Petals: A Step Towards Decentralized AI
Tags: machinelearning, ai, decentralized
Published: 2024-04-21 09:03:07

Petals offers a new way of bringing decentralized computing removing the need for expensive hardware and potentially removing the need for super-computers specifically for model training in the future by following a torrent-inspired decentralized way of training machine learning models and in this article we will be breaking down what this project exactly is, how it is ground-breaking in nature and understanding its significance and stance in the process of removing the requirement for heavy beefy GPUs.

## Introduction


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/z0ak9eit072hc08o0s3p.png)



Petals is a revolutionary decentralized AI platform introduced by [Yandex Research](https://research.yandex.com/), researchers from the [University of Washington](https://www.washington.edu/), and [HuggingFace](https://huggingface.co/) that aims to democratize access to artificial intelligence (AI) by leveraging the BitTorrent protocol for distributed computing. This innovative approach not only enhances the efficiency and speed of AI model training but also significantly reduces the environmental impact associated with traditional centralized computing methods.

### Key Goals and Principles of Petals

1. **Democratizing AI**: Petals is designed to make AI accessible to everyone, regardless of their technical expertise or financial resources. By utilizing a decentralized network, it eliminates the barriers to entry that often prevent individuals and small organizations from participating in AI development.

2. **Collaborative Model Training**: The platform facilitates collaborative model training by allowing users to contribute their computing power to train AI models. This not only accelerates the training process but also fosters a community of AI enthusiasts and professionals who can share knowledge and resources.

3. **Reducing Environmental Impact**: By distributing the computational load across a vast network of devices, Petals significantly reduces the energy consumption associated with AI development. This approach not only helps in mitigating the environmental impact of AI but also makes AI development more sustainable.

4. **Security and Privacy**: Petals places a strong emphasis on security and privacy, ensuring that users' data is protected throughout the training process. It employs advanced encryption techniques and decentralized storage solutions to safeguard against data breaches and unauthorized access.

5. **Open Source and Community-Driven**: At its core, Petals is an open-source platform, encouraging developers and researchers to contribute to its development. This open-source ethos fosters a vibrant community that continuously improves the platform, making it more robust and user-friendly.

### How Petals Works

Petals operate by leveraging the BitTorrent protocol, which is known for its efficiency in distributing large files over the internet. When a user wants to train an AI model, they can upload the model to the Petals network. The platform then distributes the training tasks across the network, with each user's device contributing a portion of its computing power to the process. Once the training is complete, the updated model is shared back with the network, allowing for continuous improvement and adaptation.

This decentralized approach not only accelerates the training process but also ensures that the computational resources are utilized efficiently. Users can choose to contribute their resources based on their availability and the rewards they receive for their contributions.

To conclude, Petals represents a significant step forward in the democratization of AI, making it accessible to a wider audience and reducing its environmental impact. By leveraging the BitTorrent protocol for distributed computing, it offers a sustainable and efficient solution for AI development. As the platform continues to evolve, it is poised to become a cornerstone of the decentralized AI ecosystem.

## How is Petals Bringing Forth Decentralization to AI?



![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/s95ent95kk7yqro1jzky.png)




Petals offers a state-of-the-art way to train complex ML models without significantly scaling hardware in the upwards direction but instead in the horizontal direction.

The core of Petals uses a BitTorrent network style protocol where every node of the network offers its compute for the training of a large and complex ML Model and therefore the process of training gets split across multiple nodes on the network making the training of these models and especially LLM Models like GPT and Llama much much faster in nature. Matter of fact, recent benchmarks say that the inference time taken for the 65 billion variant of the Llama model was around 5-6 tokens per minute blowing even the modern state-of-the-art consumer graphics card available out of the [water](https://promptengineering.org/petals-decentralized-ai-revolution/).

This is truly revolutionary in nature considering how liberating it will be allowing every individual to be able to train complicated and very big models through Petals using its distributed computing feature accelerating the training process and this could potentially lead to the training of these models on mobile devices as well due to its distributed nature and with enough node participants in the network willing to give up their idle time for compute, it could definitely soon be possible without requiring one person to have a very good graphics card in their possession which is literally in the thousands of dollars or even above $100k sometimes depending on how big your model is.

## Decentralized Compute Aided by BitTorrent


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/f6j0c7iafn5075vmwi4g.png)



Covered in a bit more detail on an old blog of [mine](https://gssakash.hashnode.dev/bittorrent-a-protocol-to-distribute-large-files-in-large-networks), we will now take a step back and understanding what the BitTorrent Protocol means exactly for the uninformed.

The BitTorrent protocol is a peer-to-peer file-sharing protocol enabling users to distribute data across the internet in a decentralized manner and unlike traditional client-server models, BitTorrent provisions and allows multiple users to share files simultaneously across the network with every user acting both like a client and server and this sharing is also known as "seeding". This decentralized approach significantly reduces the load on any single server and increases the speed and reliability of file distribution.

###  How BitTorrent Works
**1. Tracker:** The process begins with a tracker, which is a server that keeps track of all the peers (users) that are sharing a particular file. The tracker provides a list of peers to the initial user who wants to download the file.

**2. Seeding:** Once a user has downloaded a file, they can start seeding it, meaning they share the file with other users. The tracker keeps track of all seeders, ensuring that the file remains available for download.

**3. Piecewise Download:** The file is divided into small pieces, and users download these pieces from multiple peers simultaneously. This parallel downloading process significantly speeds up the download time.

**4. Choking and Unchoking:** To manage the network load, BitTorrent uses a mechanism called "choking," where a user can temporarily stop sending data to another user. This helps in balancing the load and ensuring that all users can download the file efficiently.

### Adapting BitTorrent for Decentralized Compute with Petals

Petals uses the BitTorrent protocol to enable decentralized computing where the participants contribute their computing resources to train their AI models collaboratively. This adaptation involves transforming the traditional file-sharing process that this protocol follows into a distributed computing framework built for ML model training and inference.

### How does Petals Use BitTorrent?

**1. Model Training**: In Petals, the AI Model training process is treated in a similar manner to file sharing where every large file in general in a torrent network is chopped up into multiple smaller chunks, and in the context of ML Models this can be analogous to a smaller chunk of the model training task instead.

**2. Distributed Computing**: These pieces are transferred over the network where each user's device can contribute significantly to a portion of the network's compute power to train the model and this decentralized approach allows for the parallel processing of the model training takes significantly accelerating the training process.

**3. Result Aggregation:** Once the training tasks are completed, the results are aggregated back into a complete model. This model is then shared with the network, allowing for continuous improvement and adaptation.


### The Process of Decentralized Compute with Petals

**1. Model Upload:** A user uploads the AI model to the Petals network. The model is divided into smaller tasks, each representing a portion of the model training.

**2. Task Distribution:** The tasks are distributed across the network, with each user's device assigned a portion of the tasks. This distribution is managed by the BitTorrent protocol, ensuring that the tasks are efficiently distributed and that the network load is balanced.

**3. Task Execution:** Each user's device executes its assigned tasks, contributing its computing resources to the model training process. This collaborative effort allows for the parallel processing of the model training tasks.

**4. Result Aggregation:** Once all tasks are completed, the results are aggregated back into a complete model. This model is then shared with the network, allowing for continuous improvement and adaptation.

**5. Seeding:** The completed model is then made available for other users to download and use, either for further training or for inference tasks. This process of seeding ensures that the model remains available for use within the network.

By adapting the BitTorrent protocol for decentralized computing, Petals enables a new paradigm in AI development, where the power of collective computing is harnessed to train AI models more efficiently and sustainably.



## Technical Deep Dive into Petals



![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/it0mt5jzxt891045mdx4.png)



Now, let's break down even further how exactly the Petals framework works and how it has adapted BitTorrent's protocol to its advantage to power the decentralized training of Machine Learning Models.

To understand how Petals leverages the BitTorrent protocol for decentralized training of Machine Learning (ML) models, let's delve deeper into the specifics of how the framework operates and the adaptations it makes to BitTorrent's protocol.


### BitTorrent Protocol Adaptation

**1. Task Segmentation**: Unlike traditional file sharing, where the data is divided into pieces for distribution, Petals segments ML model training takes into smaller, manageable sub-sections and each training task represents a portion of the model's training process such as training a specific layer of a specific set of parameters.

**2. P2P Networking**: Petals utilizes the peer-to-peer nature of BitTorrent to create a network of devices contributing to its compute resources and each device in the network acts like both a client and a server offering its computational power to train the model and also receives tasks from other devices.

**3. Dynamic Task Assignment**: The BitTorrent protocol's dynamic nature allows Petals to dynamically assign tasks to the devices present as a part of the network based on their availability and current workload. This ensures that the network's resources are utilized efficiently with the devices contributing to the model's training process as they become available.

**4. Result Aggregation:** After a task is completed, the results are sent back to a central server or distributed across the network. Petals then aggregates these results to update the model. This process is facilitated by the BitTorrent protocol's ability to handle large data transfers efficiently.

### Decentralized Training Process

**1. Model Initialization:** The process begins with the initialization of the ML model and this model is divided up into smaller tasks that can be distributed across the network.

**2. Task Distribution:**: Using the BitTorrent protocol, Petals distributes these tasks to the devices active on the network and this protocol ensures that the tasks are evenly distributed across the network taking into account the current workload and capacity available across the individual devices of the network.

**3. Task Execution:**  Each device executes its assigned tasks using its local computing resources. This could involve training a specific layer of the model, adjusting parameters, or performing any other task necessary for the model's training.

**4. Result Collection:** Once a task is finished, the results are sent back to the network and this could be done through a centralized server or directly through the other devices in general.

**5. Model Update:** The collected results are then used to update the models and this process is repeated until the model reaches a satisfactory level of performance or until a pre-defined number of iterations are reached.

**6. Completion and Sharing**: Once the model training is complete, the final model is made available for download or further use within the network. This could involve sharing the model with other users for inference tasks or continuing the training process with additional data.

### Advantages that Petals Offers

**1. Scalability:** Petals can potentially scale to accommodate large models and datasets offering a fast way to train these models by distributing the workload or CPU cycles required to do so across the various devices in the network.

**2. Efficiency:** By utilizing the BitTorrent protocol, Petals ensures that the network's resources are used efficiently, with the tasks being dynamically assigned based on the availability of the individual devices in the network.

**3. Reliability:** The decentralized nature of the network ensures that the training process is not dependent on a single device or server, making it more reliable and resilient to failures.

**4. Environmental Impact:** By distributing the computational load, Petals significantly reduces the environmental impact associated with the traditional centralized computing methods.

In summary, Petals adapts the BitTorrent protocol to enable decentralized training of ML models, leveraging the protocol's strengths in peer-to-peer networking, dynamic task assignment, and efficient data transfer. This approach not only accelerates the training process but also democratizes access to AI, making it possible for individuals and organizations with limited resources to contribute to the development of AI models.

## Petals Ecosystem and Adoption


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/m93pxr13gwkd3z198pb4.png)



The Petals ecosystem is widely gaining popularity in the community due to how fast it performs and how liberating it is to a lot of researchers and people working in ML especially for training LLM Models due to its nature of distributed computing.

### Current State Of the Petals Ecosystem

- **Participants:** The Petals ecosystem has seen significant growth, with thousands of participants from various backgrounds, including data scientists, developers, and enthusiasts. These participants contribute to the ecosystem by training AI models, validating data, and participating in governance decisions.

- **AI Models:** The platform supports a wide range of AI models, from image recognition to natural language processing. Participants can choose to train models in areas of interest or contribute to ongoing projects.

- **Projects and Organizations:** Several projects and organizations have adopted the Petals platform for their AI development needs. These include research institutions, tech startups, and non-profit organizations looking to leverage AI for social good.

### Potential for Further Adoption and Growth

The Petals platform has immense potential for further adoption and growth. Its decentralized approach to AI development, combined with the increasing demand for AI solutions across various sectors, positions it well for future expansion.

- **Increased Adoption:** As more industries recognize the value of AI, the demand for decentralized AI platforms like Petals is likely to grow. This could lead to a surge in participants and projects.

- **Collaboration Opportunities:** The platform's open-source nature encourages collaboration, allowing for the development of more complex and sophisticated AI models.

- **Implications for Decentralized AI Development:** The success of Petals could set a precedent for other decentralized AI platforms, potentially leading to a shift in how AI is developed and deployed like [ZKML](https://dev.to/gssakash/zkml-bringing-verifiable-and-trustless-ml-to-the-masses-2a38) for example.

In conclusion, the Petals ecosystem represents a significant step forward in the democratization of AI development. Its current state, coupled with its potential for growth and adoption, positions it as a key player in the future of decentralized AI.

### How can Petals Increase Its Adoption and Incentivise Its Users?

Petals really needs multiple operators in the network who are willing to give up their idle time for computational tasks that can be undertaken by the network for the training of ML models of somebody else and this distributed computing while in theory solves legit, will be difficult to convince people to give up their idle times until they themselves have already benefited from Petals.

Petals could potentially solve this by following a similar rewards mechanism that various blockchains already follow where miners are rewarded for their efforts and compute offered for the mining of the blocks by incentivizing them and promoting further collaboration.

Blockchain technology could prove to be the next required step for the Petals ecosystem and could be very beneficial to also make this process fully decentralized in nature. The integration of Petals with blockchain technology could offer a unique approach to decentralized AI development, addressing several challenges and enhancing the ecosystem in several ways. Here's a breakdown of how this combination can help:

**1. Transparency And Trust** 

Blockchain's Immutable Ledger: Each transaction on the blockchain is recorded in a way that is transparent and immutable. This means that once data is added to the blockchain, it cannot be altered or deleted. This feature ensures that the training data, model parameters, and validation results are all transparent and verifiable.

Trust and Security: The decentralized nature of blockchain technology means that no single entity has control over the entire network. This reduces the risk of data manipulation and ensures that the AI models are trained on a wide range of data, increasing their robustness and reliability.

**2. Decentralized Governance**

Participation and Decision Making: Blockchain allows for decentralized governance, where decisions about the platform's direction, rules, and policies can be made collectively by its participants. This ensures that the platform remains responsive to the needs and interests of its community.

Fairness and Equity: By distributing the decision-making power among all participants, blockchain helps to ensure that the platform remains fair and equitable. This is particularly important in AI development, where ensuring that models are trained on a diverse range of data is crucial.

**3. Incentivization and Reward Mechanisms**

Token Economy: Blockchain platforms often use a token economy to incentivize participation. In the Petals ecosystem, participants could earn tokens for contributing to AI model training, validating data, and participating in governance. These tokens can be used within the ecosystem or exchanged for other cryptocurrencies.

Motivation for Contribution: The prospect of earning tokens can motivate more people to contribute to the ecosystem, leading to a more robust and diverse set of AI models.

**4. Scalability and Efficiency**

Distributed Processing: Blockchain's decentralized nature allows for distributed processing of AI model training and validation. This can significantly reduce the time and computational resources required to train complex models.

Efficient Data Management: The blockchain's immutable ledger ensures that data is efficiently managed and stored, reducing the need for redundant storage and enhancing the platform's scalability.

**5. Interoperability and Accessibility**

Open Standards: Blockchain platforms often support open standards, which can facilitate interoperability between different AI models and tools. This can make it easier for developers and researchers to integrate Petals-trained models into their applications.

Accessibility: The decentralized nature of blockchain technology can make the Petals ecosystem more accessible to individuals and organizations around the world, regardless of their geographical location or technical capabilities.

In summary, the integration of Petals with blockchain technology offers a robust framework for decentralized AI development. It addresses key challenges related to transparency, trust, governance, incentivization, scalability, and interoperability, positioning the Petals ecosystem as a leading platform for AI development in the future.

## Petals + ZKML?


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1ks556k6nqpfz5y87pa2.png)



Let's now see the benefits when [ZKML](https://dev.to/gssakash/zkml-bringing-verifiable-and-trustless-ml-to-the-masses-2a38) is combined potentially with Petals.

Integrating Petals with zkML and leveraging BitTorrent technology presents a compelling approach to decentralized AI development, combining the strengths of privacy, efficiency, scalability, and peer-to-peer distribution. Here's how these technologies can be integrated:

**1. Enhanced Privacy and Scalability with zkML**

zkML for Data Training: zkML allows for the training of machine learning models without revealing the underlying data to the model. In the Petals ecosystem, participants can contribute data for model training without compromising their privacy. This is particularly useful for sensitive data, ensuring privacy while maintaining the scalability of the training process.

Privacy-Preserving Validation: The validation of AI models can be conducted privately using zkML. This ensures that the validation process does not compromise the privacy of the data used for training the models, enhancing the privacy and security of the ecosystem.

**2. Efficient Data Processing and Scalability with BitTorrent**

BitTorrent for Data Distribution: BitTorrent technology can be used to distribute the training data and models across the Petals ecosystem. This peer-to-peer distribution method is highly efficient and scalable, allowing for the rapid sharing of large datasets and models without relying on centralized servers.

Decentralized zkML Models: By leveraging BitTorrent for data distribution, Petals can develop and deploy AI models that are scalable and efficient. zkML models can be trained on a decentralized network, reducing the need for centralized servers and enhancing the platform's scalability.

**3. Incentivization and Governance**

Token-Based Rewards: The integration of zkML with Petals can also enhance the incentivization and governance mechanisms of the ecosystem. Participants can earn tokens for contributing to the training and validation of zkML models, incentivizing more individuals to participate in the ecosystem.

Decentralized Governance: The use of blockchain technology in Petals, combined with zkML and BitTorrent, can facilitate decentralized governance. Participants can collectively decide on the rules and policies of the ecosystem, ensuring that it remains fair and equitable.

**4. Interoperability and Accessibility**

Open Standards for zkML Models: By adopting open standards for zkML models, Petals can ensure that these models are interoperable with other AI tools and platforms. This can make it easier for developers and researchers to integrate Petals-trained models into their applications.

Accessibility for All: The combination of Petals with zkML and BitTorrent can make AI development more accessible to a wider range of participants, including those who may not have access to powerful computational resources. This can democratize AI development and foster innovation.

**5. Real-World Applications**

Privacy-Preserving AI Solutions: The integration of Petals with zkML and BitTorrent can enable the development of AI solutions that are both powerful and privacy-preserving. This can be particularly beneficial in sectors where privacy is a critical concern, such as healthcare, finance, and social services.


In conclusion, combining Petals with zkML and leveraging BitTorrent technology offers a promising path forward for decentralized AI development. It addresses key challenges related to privacy, scalability, 
efficiency, and governance, positioning the Petals ecosystem as a leading platform for the development and deployment of privacy-preserving AI models.

## Conclusion


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ldn7tyec971id36ym3ki.png)



Overall, Petals once it gets widely adopted could lead to the start of a new era that is decentralized model training thus empowering individuals with low compute to be able to train their models even if they are in the billions of parameters through Petals and could also potentially be incentivized for training the models of other community members provided Petals manages to implement a similar rewards mechanism which miners already enjoy in a blockchain network.

The Petals ecosystem represents a pivotal juncture in the evolution of AI development, harnessing the power of blockchain, zkML, and BitTorrent technologies to democratize access to AI technologies. By leveraging these innovative approaches, Petals not only enhances privacy and security but also significantly boosts scalability and efficiency in AI model training and validation. The integration of these technologies creates a robust framework that fosters collaboration, transparency, and fairness, ensuring that the platform remains responsive to the needs of its community.

The potential of Petals to revolutionize the AI landscape is immense, with the ability to support a wide range of applications from healthcare to environmental monitoring. As the ecosystem continues to grow and evolve, it is poised to become a cornerstone in the future of decentralized AI development. The success of Petals underscores the importance of decentralization in AI, highlighting the need for platforms that empower individuals and organizations to contribute to AI development without compromising privacy or security.

Looking ahead, the integration of Petals with zkML and BitTorrent technology promises to unlock new possibilities for AI innovation. By combining the strengths of these technologies, Petals can further enhance its capabilities, making AI development more accessible, efficient, and secure. As the platform continues to expand and attract more participants, it is set to become a beacon for the next generation of AI development, paving the way for a future where AI is truly accessible to all.

In the end, revolutionary concepts/implementations like Petals are not just about the technology; it's about the potential of a decentralized ecosystem to transform the way we approach AI development. It's about the power of collaboration, the importance of privacy, and the endless possibilities that lie ahead. As we look to the future, the Petals ecosystem stands as a testament to the innovative spirit of the AI community, a beacon of hope for a more equitable and accessible future in AI development.

And, to use Petals right now and to find out more about it, check out the references below.

## References

1. [Prompt Engineering](https://research.yandex.com/blog/petals-decentralized-inference-and-finetuning-of-large-language-models?ref=promptengineering.org)

2. [MindBlast](https://mindblastai.com/petals-the-bittorrent-of-ai-models/)

3. [Petals Code](https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing)

4. [Petals WhitePaper](https://arxiv.org/pdf/2209.01188.pdf)

5. [Petals Github](https://github.com/bigscience-workshop/petals)