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[Перевод] Простые способы ускорения обучения PyTorch-моделей

Не знаю — нужно ли вступление к статье, посвящённой ускорению машинного обучения (Machine Learning, ML)?

Ускорение обучения моделей — это именно то, в чём нуждаются все ML‑инженеры. Более быстрое обучение модели означает ускорение экспериментов, что, в свою очередь, ведёт к ускорению выпуска новых версий программных продуктов. Кроме того — чем выше скорость обучения — тем меньше ресурсов нужно на каждую итерацию обучения модели. Поэтому предлагаю перейти сразу к делу.

5043d140eb5c66a7303442457608028c.png

Контейнеризация

Конечно, сама по себе контейнеризация обучение модели не ускорит. Но эта технология нацелена на другой важный аспект функционирования моделей — на воспроизводимость работы приложения в разных окружениях. Иногда для этого достаточно воспользоваться virtualenv с фиксированными версиями библиотек. Но я рекомендую идти на шаг дальше и собирать самодостаточные Docker‑контейнеры для обучения моделей.

Это обеспечивает полное единообразие окружения при отладке модели, при её профилировании и, в итоге, при её обучении. ML‑инженеру меньше всего нужно, например, оптимизировать часть кода, который, из‑за ускорения, обеспечиваемого python12, больше не является узким местом системы. То же самое можно сказать и о некоей ошибке, которая не воспроизводится на разных версиях CUDA.

В качестве отправной точки можно воспользоваться предварительно собранными образами от NVIDIA. В них уже установлены CUDA, PyTorch и другие популярные библиотеки.

Docker‑контейнер — это оптимальное решение проблем, которые обычно описывают так: «Вообще‑то на моей машине это работает. И у меня нет ни малейшего понятия о том, почему это не работает у тебя».

Освоение профилировщика PyTorch

Прежде чем что‑либо оптимизировать — нужно понять то, сколько времени уходит на выполнение тех или иных фрагментов кода. Профилировщик PyTorch — это почти универсальный инструмент для профилирования кода, ответственного за обучение моделей. Он может фиксировать следующие показатели:

  • Данные о выполнении кода на CPU.

  • Данные о выполнении кода в ядрах CUDA.

  • Историю потребления памяти.

Это — всё, что нам нужно. И профилировщик PyTorch очень просто включить.

Для того чтобы приступить к записи событий, достаточно внедрить код, отвечающий за обучение, в контекст профилировщика:

import torch.autograd.profiler as profiler

with profiler.profile(
  activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
  on_trace_ready=torch.profiler.tensorboard_trace_handler('./logs'),
) as prof:
  train(args)

После этого можно запустить TensorBoard и посмотреть записи результатов профилирования. Тут ещё надо не забыть установить TensorBoard‑плагин torch‑tb‑profiler.

У профилировщика есть множество разнообразных параметров. Но самыми важными из них можно назвать activities и profile_memory. Можно поэкспериментировать и с другими опциями, но при этом стоит держать в голове одно простое правило: чем меньше опций включено — тем меньшую дополнительную нагрузку на систему создаст профилировщик.

Поэтому, если нужны результаты профилирования выполнения кода в ядрах CUDA — хорошо будет отключить профилирование CPU и другие возможности. В этом режиме профилирование даст результаты, максимально близкие к тем, которые характерны для реального выполнения кода.

Для того чтобы облегчить понимание записей профилировщика, можно рассмотреть возможность добавления в них пометок о контекстах, которые описывают важнейшие части кода. Если профилирование не включено, то подобные инструкции выполняться не будут.

with profiler.record_function("forward_pass"):
  result = model(**batch)

with profiler.record_function("train_step"):
  step(**result)

При таком подходе используемые метки будут видны в записях профилировщика. В результате легче будет идентифицировать разные блоки кода. Эти метки можно назначить и более мелким событиям. Например — в коде, отвечающем за прямой проход по модели.

with profiler.record_function("transformer_layer:self_attention"):
  data = self.self_attention(**data)

...

with profiler.record_function("transformer_layer:encoder_attention"):
  data = self.encoder_attention(**data, **encoder_data)

Интерпретация записей трассировщика PyTorch

После того, как записи трассировщика PyTorch собраны, откроем их в TensorBoard. Вот как выглядят результаты профилирования CPU и CUDA:

https://miro.medium.com/v2/resize:fit:2086/1*docJYzaTa0IXhTDgLv5QoA.png
Записи результатов профилирования (© Copyright 2024, PyTorch)

Сразу же найдём основные показатели, характерные для любого сеанса обучения:

  • Загрузка данных.

  • Прямой проход.

  • Обратный проход.

Обратный проход обрабатывается PyTorch в отдельном потоке (в нашем случае это — поток 16893), поэтому то, что к нему относится, легко идентифицировать.

Загрузка данных

Нам нужно, чтобы время загрузки данных стремилось бы к нулю.

И никаких компромиссов.

Это так из‑за того, что во время загрузки данных GPU бездействует. А это означает неполноценное использование имеющихся ресурсов. Но обработку данных можно совместить с вычислениями, проводимыми GPU, так как эти операции проводятся независимыми друг от друга подсистемами компьютера.

Для того чтобы увидеть области простоя GPU — достаточно взглянуть на показатели GPU Est. SM Efficiency и GPU Utilization в записях профилировщика. Области, в которых нет активности — это наши, так сказать, пациенты. Именно там GPU ничем не занят.

Вот пара простых подходов к решению этой проблемы:

  • Обработка данных в фоновых процессах (без GIL).

  • Выполнение аугментации и трансформации данных в параллельных процессах.

Если применяется DataLoader PyTorch — этого легко достичь, задав параметр num_workers. Ситуация осложняется в том случае, если используется IterableDataset, так как при таком подходе имеет место дупликация данных. Но и эту проблему можно решить, прибегнув к get_worker_info(). Речь идёт о такой настройке итераций, когда каждый воркер получает различные, непересекающиеся строки.

Для организации работы с возможностью более тонкой её настройки можно подумать о самостоятельной реализации многопроцессных платформ с применением multiprocessing.

Если вы ни разу не проверяли скорость, с которой ваш код обрабатывает данные — знайте, что улучшение этого аспекта функционирования системы может привести к колоссальному ускорению.

Заводим дружбу с распределителем памяти

Кеширующий распределитель памяти PyTorch для CUDA — это сущность, с которой, определённо, стоит подружиться.

Когда выделяют память для тензоров, используя PyTorch и работая с CUDA‑устройством, PyTorch применяет кеширующий аллокатор памяти. Это так из‑за того, что cudaMalloc/ cudaFree — это «дорогие» операции, которых мы стремимся избегать. Поэтому в PyTorch имеется механизм распределения памяти, который стремится повторно использовать фрагменты памяти, ранее выделенные с помощью cudaMalloc. В результате, если в распоряжении аллокатора PyTorch имеются подходящие блоки памяти — он сразу же, не вызывая cudaMalloc, передаст их в наше распоряжение. При таком подходе cudaMalloc вызывается лишь в начале работы программы.

Но если работают с данными переменной длины — разные прямые проходы потребуют применения промежуточных тензоров разных размеров. В результате у аллокатора PyTorch может не оказаться подходящего блока памяти. В таком случае аллокатор впадает в панику и освобождает ранее выделенные блоки памяти, вызывая cudaFree. Освобождённую память он сможет использовать для выполнения новых операций по выделению памяти.

После этого аллокатор снова начинает создавать кеш, выполняя множество «тяжёлых» операций cudaMalloc. Обнаружить эту проблему можно, взглянув в раздел профилирования памяти TensorBoard, где отображаются записи профилировщика.

Эту проблему можно увидеть и в самих записях профилировщика. Она выглядит как вызовы cudaMalloc и cudaFree.

https://miro.medium.com/v2/resize:fit:1000/1*glGIB0gdoQOAw7Dpflp2uQ.png
Аллокатор PyTorch потерял самообладание (изображение подготовлено автором)

Здесь красной линией показана память, зарезервированная аллокатором, объём которой, как видно, постоянно меняется. Это означает, что аллокатор не в состоянии эффективно обрабатывать запросы на выделение памяти.

Если же выделение памяти не заставляет аллокатор паниковать — красная линия будет абсолютно прямой.

https://miro.medium.com/v2/resize:fit:1000/1*cnclQLarMTifDCbc9_MBrw.png
Аллокатор PyTorch работает именно так, как ожидается (изображение подготовлено автором)

Как уже было сказано — подобное случается при использовании тензоров разных форм. Как это исправить?

Расширяемые сегменты памяти

Первое, что в такой ситуации стоит попробовать — это переключение аллокатора PyTorch в сравнительно новый режим:

PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True"

Если параметр expandable_segments установлен в True — это указывает аллокатору на то, что он должен выделять память для CUDA так, чтобы эта память потом могла бы быть расширена. Это позволит лучше организовать работу в ситуациях, когда программа часто требует изменения размера выделенной памяти. Например — при изменении размеров пакетов данных.

В результате можно сказать о том, что этот параметр указывает аллокатору PyTorch на то, что выделенные блоки памяти, при необходимости, могут быть расширены. А это — именно то, что нам нужно. Хотя, если вариации размеров данных слишком велики, этот подход может и не привести к решению проблемы. Если так и случилось — имеет смысл рассмотреть ещё один подход.

Сокращение разброса изменения объёмов выделяемой памяти

Ещё одно возможное решение этой проблемы заключается в том, чтобы работать с фрагментами данных примерно одинаковой формы. Это приведёт к тому, что аллокатору будет легче найти подходящий блок памяти для повторного использования.

Для того чтобы это сделать — можно, прибегнув к паддингу, выровнять размеры фрагментов данных. Или можно «прогреть» аллокатор, запустив модель, в которой используются входные данные максимального размера.

Подробности об аллокаторе памяти PyTorch можно найти в этой статье.

Приведение в порядок истории выделения памяти

Мы стремимся к тому, чтобы использовать всю доступную память GPU. Это позволяет загружать большие пакеты данных и быстрее эти данные обрабатывать. Но при увеличении пакета данных рано или поздно можно столкнуться с ошибкой нехватки памяти. Что вызывает эту ошибку?

Тут, в отладочных целях, имеет смысл просмотреть историю выделения памяти. Записать её можно с помощью PyTorch, а визуализировать с помощью memory_viz.

  • Запуск: torch.cuda.memory._record_memory_history(max_entries=100 000)

  • Сохранение: torch.cuda.memory._dump_snapshot(file_name)

  • Остановка: torch.cuda.memory._record_memory_history(enabled=None)

Визуализация данных о выделении памяти может выглядеть примерно так:

https://miro.medium.com/v2/resize:fit:700/1*39xeSNRORSW48rjIhO5MCQ.png
Визуализация данных о выделении памяти (© Copyright 2024, PyTorch)

Ось X — это время, ось Y — это общая использованная память, а цветные блоки — это тензоры. В результате тут видны моменты выделения и освобождения памяти.

Тут можно заметить узкие пики — это короткоживущие тензоры, которые занимают много памяти. Щёлкнув по тензору, можно получить сведения о том, где именно была выделена память под этот тензор. Нам нужно минимизировать эти пики, так как они ограничивают эффективное использование памяти. Можно узнать о том, что именно их вызывает, и рассмотреть возможность использования другого способа вычисления того, что нужно вычислить.

Помимо пиков тут легко выявлять утечки памяти.

https://miro.medium.com/v2/resize:fit:700/1*4R1oLVWtLl7xssZe1YNwJw.png
Обнаружение утечки памяти (© Copyright 2024, PyTorch)

Как видите — некоторый объём памяти после первого прямого прохода не был освобождён. Щёлкнув по соответствующему блоку, можно обнаружить подсказку относительно того, откуда именно взялся проблемный тензор. Вышеприведённое изображение иллюстрирует проблему, возникшую после того, как память, занятая градиентами, не была очищена после первого шага обучения. В результате соответствующие данные лежат в памяти мёртвым грузом во время прямого прохода, ограничивая нашу возможность по увеличению размера пакета данных и по размещению в этом пакете большего количества полезной информации.

Вот полезная статья о работе с памятью GPU.

Ускорение модели и использование меньшего объёма памяти

Что может быть лучше того, что описано в предыдущем заголовке? Достичь этого можно, воспользовавшись ядром FlashAttention для вычисления скалярного произведения в блоках внимания.

Если вы об этом не слышали — то знайте, что это — подход к вычислению точного скалярного произведения в блоке внимания без явного конструирования матрицы внимания. Это оптимизирует операции, выполняемые GPU, что улучшает скорость работы системы, и, кроме того, серьёзно минимизирует объём потребляемой памяти. Пожалуй, у нас просто нет причин этим не воспользоваться.

К сожалению, одна причина не пользоваться этим замечательным инструментом, всё же, есть. Это — аппаратное обеспечение. FlashAttention работает только с числами точности fp16 и bf16 на совместимом «железе». А это — NVIDIA Ampere, Hooper и так далее.

Существуют и другие библиотеки, использующие механизм Flash Attention. Поэтому, при необходимости, можно попытаться подобрать что‑то другое, лучше подходящее к конкретной кодовой базе. Например — XFormers или Transformer Engine.

А ещё можно взять и воспользоваться возможностями, которые есть в самом фреймворке PyTorch! Дело в том, что новые версии PyTorch могут использовать механизм Flash Attention там, где он применим. Для включения этого режима нужно выполнять блоки внимания в менеджере контекста, который указывает то, какую именно стратегию внимания использовать.

Оптимизация обучения в системах с несколькими GPU и с избыточностью данных — FSDP

Если для обучения моделей используют несколько GPU, то самым простым подходом в этой ситуации будет применение класса DistributedDataParallel (DDP). В результате будут созданы несколько идентичных процессов, а градиенты будут агрегироваться на шаге обратного прохода.

Но такой подход неоптимален!

Проблема заключается в следующем: когда запускают одинаковые процессы — на каждом GPU работа ведётся с одинаковыми моделями и с одинаковыми состояниями оптимизатора. Это избыточно. Решение заключается в шардинге (распределении) данных на разных GPU. Сделать это можно с помощью PyTorhc-обёртки Fully Sharded Data Parallel (FSDP, параллелизм с полным шардингом данных).

https://miro.medium.com/v2/resize:fit:1000/1*Ge8f-RxzQGit6cwO5iHbgw.png

Как работает этот механизм?

Мы уже говорили о том, что, при обучении модели на нескольких GPU с применением DDP, в распоряжении каждого процесса имеются одни и те же данные. Улучшить ситуацию можно, прибегнув к нескольким оптимизациям.

Шардинг состояния оптимизатора (ZeRO1)

При обучении модели с помощью DDP каждый процесс хранит полную копию состояний оптимизатора. При применении ZeRO1 состояния оптимизатора распределяются по всем рангам — так, что каждый из них хранит лишь некую порцию состояний оптимизатора. При выполнении обратного прохода каждый ранг нуждается в сборе лишь тех состояний оптимизатора, которые имеют отношение к его параметрам, необходимым для проведения шага оптимизации. Такое сокращение избыточности помогает беречь память.

В случае с Adam, когда размер хранимых параметров примерно в два раза превышает размер модели, шардинг состояния оптимизатора по 8 рангам означает, что один ранг хранит лишь четверть (2/8) общего размера состояния.

Шардинг градиентов (ZeRO2)

Итак, мы применяем шардинг состояний оптимизатора. А теперь модифицируем шаг оптимизатора так, чтобы подвергнуть шардингу и градиенты. Если у одного ранга имеются состояния для некоей порции параметров, это значит, что можно поступить так:

  • Агрегируем все градиенты, имеющие отношение к состояниям, которые имеются у ранга.

  • Проводим вычисления для выполнения шага оптимизации.

  • Отправляем данные шага оптимизации для порции параметров всем остальным рангам.

Как можно заметить — теперь каждому рангу не нужно хранить полную копию градиентов. Градиенты можно отправлять соответствующему рангу в тот момент, как они окажутся доступными. В результате это даёт возможность ещё сильнее снизить пиковое потребление памяти.

Шардинг параметров модели (ZeRO3)

А это — без преувеличения — настоящий прорыв.

Зачем хранить полную копию модели в каждом ранге? Прибегнем к распределению параметров по всем рангам. Это позволит точно вовремя загружать нужные параметры при выполнении прямого и обратного проходов.

Если речь идёт о больших моделях — эти оптимизации могут очень серьёзно снизить потребление памяти.

Как пользоваться FSDP?

На самом деле, пользоваться FSDP довольно просто. Достаточно обернуть модель в FSDP:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP


model = FSDP(model)

# крайне важно получить параметры из обёрнутой модели
# так как возвращается лишь их порция (распределённая часть)
optimizer = optim.Adam(model.parameters())

# проводим обучение так, как в обычных условиях
train(model, optimizer)

Ещё можно задать стратегию шардинга, применяемую FSDP. Например, можно выбрать стратегию SHARD_GRAD_OP, что позволит достичь поведения системы, похожего на её поведение при применении оптимизации ZeRO2. Подробности о других стратегиях можно найти здесь.

Кроме того, оборачивать в FSDP можно подмодули. В вышеприведённом примере используется лишь модуль FSDP, что снизит эффективность вычислений и эффективность использования памяти. Работает это так: представим, что модель содержит 100 линейных слоёв. Если прибегнуть к конструкции FSDP(model), то будет создан лишь один модуль FSDP, в который будет обёрнута вся модель. В данном случае AllGather будет собирать полный набор параметров для всех 100 линейных слоёв, а значит — не будет сэкономлена память CUDA для шардинга параметров.

Можно явным образом создавать обёртки для подмодулей или задать политику их автоматического оборачивания. Подробности о FSDP можно узнать из этого руководства.

Волшебное ускорение кода с помощью torch.compile

Для того чтобы ускорить работу модели на несколько процентов — достаточно просто включить torch.compile. Вот и вся магия.

Torch наблюдает за графом выполнения кода и пытается скомпилировать его, преобразовав так, чтобы модель могла бы выполняться эффективно, почти без обращений к Python.

Самый простой способ использования компилятора — обернуть модель в torch.compile:

import torch

model = torch.compile(model)

Такая конструкция выполняется почти мгновенно. Реальная трассировка производится только при первом прямом проходе.

У torch.compile есть множество опций, которые стоит попробовать.

Компилятор torch — это большая тема, которую я собираюсь раскрыть в будущих статьях.

Здесь можно найти руководство по torch.compile.

Итоги

В этом материале, конечно, нет полных и глубоких объяснений различных технологий. Он, скорее, представляет собой список различных способов ускорения обучения моделей, которые можно сразу же попробовать. Надеюсь, они вам пригодятся.

О, а приходите к нам работать? 🤗 💰

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  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

  • 08.10.23 01:30 davec8080

    The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.

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  • 01.10.24 14:54 Sinewclaudia

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  • 02.10.24 22:27 Emily Hunter

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  • 31.10.24 00:13 ytre89

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

  • 02.11.24 14:44 diannamendoza732

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  • 12.11.24 00:50 TERESA

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  • 17.11.24 09:31 Vivianlocke223

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    Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886  Email digital tech guard . com  Telegram: digital tech guard recovery . com  website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!

  • 21.11.24 08:02 Emily Hunter

    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

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  • 22.11.24 23:43 teresaborja

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  • 24.11.24 02:21 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

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  • 26.11.24 21:59 [email protected]

    In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com

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  • 27.11.24 00:39 [email protected]

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  • 27.11.24 09:10 Michal Novotny

    The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.

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