How to Split Model Between 2 Gpus With Keras In Tensorflow?

To split a model between two GPUs with Keras in TensorFlow, you can use the tf.distribute.MirroredStrategy for multi-GPU training. This strategy allows you to distribute the computation load of the model across multiple GPUs. First, you need to create an instance of the MirroredStrategy and then wrap the model and optimizer within the strategy.scope() context manager. This will automatically handle the distribution of computation between the two GPUs. By doing this, you can effectively utilize the resources of both GPUs and speed up the training process.

What is the limitation of using multiple GPUs for training?

One limitation of using multiple GPUs for training is the potential for diminishing returns in performance scaling as more GPUs are added. This is due to factors such as communication overhead between GPUs, memory bandwidth limitations, and bottlenecks in the system architecture. Additionally, not all neural network models or algorithms can effectively utilize multiple GPUs, which can result in inefficient use of resources. Lastly, the cost of purchasing and maintaining multiple GPUs can be prohibitive for some organizations.

What is the best practice for distributing computation across GPUs?

The best practice for distributing computation across GPUs typically involves utilizing parallel computing techniques such as data parallelism or model parallelism. Some key strategies for efficiently distributing computation across GPUs include:

1. Data parallelism: Splitting the input data into smaller batches and processing them simultaneously on different GPUs. This approach is commonly used in deep learning tasks where each GPU works on a different batch of input data and then aggregates the results.
2. Model parallelism: Splitting the model across multiple GPUs so that each GPU is responsible for processing a different portion of the model. This approach is useful for large models that do not fit into a single GPU's memory.
3. Asynchronous computing: Allowing GPUs to perform computation independently of each other and asynchronously communicate their results. This can help to reduce idle time and improve overall throughput.
4. Optimizing communication: Minimizing the amount of data exchanged between GPUs and using efficient communication protocols can help reduce bottlenecks and improve performance.
5. Load balancing: Ensuring that work is evenly distributed across GPUs by monitoring resource usage and adjusting the workload allocation as needed.
6. Profiling and tuning: Regularly profiling the performance of the distributed computation across GPUs and identifying opportunities for optimization. Tuning parameters such as batch size, learning rate, and model architecture can also help improve performance.

Overall, the key to effectively distributing computation across GPUs is to carefully design the parallelization strategy based on the specific task and hardware configuration, and continuously optimize and tune the system to achieve maximum performance.

How to handle communication between GPUs during training?

Communication between GPUs during training is typically handled automatically by deep learning frameworks such as TensorFlow and PyTorch. These frameworks have built-in support for distributed training, which allows multiple GPUs to work together on a single training task.

When using distributed training, the framework will automatically divide the data batch into smaller chunks and distribute them to different GPUs for processing. The GPUs then compute the gradients independently and communicate with each other to update the weights of the neural network.

However, there are some best practices to consider when handling communication between GPUs during training:

1. Use data parallelism: Divide the training data into smaller batches and distribute them to different GPUs using data parallelism. This allows each GPU to process a different portion of the data and speed up training.
2. Synchronize gradients: After each iteration, the gradients computed by each GPU need to be synchronized to update the weights of the neural network. This can be done using techniques such as synchronous updates or parameter server architectures.
3. Monitor and tune communication overhead: Communication between GPUs can introduce overhead, which can impact the overall training performance. It's important to monitor the communication overhead and tune the training process accordingly to minimize delays.
4. Consider using mixed precision training: Mixed precision training can reduce the memory usage and improve the training speed by using floating-point arithmetic with reduced precision. This can also help reduce the communication overhead between GPUs.

By following these best practices and leveraging the built-in distributed training support in deep learning frameworks, you can effectively handle communication between GPUs during training and optimize the performance of your deep learning models.