Scaling Deep Learning: Strategies for Distributed Training

Deep learning has revolutionized the field of artificial intelligence, enabling the development of sophisticated models that can learn from vast amounts of data. While training deep learning models on a single machine with a single GPU can yield impressive results, there are scenarios where this approach falls short. When faced with large datasets or complex models that exceed the capabilities of a single machine, it becomes necessary to scale out. This article explores the various strategies for distributed training, focusing on how to efficiently distribute workloads across multiple GPUs or machines.

Understanding the Need for Scaling Out

In many deep learning applications, a single GPU can handle training efficiently. However, as datasets grow larger or models become more complex, the limitations of a single machine become apparent. Scaling out involves adding more GPUs to a system or utilizing multiple machines within a cluster. This necessitates a robust strategy for distributing training tasks effectively.

The Challenge of Distributed Training

Distributing training across multiple devices is not a straightforward task. It involves careful consideration of the specific use case, data characteristics, and model architecture. The choice of distribution strategy can significantly impact performance, speed, and resource utilization. In this article, we will outline the primary strategies for distributed training, providing insights into their implementation using TensorFlow, while also noting that many concepts apply across different deep learning frameworks.

Data and Model Parallelism

The two primary approaches to distributed training are data parallelism and model parallelism.

Data Parallelism

In data parallelism, the dataset is divided into smaller batches, which are then distributed across multiple GPUs or machines. Each device processes its assigned batch independently, performing forward and backward passes to compute gradients. The gradients are then aggregated, typically using an all-reduce algorithm, to update the model weights.

This method is widely adopted, accounting for approximately 95% of all distributed training scenarios. Its advantages include:

• Universality: Applicable to various models and cluster configurations.
• Fast Compilation: Optimized for specific clusters, leading to quicker setup times.
• Full Hardware Utilization: Ensures that all available resources are effectively used.

However, in cases where the model is too large to fit into a single machine’s memory, model parallelism may be a more suitable option.

Model Parallelism

Model parallelism involves splitting a model into different segments, each of which is trained on a separate machine or GPU. This approach is particularly useful for large models, such as modern natural language processing architectures like GPT-2 and GPT-3, which contain billions of parameters.

Training in a Single Machine

Before delving deeper into distributed strategies, it’s essential to understand how training occurs on a single machine. Consider a simple neural network with two layers and three nodes per layer. The training process involves:

1. Data Preprocessing: Preparing the input data for the model.
2. Forward Pass: Feeding the data into the network to generate predictions.
3. Loss Calculation: Comparing predictions with actual labels to compute loss.
4. Backward Pass: Calculating gradients and updating weights based on the loss.

In a single-GPU scenario, a CPU with multiple cores can handle training efficiently, especially when leveraging multithreading. As we scale up to multiple GPUs or machines, the complexity of the training process increases, necessitating a clear understanding of distributed training strategies.

Distributed Training Strategies

Distributed training strategies can be broadly categorized into two types: synchronous and asynchronous.

Synchronous Training

In synchronous training, all workers or accelerators process different slices of input data simultaneously. After computing gradients, they communicate to aggregate these gradients before updating the model weights. This ensures that all devices maintain identical weights at each training step.

TensorFlow provides two primary strategies for synchronous training:

1. Mirrored Strategy: This strategy is designed for multiple GPUs within a single machine. Each variable in the model is mirrored across all replicas, ensuring synchronized updates.

   mirrored_strategy = tf.distribute.MirroredStrategy(devices=["/gpu:0", "/gpu:1"])
   with mirrored_strategy.scope():
      model = tf.keras.Model(inputs=inputs, outputs=x)
      model.compile(...)
      model.fit(...)

2. MultiWorker Mirrored Strategy: This strategy extends the mirrored approach to multiple machines, creating copies of all variables across all workers.

   multi_worker_mirrored_strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
   os.environ["TF_CONFIG"] = json.dumps({
      "cluster": {
          "worker": ["host1:port", "host2:port", "host3:port"]
      },
      "task": {
          "type": "worker",
          "index": 1
      }
   })
   with multi_worker_mirrored_strategy.scope():
      model = tf.keras.Model(inputs=inputs, outputs=x)
      model.compile(...)
      model.fit(...)

Asynchronous Training

Asynchronous training allows workers to operate independently, without waiting for others to complete their tasks. This approach can be beneficial in scenarios where workers have varying capabilities or are subject to downtime.

One common technique for asynchronous training is the Parameter Server Strategy, where some devices act as parameter servers, holding the model parameters and updating them based on gradients sent from training workers.

ps_strategy = tf.distribute.experimental.ParameterServerStrategy()
os.environ["TF_CONFIG"] = json.dumps({
    "cluster": {
        "worker": ["host1:port", "host2:port", "host3:port"],
        "ps": ["host4:port", "host5:port"]
    },
    "task": {
        "type": "worker",
        "index": 1
    }
})

Conclusion

In this article, we explored the various strategies for distributed training in deep learning, focusing on data and model parallelism. We discussed the importance of selecting the right strategy based on the specific requirements of your application, data, and model architecture.

With a solid understanding of these concepts, you are now better equipped to tackle the challenges of scaling deep learning training. As we move forward in this series, we will delve into deploying trained models, serving them to users, and scaling applications in the cloud.

Stay tuned for upcoming articles where we will cover topics such as API development using Flask, containerization with Docker, and deploying applications using Kubernetes. If you’re interested in these topics, consider subscribing to our newsletter to stay updated on our latest content.

To be continued…