import argparse
import logging
import os
from pathlib import Path
from datatrove.executor import LocalPipelineExecutor
from datatrove.executor.slurm import SlurmPipelineExecutor
from datatrove.pipeline.base import PipelineStep
from huggingface_hub import HfApi
from huggingface_hub.constants import REPOCARD_NAME
from examples.port_datasets.droid_rlds.port_droid import DROID_SHARDS
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDatasetMetadata
from lerobot.common.datasets.utils import create_lerobot_dataset_card
from lerobot.common.utils.utils import init_logging
class UploadDataset(PipelineStep):
def __init__(
self,
repo_id: str,
branch: str | None = None,
revision: str | None = None,
tags: list | None = None,
license: str | None = "apache-2.0",
private: bool = False,
distant_repo_id: str | None = None,
**card_kwargs,
):
super().__init__()
self.repo_id = repo_id
self.distant_repo_id = self.repo_id if distant_repo_id is None else distant_repo_id
self.branch = branch
self.tags = tags
self.license = license
self.private = private
self.card_kwargs = card_kwargs
self.revision = revision if revision else CODEBASE_VERSION
if os.environ.get("HF_HUB_ENABLE_HF_TRANSFER", "0") != "1":
logging.warning(
'HF_HUB_ENABLE_HF_TRANSFER is not set to "1". Install hf_transfer and set the env '
"variable for faster uploads:\npip install hf-transfer\nexport HF_HUB_ENABLE_HF_TRANSFER=1"
)
self.create_repo()
def create_repo(self):
logging.info(f"Loading meta data from {self.repo_id}...")
meta = LeRobotDatasetMetadata(self.repo_id)
logging.info(f"Creating repo {self.distant_repo_id}...")
hub_api = HfApi()
hub_api.create_repo(
repo_id=self.distant_repo_id,
private=self.private,
repo_type="dataset",
exist_ok=True,
)
if self.branch:
hub_api.create_branch(
repo_id=self.distant_repo_id,
branch=self.branch,
revision=self.revision,
repo_type="dataset",
exist_ok=True,
)
if not hub_api.file_exists(
self.distant_repo_id, REPOCARD_NAME, repo_type="dataset", revision=self.branch
):
card = create_lerobot_dataset_card(
tags=self.tags, dataset_info=meta.info, license=self.license, **self.card_kwargs
)
card.push_to_hub(repo_id=self.distant_repo_id, repo_type="dataset", revision=self.branch)
def list_files_recursively(directory):
base_path = Path(directory)
return [str(file.relative_to(base_path)) for file in base_path.rglob("*") if file.is_file()]
logging.info(f"Listing all local files from {self.repo_id}...")
self.file_paths = list_files_recursively(meta.root)
self.file_paths = sorted(self.file_paths)
def create_chunks(self, lst, n):
from itertools import islice
it = iter(lst)
return [list(islice(it, size)) for size in [len(lst) // n + (i < len(lst) % n) for i in range(n)]]
def create_commits(self, additions):
import logging
import math
import random
import time
from huggingface_hub import create_commit
from huggingface_hub.utils import HfHubHTTPError
FILES_BETWEEN_COMMITS = 10 # noqa: N806
BASE_DELAY = 0.1 # noqa: N806
MAX_RETRIES = 12 # noqa: N806
# Split the files into smaller chunks for faster commit
# and avoiding "A commit has happened since" error
num_chunks = math.ceil(len(additions) / FILES_BETWEEN_COMMITS)
chunks = self.create_chunks(additions, num_chunks)
for chunk in chunks:
retries = 0
while True:
try:
create_commit(
self.distant_repo_id,
repo_type="dataset",
operations=chunk,
commit_message=f"DataTrove upload ({len(chunk)} files)",
revision=self.branch,
)
logging.info("create_commit completed!")
break
except HfHubHTTPError as e:
if "A commit has happened since" in e.server_message:
if retries >= MAX_RETRIES:
logging.error(f"Failed to create commit after {MAX_RETRIES=}. Giving up.")
raise e
logging.info("Commit creation race condition issue. Waiting...")
time.sleep(BASE_DELAY * 2**retries + random.uniform(0, 2))
retries += 1
else:
raise e
def run(self, data=None, rank: int = 0, world_size: int = 1):
import logging
from datasets.utils.tqdm import disable_progress_bars
from huggingface_hub import CommitOperationAdd, preupload_lfs_files
from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata
from lerobot.common.utils.utils import init_logging
init_logging()
disable_progress_bars()
chunks = self.create_chunks(self.file_paths, world_size)
file_paths = chunks[rank]
if len(file_paths) == 0:
raise ValueError(file_paths)
logging.info("Pre-uploading LFS files...")
for i, path in enumerate(file_paths):
logging.info(f"{i}: {path}")
meta = LeRobotDatasetMetadata(self.repo_id)
additions = [
CommitOperationAdd(path_in_repo=path, path_or_fileobj=meta.root / path) for path in file_paths
]
preupload_lfs_files(
repo_id=self.distant_repo_id, repo_type="dataset", additions=additions, revision=self.branch
)
logging.info("Creating commits...")
self.create_commits(additions)
logging.info("Done!")
def make_upload_executor(
repo_id, job_name, logs_dir, workers, partition, cpus_per_task, mem_per_cpu, slurm=True
):
kwargs = {
"pipeline": [
UploadDataset(repo_id),
],
"logging_dir": str(logs_dir / job_name),
}
if slurm:
kwargs.update(
{
"job_name": job_name,
"tasks": DROID_SHARDS,
"workers": workers,
"time": "08:00:00",
"partition": partition,
"cpus_per_task": cpus_per_task,
"sbatch_args": {"mem-per-cpu": mem_per_cpu},
}
)
executor = SlurmPipelineExecutor(**kwargs)
else:
kwargs.update(
{
"tasks": DROID_SHARDS,
"workers": 1,
}
)
executor = LocalPipelineExecutor(**kwargs)
return executor
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--repo-id",
type=str,
help="Repositery identifier on Hugging Face: a community or a user name `/` the name of the dataset, required when push-to-hub is True.",
)
parser.add_argument(
"--logs-dir",
type=Path,
help="Path to logs directory for `datatrove`.",
)
parser.add_argument(
"--job-name",
type=str,
default="upload_droid",
help="Job name used in slurm, and name of the directory created inside the provided logs directory.",
)
parser.add_argument(
"--slurm",
type=int,
default=1,
help="Launch over slurm. Use `--slurm 0` to launch sequentially (useful to debug).",
)
parser.add_argument(
"--workers",
type=int,
default=50,
help="Number of slurm workers. It should be less than the maximum number of shards.",
)
parser.add_argument(
"--partition",
type=str,
help="Slurm partition. Ideally a CPU partition. No need for GPU partition.",
)
parser.add_argument(
"--cpus-per-task",
type=int,
default=8,
help="Number of cpus that each slurm worker will use.",
)
parser.add_argument(
"--mem-per-cpu",
type=str,
default="1950M",
help="Memory per cpu that each worker will use.",
)
init_logging()
args = parser.parse_args()
kwargs = vars(args)
kwargs["slurm"] = kwargs.pop("slurm") == 1
upload_executor = make_upload_executor(**kwargs)
upload_executor.run()
if __name__ == "__main__":
main()