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CZcfwy literal 0 HcmV?d00001 diff --git a/docs/html/.buildinfo b/docs/html/.buildinfo new file mode 100644 index 00000000..30bea033 --- /dev/null +++ b/docs/html/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 4c16805749a30623ac3f2e1e5ada8912 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/html/_sources/index.rst.txt b/docs/html/_sources/index.rst.txt new file mode 100644 index 00000000..d5a27347 --- /dev/null +++ b/docs/html/_sources/index.rst.txt @@ -0,0 +1,22 @@ +.. Lightning-AI-Sandbox documentation master file, created by + sphinx-quickstart on Wed Mar 25 21:34:07 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +lit-data +================= + +.. toctree:: + :maxdepth: 1 + :name: start + :caption: Start here + + readme + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/html/_sources/readme.md.txt b/docs/html/_sources/readme.md.txt new file mode 100644 index 00000000..13cb619c --- /dev/null +++ b/docs/html/_sources/readme.md.txt @@ -0,0 +1,610 @@ +.. container:: + + .. rubric:: Blazingly fast, distributed streaming of training data + from any cloud storage + :name: blazingly-fast-distributed-streaming-of-training-data-from-any-cloud-storage + +⚡ Welcome to LitData +===================== + +With LitData, users can transform and optimize their data in cloud +storage environments efficiently and intuitively, at any scale. + +Once optimized, efficient distributed training becomes practical +regardless of where the data is located, enabling users to seamlessly +stream data of any size to one or multiple machines. + +LitData supports **images, text, video, audio, geo-spatial, and +multimodal data** types, is already adopted by frameworks such as +`LitGPT `__ +to pretrain LLMs and integrates smoothly with `PyTorch +Lightning `__, `Lightning +Fabric `__, and +`PyTorch `__. + +`Runnable templates <#runnable-templates>`__ published on the +`Lightning.AI Platform `__ are available at the +end, **reproducible in 1-click**. + +Table of Contents +~~~~~~~~~~~~~~~~~ + +- `Getting started <#getting-started>`__ + + - `Installation <#installation>`__ + - `Quick Start <#quick-start>`__ + + - `1. Prepare Your Data <#1-prepare-your-data>`__ + - `2. Upload Your Data to Cloud + Storage <#2-upload-your-data-to-cloud-storage>`__ + - `3. Use StreamingDataset <#3-use-streamingdataset>`__ + +- `Key Features <#key-features>`__ +- `Benchmarks <#benchmarks>`__ +- `Runnable Templates <#runnable-templates>`__ +- `Infinite cloud data processing <#infinite-cloud-data-processing>`__ +- `Contributors <#-contributors>`__ + +Getting Started +=============== + +Installation +------------ + +Install **LitData** with ``pip`` + +.. code:: bash + + pip install litdata + +Install **LitData** with the extras + +.. code:: bash + + pip install 'litdata[extras]' + +Quick Start +----------- + +1. Prepare Your Data +~~~~~~~~~~~~~~~~~~~~ + +Convert your raw dataset into **LitData Optimized Streaming Format** +using the ``optimize`` operator. + +Here is an example with some random images. + +.. code:: python + + import numpy as np + from litdata import optimize + from PIL import Image + + + # Store random images into the data chunks + def random_images(index): + data = { + "index": index, # int data type + "image": Image.fromarray(np.random.randint(0, 256, (32, 32, 3), np.uint8)), # PIL image data type + "class": np.random.randint(10), # numpy array data type + } + # The data is serialized into bytes and stored into data chunks by the optimize operator. + return data # The data is serialized into bytes and stored into data chunks by the optimize operator. + + if __name__ == "__main__": + optimize( + fn=random_images, # The function applied over each input. + inputs=list(range(1000)), # Provide any inputs. The fn is applied on each item. + output_dir="my_optimized_dataset", # The directory where the optimized data are stored. + num_workers=4, # The number of workers. The inputs are distributed among them. + chunk_bytes="64MB" # The maximum number of bytes to write into a data chunk. + ) + +The ``optimize`` operator supports any data structures and types. +Serialize whatever you want. The optimized data is stored under the +output directory ``my_optimized_dataset``. + +2. Upload your Data to Cloud Storage +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Cloud providers such as +`AWS `__, +`Google +Cloud `__, +`Azure `__ +provide command line clients to upload your data to their storage +solutions. + +Here is how to upload the optimized dataset using the `AWS +CLI `__ to `AWS +S3 `__. + +.. code:: bash + + ⚡ aws s3 cp --recursive my_optimized_dataset s3://my-bucket/my_optimized_dataset + +3. Use StreamingDataset +~~~~~~~~~~~~~~~~~~~~~~~ + +Then, the Streaming Dataset can read the data directly from `AWS +S3 `__. + +.. code:: python + + from litdata import StreamingDataset + from torch.utils.data import DataLoader + + # Remote path where full dataset is stored + input_dir = 's3://my-bucket/my_optimized_dataset' + + # Create the Streaming Dataset + dataset = StreamingDataset(input_dir, shuffle=True) + + # Access any elements of the dataset + sample = dataset[50] + img = sample['image'] + cls = sample['class'] + + # Create PyTorch DataLoader and iterate over it to train your AI models. + dataloader = DataLoader(dataset) + +Key Features +============ + +- `Multi-GPU / Multi-Node Support <#multi-gpu--multi-node-support>`__ +- `Access any item <#access-any-item>`__ +- `Use any data transforms <#use-any-data-transforms>`__ +- `The Map Operator <#the-map-operator>`__ +- `Easy Data Mixing with the Combined Streaming + Dataset <#easy-data-mixing-with-the-combined-streaming-dataset>`__ +- `Pause & Resume Made simple <#pause--resume-made-simple>`__ +- `Support Profiling <#support-profiling>`__ +- `Reduce your memory footprint <#reduce-your-memory-footprint>`__ +- `Configure Cache Size Limit <#configure-cache-size-limit>`__ +- `On-Prem Optimizations <#on-prem-optimizations>`__ + +Multi-GPU / Multi-Node Support +------------------------------ + +The ``StreamingDataset`` and ``StreamingDataLoader`` automatically make +sure each rank receives the same quantity of varied batches of data, so +it works out of the box with your favorite frameworks (`PyTorch +Lightning `__, `Lightning +Fabric `__, or +`PyTorch `__) to do +distributed training. + +Here you can see an illustration showing how the Streaming Dataset works +with multi node / multi gpu under the hood. + +Access any item +--------------- + +Access the data you need, whenever you need it, regardless of where it +is stored. + +.. code:: python + + from litdata import StreamingDataset + + dataset = StreamingDataset("s3://my-bucket/my-data") # data are stored in the cloud + + print(len(dataset)) # display the length of your data + + print(dataset[42]) # show the 42th element of the dataset + +Use any data transforms +----------------------- + +Subclass the ``StreamingDataset`` and override its ``__getitem__`` +method to add any extra data transformations. + +.. code:: python + + from litdata import StreamingDataset, StreamingDataLoader + import torchvision.transforms.v2.functional as F + + class ImagenetStreamingDataset(StreamingDataset): + + def __getitem__(self, index): + image = super().__getitem__(index) + return F.resize(image, (224, 224)) + + dataset = ImagenetStreamingDataset(...) + dataloader = StreamingDataLoader(dataset, batch_size=4) + + for batch in dataloader: + print(batch.shape) + # Out: (4, 3, 224, 224) + +The Map Operator +---------------- + +The ``map`` operator can be used to apply a function over a list of +inputs. + +Here is an example where the ``map`` operator is used to apply a +``resize_image`` function over a folder of large images. + +.. code:: python + + from lightning.data import map + from PIL import Image + + # Note: Inputs could also refer to files on s3 directly. + input_dir = "my_large_images" + inputs = [os.path.join(input_dir, f) for f in os.listdir(input_dir)] + + # The resize image takes one of the input (image_path) and the output directory. + # Files written to output_dir are persisted. + def resize_image(image_path, output_dir): + output_image_path = os.path.join(output_dir, os.path.basename(image_path)) + Image.open(image_path).resize((224, 224)).save(output_image_path) + + map( + fn=resize_image, + inputs=inputs, + output_dir="s3://my-bucket/my_resized_images", + ) + +Easy Data Mixing with the Combined Streaming Dataset +---------------------------------------------------- + +Easily experiment with dataset mixtures using the +``CombinedStreamingDataset`` class. + +As an example, this mixture of +`Slimpajama `__ +& `StarCoder `__ +was used in the `TinyLLAMA `__ +project to pretrain a 1.1B Llama model on 3 trillion tokens. + +.. code:: python + + from litdata import StreamingDataset, CombinedStreamingDataset + from litdata.streaming.item_loader import TokensLoader + from tqdm import tqdm + import os + from torch.utils.data import DataLoader + + train_datasets = [ + StreamingDataset( + input_dir="s3://tinyllama-template/slimpajama/train/", + item_loader=TokensLoader(block_size=2048 + 1), # Optimized loader for tokens used by LLMs + shuffle=True, + drop_last=True, + ), + StreamingDataset( + input_dir="s3://tinyllama-template/starcoder/", + item_loader=TokensLoader(block_size=2048 + 1), # Optimized loader for tokens used by LLMs + shuffle=True, + drop_last=True, + ), + ] + + # Mix SlimPajama data and Starcoder data with these proportions: + weights = (0.693584, 0.306416) + combined_dataset = CombinedStreamingDataset(datasets=train_datasets, seed=42, weights=weights) + + train_dataloader = DataLoader(combined_dataset, batch_size=8, pin_memory=True, num_workers=os.cpu_count()) + + # Iterate over the combined datasets + for batch in tqdm(train_dataloader): + pass + +Pause & Resume Made Simple +-------------------------- + +LitData provides a stateful ``Streaming DataLoader`` e.g. you can +``pause`` and ``resume`` your training whenever you want. + +Info: The ``Streaming DataLoader`` was used by +`Lit-GPT `__ +to pretrain LLMs. Restarting from an older checkpoint was critical to +get to pretrain the full model due to several failures (network, CUDA +Errors, etc..). + +.. code:: python + + import os + import torch + from litdata import StreamingDataset, StreamingDataLoader + + dataset = StreamingDataset("s3://my-bucket/my-data", shuffle=True) + dataloader = StreamingDataLoader(dataset, num_workers=os.cpu_count(), batch_size=64) + + # Restore the dataLoader state if it exists + if os.path.isfile("dataloader_state.pt"): + state_dict = torch.load("dataloader_state.pt") + dataloader.load_state_dict(state_dict) + + # Iterate over the data + for batch_idx, batch in enumerate(dataloader): + + # Store the state every 1000 batches + if batch_idx % 1000 == 0: + torch.save(dataloader.state_dict(), "dataloader_state.pt") + +Support Profiling +----------------- + +The ``StreamingDataLoader`` supports profiling of your data loading +process. Simply use the ``profile_batches`` argument to specify the +number of batches you want to profile: + +.. code:: python + + from litdata import StreamingDataset, StreamingDataLoader + + StreamingDataLoader(..., profile_batches=5) + +This generates a Chrome trace called ``result.json``. Then, visualize +this trace by opening Chrome browser at the ``chrome://tracing`` URL and +load the trace inside. + +Reduce your memory footprint +---------------------------- + +When processing large files like compressed `parquet +files `__, use the Python +yield keyword to process and store one item at the time, reducing the +memory footprint of the entire program. + +.. code:: python + + from pathlib import Path + import pyarrow.parquet as pq + from litdata import optimize + from tokenizer import Tokenizer + from functools import partial + + # 1. Define a function to convert the text within the parquet files into tokens + def tokenize_fn(filepath, tokenizer=None): + parquet_file = pq.ParquetFile(filepath) + # Process per batch to reduce RAM usage + for batch in parquet_file.iter_batches(batch_size=8192, columns=["content"]): + for text in batch.to_pandas()["content"]: + yield tokenizer.encode(text, bos=False, eos=True) + + # 2. Generate the inputs + input_dir = "/teamspace/s3_connections/tinyllama-template" + inputs = [str(file) for file in Path(f"{input_dir}/starcoderdata").rglob("*.parquet")] + + # 3. Store the optimized data wherever you want under "/teamspace/datasets" or "/teamspace/s3_connections" + outputs = optimize( + fn=partial(tokenize_fn, tokenizer=Tokenizer(f"{input_dir}/checkpoints/Llama-2-7b-hf")), # Note: Use HF tokenizer or any others + inputs=inputs, + output_dir="/teamspace/datasets/starcoderdata", + chunk_size=(2049 * 8012), # Number of tokens to store by chunks. This is roughly 64MB of tokens per chunk. + ) + +Configure Cache Size Limit +-------------------------- + +Adapt the local caching limit of the ``StreamingDataset``. This is +useful to make sure the downloaded data chunks are deleted when used and +the disk usage stays low. + +.. code:: python + + from litdata import StreamingDataset + + dataset = StreamingDataset(..., max_cache_size="10GB") + +On-Prem Optimizations +--------------------- + +On-prem compute nodes can mount and use a network drive. A network drive +is a shared storage device on a local area network. In order to reduce +their network overload, the ``StreamingDataset`` supports ``caching`` +the data chunks. + +.. code:: python + + from lightning.data import StreamingDataset + + dataset = StreamingDataset(input_dir="local:/data/shared-drive/some-data") + +Benchmarks +========== + +In order to measure the effectiveness of LitData, we used a commonly +used dataset for benchmarks: +`Imagenet-1.2M `__ where the training set +contains ``1,281,167 images``. + +To align with other benchmarks, we measured the streaming speed +(``images per second``) loaded from `AWS +S3 `__ for several frameworks. + +Reproduce our benchmark **by running** this +`Studio `__. + +Imagenet-1.2M Streaming from AWS S3 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We can observe LitData is up to 85 % faster than the second best. Higher +is better in the table below. + ++-------------+-------------+-------------+-------------+-------------+ +| Framework | Images / | Images / | Images / | Images / | +| | sec 1st | sec 2nd | sec 1st | sec 2nd | +| | Epoch | Epoch | Epoch | Epoch | +| | (float32) | (float32) | (torch16) | (torch16) | ++=============+=============+=============+=============+=============+ +| PL Data | **5800.34** | **6589.98** | **6282.17** | **7221.88** | ++-------------+-------------+-------------+-------------+-------------+ +| Web Dataset | 3134.42 | 3924.95 | 3343.40 | 4424.62 | ++-------------+-------------+-------------+-------------+-------------+ +| Mosaic ML | 2898.61 | 5099.93 | 2809.69 | 5158.98 | ++-------------+-------------+-------------+-------------+-------------+ + +Imagenet-1.2M Conversion +~~~~~~~~~~~~~~~~~~~~~~~~ + +We measured how fast the 1.2 million images can converted into a +streamable format. Faster is better in the table below. + ++-------------+-------------+-------------+-------------+-------------+ +| Framework | Train | Val | Dataset | # Files | +| | Conversion | Conversion | Size | | +| | Time | Time | | | ++=============+=============+=============+=============+=============+ +| PL Data | **10:05 | **00:30 | **143.1 | 2.339 | +| | min** | min** | GB** | | ++-------------+-------------+-------------+-------------+-------------+ +| Web Dataset | 32:36 min | 01:22 min | 147.8 GB | 1.144 | ++-------------+-------------+-------------+-------------+-------------+ +| Mosaic ML | 49:49 min | 01:04 min | **143.1 | 2.298 | +| | | | GB** | | ++-------------+-------------+-------------+-------------+-------------+ + +Runnable Templates +================== + +Fastest way to learn is with `Studios `__. + +`Studios `__ are reproducible cloud IDE +with data, code, dependencies, e.g. so redo everything yourself with +ease! + +We’ve published `public templates `__ that +demonstrates how best to use the LitData framework at scale and with +several data types. + +Sign up `here `__ and run your first Studio for +free. + ++---------------------------------+---+--------------------------------+ +| Studio | D | Dataset | +| | a | | +| | t | | +| | a | | +| | t | | +| | y | | +| | p | | +| | e | | ++=================================+===+================================+ +| `Use or explore | I | `L | +| LAION-400MILLION | m | AION-400M `__ | +| ataset `__ | & | | +| | T | | +| | e | | +| | x | | +| | t | | ++---------------------------------+---+--------------------------------+ +| `Convert GeoSpatial data to | I | `Chesapeake Roads Spatial | +| Lightning | m | Context `__ | +| ng `__ | & | | +| | M | | +| | a | | +| | s | | +| | k | | ++---------------------------------+---+--------------------------------+ +| `Benchmark cloud data-loading | I | `Imagenet | +| li | m | 1M `__ | +| loud-data-loading-libraries>`__ | e | | +| | & | | +| | L | | +| | a | | +| | b | | +| | e | | +| | l | | ++---------------------------------+---+--------------------------------+ +| `Prepare the TinyLlama 1T token | T | `SlimPajama < | +| dataset `__ | +| -tinyllama-1t-token-dataset>`__ | t | & | +| | | `StarCode | +| | | r `__ | ++---------------------------------+---+--------------------------------+ +| `Tokenize 2M Swedish Wikipedia | T | `Swedish | +| A | e | Wikipedia `__ | +| ightning-ai/studios/tokenize-2m | t | | +| -swedish-wikipedia-articles>`__ | | | ++---------------------------------+---+--------------------------------+ +| `Embed English Wikipedia under | T | `English | +| 5 | e | Wikipedia `__ | +| ollars `__ | | | ++---------------------------------+---+--------------------------------+ +| `Convert parquets to Lightning | P | Randomly Generated data | +| Str | a | | +| eaming `__ | u | | +| | e | | +| | t | | +| | F | | +| | i | | +| | l | | +| | e | | +| | s | | ++---------------------------------+---+--------------------------------+ + +Infinite cloud data processing +============================== + +If you want to scale data processing, you typically need more machines +and if you do this yourself, this becomes very tedious and can take a +long time to get there. + +Instead, create a free account on the +`Lightning.ai `__ platform and use as many +machines as you need from code. + +On the platform, simply specify the number of nodes and the machine type +you need as follows: + +.. code:: python + + from litdata import map, Machine + + map( + ... + num_nodes=32, + machine=Machine.DATA_PREP, # Select between dozens of optimized machines + ) + +Also, the ``optimize`` operator can do the same to make immense datasets +streamable as follows: + +.. code:: python + + from litdata import optimize, Machine + + optimize( + ... + num_nodes=32, + machine=Machine.DATA_PREP, # Select between dozens of optimized machines + ) + +Within the `LAION 400 MILLION +Studio `__, +we utilized 32 machines, each equipped with 32 CPUs, to execute the +``optimize`` operator, enabling the download of 400 million images in +just 2 hours. Below is a screenshot of that job within the +`Lightning.ai `__ platform. You can execute it +yourself +`here `__. + +.. container:: + +⚡ Contributors +=============== + +We welcome any contributions, pull requests, or issues. If you use the +Streaming Dataset for your own project, please reach out to us on +`Discord `__. diff --git a/docs/html/_static/basic.css b/docs/html/_static/basic.css new file mode 100644 index 00000000..7577acb1 --- /dev/null +++ b/docs/html/_static/basic.css @@ -0,0 +1,903 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + 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.dropdown-title{background-color:hover_background;color:#792ee5} + +/*# sourceMappingURL=theme.css.map */ diff --git a/docs/html/_static/doctools.js b/docs/html/_static/doctools.js new file mode 100644 index 00000000..d06a71d7 --- /dev/null +++ b/docs/html/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if 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s="UL"===i.nodeName||"OL"===i.nodeName?he:ce;n=(n=o.default.makeArray(o.default(i).find(s)))[n.length-1]}var l=o.default.Event("hide.bs.tab",{relatedTarget:this._element}),r=o.default.Event("show.bs.tab",{relatedTarget:n});if(n&&o.default(n).trigger(l),o.default(this._element).trigger(r),!r.isDefaultPrevented()&&!l.isDefaultPrevented()){a&&(e=document.querySelector(a)),this._activate(this._element,i);var u=function(){var e=o.default.Event("hidden.bs.tab",{relatedTarget:t._element}),i=o.default.Event("shown.bs.tab",{relatedTarget:n});o.default(n).trigger(e),o.default(t._element).trigger(i)};e?this._activate(e,e.parentNode,u):u()}}},e.dispose=function(){o.default.removeData(this._element,le),this._element=null},e._activate=function(t,e,n){var i=this,a=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?o.default(e).children(ce):o.default(e).find(he))[0],s=n&&a&&o.default(a).hasClass(fe),l=function(){return i._transitionComplete(t,a,n)};if(a&&s){var 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t=this,e=o.default.Event("show.bs.toast");if(o.default(this._element).trigger(e),!e.isDefaultPrevented()){this._clearTimeout(),this._config.animation&&this._element.classList.add("fade");var n=function(){t._element.classList.remove(ye),t._element.classList.add(ve),o.default(t._element).trigger("shown.bs.toast"),t._config.autohide&&(t._timeout=setTimeout((function(){t.hide()}),t._config.delay))};if(this._element.classList.remove(_e),d.reflow(this._element),this._element.classList.add(ye),this._config.animation){var i=d.getTransitionDurationFromElement(this._element);o.default(this._element).one(d.TRANSITION_END,n).emulateTransitionEnd(i)}else n()}},e.hide=function(){if(this._element.classList.contains(ve)){var t=o.default.Event("hide.bs.toast");o.default(this._element).trigger(t),t.isDefaultPrevented()||this._close()}},e.dispose=function(){this._clearTimeout(),this._element.classList.contains(ve)&&this._element.classList.remove(ve),o.default(this._element).off(be),o.default.removeData(this._element,me),this._element=null,this._config=null},e._getConfig=function(t){return t=r({},Ee,o.default(this._element).data(),"object"==typeof t&&t?t:{}),d.typeCheckConfig("toast",t,this.constructor.DefaultType),t},e._setListeners=function(){var t=this;o.default(this._element).on(be,'[data-dismiss="toast"]',(function(){return t.hide()}))},e._close=function(){var t=this,e=function(){t._element.classList.add(_e),o.default(t._element).trigger("hidden.bs.toast")};if(this._element.classList.remove(ve),this._config.animation){var n=d.getTransitionDurationFromElement(this._element);o.default(this._element).one(d.TRANSITION_END,e).emulateTransitionEnd(n)}else 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file mode 100644 index 00000000..bb1aaae3 --- /dev/null +++ b/docs/html/_static/js/vendor/popper.min.js @@ -0,0 +1,5 @@ +/* + Copyright (C) Federico Zivolo 2020 + Distributed under the MIT License (license terms are at http://opensource.org/licenses/MIT). + */(function(e,t){'object'==typeof exports&&'undefined'!=typeof module?module.exports=t():'function'==typeof define&&define.amd?define(t):e.Popper=t()})(this,function(){'use strict';function e(e){return e&&'[object Function]'==={}.toString.call(e)}function t(e,t){if(1!==e.nodeType)return[];var o=e.ownerDocument.defaultView,n=o.getComputedStyle(e,null);return t?n[t]:n}function o(e){return'HTML'===e.nodeName?e:e.parentNode||e.host}function n(e){if(!e)return document.body;switch(e.nodeName){case'HTML':case'BODY':return e.ownerDocument.body;case'#document':return e.body;}var i=t(e),r=i.overflow,p=i.overflowX,s=i.overflowY;return /(auto|scroll|overlay)/.test(r+s+p)?e:n(o(e))}function i(e){return 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* namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // 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Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/html/_static/sphinx_highlight.js b/docs/html/_static/sphinx_highlight.js new file mode 100644 index 00000000..aae669d7 --- /dev/null +++ b/docs/html/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

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.. container::

+

.. rubric:: Blazingly fast, distributed streaming of training data +from any cloud storage +:name: blazingly-fast-distributed-streaming-of-training-data-from-any-cloud-storage

+
+

⚡ Welcome to LitData

+

With LitData, users can transform and optimize their data in cloud +storage environments efficiently and intuitively, at any scale.

+

Once optimized, efficient distributed training becomes practical +regardless of where the data is located, enabling users to seamlessly +stream data of any size to one or multiple machines.

+

LitData supports images, text, video, audio, geo-spatial, and +multimodal data types, is already adopted by frameworks such as +LitGPT <https://github.com/Lightning-AI/litgpt/blob/main/litgpt/data/lit_data.py>__ +to pretrain LLMs and integrates smoothly with PyTorch Lightning <https://lightning.ai/docs/pytorch/stable/>, Lightning Fabric <https://lightning.ai/docs/fabric/stable/>, and +PyTorch <https://pytorch.org/docs/stable/index.html>__.

+

Runnable templates <#runnable-templates>__ published on the +Lightning.AI Platform <https://lightning.ai>__ are available at the +end, reproducible in 1-click.

+

Table of Contents

+

+-  `Getting started <#getting-started>`__
+
+   -  `Installation <#installation>`__
+   -  `Quick Start <#quick-start>`__
+
+      -  `1. Prepare Your Data <#1-prepare-your-data>`__
+      -  `2. Upload Your Data to Cloud
+         Storage <#2-upload-your-data-to-cloud-storage>`__
+      -  `3. Use StreamingDataset <#3-use-streamingdataset>`__
+
+-  `Key Features <#key-features>`__
+-  `Benchmarks <#benchmarks>`__
+-  `Runnable Templates <#runnable-templates>`__
+-  `Infinite cloud data processing <#infinite-cloud-data-processing>`__
+-  `Contributors <#-contributors>`__
+
+Getting Started
+===============
+
+Installation
+------------
+
+Install **LitData** with ``pip``
+
+.. code:: bash
+
+   pip install litdata
+
+Install **LitData** with the extras
+
+.. code:: bash
+
+   pip install 'litdata[extras]'
+
+Quick Start
+-----------
+
+1. Prepare Your Data
+
+
+

Convert your raw dataset into LitData Optimized Streaming Format +using the optimize operator.

+

Here is an example with some random images.

+

.. code:: python

+

import numpy as np +from litdata import optimize +from PIL import Image

+
+
+

Store random images into the data chunks

+

def random_images(index): +data = { +“index”: index, # int data type +“image”: Image.fromarray(np.random.randint(0, 256, (32, 32, 3), np.uint8)), # PIL image data type +“class”: np.random.randint(10), # numpy array data type +} +# The data is serialized into bytes and stored into data chunks by the optimize operator. +return data # The data is serialized into bytes and stored into data chunks by the optimize operator.

+

if name == “main”: +optimize( +fn=random_images, # The function applied over each input. +inputs=list(range(1000)), # Provide any inputs. The fn is applied on each item. +output_dir=”my_optimized_dataset”, # The directory where the optimized data are stored. +num_workers=4, # The number of workers. The inputs are distributed among them. +chunk_bytes=”64MB” # The maximum number of bytes to write into a data chunk. +)

+

The optimize operator supports any data structures and types. +Serialize whatever you want. The optimized data is stored under the +output directory my_optimized_dataset.

+
    +

  1. Upload your Data to Cloud Storage

    2. +
+

+Cloud providers such as
+`AWS <https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html>`__,
+`Google
+Cloud <https://cloud.google.com/storage/docs/uploading-objects?hl=en#upload-object-cli>`__,
+`Azure <https://learn.microsoft.com/en-us/azure/import-export/storage-import-export-data-to-files?tabs=azure-portal-preview>`__
+provide command line clients to upload your data to their storage
+solutions.
+
+Here is how to upload the optimized dataset using the `AWS
+CLI <https://aws.amazon.com/s3>`__ to `AWS
+S3 <https://aws.amazon.com/s3/>`__.
+
+.. code:: bash
+
+   ⚡ aws s3 cp --recursive my_optimized_dataset s3://my-bucket/my_optimized_dataset
+
+3. Use StreamingDataset
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Then, the Streaming Dataset can read the data directly from `AWS
+S3 <https://aws.amazon.com/s3/>`__.
+
+.. code:: python
+
+   from litdata import StreamingDataset
+   from torch.utils.data import DataLoader
+
+   # Remote path where full dataset is stored
+   input_dir = 's3://my-bucket/my_optimized_dataset'
+
+   # Create the Streaming Dataset
+   dataset = StreamingDataset(input_dir, shuffle=True)
+
+   # Access any elements of the dataset
+   sample = dataset[50]
+   img = sample['image']
+   cls = sample['class']
+
+   # Create PyTorch DataLoader and iterate over it to train your AI models.
+   dataloader = DataLoader(dataset)
+
+Key Features
+============
+
+-  `Multi-GPU / Multi-Node Support <#multi-gpu--multi-node-support>`__
+-  `Access any item <#access-any-item>`__
+-  `Use any data transforms <#use-any-data-transforms>`__
+-  `The Map Operator <#the-map-operator>`__
+-  `Easy Data Mixing with the Combined Streaming
+   Dataset <#easy-data-mixing-with-the-combined-streaming-dataset>`__
+-  `Pause & Resume Made simple <#pause--resume-made-simple>`__
+-  `Support Profiling <#support-profiling>`__
+-  `Reduce your memory footprint <#reduce-your-memory-footprint>`__
+-  `Configure Cache Size Limit <#configure-cache-size-limit>`__
+-  `On-Prem Optimizations <#on-prem-optimizations>`__
+
+Multi-GPU / Multi-Node Support
+------------------------------
+
+The ``StreamingDataset`` and ``StreamingDataLoader`` automatically make
+sure each rank receives the same quantity of varied batches of data, so
+it works out of the box with your favorite frameworks (`PyTorch
+Lightning <https://lightning.ai/docs/pytorch/stable/>`__, `Lightning
+Fabric <https://lightning.ai/docs/fabric/stable/>`__, or
+`PyTorch <https://pytorch.org/docs/stable/index.html>`__) to do
+distributed training.
+
+Here you can see an illustration showing how the Streaming Dataset works
+with multi node / multi gpu under the hood.
+
+Access any item
+---------------
+
+Access the data you need, whenever you need it, regardless of where it
+is stored.
+
+.. code:: python
+
+   from litdata import StreamingDataset
+
+   dataset = StreamingDataset("s3://my-bucket/my-data") # data are stored in the cloud
+
+   print(len(dataset)) # display the length of your data
+
+   print(dataset[42]) # show the 42th element of the dataset
+
+Use any data transforms
+-----------------------
+
+Subclass the ``StreamingDataset`` and override its ``__getitem__``
+method to add any extra data transformations.
+
+.. code:: python
+
+   from litdata import StreamingDataset, StreamingDataLoader
+   import torchvision.transforms.v2.functional as F
+
+   class ImagenetStreamingDataset(StreamingDataset):
+
+       def __getitem__(self, index):
+           image = super().__getitem__(index)
+           return F.resize(image, (224, 224))
+
+   dataset = ImagenetStreamingDataset(...)
+   dataloader = StreamingDataLoader(dataset, batch_size=4)
+
+   for batch in dataloader:
+       print(batch.shape)
+       # Out: (4, 3, 224, 224)
+
+The Map Operator
+----------------
+
+The ``map`` operator can be used to apply a function over a list of
+inputs.
+
+Here is an example where the ``map`` operator is used to apply a
+``resize_image`` function over a folder of large images.
+
+.. code:: python
+
+   from lightning.data import map
+   from PIL import Image
+
+   # Note: Inputs could also refer to files on s3 directly.
+   input_dir = "my_large_images"
+   inputs = [os.path.join(input_dir, f) for f in os.listdir(input_dir)]
+
+   # The resize image takes one of the input (image_path) and the output directory. 
+   # Files written to output_dir are persisted.
+   def resize_image(image_path, output_dir):
+     output_image_path = os.path.join(output_dir, os.path.basename(image_path))
+     Image.open(image_path).resize((224, 224)).save(output_image_path)
+     
+   map(
+       fn=resize_image,
+       inputs=inputs, 
+       output_dir="s3://my-bucket/my_resized_images",
+   )
+
+Easy Data Mixing with the Combined Streaming Dataset
+----------------------------------------------------
+
+Easily experiment with dataset mixtures using the
+``CombinedStreamingDataset`` class.
+
+As an example, this mixture of
+`Slimpajama <https://huggingface.co/datasets/cerebras/SlimPajama-627B>`__
+& `StarCoder <https://huggingface.co/datasets/bigcode/starcoderdata>`__
+was used in the `TinyLLAMA <https://github.com/jzhang38/TinyLlama>`__
+project to pretrain a 1.1B Llama model on 3 trillion tokens.
+
+.. code:: python
+
+   from litdata import StreamingDataset, CombinedStreamingDataset
+   from litdata.streaming.item_loader import TokensLoader
+   from tqdm import tqdm
+   import os
+   from torch.utils.data import DataLoader
+
+   train_datasets = [
+       StreamingDataset(
+           input_dir="s3://tinyllama-template/slimpajama/train/",
+           item_loader=TokensLoader(block_size=2048 + 1), # Optimized loader for tokens used by LLMs 
+           shuffle=True,
+           drop_last=True,
+       ),
+       StreamingDataset(
+           input_dir="s3://tinyllama-template/starcoder/",
+           item_loader=TokensLoader(block_size=2048 + 1), # Optimized loader for tokens used by LLMs 
+           shuffle=True,
+           drop_last=True,
+       ),
+   ]
+
+   # Mix SlimPajama data and Starcoder data with these proportions:
+   weights = (0.693584, 0.306416)
+   combined_dataset = CombinedStreamingDataset(datasets=train_datasets, seed=42, weights=weights)
+
+   train_dataloader = DataLoader(combined_dataset, batch_size=8, pin_memory=True, num_workers=os.cpu_count())
+
+   # Iterate over the combined datasets
+   for batch in tqdm(train_dataloader):
+       pass
+
+Pause & Resume Made Simple
+--------------------------
+
+LitData provides a stateful ``Streaming DataLoader`` e.g. you can
+``pause`` and ``resume`` your training whenever you want.
+
+Info: The ``Streaming DataLoader`` was used by
+`Lit-GPT <https://github.com/Lightning-AI/lit-gpt/blob/main/pretrain/tinyllama.py>`__
+to pretrain LLMs. Restarting from an older checkpoint was critical to
+get to pretrain the full model due to several failures (network, CUDA
+Errors, etc..).
+
+.. code:: python
+
+   import os
+   import torch
+   from litdata import StreamingDataset, StreamingDataLoader
+
+   dataset = StreamingDataset("s3://my-bucket/my-data", shuffle=True)
+   dataloader = StreamingDataLoader(dataset, num_workers=os.cpu_count(), batch_size=64)
+
+   # Restore the dataLoader state if it exists
+   if os.path.isfile("dataloader_state.pt"):
+       state_dict = torch.load("dataloader_state.pt")
+       dataloader.load_state_dict(state_dict)
+
+   # Iterate over the data
+   for batch_idx, batch in enumerate(dataloader):
+     
+       # Store the state every 1000 batches
+       if batch_idx % 1000 == 0:
+           torch.save(dataloader.state_dict(), "dataloader_state.pt")
+
+Support Profiling
+-----------------
+
+The ``StreamingDataLoader`` supports profiling of your data loading
+process. Simply use the ``profile_batches`` argument to specify the
+number of batches you want to profile:
+
+.. code:: python
+
+   from litdata import StreamingDataset, StreamingDataLoader
+
+   StreamingDataLoader(..., profile_batches=5)
+
+This generates a Chrome trace called ``result.json``. Then, visualize
+this trace by opening Chrome browser at the ``chrome://tracing`` URL and
+load the trace inside.
+
+Reduce your memory footprint
+----------------------------
+
+When processing large files like compressed `parquet
+files <https://en.wikipedia.org/wiki/Apache_Parquet>`__, use the Python
+yield keyword to process and store one item at the time, reducing the
+memory footprint of the entire program.
+
+.. code:: python
+
+   from pathlib import Path
+   import pyarrow.parquet as pq
+   from litdata import optimize
+   from tokenizer import Tokenizer
+   from functools import partial
+
+   # 1. Define a function to convert the text within the parquet files into tokens
+   def tokenize_fn(filepath, tokenizer=None):
+       parquet_file = pq.ParquetFile(filepath)
+       # Process per batch to reduce RAM usage
+       for batch in parquet_file.iter_batches(batch_size=8192, columns=["content"]):
+           for text in batch.to_pandas()["content"]:
+               yield tokenizer.encode(text, bos=False, eos=True)
+
+   # 2. Generate the inputs
+   input_dir = "/teamspace/s3_connections/tinyllama-template"
+   inputs = [str(file) for file in Path(f"{input_dir}/starcoderdata").rglob("*.parquet")]
+
+   # 3. Store the optimized data wherever you want under "/teamspace/datasets" or "/teamspace/s3_connections"
+   outputs = optimize(
+       fn=partial(tokenize_fn, tokenizer=Tokenizer(f"{input_dir}/checkpoints/Llama-2-7b-hf")), # Note: Use HF tokenizer or any others
+       inputs=inputs,
+       output_dir="/teamspace/datasets/starcoderdata",
+       chunk_size=(2049 * 8012), # Number of tokens to store by chunks. This is roughly 64MB of tokens per chunk.
+   )
+
+Configure Cache Size Limit
+--------------------------
+
+Adapt the local caching limit of the ``StreamingDataset``. This is
+useful to make sure the downloaded data chunks are deleted when used and
+the disk usage stays low.
+
+.. code:: python
+
+   from litdata import StreamingDataset
+
+   dataset = StreamingDataset(..., max_cache_size="10GB")
+
+On-Prem Optimizations
+---------------------
+
+On-prem compute nodes can mount and use a network drive. A network drive
+is a shared storage device on a local area network. In order to reduce
+their network overload, the ``StreamingDataset`` supports ``caching``
+the data chunks.
+
+.. code:: python
+
+   from lightning.data import StreamingDataset
+
+   dataset = StreamingDataset(input_dir="local:/data/shared-drive/some-data")
+
+Benchmarks
+==========
+
+In order to measure the effectiveness of LitData, we used a commonly
+used dataset for benchmarks:
+`Imagenet-1.2M <https://www.image-net.org/>`__ where the training set
+contains ``1,281,167 images``.
+
+To align with other benchmarks, we measured the streaming speed
+(``images per second``) loaded from `AWS
+S3 <https://aws.amazon.com/s3/>`__ for several frameworks.
+
+Reproduce our benchmark **by running** this
+`Studio <https://lightning.ai/lightning-ai/studios/benchmark-cloud-data-loading-libraries>`__.
+
+Imagenet-1.2M Streaming from AWS S3
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We can observe LitData is up to 85 % faster than the second best. Higher
+is better in the table below.
+
++-------------+-------------+-------------+-------------+-------------+
+| Framework   | Images /    | Images /    | Images /    | Images /    |
+|             | sec 1st     | sec 2nd     | sec 1st     | sec 2nd     |
+|             | Epoch       | Epoch       | Epoch       | Epoch       |
+|             | (float32)   | (float32)   | (torch16)   | (torch16)   |
++=============+=============+=============+=============+=============+
+| PL Data     | **5800.34** | **6589.98** | **6282.17** | **7221.88** |
++-------------+-------------+-------------+-------------+-------------+
+| Web Dataset | 3134.42     | 3924.95     | 3343.40     | 4424.62     |
++-------------+-------------+-------------+-------------+-------------+
+| Mosaic ML   | 2898.61     | 5099.93     | 2809.69     | 5158.98     |
++-------------+-------------+-------------+-------------+-------------+
+
+Imagenet-1.2M Conversion
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+We measured how fast the 1.2 million images can converted into a
+streamable format. Faster is better in the table below.
+
++-------------+-------------+-------------+-------------+-------------+
+| Framework   | Train       | Val         | Dataset     | # Files     |
+|             | Conversion  | Conversion  | Size        |             |
+|             | Time        | Time        |             |             |
++=============+=============+=============+=============+=============+
+| PL Data     | **10:05     | **00:30     | **143.1     | 2.339       |
+|             | min**       | min**       | GB**        |             |
++-------------+-------------+-------------+-------------+-------------+
+| Web Dataset | 32:36 min   | 01:22 min   | 147.8 GB    | 1.144       |
++-------------+-------------+-------------+-------------+-------------+
+| Mosaic ML   | 49:49 min   | 01:04 min   | **143.1     | 2.298       |
+|             |             |             | GB**        |             |
++-------------+-------------+-------------+-------------+-------------+
+
+Runnable Templates
+==================
+
+Fastest way to learn is with `Studios <https://lightning.ai/studios>`__.
+
+`Studios <https://lightning.ai/studios>`__ are reproducible cloud IDE
+with data, code, dependencies, e.g. so redo everything yourself with
+ease!
+
+We’ve published `public templates <https://lightning.ai/studios>`__ that
+demonstrates how best to use the LitData framework at scale and with
+several data types.
+
+Sign up `here <https://lightning.ai/>`__ and run your first Studio for
+free.
+
++---------------------------------+---+--------------------------------+
+| Studio                          | D | Dataset                        |
+|                                 | a |                                |
+|                                 | t |                                |
+|                                 | a |                                |
+|                                 | t |                                |
+|                                 | y |                                |
+|                                 | p |                                |
+|                                 | e |                                |
++=================================+===+================================+
+| `Use or explore                 | I | `L                             |
+| LAION-400MILLION                | m | AION-400M <https://laion.ai/bl |
+| d                               | a | og/laion-400-open-dataset/>`__ |
+| ataset <https://lightning.ai/li | g |                                |
+| ghtning-ai/studios/use-or-explo | e |                                |
+| re-laion-400million-dataset>`__ | & |                                |
+|                                 | T |                                |
+|                                 | e |                                |
+|                                 | x |                                |
+|                                 | t |                                |
++---------------------------------+---+--------------------------------+
+| `Convert GeoSpatial data to     | I | `Chesapeake Roads Spatial      |
+| Lightning                       | m | Context <https://github.com    |
+| Streami                         | a | /isaaccorley/chesapeakersc>`__ |
+| ng <https://lightning.ai/lightn | g |                                |
+| ing-ai/studios/convert-spatial- | e |                                |
+| data-to-lightning-streaming>`__ | & |                                |
+|                                 | M |                                |
+|                                 | a |                                |
+|                                 | s |                                |
+|                                 | k |                                |
++---------------------------------+---+--------------------------------+
+| `Benchmark cloud data-loading   | I | `Imagenet                      |
+| li                              | m | 1M <https://paperswithcode.    |
+| braries <https://lightning.ai/l | a | com/sota/image-classification- |
+| ightning-ai/studios/benchmark-c | g | on-imagenet?tag_filter=171>`__ |
+| loud-data-loading-libraries>`__ | e |                                |
+|                                 | & |                                |
+|                                 | L |                                |
+|                                 | a |                                |
+|                                 | b |                                |
+|                                 | e |                                |
+|                                 | l |                                |
++---------------------------------+---+--------------------------------+
+| `Prepare the TinyLlama 1T token | T | `SlimPajama <                  |
+| dataset <https://lightning.ai/l | e | https://huggingface.co/dataset |
+| ightning-ai/studios/prepare-the | x | s/cerebras/SlimPajama-627B>`__ |
+| -tinyllama-1t-token-dataset>`__ | t | &                              |
+|                                 |   | `StarCode                      |
+|                                 |   | r <https://huggingface.co/data |
+|                                 |   | sets/bigcode/starcoderdata>`__ |
++---------------------------------+---+--------------------------------+
+| `Tokenize 2M Swedish Wikipedia  | T | `Swedish                       |
+| A                               | e | Wikipedia <https://hugging     |
+| rticles <https://lightning.ai/l | x | face.co/datasets/wikipedia>`__ |
+| ightning-ai/studios/tokenize-2m | t |                                |
+| -swedish-wikipedia-articles>`__ |   |                                |
++---------------------------------+---+--------------------------------+
+| `Embed English Wikipedia under  | T | `English                       |
+| 5                               | e | Wikipedia <https://hugging     |
+| d                               | x | face.co/datasets/wikipedia>`__ |
+| ollars <https://lightning.ai/li | t |                                |
+| ghtning-ai/studios/embed-englis |   |                                |
+| h-wikipedia-under-5-dollars>`__ |   |                                |
++---------------------------------+---+--------------------------------+
+| `Convert parquets to Lightning  | P | Randomly Generated data        |
+| Str                             | a |                                |
+| eaming <https://lightning.ai/li | r |                                |
+| ghtning-ai/studios/convert-parq | q |                                |
+| uets-to-lightning-streaming>`__ | u |                                |
+|                                 | e |                                |
+|                                 | t |                                |
+|                                 | F |                                |
+|                                 | i |                                |
+|                                 | l |                                |
+|                                 | e |                                |
+|                                 | s |                                |
++---------------------------------+---+--------------------------------+
+
+Infinite cloud data processing
+==============================
+
+If you want to scale data processing, you typically need more machines
+and if you do this yourself, this becomes very tedious and can take a
+long time to get there.
+
+Instead, create a free account on the
+`Lightning.ai <https://lightning.ai/>`__ platform and use as many
+machines as you need from code.
+
+On the platform, simply specify the number of nodes and the machine type
+you need as follows:
+
+.. code:: python
+
+   from litdata import map, Machine
+
+   map(
+     ...
+     num_nodes=32,
+     machine=Machine.DATA_PREP, # Select between dozens of optimized machines
+   )
+
+Also, the ``optimize`` operator can do the same to make immense datasets
+streamable as follows:
+
+.. code:: python
+
+   from litdata import optimize, Machine
+
+   optimize(
+     ...
+     num_nodes=32,
+     machine=Machine.DATA_PREP, # Select between dozens of optimized machines
+   )
+
+Within the `LAION 400 MILLION
+Studio <https://lightning.ai/lightning-ai/studios/use-or-explore-laion-400million-dataset>`__,
+we utilized 32 machines, each equipped with 32 CPUs, to execute the
+``optimize`` operator, enabling the download of 400 million images in
+just 2 hours. Below is a screenshot of that job within the
+`Lightning.ai <https://lightning.ai/>`__ platform. You can execute it
+yourself
+`here <https://lightning.ai/lightning-ai/studios/use-or-explore-laion-400million-dataset>`__.
+
+.. container::
+
+⚡ Contributors
+===============
+
+We welcome any contributions, pull requests, or issues. If you use the
+Streaming Dataset for your own project, please reach out to us on
+`Discord <https://discord.com/invite/XncpTy7DSt>`__.
+
+
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