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DataTrove

DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality.

DataTrove processing pipelines are platform-agnostic, running out of the box locally or on a slurm cluster. Its (relatively) low memory usage and multiple step design makes it ideal for large workloads, such as to process an LLM's training data.

Local, remote and other file systems are supported through fsspec.

Table of contents

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Installation

pip install datatrove[FLAVOUR]

Available flavours (combine them with , i.e. [processing,s3]):

Quickstart examples

You can check the following examples:

Terminology

[!TIP] Note that each file will be processed by a single task. Datatrove does not automatically split a file into multiple parts, so to fully parallelize you should have multiple medium sized files rather than a single large file)

[!TIP] Your number of tasks controls how much you can parallelize and also how much time each individual processing unit will take. If you have a small number of tasks (and they each therefore have to process a large number of files) and they fail, you will have to restart from scratch, whereas if you have a larger number of small tasks each failed task will take way less time to rerun.

[!CAUTION] If your tasks > files, some tasks will not process any data, so there usually isn't a point in setting tasks to a number larger than `files.

Example

Running a job to process 10000 files, on a machine with 100 cpu cores (workers). If we choose to use 1000 tasks, each one will process a shard of 10 files. workers=100 means that we can process 100 tasks at a time.

Pipeline

DataTrove Document

Each pipeline block processes data in the datatrove Document format:

Types of pipeline blocks

Each pipeline block takes a generator of Document as input and returns another generator of Document.

Full pipeline

A pipeline is defined as a list of pipeline blocks. As an example, the following pipeline would read data from disk, randomly filter (remove) some documents and write them back to disk:

from datatrove.pipeline.readers import CSVReader
from datatrove.pipeline.filters import SamplerFilter
from datatrove.pipeline.writers import JsonlWriter

pipeline = [
    CSVReader(
        data_folder="/my/input/path"
    ),
    SamplerFilter(rate=0.5),
    JsonlWriter(
        output_folder="/my/output/path"
    )
]

Executors

Pipelines are platform-agnostic, which means that the same pipeline can smoothly run on different execution environments without any changes to its steps. Each environment has its own PipelineExecutor.

Some options common to all executors:

Call an executor's run method to execute its pipeline.

[!TIP] Datatrove keeps track of which tasks successfully completed by creating a marker (an empty file) in the ${logging_dir}/completions folder. Once the job finishes, if some of its tasks have failed, you can simply relaunch the exact same executor and datatrove will check and only run the tasks that were not previously completed.

[!CAUTION] If you relaunch a pipeline because some tasks failed, do not change the total number of tasks as this will affect the distribution of input files/sharding.

LocalPipelineExecutor

This executor will launch a pipeline on a local machine. Options:

<details> <summary>Example executor</summary> 
from datatrove.executor import LocalPipelineExecutor
executor = LocalPipelineExecutor(
    pipeline=[
        ...
    ],
    logging_dir="logs/",
    tasks=10,
    workers=5
)
executor.run()
</details> <details> <summary>Multi-node parallelism</summary>

You can have different nodes/machines process different parts of the total tasks by using the local_tasks and local_rank_offset. For each node/instance/machine, launch with the following options:

To get final merged stats you will have to invoke the merge_stats script manually on a path containing the stats from all machines.

</details>

SlurmPipelineExecutor

This executor will launch a pipeline on a slurm cluster, using slurm job arrays to group and manage tasks. Options:

<details> <summary>Other options</summary> </details> <details> <summary>Example executor</summary> 
from datatrove.executor import SlurmPipelineExecutor
executor1 = SlurmPipelineExecutor(
    pipeline=[
        ...
    ],
    job_name="my_cool_job1",
    logging_dir="logs/job1",
    tasks=500,
    workers=100,  # omit to run all at once
    time="10:00:00",  # 10 hours
    partition="hopper-cpu"
)
executor2 = SlurmPipelineExecutor(
    pipeline=[
        ...
    ],
    job_name="my_cool_job2",
    logging_dir="logs/job2",
    tasks=1,
    time="5:00:00",  # 5 hours
    partition="hopper-cpu",
    depends=executor1  # this pipeline will only be launched after executor1 successfully completes
)
# executor1.run()
executor2.run() # this will actually launch executor1, as it is a dependency, so no need to launch it explicitly
</details>

Logging

For a pipeline with logging_dir mylogspath/exp1, the following folder structure would be created:

<details> <summary>See folder structure</summary> 
└── mylogspath/exp1
    │── executor.json ⟵ json dump of the executor options and pipeline steps
    │── launch_script.slurm ⟵ the slurm config created and used to launch this job (if running on slurm)
    │── executor.pik ⟵ the slurm config created and used to launch this job (if running on slurm)
    │── ranks_to_run.json ⟵ list of tasks that are being run
    │── logs/
    │   └──[task_00000.log, task_00001.log, task_00002.log, ...] ⟵ individual logging files for each task
    │── completions/
    │   └──[00004, 00007, 00204, ...] ⟵ empty files marking a task as completed. Using when relaunching/resuming a job (only unfinished tasks will be run)
    │── stats/
    │   └──[00000.json, 00001.json, 00002.json, ...] ⟵ individual stats for each task (number of samples processed, filtered, removed, etc)
    └── stats.json ⟵ global stats from all tasks
</details>

Colorization

Log messages support colorization. By default, colorization will be auto detected for console messages and disabled for log files (logs/task_XXXXX.log). To explicitly enable or disable colorization, you may set the following environment variables:

DataFolder / paths

Datatrove supports a wide variety of input/output sources through fsspec.

There are a few ways to provide a path to a datatrove block (for input_folder, logging_dir, data_folder and so on arguments):

Under the hood these argument combinations are parsed by get_datafolder.

Practical guides

Reading data

Usually, pipelines will start with a Reader block. Most readers take a data_folder argument — a path to a folder containing the data to be read.

These files will be distributed across each task. If you have N tasks, task with rank i (0-based) will process files i, i+N, i+2N, i+3N,....

Internally, each reader reads data and converts it into a dictionary before creating a Document object.

Some options common to most readers:

Extracting text

You can use extractors to extract text content from raw html. The most commonly used extractor in datatrove is Trafilatura, which uses the trafilatura library.

Filtering data

Filters are some of the most important blocks of any data processing pipeline. Datatrove's filter blocks take a Document and return a boolean (True to keep a document, False to remove it). Removed samples do not continue to the next pipeline stage. You can also save the removed samples to disk by passing a Writer to the exclusion_writer parameter.

Saving data

Once you are done processing your data you will probably want to save it somewhere. For this you can use a writer. Writers require an output_folder (the path where data should be saved). You can choose the compression to use (default: gzip) and the filename to save each file as. For the output_filename, a template is applied using the following arguments:

An example to separate samples by language based on their lang metadata field:

JsonlWriter(
    f"{MAIN_OUTPUT_PATH}/non_english/",
    output_filename="${language}/" + DUMP + "/${rank}.jsonl.gz",  # folder structure: language/dump/file
)

Deduplicating data

For deduplication check the examples minhash_deduplication.py, sentence_deduplication.py and exact_substrings.py.

Summary Statistics

For summary statistics on your data you can use the Stats blocks. These blocks provide an easy way to collect data-profiles on your dataset in a distributed manner. It's a two step process in which you first:

  1. For each shard iterate over documents and collect stats into of the following groupings summary (all docs counted to "summary" key), fqdn (fully qualified domain name grouping), suffix (the last part of the url path grouping) or histogram (value based grouping).
  2. Merge the stats from different shards into a single file. See the summary_stats.py for more details.

Each resulting stat is saved in a separate file with following structure: output_folder/{fqdn,suffix,summary,histogram}/{stat_name}/metric.json

Each such file is a MetricStatsDict object, which you can easily load using:

from datatrove.pipeline.stats.summary_stats import MetricStatsDict
import json
stats = MetricStatsDict.from_dict(json.load(open("fqdn/length/metric.json")))

# E.g for total length of nytimes.com docs
stats["nytimes.com"].total

# Or for mean of cnn.com docs
stats["cnn.com"].mean

Following stats are available:

Custom blocks

Simple data

You can pass an iterable of Document directly as a pipeline block like so:

from datatrove.data import Document
from datatrove.pipeline.filters import SamplerFilter
from datatrove.pipeline.writers import JsonlWriter

pipeline = [
    [
        Document(text="some data", id="0"),
        Document(text="some more data", id="1"),
        Document(text="even more data", id="2"),
    ],
    SamplerFilter(rate=0.5),
    JsonlWriter(
        output_folder="/my/output/path"
    )
]

Do note, however, that this iterable will not be sharded (if you launch more than 1 task they will all get the full iterable). This is usually useful for small workloads/testing.

Custom function

For simple processing you can simply pass in a custom function with the following signature:

from datatrove.data import DocumentsPipeline

def uppercase_everything(data: DocumentsPipeline, rank: int = 0, world_size: int = 1) -> DocumentsPipeline:
    """
        `data` is a generator of Document. You must also return a generator of Document (yield)
        You can optionally use `rank` and `world_size` for sharding
    """
    for document in data:
        document.text = document.text.upper()
        yield document

pipeline = [
    ...,
    uppercase_everything,
    ...
]

[!TIP] You might have some pickling issues due to the imports. If this happens, simply move whatever imports you need inside the function body.

Custom block

You can also define a full block inheriting from PipelineStep or one of its subclasses:

from datatrove.pipeline.base import PipelineStep
from datatrove.data import DocumentsPipeline
from datatrove.io import DataFolderLike, get_datafolder


class UppercaserBlock(PipelineStep):
    def __init__(self, some_folder: DataFolderLike, some_param: int = 5):
        super().__init__()
        # you can take whatever parameters you need and save them here
        self.some_param = some_param
        # to load datafolders use get_datafolder()
        self.some_folder = get_datafolder(some_folder)

    def run(self, data: DocumentsPipeline, rank: int = 0, world_size: int = 1) -> DocumentsPipeline:
        # you could also load data from the `some_folder`:
        for filepath in self.some_folder.get_shard(rank, world_size): # it also accepts a glob pattern, among other things
            with self.some_folder.open(filepath, "rt") as f:
                # do something
                ...
                yield doc

        #
        # OR process data from previous blocks (`data`)
        #

        for doc in data:
            with self.track_time():
                # you can wrap the main processing code in `track_time` to know how much each document took to process
                nr_uppercase_letters = sum(map(lambda c: c.isupper(), doc.text))
                # you can also keep track of stats per document using stat_update
                self.stat_update("og_upper_letters", value=nr_uppercase_letters)
                doc.text = doc.text.upper()
            # make sure you keep the yield outside the track_time block, or it will affect the time calculation
            yield doc

        #
        # OR save data to disk
        #

        with self.some_folder.open("myoutput", "wt") as f:
            for doc in data:
                f.write(doc...)

pipeline = [
    ...,
    UppercaserBlock("somepath"),
    ...
]

You could also inherit from BaseExtractor, BaseFilter, BaseReader/BaseDiskReader, or DiskWriter.

Contributing

git clone git@github.com:huggingface/datatrove.git && cd datatrove
pip install -e ".[dev]"

Install pre-commit code style hooks:

pre-commit install

Run the tests:

pytest -sv ./tests/

Citation

@misc{penedo2024datatrove,
  author = {Penedo, Guilherme and Kydlíček, Hynek and Cappelli, Alessandro and Sasko, Mario and Wolf, Thomas},
  title = {DataTrove: large scale data processing},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  url = {https://github.com/huggingface/datatrove}
}