Add files using upload-large-folder tool

batch_submit.sh

@@ -0,0 +1,45 @@
+#/bin/bash
+
+
+# for offset in $(seq 0 100 6000); do
+#     echo $offset
+# done
+
+
+# Store the output in an array
+folders=($(ls /gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2/data/))
+
+
+# Iterate over all indices
+# for i in {0..94}; do
+#     echo "${folders[$i]}"
+# done
+
+# Iterate over a range of indices (from 5 to 10)
+# 已提交：0..94
+for i in {63..94}; do
+    sleep 2s
+    echo processing "${folders[$i]}"
+    task_name=$(echo "${folders[$i]}" | awk '{print tolower($0)}')
+    processed_script=$(echo -n "bash /gpfs/public/research/liyizhi/datafilter/minhash.sh ${folders[$i]}" | base64 -w 0)
+
+    curl 'https://arsenal.dh3.ai/api/v1/workloads' \
+      -H 'accept: application/json, text/plain, */*' \
+      -H 'accept-language: en-GB,en;q=0.9,zh-CN;q=0.8,zh;q=0.7,en-US;q=0.6' \
+      -H 'cache-control: no-cache' \
+      -H 'content-type: application/json' \
+      -H 'cookie: Arsenal-UserName=%E6%9D%8E%E4%B8%80%E4%B9%8B; Arsenal-Token=deZ50%2FVRKNk5LI3e5DMwDiAJNq1unS2y5WicS3kAniyPUnbVcMd5vMgbwuZYdagTuht07DYOGl7HfStjsUbwJXt4yG6MWFZr1w%3D%3D; Arsenal-UserId=ou-4abbe30b0a0f07192d8dbdd0e8d0566a; Arsenal-Token-Expire=1729027014; Arsenal-IsAdmin=false; Arsenal-IsQueueAdmin=false; Arsenal-UserAvatar=https%3A%2F%2Fs3-imfile.feishucdn.com%2Fstatic-resource%2Fv1%2Fv3_00ae_189cc1d3-aeb5-4aaa-996e-5b64ba7f30dg~%3Fimage_size%3D640x640%26cut_type%3D%26quality%3D%26format%3Dimage%26sticker_format%3D.webp' \
+      -H 'dnt: 1' \
+      -H 'origin: https://arsenal.dh3.ai' \
+      -H 'pragma: no-cache' \
+      -H 'priority: u=1, i' \
+      -H 'referer: https://arsenal.dh3.ai/arsenal/workloads/create?from=xcs-filter-2013-20-bv8wj' \
+      -H 'sec-ch-ua: "Google Chrome";v="129", "Not=A?Brand";v="8", "Chromium";v="129"' \
+      -H 'sec-ch-ua-mobile: ?0' \
+      -H 'sec-ch-ua-platform: "Windows"' \
+      -H 'sec-fetch-dest: empty' \
+      -H 'sec-fetch-mode: cors' \
+      -H 'sec-fetch-site: same-origin' \
+      -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36' \
+      --data-raw '{"workspace":"polaris-dev-cpu","priority":1,"displayName":"'${task_name}'","gvk":{"kind":"PyTorchJob"},"description":"","entryPoint":"'${processed_script}'","tensorboard":"","isSSHEnabled":true,"isTolerateAll":false,"tolerations":{},"image":"nvcr.io/nvidia/pytorch:23.10-py3","maxRetry":0,"env":{},"resources":[{"name":"Master","replica":1,"cpu":"50","memory":"400Gi","gpu":"","gpuType":"","shareMemory":"200Gi","ephemeralStorage":"50Gi"}],"ttlSecondsAfterFinished":10,"schedulerTime":0,"customerLabels":{}}'
+done

count

@@ -0,0 +1,95 @@
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+774129312

count.txt

@@ -0,0 +1,190 @@
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count_parquet.py

@@ -0,0 +1,15 @@
+import os
+import pyarrow.parquet as pq
+
+parquet_file = pq.ParquetFile("/gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2/data/CC-MAIN-2013-20/train-00013-of-00058.parquet")
+
+print(parquet_file.metadata.num_rows)
+
+# sum = 0
+# for a, b, c in os.walk("/gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2/data/"):
+#     if len(b) == 0:
+#         for name in c:
+#             parquet_file = pq.ParquetFile(os.path.join(a, name))
+#             num_rows = parquet_file.metadata.num_rows
+#             sum += num_rows
+# print(sum)

custom_minhash.py

@@ -0,0 +1,550 @@
+import os
+import re
+import heapq
+import pickle
+import struct
+import contextlib
+import numpy as np
+from typing import Generator
+from pathlib import Path
+from dataclasses import dataclass, field
+from fsspec.spec import AbstractBufferedFile
+from datatrove.data import DocumentsPipeline
+from datatrove.io import DataFolderLike, get_datafolder
+from datatrove.pipeline.base import PipelineStep
+from datatrove.pipeline.writers.disk_base import DiskWriter
+from datatrove.utils.binaryio import read_tuples_from_file, seek_to_start
+from datatrove.utils.hashing import HashConfig, create_hash_func
+from datatrove.utils.logging import logger
+from datatrove.utils.text import TextNormConfig, ngrams
+from datatrove.utils.typeshelper import StatHints
+
+
+# http://en.wikipedia.org/wiki/Mersenne_prime
+_mersenne_prime = np.uint64((1 << 61) - 1)
+
+SENTINEL = (1 << 32) - 1
+
+dataset_map = {}
+datasets = []
+cnt = 0
+
+def transform(dataset_idx: int, line_no: int) -> int:
+    global cnt
+    global dataset_map
+    global datasets
+    if dataset_idx not in dataset_map:
+        dataset_map[dataset_idx] = cnt
+        datasets.append(dataset_idx)
+        cnt += 1
+    return line_no * 10000 + dataset_map[dataset_idx]
+
+
+@dataclass
+class CustomMinhashConfig:
+    """Configuration for Min-Hash deduplication
+
+    Args:
+        n_grams: n-grams size to use
+        num_buckets: number of buckets to use
+        seed: random seed used to generate the hash function parameters. Should be the same on all workers to ensure they all have the same parameters
+    """
+
+    n_grams: int = 13
+    num_buckets: int = 9
+    num_hashs: int = 128
+    seed: int = 1
+
+    norm_config: TextNormConfig = field(default_factory=TextNormConfig)
+    hash_config: HashConfig = field(default_factory=HashConfig)
+
+    def __str__(self):
+        return f"{self.n_grams}ng_{self.num_buckets}bs_{self.hash_config}"
+
+
+@dataclass(order=True)
+class HashSig:
+    """Hash signature for a given document in a given bucket
+
+    Args:
+        sig: tuple of hashes
+        file_id: file id
+        doc_id: document id
+        reader_id: reader id. Used to know from where the next signature should be requested
+    """
+
+    sig: tuple[int]
+    doc_id: int
+
+    data_index: int
+    reader_id: int
+
+    def is_from_index(self):
+        return False
+
+
+def read_sigs(
+    file: AbstractBufferedFile,
+    config: CustomMinhashConfig,
+    index_file: bool = False,
+    min_hash: int = 0,
+    max_hash: int = _mersenne_prime,
+    ensure_order: bool = True,
+    lines_to_buffer: int = 5,
+    reader_id: int = 0,
+) -> Generator:
+    """Read signatures from a file
+
+    Args:
+        file: file to read from
+        reader_id: reader id
+        config: minhash configuration (a MinhashConfig object)
+        index_file: is index file
+    """
+    line_format = f"{config.n_grams}{config.hash_config.struct_format}{'I' if not index_file else ''}"
+    with file as f:
+        if f.size == 0:
+            return
+        seek_to_start(f, min_hash, line_format, config.hash_config.struct_format)
+        last = None
+        # CC-MAIN-2013-20_00000.minhash.sig
+        file_stem = Path(file.path).name.removesuffix(".minhash.sig")
+
+        data_index = 0
+        if len(file_stem) == 21:
+            dataset_year = int(file_stem[8:12])
+            dataset_num = int(file_stem[13:15])
+            parquet_num = int(file_stem[16:21])
+            # 13 20 00000
+            data_index = ((dataset_year % 100) * 100 + dataset_num) * 100000 + parquet_num
+
+        for data in read_tuples_from_file(f, line_format, lines_to_buffer=lines_to_buffer):
+            sigdata = data if index_file else data[:-1]
+            assert sigdata[0] >= min_hash and (
+                ensure_order is False or last is None or sigdata >= last
+            ), f"Hash order error. {f.tell()=}, {min_hash=}, {sigdata=}, {last=}"
+            if sigdata[0] >= max_hash:
+                break
+            last = sigdata
+            yield (
+                HashSig(sig=sigdata, doc_id=-1, data_index=data_index, reader_id=reader_id)
+                if index_file
+                else HashSig(sig=sigdata, doc_id=data[-1], data_index=data_index, reader_id=reader_id)
+            )
+
+
+class CustomMinhashDedupSignature(PipelineStep):
+    """Minhash Deduplication: First Pipeline Step
+
+        Compute the minhash signature for each document and write it to disk.
+
+    Args:
+        output_folder: output folder
+        config: minhash configuration (a MinhashConfig object)
+    """
+
+    type = "🫂 - DEDUP"
+    name = "🎯 MinHash stage 1"
+
+    def __init__(self, output_folder: DataFolderLike, config: CustomMinhashConfig = None, naming_prefix: str = ''):
+        super().__init__()
+        self.output_folder = get_datafolder(output_folder)
+        self.config = config or CustomMinhashConfig()
+        self.num_hashes = self.config.num_hashs
+        self.naming_prefix = naming_prefix
+        self._parameters = None
+        self._hash_func = create_hash_func(self.config.hash_config)
+
+    @property
+    def parameters(self):
+        """Minhash parameters
+
+        Create parameters for a random bijective permutation function
+        that maps a 32/64-bit hash value to another 32/64-bit hash value.
+        http://en.wikipedia.org/wiki/Universal_hashing
+
+        Note: For 64-bit hashes the upper-bound for codomain is not [0,2**64) but [0,2**61 - 1)
+        """
+        if self._parameters is None:
+            gen = np.random.RandomState(self.config.seed)
+            self._parameters = np.array(
+                [
+                    (
+                        gen.randint(1, _mersenne_prime, dtype=np.uint64),
+                        gen.randint(0, _mersenne_prime, dtype=np.uint64),
+                    )
+                    for _ in range(self.num_hashes)
+                ],
+                dtype=np.uint64,
+            ).T
+        return self._parameters
+
+    def get_signature(self, shingles: np.ndarray) -> list[list[int]]:
+        """Get the signature for a set of shingles (n-grams)
+
+        Args:
+            shingles: shingles (n-grams) numpy uint64 array of size (N, 1)
+
+        Returns:
+            list (num buckets) of lists of integers (hashes)
+        """
+        a, b = self.parameters
+        phv = (shingles * a + b) % _mersenne_prime
+        if self.config.hash_config.precision == 32:
+            phv = np.bitwise_and(phv, self.config.hash_config.max)
+        return [
+            x.tolist()
+            for x in np.split(np.min(phv, axis=0)[:self.config.num_buckets * self.config.n_grams], self.config.num_buckets)
+        ]
+
+    def get_shingles(self, text: str) -> np.ndarray:
+        """Get shingles (hashed n-grams) from a string of text
+
+        Shingles are created by hashing n-grams of simplified text (lower cases, whitespace normalized, no punctuation, etc).
+
+        Args:
+            text: input text
+
+        Returns:
+            numpy array of shingles: dtype = uint64, shape = (number of n_grams in string, 1)
+        """
+        return np.fromiter(
+            [
+                self._hash_func("".join(x))
+                for x in ngrams(text, self.config.n_grams)
+            ],
+            dtype=np.uint64,
+        ).reshape((-1, 1))
+
+    # def write_buckets(self, batch_id, rank, band_hash_value_list):
+    #     try:
+    #         for bucket in range(self.config.num_buckets):
+    #             with self.output_folder.open(f"{bucket:02d}/{batch_id:05d}_{rank:05d}.minhash.pkl", mode="wb") as fout:
+    #                 pickle.dump(band_hash_value_list[bucket], fout)
+    #     except Exception as e:
+    #         logger.error(f"dump minhash fail, error: {e}")
+
+    def run(self, data: DocumentsPipeline, rank: int = 0, world_size: int = 1) -> DocumentsPipeline:
+        buckets = [
+            self.output_folder.open(f"bucket_{bi:03d}/{self.naming_prefix}_{rank:05d}.minhash.sig", mode="wb")
+            for bi in range(self.config.num_buckets)
+        ]
+        with self.track_time():
+            # batch_id = 0
+            for doc_idx, doc in enumerate(data):
+                self.stat_update(StatHints.total)
+
+                shingles = self.get_shingles(doc.text)
+                if shingles.size != 0:
+                    sig = self.get_signature(shingles)
+                    for bi, (bucket, bucket_sig) in enumerate(zip(buckets, sig)):
+                        # print(f"{self.n_grams=} {bucket_sig=}")
+                        bucket.write(
+                            struct.pack(
+                                f"<{self.config.n_grams}{self.config.hash_config.struct_format}I",
+                                *bucket_sig,
+                                doc_idx,
+                            )
+                        )
+            for file in buckets:
+                file.close()
+
+            logger.info("Sorting buckets...")
+            for bi in range(len(buckets)):
+                # read one by one, sort and write back
+                sigs = sorted(
+                    read_sigs(
+                        self.output_folder.open(f"bucket_{bi:03d}/{self.naming_prefix}_{rank:05d}.minhash.sig", mode="rb"),
+                        self.config,
+                        ensure_order=False,
+                        lines_to_buffer=-1,  # load everything in one go
+                    )
+                )
+                with self.output_folder.open(f"bucket_{bi:03d}/{self.naming_prefix}_{rank:05d}.minhash.sig", mode="wb") as fo:
+                    for sig in sigs:
+                        fo.write(
+                            struct.pack(
+                                f"<{self.config.n_grams}{self.config.hash_config.struct_format}I",
+                                *sig.sig,
+                                sig.doc_id,
+                            )
+                        )
+
+
+class CustomMinhashDedupBuckets(PipelineStep):
+    """Minhash Deduplication: Second Pipeline Step
+
+        Find duplicate pairs from the signatures and possibly an index. Can also save an index with the new signatures.
+
+    Args:
+        input_folder: input folder containing the signature from step 1
+        output_folder: output folder where results (document duplicate pairs) will be saved
+        index_folder: index folder. If set, we will load all index files in this folder and use them as a reference for deduplicating the current dataset (remove any matches on our dataset with signatures from the index)
+        config: minhash configuration (a MinhashConfig object)
+        only_dedup_in_index: only deduplicate versus index (ignore any matches between 2 documents in our input dataset)
+        create_index_name: create index name. If this parameter is set, index files will be created with this name that other datasets can use as a reference for dedup. Set to `None` to disable index file creation.
+    """
+
+    type = "🫂 - DEDUP"
+    name = "🎯 MinHash stage 2"
+
+    def __init__(
+        self,
+        input_folder: DataFolderLike,
+        output_folder: DataFolderLike,
+        config: CustomMinhashConfig = None,
+        lines_to_buffer: int = 5,
+    ):
+        super().__init__()
+        self.input_folder = get_datafolder(input_folder)
+        self.output_folder = get_datafolder(output_folder)
+        self.config = config or CustomMinhashConfig()
+        self.lines_to_buffer = lines_to_buffer
+
+    def get_worker_hash_range(self, sig_files, rank, world_size):
+        workers_per_bucket = world_size // self.config.num_buckets
+        bucket, bucket_worker = divmod(rank, workers_per_bucket)
+        hash_min, hash_max = (
+            0,
+            _mersenne_prime if self.config.hash_config.precision == 64 else self.config.hash_config.max,
+        )
+        if workers_per_bucket > 1 and len(sig_files):
+            # take the first file and find bucket_worker boundaries. all workers in a bucket process the same set of
+            # files, so this should be consistent across workers (and span the entire range of hashes)
+            with self.input_folder.open(sig_files[0], mode="rb") as f:
+                line_size = struct.calcsize(f"{self.config.n_grams}{self.config.hash_config.struct_format}I")
+                L, rem = divmod(f.size, line_size)
+                assert rem == 0, "file size not divisible by line size"
+                assert L >= workers_per_bucket, f"tried to use {workers_per_bucket=} but there are only {L} lines"
+                if bucket_worker > 0:
+                    # not first
+                    f.seek(line_size * (L // workers_per_bucket) * bucket_worker, os.SEEK_SET)
+                    hash_min = struct.unpack(
+                        self.config.hash_config.struct_format,
+                        f.read(struct.calcsize(self.config.hash_config.struct_format)),
+                    )[0]
+                if bucket_worker + 1 < workers_per_bucket:
+                    # not last
+                    f.seek(line_size * (L // workers_per_bucket) * (bucket_worker + 1), os.SEEK_SET)
+                    hash_max = struct.unpack(
+                        self.config.hash_config.struct_format,
+                        f.read(struct.calcsize(self.config.hash_config.struct_format)),
+                    )[0]
+        return hash_min, hash_max
+
+    def run(self, data: DocumentsPipeline = None, rank: int = 0, world_size: int = 1):
+        assert data is None, "You should not use an input block before MinhashDedupBuckets"
+        assert (world_size % self.config.num_buckets) == 0, "Number of tasks must be divisible by num_buckets"
+        workers_per_bucket = world_size // self.config.num_buckets
+        bucket, bucket_worker = divmod(rank, workers_per_bucket)
+
+        with self.track_time():
+            sig_files = self.input_folder.list_files(subdirectory=f"bucket_{bucket:03d}")
+            hash_min, hash_max = self.get_worker_hash_range(sig_files, rank, world_size)
+
+            logger.info(
+                f"Running worker {bucket_worker + 1}/{workers_per_bucket} on bucket {bucket:03d}. "
+                f"Hash range: {[hash_min, hash_max]}"
+            )
+
+            sig_readers = [
+                read_sigs(
+                    file,
+                    self.config,
+                    min_hash=hash_min,
+                    max_hash=hash_max,
+                    lines_to_buffer=self.lines_to_buffer,
+                    reader_id=file_i,
+                )
+                for file_i, file in enumerate(self.input_folder.open_files(sig_files, mode="rb"))
+            ]
+
+            pq = [x for x in [next(sig_reader, None) for sig_reader in sig_readers] if x is not None]
+            heapq.heapify(pq)
+            logger.info("Finished initializing signatures priority queue.")
+
+            with self.output_folder.open(f"{bucket:05d}_{bucket_worker:02d}.dups", mode="wb") as out_f:
+                last: HashSig | None = None
+                while pq:
+                    v: HashSig = heapq.heappop(pq)
+                    assert last is None or v >= last, f"Sig queue sort error. {v=} < {last=}"
+                    if last and last.sig == v.sig:
+                        # write (file_id1, doc_id1, file_id2, doc_id2)
+                        out_f.write(
+                            struct.pack("<4I", last.data_index, last.doc_id, v.data_index, v.doc_id)
+                        )
+                        self.stat_update("total_matches")
+                    last = v
+                    next_sig = next(sig_readers[v.reader_id], None)
+                    if next_sig:
+                        assert next_sig >= v, f"Next sig sort error. {next_sig=} < {v=}"
+                        heapq.heappush(pq, next_sig)
+
+
+class CustomMinhashDedupCluster(PipelineStep):
+    """Minhash Deduplication: Third Pipeline Step
+
+    Cluster the documents using the previously found duplicate pairs. If A-B and B-C are duplicate pairs, then we will have the A-B-C cluster. Only one document per cluster will be kept after filtering
+    """
+
+    type = "🫂 - DEDUP"
+    name = "🎯 MinHash stage 3"
+
+    def __init__(
+        self,
+        input_folder: DataFolderLike,
+        output_folder: DataFolderLike,
+        config: CustomMinhashConfig = None,
+        ignore_index_matches: bool = False,
+        lines_to_buffer: int = 5,
+    ):
+        super().__init__()
+        self.input_folder = get_datafolder(input_folder)
+        self.output_folder = get_datafolder(output_folder)
+        self.config = config or CustomMinhashConfig()
+        self.ignore_index_matches = ignore_index_matches
+        self.lines_to_buffer = lines_to_buffer
+
+    def run(self, data: DocumentsPipeline = None, _: int = 0, world_size: int = 1):
+        global datasets
+        global dataset_map
+
+        dup_files = self.input_folder.list_files(glob_pattern="*.dups")
+        assert (
+            len(dup_files) % self.config.num_buckets
+        ) == 0, "Number of .dups files should be divisible by number of buckets"
+        assert world_size == 1, "World size must be 1 for clustering"
+        union_set = np.arange(0, 1_500_000 * 10_000, dtype=np.uint64)
+        exists = np.zeros(1_500_000 * 10_000, dtype=bool)
+
+        max_no = 0
+
+        def parent(x):
+            exists[x] = 1
+            if union_set[x] == x:
+                return x
+            # Path Compression
+            union_set[x] = parent(union_set[x])
+            return union_set[x]
+
+        with self.track_time():
+            for dup_file in dup_files:
+                with self.input_folder.open(dup_file, "rb") as dupf:
+                    logger.info(f"Processing {dup_file}")
+                    for f1, d1, f2, d2 in read_tuples_from_file(dupf, "4I", lines_to_buffer=self.lines_to_buffer):
+                        a, b = transform(f1, d1), transform(f2, d2)
+                        if a > max_no:
+                            max_no = a
+                        if b > max_no:
+                            max_no = b
+                        union_set[parent(b)] = parent(a)
+
+            logger.info("Outputing")
+            with self.output_folder.get_output_file_manager(mode="wb") as output_mg:
+                for node in range(max_no + 1):
+                    if exists[node]:
+                        self.stat_update("duplicates")
+                        p = parent(node)
+                        if node != p:
+                            dataset_idx = datasets[node % 10000]
+                            dataset_year = dataset_idx // 10000000
+                            dataset_num = (dataset_idx // 100000) % 100
+                            parquet_num = dataset_idx % 100000
+                            output_mg.write(f"CC-MAIN-20{dataset_year:02d}-{dataset_num:02d}_{parquet_num:05d}.remove", struct.pack("<I", node // 10000))
+                            self.stat_update("to_remove")
+
+
+class CustomMinhashDedupFilter(PipelineStep):
+    """Minhash Deduplication: Fourth (and final) Pipeline Step
+
+    Filter the documents based on the minhash clusters to keep only one per cluster
+    """
+
+    type = "🫂 - DEDUP"
+    name = "🎯 MinHash stage 4"
+
+    def __init__(
+        self,
+        remove_id_input_folder: DataFolderLike,
+        sig_input_folder: DataFolderLike,
+        exclusion_writer: DiskWriter = None,
+        lines_to_buffer: int = 5,
+        naming_prefix: str = '',
+        config: CustomMinhashConfig = None,
+    ):
+        super().__init__()
+        self.remove_id_folder = get_datafolder(remove_id_input_folder)
+        self.sig_folder = get_datafolder(sig_input_folder)
+        self.exclusion_writer = exclusion_writer
+        self.lines_to_buffer = lines_to_buffer
+        self.naming_prefix = naming_prefix
+        self.config = config or CustomMinhashConfig()
+
+    def run(self, data: DocumentsPipeline, rank: int = 0, world_size: int = 1):
+        files = self.remove_id_folder.list_files(glob_pattern=f"{self.naming_prefix}*{rank:05d}.remove")
+        if not files or len(files) == 0:
+            logger.info(f"Found 0 files by pattern {self.naming_prefix}*{rank:05d}.remove, maybe no dups")
+            for bucket in range(self.config.num_buckets):
+                for sig_file_name in self.sig_folder.list_files(glob_pattern=f"bucket_{bucket:03d}/{self.naming_prefix}*{rank:05d}.minhash.sig"):
+                    file_name = Path(sig_file_name).name.removesuffix(".minhash.sig")
+                    save_docs = []
+                    for sig in read_sigs(
+                        self.sig_folder.open(f"bucket_{bucket:03d}/{file_name}.minhash.sig", "rb"),
+                        self.config,
+                        ensure_order=False,
+                    ):
+                        self.stat_update(StatHints.total)
+                        save_doc = {}
+                        save_doc['doc_id'] = file_name + ':' + str(sig.doc_id)
+                        save_doc['hash'] = bytes(np.array(sig.sig).astype(np.uint64).byteswap().data)
+                        save_docs.append(save_doc)
+                        self.stat_update(StatHints.forwarded)
+                    with self.sig_folder.open(f"bucket_{bucket:03d}/{file_name}.pkl", "wb") as out_file:
+                        pickle.dump(save_docs, out_file)
+
+            return
+
+        single_int = struct.Struct("<I")
+        for file in files:
+            logger.info(f"Processing {file}")
+
+            remove_id_file = self.remove_id_folder.open(file, "rb")
+            logger.info(remove_id_file)
+            remove_ids = set()
+            while True:
+                chunk = remove_id_file.read(single_int.size)
+                if not chunk:
+                    break
+                remove_ids.add(single_int.unpack(chunk))
+            remove_id_file.close()
+
+            file_name = Path(file).name.removesuffix(".remove")
+            for bucket in range(self.config.num_buckets):
+                save_docs = []
+                for sig in read_sigs(
+                    self.sig_folder.open(f"bucket_{bucket:03d}/{file_name}.minhash.sig", "rb"),
+                    self.config,
+                    ensure_order=False,
+                ):
+                    self.stat_update(StatHints.total)
+                    if sig.doc_id in remove_ids:
+                        continue
+                    save_doc = {}
+                    save_doc['doc_id'] = file_name + ':' + str(sig.doc_id)
+                    save_doc['hash'] = bytes(np.array(sig.sig).astype(np.uint64).byteswap().data)
+                    save_docs.append(save_doc)
+                    self.stat_update(StatHints.forwarded)
+                with self.sig_folder.open(f"bucket_{bucket:03d}/{file_name}.pkl", "wb") as out_file:
+                    pickle.dump(save_docs, out_file)
+            #     for bucket_id in range(self.config.num_buckets):
+            #         save_doc = {}
+            #         save_doc[self.config.doc_id_field_name] = doc.metadata['dump'] + ':' + doc.id
+            #         save_doc[self.config.hash_field_name] = hash_value_list[bucket_id]
+            #         bucket_id = int.from_bytes(hash_value_list[bucket_id], 'big') % self.config.num_buckets
+            #         band_hash_value_list[bucket_id].append(save_doc)
+            #     if doc_idx % self.config.write_line_num == 0 and doc_idx != 0:
+            #         self.write_bands(rank, batch_id, band_hash_value_list)
+            #         batch_id += 1
+            #         band_hash_value_list = [[[] for _ in range(self.config.num_buckets)] for _ in range(self.config.num_buckets)]
+            # if len(band_hash_value_list[0]) > 0:
+            #     self.write_bands(rank, batch_id, band_hash_value_list)

etcd.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+set -ex
+
+apt update
+apt install -y curl
+
+ETCD_VER=v3.4.34
+
+# choose either URL
+GOOGLE_URL=https://storage.googleapis.com/etcd
+GITHUB_URL=https://github.com/etcd-io/etcd/releases/download
+DOWNLOAD_URL=${GOOGLE_URL}
+
+rm -f /tmp/etcd-${ETCD_VER}-linux-amd64.tar.gz
+rm -rf /tmp/etcd-download-test && mkdir -p /tmp/etcd-download-test
+
+curl -L ${DOWNLOAD_URL}/${ETCD_VER}/etcd-${ETCD_VER}-linux-amd64.tar.gz -o /tmp/etcd-${ETCD_VER}-linux-amd64.tar.gz
+tar xzvf /tmp/etcd-${ETCD_VER}-linux-amd64.tar.gz -C /tmp/etcd-download-test --strip-components=1
+rm -f /tmp/etcd-${ETCD_VER}-linux-amd64.tar.gz
+
+/tmp/etcd-download-test/etcd --version
+/tmp/etcd-download-test/etcdctl version
+/tmp/etcd-download-test/etcd --listen-client-urls 'http://0.0.0.0:2379' --advertise-client-urls 'http://0.0.0.0:2379'

get_file.sh

@@ -0,0 +1,4 @@
+#/bin/bash
+
+head -n 1 filelist.txt > ~/filename
+sed -i '1d' filelist.txt

minhash.py

@@ -0,0 +1,127 @@
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.pipeline.readers import ParquetReader
+from datatrove.utils.hashing import HashConfig
+from custom_minhash import (
+    CustomMinhashConfig,
+    CustomMinhashDedupSignature,
+    CustomMinhashDedupBuckets,
+    CustomMinhashDedupCluster,
+    CustomMinhashDedupFilter,
+)
+from datatrove.pipeline.readers import JsonlReader
+from datatrove.pipeline.tokens import TokensCounter
+from datatrove.pipeline.writers.jsonl import JsonlWriter
+from datatrove.utils.hashing import HashConfig
+ 
+import argparse
+from glob import glob
+
+custom_minhash_config = CustomMinhashConfig(
+    hash_config=HashConfig(precision=32, hash_fc='sha1'),
+)
+ 
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Demo script with argparse")
+    parser.add_argument("--sub-folder", type=str, required=True, help="Subfolder path")
+    parser.add_argument("--offset", type=int, default=0, required=False, help="task offset")
+    # parser.add_argument("--local-task", type=int, default=2, required=False, help="local task number")
+
+    args = parser.parse_args()
+    print(args)
+
+    MINHASH_BASE_PATH = "minhash"
+     
+    LOGS_FOLDER = "minhash_logs"
+    
+    n_file = len(glob(f"/gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2/data/{args.sub_folder}/*.parquet"))
+    TOTAL_TASKS = n_file
+    print(f"Total files in {args.sub_folder}: {n_file}")
+
+    INPUT_READER = ParquetReader(
+        "/gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2", 
+        glob_pattern=f"data/{args.sub_folder}/*.parquet",
+        batch_size=100_000)
+    # INPUT_READER = ParquetReader("data/CC-MAIN-2013-20/", limit=3)
+    # INPUT_READER = JsonlReader('jsonl')
+    
+    # stage 1 computes minhash signatures for each task (each task gets a set of files)
+    stage1 = LocalPipelineExecutor(
+        # job_name="mh1",
+        pipeline=[
+            INPUT_READER,
+            CustomMinhashDedupSignature(
+                output_folder=f"{MINHASH_BASE_PATH}/signatures",
+                config=custom_minhash_config,
+                naming_prefix=args.sub_folder,
+            ),
+        ],
+        tasks=TOTAL_TASKS,
+        # local_tasks=LOCAL_TASKS,
+        # local_rank_offset=TASK_OFFSET,
+        logging_dir=f"{LOGS_FOLDER}/signatures",
+        # slurm_logs_folder=f"{LOCAL_LOGS_FOLDER}/signatures/slurm_logs",
+    )
+     
+    # stage 2 finds matches between signatures in each bucket
+    stage2 = LocalPipelineExecutor(
+        # job_name="mh2",
+        pipeline=[
+            CustomMinhashDedupBuckets(
+                input_folder=f"{MINHASH_BASE_PATH}/signatures",
+                output_folder=f"{MINHASH_BASE_PATH}/buckets",
+                config=custom_minhash_config,
+            ),
+        ],
+        tasks=custom_minhash_config.num_buckets,
+        local_tasks=1,
+        local_rank_offset=args.offset,
+        # time="90:00:00",
+        # partition="hopper-prod",
+        logging_dir=f"{LOGS_FOLDER}/buckets",
+        # depends=stage1,
+    )
+    
+    # stage 3 creates clusters of duplicates using the results from all buckets
+    stage3 = LocalPipelineExecutor(
+        # job_name="mh3",
+        pipeline=[
+            CustomMinhashDedupCluster(
+                input_folder=f"{MINHASH_BASE_PATH}/buckets",
+                output_folder=f"{MINHASH_BASE_PATH}/remove_ids",
+                config=custom_minhash_config,
+            ),
+        ],
+        # tasks=1,
+        # time="90:00:00",
+        # partition="hopper-prod",
+        logging_dir=f"{LOGS_FOLDER}/clusters",
+        # mem_per_cpu_gb=70,
+        # cpus_per_task=2,
+        # depends=stage2,
+        # slurm_logs_folder=f"{LOCAL_LOGS_FOLDER}/clusters/slurm_logs",
+    )
+    
+    # stage 4 reads the original input data and removes all but 1 sample per duplicate cluster
+    # the data must match exactly stage 1, so number of tasks and the input source must be the same
+    stage4 = LocalPipelineExecutor(
+        # job_name="mh4",
+        pipeline=[
+            INPUT_READER,
+            TokensCounter(),  # nice way to see how many tokens we had before and after deduplication
+            CustomMinhashDedupFilter(
+                remove_id_input_folder=f"{MINHASH_BASE_PATH}/remove_ids",
+                sig_input_folder=f"{MINHASH_BASE_PATH}/signatures",
+                exclusion_writer=JsonlWriter(f"{MINHASH_BASE_PATH}/removed"),
+                config=custom_minhash_config,
+                naming_prefix=args.sub_folder,
+            ),
+        ],
+        tasks=TOTAL_TASKS,
+        # time="50:00:00",
+        # partition="hopper-cpu",
+        logging_dir=f"{LOGS_FOLDER}/filter",
+        depends=stage3,
+        # slurm_logs_folder=f"{LOCAL_LOGS_FOLDER}/filter/slurm_logs",
+    )
+    
+    stage3.run()

remove_count.txt

@@ -0,0 +1,6549 @@
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stage2.sh

@@ -0,0 +1,39 @@
+#/bin/bash
+
+# CC-MAIN-2013-20  CC-MAIN-2014-52  CC-MAIN-2015-40  CC-MAIN-2016-44  CC-MAIN-2017-34  CC-MAIN-2018-22  CC-MAIN-2019-09  CC-MAIN-2019-47  CC-MAIN-2020-45  CC-MAIN-2021-43  CC-MAIN-2023-14
+# CC-MAIN-2013-48  CC-MAIN-2015-06  CC-MAIN-2015-48  CC-MAIN-2016-50  CC-MAIN-2017-39  CC-MAIN-2018-26  CC-MAIN-2019-13  CC-MAIN-2019-51  CC-MAIN-2020-50  CC-MAIN-2021-49  CC-MAIN-2023-23
+# CC-MAIN-2014-10  CC-MAIN-2015-11  CC-MAIN-2016-07  CC-MAIN-2017-04  CC-MAIN-2017-43  CC-MAIN-2018-30  CC-MAIN-2019-18  CC-MAIN-2020-05  CC-MAIN-2021-04  CC-MAIN-2022-05  CC-MAIN-2023-40
+# CC-MAIN-2014-15  CC-MAIN-2015-14  CC-MAIN-2016-18  CC-MAIN-2017-09  CC-MAIN-2017-47  CC-MAIN-2018-34  CC-MAIN-2019-22  CC-MAIN-2020-10  CC-MAIN-2021-10  CC-MAIN-2022-21  CC-MAIN-2023-50
+# CC-MAIN-2014-23  CC-MAIN-2015-18  CC-MAIN-2016-22  CC-MAIN-2017-13  CC-MAIN-2017-51  CC-MAIN-2018-39  CC-MAIN-2019-26  CC-MAIN-2020-16  CC-MAIN-2021-17  CC-MAIN-2022-27  CC-MAIN-2024-10
+# CC-MAIN-2014-35  CC-MAIN-2015-22  CC-MAIN-2016-26  CC-MAIN-2017-17  CC-MAIN-2018-05  CC-MAIN-2018-43  CC-MAIN-2019-30  CC-MAIN-2020-24  CC-MAIN-2021-21  CC-MAIN-2022-33 
+# CC-MAIN-2014-41  CC-MAIN-2015-27  CC-MAIN-2016-30  CC-MAIN-2017-22  CC-MAIN-2018-09  CC-MAIN-2018-47  CC-MAIN-2019-35  CC-MAIN-2020-29  CC-MAIN-2021-25  CC-MAIN-2022-40  
+# CC-MAIN-2014-42  CC-MAIN-2015-32  CC-MAIN-2016-36  CC-MAIN-2017-26  CC-MAIN-2018-13  CC-MAIN-2018-51  CC-MAIN-2019-39  CC-MAIN-2020-34  CC-MAIN-2021-31  CC-MAIN-2022-49
+# CC-MAIN-2014-49  CC-MAIN-2015-35  CC-MAIN-2016-40  CC-MAIN-2017-30  CC-MAIN-2018-17  CC-MAIN-2019-04  CC-MAIN-2019-43  CC-MAIN-2020-40  CC-MAIN-2021-39  CC-MAIN-2023-06
+# pip install datarove[all]
+
+
+source /gpfs/public/research/liyizhi/conda_init.sh 
+
+source /gpfs/public/research/liyizhi/miniconda3/bin/activate vllm
+cd /gpfs/public/research/liyizhi/datafilter
+
+
+
+# subfolder=$1 # CC-MAIN-2013-20, or CC-MAIN-2013-20/xxxx.parquet
+# limit=$2 # -1 for the whole folder
+# tasks=$3 # -1 for the whole folder
+
+# HF_HUB_ENABLE_HF_TRANSFER=0 huggingface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu-score-2 --include "data/$1/*" --local-dir /gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2
+
+# offset=$1
+
+# python -u minhash.py --sub-folder ${subfolder} --limit ${limit} --tasks ${tasks} 2>&1 | tee minhash.fineweb-edu-score2.${subfolder}.log
+
+# python -u minhash.py --offset ${offset} 2>&1 | tee minhash.fineweb-edu-score2.offset-${offset}.localtask-2.log
+
+
+bucket=$1 # [0, 9)
+python -u stage2.py --bucket ${bucket} 2>&1 | tee minhash.fineweb-edu-score2.bucket${bucket}.log
+
+
+# bash /gpfs/public/research/liyizhi/datafilter/minhash.sh 100

stage3.go

@@ -0,0 +1,264 @@
+package main
+
+import (
+	"bufio"
+	"context"
+	"encoding/binary"
+	"fmt"
+	"io"
+	"io/fs"
+	"log"
+	"os"
+	"path"
+	"runtime"
+	"sync"
+	"sync/atomic"
+
+	"golang.org/x/sync/errgroup"
+)
+
+const DIR string = "minhash"
+const INPUT_DIR string = "buckets"
+const OUTPUT_DIR string = "remove_ids"
+const MAX_LINE_NO uint64 = 1500000
+
+var dataset_index_map map[uint64]uint16
+var dataset_index_arr []uint64
+var cnt uint16
+var union_set []uint64
+var union_set_lock []*sync.Mutex
+var exists []bool
+var max_index uint64
+
+func transform(dataset_index, line_no uint64, lock *sync.Mutex) uint64 {
+	lock.Lock()
+	if _, ok := dataset_index_map[dataset_index]; !ok {
+		dataset_index_map[dataset_index] = cnt
+		dataset_index_arr = append(dataset_index_arr, dataset_index)
+		cnt++
+	}
+	index := dataset_index_map[dataset_index]
+	lock.Unlock()
+
+	return line_no*10000 + uint64(index)
+}
+
+func parent(x uint64) uint64 {
+	exists[x] = true
+
+	root := x
+	union_set_lock[root].Lock()
+	for union_set[root] != root {
+		root = union_set[root]
+		union_set_lock[root].Lock()
+	}
+
+	xx := x
+	for union_set[x] != x {
+		x, union_set[x] = union_set[x], root
+		union_set_lock[x].Unlock()
+	}
+	union_set_lock[xx].Unlock()
+
+	return root
+}
+
+func merge(a, b uint64) {
+	fa_a := parent(a)
+	fa_b := parent(b)
+
+	union_set_lock[fa_b].Lock()
+	union_set[fa_b] = fa_a
+	union_set_lock[fa_b].Unlock()
+}
+
+func main() {
+	files, err := os.ReadDir(path.Join(DIR, INPUT_DIR))
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	if err = os.MkdirAll(path.Join(DIR, OUTPUT_DIR), fs.ModePerm); err != nil {
+		log.Fatal(err)
+	}
+
+	input_files := make(chan string, 1000)
+	for _, file := range files {
+		if file.IsDir() {
+			continue
+		}
+		input_files <- file.Name()
+	}
+	close(input_files)
+
+	eg, _ := errgroup.WithContext(context.Background())
+
+	concurrency := runtime.NumCPU() - 1
+	dataset_index_lock := &sync.Mutex{}
+	dataset_index_map = make(map[uint64]uint16)
+	dataset_index_arr = make([]uint64, 0)
+	cnt = 0
+
+	union_set = make([]uint64, MAX_LINE_NO*10000)
+	exists = make([]bool, MAX_LINE_NO*10000)
+	union_set_lock = make([]*sync.Mutex, MAX_LINE_NO*10000)
+
+	start_index_chan := make(chan uint64, concurrency)
+	chunk_size := MAX_LINE_NO * 10000 / uint64(concurrency-1)
+	for i := 0; i < concurrency; i++ {
+		start_index_chan <- chunk_size * uint64(i)
+	}
+	close(start_index_chan)
+	signal := make(chan struct{}, concurrency)
+
+	for i := 0; i < concurrency; i++ {
+		go func() {
+			for start_index := range start_index_chan {
+				if start_index >= MAX_LINE_NO*10000 {
+					signal <- struct{}{}
+					return
+				}
+				end_index := start_index + chunk_size
+				if end_index > MAX_LINE_NO*10000 {
+					end_index = MAX_LINE_NO * 10000
+				}
+				for j := start_index; j < end_index; j++ {
+					union_set[j] = j
+					union_set_lock[j] = &sync.Mutex{}
+				}
+				signal <- struct{}{}
+			}
+		}()
+	}
+
+	for i := 0; i < concurrency; i++ {
+		<-signal
+	}
+
+	max_index_lock := &sync.Mutex{}
+	for i := 0; i < concurrency; i++ {
+		eg.Go(func() error {
+			for file_name := range input_files {
+				log.Println("Processing", file_name)
+
+				file, err := os.OpenFile(path.Join(DIR, INPUT_DIR, file_name), os.O_RDONLY, 0664)
+				if err != nil {
+					return err
+				}
+				defer file.Close()
+
+				reader := bufio.NewReader(file)
+
+				buffer := make([]byte, 16)
+				data := make([]uint64, 4)
+
+				var max_index_here uint64 = 0
+				for {
+					if _, err = reader.Read(buffer); err != nil {
+						if err == io.EOF {
+							break
+						}
+						return err
+					}
+
+					for i := 0; i < 4; i++ {
+						data[i] = uint64(binary.LittleEndian.Uint32(buffer[i*4 : i*4+4]))
+					}
+					a := transform(data[0], data[1], dataset_index_lock)
+					b := transform(data[2], data[3], dataset_index_lock)
+					merge(a, b)
+
+					if a < b {
+						a = b
+					}
+					if a > max_index_here {
+						max_index_here = a
+					}
+				}
+
+				max_index_lock.Lock()
+				if max_index_here > max_index {
+					max_index = max_index_here
+				}
+				max_index_lock.Unlock()
+			}
+
+			return nil
+		})
+	}
+
+	if err = eg.Wait(); err != nil {
+		log.Fatal(err)
+	}
+
+	writers := make([]*bufio.Writer, 0)
+	writer_locks := make([]*sync.Mutex, 0)
+	for _, dataset_index := range dataset_index_arr {
+		dataset_year := dataset_index / 10000000
+		dataset_num := (dataset_index / 100000) % 100
+		parquet_num := dataset_index % 100000
+		file_name := fmt.Sprintf("CC-MAIN-20%02d-%02d_%05d.remove", dataset_year, dataset_num, parquet_num)
+		file, err := os.OpenFile(path.Join(DIR, OUTPUT_DIR, file_name), os.O_RDWR|os.O_CREATE, 0644)
+		if err != nil {
+			log.Fatal(err)
+			return
+		}
+		defer file.Close()
+		writer := bufio.NewWriter(file)
+		defer writer.Flush()
+		writers = append(writers, writer)
+		writer_locks = append(writer_locks, &sync.Mutex{})
+	}
+
+	eg, _ = errgroup.WithContext(context.Background())
+	start_index_chan = make(chan uint64, concurrency)
+	chunk_size = max_index / uint64(concurrency-1)
+	for i := 0; i < concurrency; i++ {
+		start_index_chan <- chunk_size * uint64(i)
+	}
+	close(start_index_chan)
+
+	duplicates := atomic.Uint64{}
+	to_remove := atomic.Uint64{}
+	for i := 0; i < concurrency; i++ {
+		eg.Go(func() error {
+			for start_index := range start_index_chan {
+				if start_index >= max_index+1 {
+					return nil
+				}
+				end_index := start_index + chunk_size
+				if end_index > max_index+1 {
+					end_index = max_index + 1
+				}
+
+				buffer := make([]byte, 4)
+				for j := start_index; j < end_index; j++ {
+					if exists[j] {
+						duplicates.Add(1)
+						fa := parent(j)
+						if fa != j {
+							binary.LittleEndian.PutUint32(buffer, uint32(j/10000))
+							writer_no := j % 10000
+
+							writer_locks[writer_no].Lock()
+							if _, err := writers[writer_no].Write(buffer); err != nil {
+								return err
+							}
+							writer_locks[writer_no].Unlock()
+
+							to_remove.Add(1)
+						}
+					}
+				}
+			}
+			return nil
+		})
+	}
+
+	if err = eg.Wait(); err != nil {
+		log.Fatal(err)
+	}
+
+	log.Println("Duplicates:", duplicates.Load())
+	log.Println("To remove:", to_remove.Load())
+}

stage3.py

@@ -0,0 +1,52 @@
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.pipeline.readers import ParquetReader
+from datatrove.utils.hashing import HashConfig
+from custom_minhash import (
+    CustomMinhashConfig,
+    CustomMinhashDedupSignature,
+    CustomMinhashDedupBuckets,
+    CustomMinhashDedupCluster,
+    CustomMinhashDedupFilter,
+)
+from datatrove.pipeline.readers import JsonlReader
+from datatrove.pipeline.tokens import TokensCounter
+from datatrove.pipeline.writers.jsonl import JsonlWriter
+from datatrove.utils.hashing import HashConfig
+ 
+custom_minhash_config = CustomMinhashConfig(
+    hash_config=HashConfig(precision=32, hash_fc='sha1'),
+)
+ 
+if __name__ == '__main__':
+    MINHASH_BASE_PATH = "minhash"
+     
+    LOGS_FOLDER = "minhash_logs"
+    
+    # INPUT_READER = ParquetReader(
+    #     "/gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2", 
+    #     glob_pattern=f"data/{args.sub_folder}/*.parquet",
+    #     batch_size=100_000)
+    # INPUT_READER = ParquetReader("data/CC-MAIN-2013-20/", limit=3)
+    # INPUT_READER = JsonlReader('jsonl')
+    
+    # stage 3 creates clusters of duplicates using the results from all buckets
+    stage3 = LocalPipelineExecutor(
+        # job_name="mh3",
+        pipeline=[
+            CustomMinhashDedupCluster(
+                input_folder=f"{MINHASH_BASE_PATH}/buckets",
+                output_folder=f"{MINHASH_BASE_PATH}/remove_ids_unsorted",
+                config=custom_minhash_config,
+            ),
+        ],
+        # tasks=1,
+        # time="90:00:00",
+        # partition="hopper-prod",
+        logging_dir=f"{LOGS_FOLDER}/clusters_unsorted",
+        # mem_per_cpu_gb=70,
+        # cpus_per_task=2,
+        # depends=stage2,
+        # slurm_logs_folder=f"{LOCAL_LOGS_FOLDER}/clusters/slurm_logs",
+    )
+    
+    stage3.run()

stage3.sh

@@ -0,0 +1,38 @@
+#/bin/bash
+
+# CC-MAIN-2013-20  CC-MAIN-2014-52  CC-MAIN-2015-40  CC-MAIN-2016-44  CC-MAIN-2017-34  CC-MAIN-2018-22  CC-MAIN-2019-09  CC-MAIN-2019-47  CC-MAIN-2020-45  CC-MAIN-2021-43  CC-MAIN-2023-14
+# CC-MAIN-2013-48  CC-MAIN-2015-06  CC-MAIN-2015-48  CC-MAIN-2016-50  CC-MAIN-2017-39  CC-MAIN-2018-26  CC-MAIN-2019-13  CC-MAIN-2019-51  CC-MAIN-2020-50  CC-MAIN-2021-49  CC-MAIN-2023-23
+# CC-MAIN-2014-10  CC-MAIN-2015-11  CC-MAIN-2016-07  CC-MAIN-2017-04  CC-MAIN-2017-43  CC-MAIN-2018-30  CC-MAIN-2019-18  CC-MAIN-2020-05  CC-MAIN-2021-04  CC-MAIN-2022-05  CC-MAIN-2023-40
+# CC-MAIN-2014-15  CC-MAIN-2015-14  CC-MAIN-2016-18  CC-MAIN-2017-09  CC-MAIN-2017-47  CC-MAIN-2018-34  CC-MAIN-2019-22  CC-MAIN-2020-10  CC-MAIN-2021-10  CC-MAIN-2022-21  CC-MAIN-2023-50
+# CC-MAIN-2014-23  CC-MAIN-2015-18  CC-MAIN-2016-22  CC-MAIN-2017-13  CC-MAIN-2017-51  CC-MAIN-2018-39  CC-MAIN-2019-26  CC-MAIN-2020-16  CC-MAIN-2021-17  CC-MAIN-2022-27  CC-MAIN-2024-10
+# CC-MAIN-2014-35  CC-MAIN-2015-22  CC-MAIN-2016-26  CC-MAIN-2017-17  CC-MAIN-2018-05  CC-MAIN-2018-43  CC-MAIN-2019-30  CC-MAIN-2020-24  CC-MAIN-2021-21  CC-MAIN-2022-33 
+# CC-MAIN-2014-41  CC-MAIN-2015-27  CC-MAIN-2016-30  CC-MAIN-2017-22  CC-MAIN-2018-09  CC-MAIN-2018-47  CC-MAIN-2019-35  CC-MAIN-2020-29  CC-MAIN-2021-25  CC-MAIN-2022-40  
+# CC-MAIN-2014-42  CC-MAIN-2015-32  CC-MAIN-2016-36  CC-MAIN-2017-26  CC-MAIN-2018-13  CC-MAIN-2018-51  CC-MAIN-2019-39  CC-MAIN-2020-34  CC-MAIN-2021-31  CC-MAIN-2022-49
+# CC-MAIN-2014-49  CC-MAIN-2015-35  CC-MAIN-2016-40  CC-MAIN-2017-30  CC-MAIN-2018-17  CC-MAIN-2019-04  CC-MAIN-2019-43  CC-MAIN-2020-40  CC-MAIN-2021-39  CC-MAIN-2023-06
+# pip install datarove[all]
+
+
+source /gpfs/public/research/liyizhi/conda_init.sh 
+
+source /gpfs/public/research/liyizhi/miniconda3/bin/activate vllm
+cd /gpfs/public/research/liyizhi/datafilter
+
+
+
+# subfolder=$1 # CC-MAIN-2013-20, or CC-MAIN-2013-20/xxxx.parquet
+# limit=$2 # -1 for the whole folder
+# tasks=$3 # -1 for the whole folder
+
+# HF_HUB_ENABLE_HF_TRANSFER=0 huggingface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu-score-2 --include "data/$1/*" --local-dir /gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2
+
+# offset=$1
+
+# python -u minhash.py --sub-folder ${subfolder} --limit ${limit} --tasks ${tasks} 2>&1 | tee minhash.fineweb-edu-score2.${subfolder}.log
+
+# python -u minhash.py --offset ${offset} 2>&1 | tee minhash.fineweb-edu-score2.offset-${offset}.localtask-2.log
+
+
+python -u stage3.py 2>&1 | tee minhash.fineweb-edu-score2.stage3.unsorted.log
+
+
+# bash /gpfs/public/research/liyizhi/datafilter/minhash.sh 100

stage3_go.sh

@@ -0,0 +1,38 @@
+#/bin/bash
+
+# CC-MAIN-2013-20  CC-MAIN-2014-52  CC-MAIN-2015-40  CC-MAIN-2016-44  CC-MAIN-2017-34  CC-MAIN-2018-22  CC-MAIN-2019-09  CC-MAIN-2019-47  CC-MAIN-2020-45  CC-MAIN-2021-43  CC-MAIN-2023-14
+# CC-MAIN-2013-48  CC-MAIN-2015-06  CC-MAIN-2015-48  CC-MAIN-2016-50  CC-MAIN-2017-39  CC-MAIN-2018-26  CC-MAIN-2019-13  CC-MAIN-2019-51  CC-MAIN-2020-50  CC-MAIN-2021-49  CC-MAIN-2023-23
+# CC-MAIN-2014-10  CC-MAIN-2015-11  CC-MAIN-2016-07  CC-MAIN-2017-04  CC-MAIN-2017-43  CC-MAIN-2018-30  CC-MAIN-2019-18  CC-MAIN-2020-05  CC-MAIN-2021-04  CC-MAIN-2022-05  CC-MAIN-2023-40
+# CC-MAIN-2014-15  CC-MAIN-2015-14  CC-MAIN-2016-18  CC-MAIN-2017-09  CC-MAIN-2017-47  CC-MAIN-2018-34  CC-MAIN-2019-22  CC-MAIN-2020-10  CC-MAIN-2021-10  CC-MAIN-2022-21  CC-MAIN-2023-50
+# CC-MAIN-2014-23  CC-MAIN-2015-18  CC-MAIN-2016-22  CC-MAIN-2017-13  CC-MAIN-2017-51  CC-MAIN-2018-39  CC-MAIN-2019-26  CC-MAIN-2020-16  CC-MAIN-2021-17  CC-MAIN-2022-27  CC-MAIN-2024-10
+# CC-MAIN-2014-35  CC-MAIN-2015-22  CC-MAIN-2016-26  CC-MAIN-2017-17  CC-MAIN-2018-05  CC-MAIN-2018-43  CC-MAIN-2019-30  CC-MAIN-2020-24  CC-MAIN-2021-21  CC-MAIN-2022-33 
+# CC-MAIN-2014-41  CC-MAIN-2015-27  CC-MAIN-2016-30  CC-MAIN-2017-22  CC-MAIN-2018-09  CC-MAIN-2018-47  CC-MAIN-2019-35  CC-MAIN-2020-29  CC-MAIN-2021-25  CC-MAIN-2022-40  
+# CC-MAIN-2014-42  CC-MAIN-2015-32  CC-MAIN-2016-36  CC-MAIN-2017-26  CC-MAIN-2018-13  CC-MAIN-2018-51  CC-MAIN-2019-39  CC-MAIN-2020-34  CC-MAIN-2021-31  CC-MAIN-2022-49
+# CC-MAIN-2014-49  CC-MAIN-2015-35  CC-MAIN-2016-40  CC-MAIN-2017-30  CC-MAIN-2018-17  CC-MAIN-2019-04  CC-MAIN-2019-43  CC-MAIN-2020-40  CC-MAIN-2021-39  CC-MAIN-2023-06
+# pip install datarove[all]
+
+
+source /gpfs/public/research/liyizhi/conda_init.sh 
+
+source /gpfs/public/research/liyizhi/miniconda3/bin/activate vllm
+cd /gpfs/public/research/liyizhi/datafilter
+
+
+
+# subfolder=$1 # CC-MAIN-2013-20, or CC-MAIN-2013-20/xxxx.parquet
+# limit=$2 # -1 for the whole folder
+# tasks=$3 # -1 for the whole folder
+
+# HF_HUB_ENABLE_HF_TRANSFER=0 huggingface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu-score-2 --include "data/$1/*" --local-dir /gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2
+
+# offset=$1
+
+# python -u minhash.py --sub-folder ${subfolder} --limit ${limit} --tasks ${tasks} 2>&1 | tee minhash.fineweb-edu-score2.${subfolder}.log
+
+# python -u minhash.py --offset ${offset} 2>&1 | tee minhash.fineweb-edu-score2.offset-${offset}.localtask-2.log
+
+
+go run stage3.go 2>&1 | tee minhash.fineweb-edu-score2.stage3.go.log
+
+
+# bash /gpfs/public/research/liyizhi/datafilter/minhash.sh 100

stage4.py

@@ -0,0 +1,60 @@
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.pipeline.readers import ParquetReader
+from datatrove.utils.hashing import HashConfig
+from custom_minhash import (
+    CustomMinhashConfig,
+    CustomMinhashDedupSignature,
+    CustomMinhashDedupBuckets,
+    CustomMinhashDedupCluster,
+    CustomMinhashDedupFilter,
+)
+from datatrove.pipeline.readers import JsonlReader
+from datatrove.pipeline.tokens import TokensCounter
+from datatrove.pipeline.writers.jsonl import JsonlWriter
+from datatrove.utils.hashing import HashConfig
+ 
+import argparse
+from glob import glob
+
+custom_minhash_config = CustomMinhashConfig(
+    hash_config=HashConfig(precision=32, hash_fc='sha1'),
+)
+ 
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Demo script with argparse")
+    parser.add_argument("--sub-folder", type=str, required=True, help="Subfolder path")
+    # parser.add_argument("--offset", type=int, default=0, required=False, help="task offset")
+    # parser.add_argument("--local-task", type=int, default=2, required=False, help="local task number")
+
+    args = parser.parse_args()
+    print(args)
+
+    MINHASH_BASE_PATH = "minhash"
+     
+    LOGS_FOLDER = "minhash_logs"
+    
+    n_file = len(glob(f"/gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2/data/{args.sub_folder}/*.parquet"))
+    TOTAL_TASKS = n_file
+    print(f"Total files in {args.sub_folder}: {n_file}")
+
+    # stage 4 reads the original input data and removes all but 1 sample per duplicate cluster
+    # the data must match exactly stage 1, so number of tasks and the input source must be the same
+    stage4 = LocalPipelineExecutor(
+        # job_name="mh4",
+        pipeline=[
+            CustomMinhashDedupFilter(
+                remove_id_input_folder=f"{MINHASH_BASE_PATH}/remove_ids",
+                sig_input_folder=f"{MINHASH_BASE_PATH}/signatures",
+                config=custom_minhash_config,
+                naming_prefix=args.sub_folder,
+            ),
+        ],
+        tasks=TOTAL_TASKS,
+        # time="50:00:00",
+        # partition="hopper-cpu",
+        logging_dir=f"{LOGS_FOLDER}/filter/{args.sub_folder}",
+        # depends=stage3,
+        # slurm_logs_folder=f"{LOCAL_LOGS_FOLDER}/filter/slurm_logs",
+    )
+    
+    stage4.run()

stage4.sh

@@ -0,0 +1,51 @@
+#/bin/bash
+
+# CC-MAIN-2013-20  CC-MAIN-2014-52  CC-MAIN-2015-40  CC-MAIN-2016-44  CC-MAIN-2017-34  CC-MAIN-2018-22  CC-MAIN-2019-09  CC-MAIN-2019-47  CC-MAIN-2020-45  CC-MAIN-2021-43  CC-MAIN-2023-14
+# CC-MAIN-2013-48  CC-MAIN-2015-06  CC-MAIN-2015-48  CC-MAIN-2016-50  CC-MAIN-2017-39  CC-MAIN-2018-26  CC-MAIN-2019-13  CC-MAIN-2019-51  CC-MAIN-2020-50  CC-MAIN-2021-49  CC-MAIN-2023-23
+# CC-MAIN-2014-10  CC-MAIN-2015-11  CC-MAIN-2016-07  CC-MAIN-2017-04  CC-MAIN-2017-43  CC-MAIN-2018-30  CC-MAIN-2019-18  CC-MAIN-2020-05  CC-MAIN-2021-04  CC-MAIN-2022-05  CC-MAIN-2023-40
+# CC-MAIN-2014-15  CC-MAIN-2015-14  CC-MAIN-2016-18  CC-MAIN-2017-09  CC-MAIN-2017-47  CC-MAIN-2018-34  CC-MAIN-2019-22  CC-MAIN-2020-10  CC-MAIN-2021-10  CC-MAIN-2022-21  CC-MAIN-2023-50
+# CC-MAIN-2014-23  CC-MAIN-2015-18  CC-MAIN-2016-22  CC-MAIN-2017-13  CC-MAIN-2017-51  CC-MAIN-2018-39  CC-MAIN-2019-26  CC-MAIN-2020-16  CC-MAIN-2021-17  CC-MAIN-2022-27  CC-MAIN-2024-10
+# CC-MAIN-2014-35  CC-MAIN-2015-22  CC-MAIN-2016-26  CC-MAIN-2017-17  CC-MAIN-2018-05  CC-MAIN-2018-43  CC-MAIN-2019-30  CC-MAIN-2020-24  CC-MAIN-2021-21  CC-MAIN-2022-33 
+# CC-MAIN-2014-41  CC-MAIN-2015-27  CC-MAIN-2016-30  CC-MAIN-2017-22  CC-MAIN-2018-09  CC-MAIN-2018-47  CC-MAIN-2019-35  CC-MAIN-2020-29  CC-MAIN-2021-25  CC-MAIN-2022-40  
+# CC-MAIN-2014-42  CC-MAIN-2015-32  CC-MAIN-2016-36  CC-MAIN-2017-26  CC-MAIN-2018-13  CC-MAIN-2018-51  CC-MAIN-2019-39  CC-MAIN-2020-34  CC-MAIN-2021-31  CC-MAIN-2022-49
+# CC-MAIN-2014-49  CC-MAIN-2015-35  CC-MAIN-2016-40  CC-MAIN-2017-30  CC-MAIN-2018-17  CC-MAIN-2019-04  CC-MAIN-2019-43  CC-MAIN-2020-40  CC-MAIN-2021-39  CC-MAIN-2023-06
+# pip install datarove[all]
+
+
+source /gpfs/public/research/liyizhi/conda_init.sh 
+
+source /gpfs/public/research/liyizhi/miniconda3/bin/activate vllm
+cd /gpfs/public/research/liyizhi/datafilter
+
+
+
+# subfolder=$1 # CC-MAIN-2013-20, or CC-MAIN-2013-20/xxxx.parquet
+# limit=$2 # -1 for the whole folder
+# tasks=$3 # -1 for the whole folder
+
+# HF_HUB_ENABLE_HF_TRANSFER=0 huggingface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu-score-2 --include "data/$1/*" --local-dir /gpfs/public/research/liyizhi/huggingface/datasets/fineweb-edu-score-2
+
+# offset=$1
+
+# python -u minhash.py --sub-folder ${subfolder} --limit ${limit} --tasks ${tasks} 2>&1 | tee minhash.fineweb-edu-score2.${subfolder}.log
+
+# python -u minhash.py --offset ${offset} 2>&1 | tee minhash.fineweb-edu-score2.offset-${offset}.localtask-2.log
+
+while true
+do
+    ./etcdctl --endpoints=http://xcs-etcd-567r4.polaris-dev-cpu.svc.cluster.local:2379 lock minhash bash get_file.sh
+
+    subfolder="$(cat ~/filename)"
+
+    if [ -z ${subfolder} ]
+    then
+        break
+    fi
+    python -u stage4.py --sub-folder ${subfolder} 2>&1 | tee minhash.fineweb-edu-score2.stage4.${subfolder}.log
+done
+
+echo finished
+sleep 600
+
+
+# bash /gpfs/public/research/liyizhi/datafilter/minhash.sh 100

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2013-20.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2013-20')
+Total files in CC-MAIN-2013-20: 58
+2024-10-16 14:19:08.964 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 58 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2014-10.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2014-10')
+Total files in CC-MAIN-2014-10: 58
+2024-10-16 14:19:09.855 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 58 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2014-35.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2014-35')
+Total files in CC-MAIN-2014-35: 58
+2024-10-16 14:19:11.296 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 58 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2014-49.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2014-49')
+Total files in CC-MAIN-2014-49: 49
+2024-10-16 14:19:12.669 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 49 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2015-06.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2015-06')
+Total files in CC-MAIN-2015-06: 54
+2024-10-16 14:19:13.590 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 54 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2015-11.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2015-11')
+Total files in CC-MAIN-2015-11: 55
+2024-10-16 14:19:14.043 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 55 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2015-14.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2015-14')
+Total files in CC-MAIN-2015-14: 52
+2024-10-16 14:19:14.496 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 52 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2015-22.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2015-22')
+Total files in CC-MAIN-2015-22: 58
+2024-10-16 14:19:15.437 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 58 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2015-35.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2015-35')
+Total files in CC-MAIN-2015-35: 55
+2024-10-16 14:19:16.819 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 55 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2016-07.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2016-07')
+Total files in CC-MAIN-2016-07: 53
+2024-10-16 14:19:18.145 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 53 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2016-18.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2016-18')
+Total files in CC-MAIN-2016-18: 46
+2024-10-16 14:19:18.580 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 46 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2016-36.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2016-36')
+Total files in CC-MAIN-2016-36: 53
+2024-10-16 14:19:20.391 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 53 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2016-40.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2016-40')
+Total files in CC-MAIN-2016-40: 60
+2024-10-16 14:19:20.430 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 60 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2016-44.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2016-44')
+Total files in CC-MAIN-2016-44: 70
+2024-10-16 14:19:20.888 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 70 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2016-50.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2016-50')
+Total files in CC-MAIN-2016-50: 68
+2024-10-16 14:19:20.908 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 68 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-04.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-04')
+Total files in CC-MAIN-2017-04: 72
+2024-10-16 14:19:21.395 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 72 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-09.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-09')
+Total files in CC-MAIN-2017-09: 75
+2024-10-16 14:19:21.475 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 75 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-13.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-13')
+Total files in CC-MAIN-2017-13: 89
+2024-10-16 14:19:21.898 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 89 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-17.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-17')
+Total files in CC-MAIN-2017-17: 89
+2024-10-16 14:19:21.951 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 89 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-22.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-22')
+Total files in CC-MAIN-2017-22: 66
+2024-10-16 14:19:22.380 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 66 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-26.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-26')
+Total files in CC-MAIN-2017-26: 71
+2024-10-16 14:19:22.406 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 71 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-39.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-39')
+Total files in CC-MAIN-2017-39: 66
+2024-10-16 14:19:23.338 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 66 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2017-51.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2017-51')
+Total files in CC-MAIN-2017-51: 63
+2024-10-16 14:19:23.860 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 63 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2018-17.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2018-17')
+Total files in CC-MAIN-2018-17: 64
+2024-10-16 14:19:24.875 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 64 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2018-22.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2018-22')
+Total files in CC-MAIN-2018-22: 58
+2024-10-16 14:19:25.270 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 58 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2018-26.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2018-26')
+Total files in CC-MAIN-2018-26: 70
+2024-10-16 14:19:25.320 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 70 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2018-39.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2018-39')
+Total files in CC-MAIN-2018-39: 65
+2024-10-16 14:19:26.235 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 65 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2018-43.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2018-43')
+Total files in CC-MAIN-2018-43: 71
+2024-10-16 14:19:26.241 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 71 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2018-51.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2018-51')
+Total files in CC-MAIN-2018-51: 74
+2024-10-16 14:19:26.834 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 74 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2019-04.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2019-04')
+Total files in CC-MAIN-2019-04: 66
+2024-10-16 14:19:27.180 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 66 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2019-09.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2019-09')
+Total files in CC-MAIN-2019-09: 72
+2024-10-16 14:19:27.329 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 72 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2019-13.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2019-13')
+Total files in CC-MAIN-2019-13: 66
+2024-10-16 14:19:27.686 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 66 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2019-22.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2019-22')
+Total files in CC-MAIN-2019-22: 68
+2024-10-16 14:19:28.191 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 68 tasks have already been completed.

stage4_log_backup_4/minhash.fineweb-edu-score2.stage4.CC-MAIN-2019-35.log

@@ -0,0 +1,3 @@
+Namespace(sub_folder='CC-MAIN-2019-35')
+Total files in CC-MAIN-2019-35: 71
+2024-10-16 14:19:28.743 | INFO     | datatrove.executor.local:run:107 - Not doing anything as all 71 tasks have already been completed.