts/minhash_lsh.py

@@ -24,7 +24,8 @@
 from nemo_curator.gpu_deduplication.jaccard_utils.doc_id_mapping import (
     convert_str_id_to_int,
 )
-from nemo_curator.gpu_deduplication.utils import create_logger, parse_nc_args
+from nemo_curator.gpu_deduplication.utils import parse_nc_args
+from nemo_curator.log import create_logger
 from nemo_curator.utils.distributed_utils import get_client