Collaborative Learning: Exploring Distributed Training for Machine Learning Models

In the constantly evolving landscape of machine learning, one thing is clear: managing large amounts of data forms a big challenge. With datasets growing exponentially in size and complexity, the demand for robust computational resources grows as well. Especially in the field of large language models, where parameter sizes are reaching into millions or even billions. Distributed training offers a solution by bundling the computational power of multiple machines. In this blog, we’ll delve into the world of distributed training, uncovering its benefits and challenges, and exploring the groundbreaking opportunities it presents.

What is distributed training?

Distributed training is a method where the task of training machine learning models is distributed across multiple processors, known as worker nodes.[1] These nodes work together to speed up the training process. Distributed training is especially useful for training computational intensive models, like neural networks, on large datasets, as it allows for simultaneously training of different model layers on separate nodes. Distributed training is primarily categorized into two types: data parallelism and model parallelism.

Data Parallelism

Data parallelism involves partitioning the dataset across multiple worker nodes to process it in parallel.[1] Each node trains a copy of the model on a different subset of data. The nodes can share their results either synchronously or asynchronously.

In synchronous training, gradients are calculated for each set of data and combined, after which parameters are updated synchronously, as of the model was trained on the complete dataset.

In asynchronous training, all workers train independently and the parameters are updated without waiting for the others.[2] An advantage of this way of processing is that it is more robust to machine failures. If one machine fails, the other machines continue to process their data part and updating the model parameters, whereas in synchronous parallelism, the training process is delayed if one machine fails.

Synchronous and asynchronous data parallel training.[1]

Model Parallelism

While data parallelism is a straightforward and efficient method for small models, model parallelism becomes useful when the model gets too large to fit into a single worker node. In this approach, the model is divided into parts that are trained simultaneously on different nodes.[1] All nodes use the same dataset and share the model parameters. This distributed training method is much more difficult to implement and is most effective for models with naturally parallel architectures, like in deep learning architecture.

Model parallel training.[1]

An example of a communication technique used in the model parallel distributed training method is all-reduce. In all-reduce, nodes collaborate to compute and aggregate partial results, like gradients, and then distribute the combined outcome to all participating nodes.[10] It is an optimized algorithm that minimizes synchronization delay, enhancing overall efficiency.

Distributed Training Frameworks

There are multiple frameworks available which help with implementing distributed training in your solutions. Depending on the framework used, distributed training can be implemented in multiple ways, which are all based on some form of data and/or model parallelism.

TensorFlow

TensorFlow has following distributed training strategies available[1][10]:

• tf.distribute.MirroredStrategy is TensorFlow’s synchronous distributed training strategy for single-machine, multi-GPU setups. It replicates the model on each GPU, ensuring that each variable is synchronized across the replicas using all-reduce algorithms. This strategy is beneficial for those who have access to a single machine with multiple GPUs and wish to scale up their training without delving into the complexities of a distributed system.
• tf.distribute.MultiWorkerMirroredStrategy applies synchronous distributed training across multiple workers. It uses the TF_CONFIG environment variable to define the cluster’s configuration, allowing for easy scaling by adding more machines to the network.
• tf.distribute.TPUStrategy extends TensorFlow’s distributed training capabilities to Google’s Tensor Processing Units (TPUs). This strategy is optimized for high-speed, large-scale machine learning training tasks, making it an excellent choice for those who require accelerated training times.
• tf.distribute.CentralStorageStrategy is suitable for smaller-scale distributed training. It keeps model parameters on a single device and performs computations across multiple GPUs. If only one GPU is available, both variables and operations are stored on that GPU, simplifying the management process.
• tf.distribute.ParameterServerStrategy is an asynchronous distributed training method that uses a separate server to store parameters. Other training servers read and update these parameters during each training step. This strategy is particular useful for scaling up model training across multiple machines, as it decouples parameter management from computation, which can lead to improved efficiency.

PyTorch

PyTorch offers a variety of strategies for distributed training as well, each suited to different scenarios and scales of machine learning models[9][11]:

• torch.nn.DataParallel is designed for single-machine, multi-GPU scenarios and is somewhat similar to TensorFlow’s MirroredStrategy.
• torch.nn.parallel.DistributedDataParallel (DDP) implements data parallelism at the module level and can run across multiple machines. It is more efficient than DataParallel and is the recommended approach for multi-GPU training across single or multiple machines.
• torch.distributed.FullySharedDataParallel (FSDP) is a type of data parallelism that shards model parameters, optimizer states, and gradients across parallel workers. This reduces the GPU memory footprint, allowing for training of larger models or larger batch sizes.
• torch.distributed.rpc framework in PyTorch provides mechanisms for multi-machine model training. It allows for remote communication and higher-level API to automatically differentiate models split across several machines.

Both TensorFlow and PyTorch provide strategies that facilitate distributed training, each with distinct approaches to model replication and synchronized updates across GPUs. While they both offer the possibility to implement distributed training efficiently, their methods for parameter management and memory optimization differ. For those interested in exploring these frameworks further, the official documentation of TensorFlow and Pytorch offers in-depth information on how to implement their strategies for optimal results in distributed machine learning tasks.[1][9]

Why do we need distributed training?

Implementing distributed training in machine learning offers multiple advantages. As already mentioned, one of the primary benefits is the ability to handle large-scale models and datasets, which would otherwise be impractical or impossible to process on a single machine.[3] This scalability allows for the training of more complex models, leading to potentially more accurate and sophisticated predictions.

Another significant advantage is reducing of training time.[4] By distributing the computational workload across multiple devices or nodes, each contributing to the overall training process, distributed training can significantly reduce the time required to train models, enabling faster iteration and development cycles.

Fault tolerance can be implemented by multiple distributed training frameworks, like TensorFlow. In the event of a node failure, the system can continue to operate, with other nodes compensating for the failed one, thus ensuring that the training process is not severely disrupted. This makes distributed training robust against hardware failures and system interruptions.[4]

Moreover, distributed training frameworks often come with built-in support for mixed precision training which can further accelerate training by utilizing lower-precision arithmetic where it does not significantly impact model accuracy.[4] In mixed precision training, half-precision format (16-bit) is used for performing operations and single precision (32-bit) to store minimal information. By this, memory requirements can be reduced, allowing to use larger models or batch sizes.

Lastly, distributed training enables the use of specialized hardware, such as GPUs or TPUs, across different nodes, which can provide significant performance boosts over traditional CPU-based training.[6] This hardware acceleration is crucial for training state-of-the-art models that require extensive computational power.

So, distributed training in machine learning is a powerful technique that offers scalability, speed, fault tolerance, and hardware optimization. These advantages make it an essential tool for tackling the increasingly complex and data-intensive challenges in the field of artificial intelligence.

Challenges in Distributed Training

When implementing distributed training in machine learning solutions, it’s good to be aware of the various difficulties that can hinder the process. As distributed training offers several advantages, the implementation also comes with some challenges.

One common pitfall is the underestimation of network latency, which can lead to significant delays in synchronization and reduce the overall speedup gained from distribution. To deal with this, implementing efficient communication protocols and gradient compression techniques can help mitigate the impact of latency. Additionally, using decentralized training approaches, synchronization delays can be reduces as well. This means that instead of aggregating data or model updates at a central location, each node in the network collaborates directly with others, meaning that less communication is needed between all nodes.[5]

Data sharding can also be a problem if not done carefully. Poorly distributed data can result in some nodes training on non-representative samples, which can skew the model’s learning process.[6] To avoid this, partitioning strategies can be implemented that ensure a representative sample of data on each node.

When errors occur, debugging becomes more challenging in distributed systems. Errors may be harder to reproduce and trace back to their source due to the complexity of the system. Specialized debugging tools that provide a global view of the system and allow for tracing errors across nodes, can speed up the debugging process. An example is TensorFlow’s Profiler, which can give inside in performances issues.[10]

Also version mismatches between different nodes’ software can cause unexpected behavior and crashes which can be time-consuming to resolve. Regularly updating and testing the software on all nodes can help maintain consistency and prevent version mismatches.[8]

And lastly, by actively monitoring and logging the training process, there’s an increased chance of identifying errors sooner. Enhancing monitoring and logging with distributed monitoring systems that aggregate logs from all nodes, can provide deeper insights into the overall training process.

By being aware of these pitfalls, you can increase the likelihood of a successful implementation of distributed training in your machine learning projects.

Conclusion

In conclusion, distributed training is an interesting strategy to implement in your machine learning solutions, particularly when it comes to addressing the challenges of large-scale data and model management. By maximizing the collective power of multiple computational nodes, it significantly accelerates the training process, enhances model accuracy, and offers a scalable solution that grows with computational needs. Moreover, it introduces cost efficiencies and increases the fault tolerance of our systems, ensuring that machine learning tasks can be performed with greater reliability and less downtime. With the support of robust frameworks like TensorFlow and PyTorch, distributed training gives the possibility to build more advanced and intelligent applications in an efficient and sustainable manner.

References

1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … & Zheng, X. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint arXiv:1603.04467.
2. Dean, J., Corrado, G. S., Monga, R., Chen, K., Devin, M., Le, Q. V., Mao, M. Z., Ranzato, M’A., Senior, A., Tucker, P., Yang, K., & Ng, A. Y. (2012). Large Scale Distributed Deep Networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012).
3. Hu, C.-H., Chen, Z., & Larsson, E. G. (2024). Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach. arXiv preprint arXiv:2405.12046.
4. Chahal, K., Grover, M. S., Dey, K., & Shah, R. R. (2018). A Hitchhiker’s Guide On Distributed Training of Deep Neural Networks. arXiv preprint arXiv:1810.11787.
5. Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., & Liu, J. (2017). Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent. In Advances in Neural Information Processing Systems 30 (pp. 5330–5340).
6. Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2019). A Survey on Distributed Machine Learning. arXiv preprint arXiv:1912.09789.
7. Mungoli, N. (2023). Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency. arXiv preprint arXiv:2304.13738.
8. Tang, Y. (2024). Distributed Machine Learning Patterns. Manning Publications. ISBN: 9781617299025.
9. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., … & Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32 (NeurIPS 2019).
10. TensorFlow. (2024). Distributed training with TensorFlow. Retrieved from TensorFlow Core Guide.
11. Li, S. (2023). Distributed training overview. Retrieved from PyTorch Tutorials.