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Distributed Training on Multiple GPUs

Distributed Training Gpu

AI models trained at scale by data scientists or machine learning enthusiasts will inevitably reach a limit. As dataset sizes grow, processing times can increase from minutes to hours to days to weeks. Data scientists use distributed training for machine learning models and multiple GPU to speed up the development of complete AI models in a shorter time.

We will go over why distributed training with multiple GPUs like the A100 is best for larger datasets, the advantages of GPUs over CPUs for machine learning, and how to train machine learning models correctly.

Why Are GPUs Good For Training Neural Networks?

The training stage is the most resource-intensive stage of creating a neural network or machine learning model. Data inputs are necessary for a neural network during its training phase. The model generates a pertinent prediction using layers of processed data based on adjustments made between datasets.

Following datasets calculate weights and parameters to train machine prediction accuracy. The initial round of input data essentially serves as a baseline for the machine learning model to understand.

Waiting a few minutes is reasonable for datasets that are small or simple. However, training times could grow to be hours, days, or even longer as the size or volume of input data increases.

Large amounts of data, such as repeated calculations on millions of floating-point numbers, are difficult for CPUs to handle. Operations like matrix multiplication and vector addition make up deep neural networks.

Switching distributed training with multiple GPUs is one way to accelerate this procedure. Depending on how many tensor cores are devoted to the training phase, GPUs for distributed training can move the process along more quickly than CPUs.

The original purpose of graphics processing units (GPUs) was to handle the repetitive calculations involved in extrapolating and positioning thousands of triangles for video game graphics. GPUs are perfect for the quick data flow required for neural network training through hundreds of epochs (or model iterations), which makes them ideal for deep learning training. They also have a large memory bandwidth and an innate ability to carry out millions of calculations.

Check out our blog post on applications for GPU-based AI and machine learning models for more information on why GPUs are better for machine and deep learning models.

What is Distributed Training In Machine Learning?

The training phase’s workload is divided among several processors during distributed training. Each mini-processor copies the machine learning model on a different batch of training data as the data is divided and analyzed in parallel. These mini-processors cooperate in expediting training without compromising the accuracy of the machine learning model.

Results are transmitted among processors (either when the batch is completed entirely or as each processor finishes its batch). Until the model achieves the desired result, the subsequent iteration or epoch starts with a slightly newly trained model.

Model and data parallelism are the two most popular methods for dividing training between mini-processors (in our case, GPUs).

Data Parallelism

Data parallelism divides the data into smaller portions and distributes them among all GPUs for evaluation using the same AI model. After completing a forward pass, the GPUs output a gradient or the model’s learned parameters. The gradients are compiled, averaged, and reduced to a single value to finally update the model parameters for the training of the next epoch because there are multiple gradients but only one AI model to train. Both synchronous and asynchronous methods are possible here.

Our GPU groups must wait for all other GPUs to finish calculating gradients before averaging and reducing the results to update the model parameters in synchronous data parallelism. The model can move on to the next epoch after updated parameters.

GPUs can train independently without coordinating a synchronized gradient calculation thanks to asynchronous data parallelism. Gradients are instead transmitted once they are finished back to the parameter server. Asynchronous because neither GPU waits for the other to finish processing nor performs gradient averaging. It is a little more expensive to use asynchronous data parallelism because it needs a separate parameter server for the model’s learning section.

The most computationally demanding step is calculating the gradients and averaging the training data at each step. GPUs have accelerated this step because the calculations are repetitive, resulting in quicker results. Although data parallelism is comparatively easy to use and cost-effective, there are times when the model is too big to fit on a single mini-processor.

Model Parallelism

Unlike splitting the data, model parallelism divides the workload to train the model (or the model) across the worker GPUs. To maximize GPU usage, segmenting the model assigns particular tasks to a single worker or multiple workers. Model parallelism can be compared to an AI assembly line that builds multi-layer networks to process large datasets that are impractical for data parallelism. Model parallelism requires a specialist to decide how to divide the model, but the result is better utilization and efficiency.

Is Multi-GPU Distributed Training Faster?

Even though purchasing multiple GPUs can be expensive, it is the fastest option. Additionally expensive and unable to scale like GPUs are CPUs. Distributed training of your machine learning models across multiple layers and GPUs improves training phase productivity and efficiency.

Naturally, this means less time spent training your models, but it also enables you to produce (and reproduce) results more quickly and address issues before they become major ones. The difference between weeks of training and hours or minutes of training in terms of yielding results for your effort (depending on the number of GPUs in use).

The next challenge you must overcome is how to begin using multiple GPUs for distributed training in your machine learning models.

How Do I Train With Multiple GPUs?

It’s crucial first to determine whether data parallelism or model parallelism will be required if you want to handle distributed training with multiple GPUs. Your dataset’s size and scope will determine this choice.

Can the entire model be run on each GPU using the dataset? Or will running different parts of the model on various GPUs using bigger datasets take less time? Data Parallelism is typically the default choice for distributed learning. Before diving deeper into model parallelism or asynchronous data parallelism, where a separate dedicated parameter server is required, start with synchronous data parallelism.

It is now possible to connect your GPUs in your distributed training process.

  • Depending on your decision regarding parallelism, divide your data. You could, for instance, divide the current data batch (the global batch) into eight sub-batches (local batches). With eight GPUs and 512 samples in the global batch, the eight local batches will each contain 64 samples.
  • The eight GPUs, or mini-processors, each independently run a local batch: forward pass, backward pass, output the gradient of the weights, etc.
    All eight mini-processors effectively blend weight changes from local gradients, keeping everything in sync and ensuring that the model has been adequately trained (when using synchronous data parallelism).
  • It’s crucial to remember that one GPU will need to host the data gathered and the training results from the other GPUs. If you are not paying close attention, you might experience the problem of one GPU running out of memory.

Except for this, the advantages of distributed training with multiple GPUs far outweigh the disadvantage. When you select the appropriate data parallelization for your model, each GPU reduces the time spent in the training stage, improves model effectiveness, and produces more high-end results.

More Information On Distributed Training

Technology-wise, neural networks are incredibly complex, and the training process can be challenging. Data science can change our world by utilizing and learning more about how to use additional hardware to produce more efficient models in less time. GPUs for distributed training are well worth the initial outlay since it takes weeks or months to create more powerful neural networks as opposed to years or decades.

Start working on distributed training and deep learning today, we urge you. Visit our blog for more posts on various topics, including distributed training, best GPUs for neural networks, and machine learning.

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