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

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To split a model between two GPUs using Keras in TensorFlow, you can use the tf.distribute.Strategy API. This API allows you to distribute the computation of your model across multiple devices, such as GPUs.


First, you need to create a MirroredStrategy object which represents the synchronization strategy for distributing a model across multiple devices. Then, you can use this strategy to define and compile your model. When defining the model, make sure to wrap the model creation inside the strategy.scope() context manager to ensure that variables are created on the correct device.


Once you have defined and compiled your model using the strategy, you can fit the model to your data just like you normally would with the model.fit() method. The strategy will automatically distribute the computation across the two GPUs.


In summary, to split a model between two GPUs with Keras in TensorFlow, you need to use the tf.distribute.Strategy API to define and compile your model, and then fit the model to your data using the strategy for automatic distribution of computation across the two GPUs.

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How to distribute the computational load evenly between 2 GPUs when splitting a model?

  1. Use a data parallel approach: In this approach, you can split the dataset into two equal parts and assign each part to a different GPU. This way, both GPUs will be processing different parts of the dataset simultaneously, thereby distributing the computational load evenly between them.
  2. Split the model architecture: You can also split the layers of your model between the two GPUs. For example, you can assign the first half of the layers to one GPU and the second half to the other GPU. This way, both GPUs will be responsible for processing different parts of the model, helping distribute the computational load evenly.
  3. Utilize model parallelism: In this approach, different parts of the model are computed on different GPUs. This requires more careful planning and implementation, as the communication between GPUs needs to be managed effectively. However, this approach can help distribute the computational load more evenly between the GPUs compared to other methods.
  4. Fine-tune the distribution based on GPU capabilities: Depending on the capabilities of the two GPUs (such as memory size, computation power, etc.), you may need to fine-tune the distribution of the workload to ensure that both GPUs are being utilized optimally. Experiment with different configurations to find the best setup for your specific hardware and model.
  5. Monitor and optimize performance: Once you have distributed the computational load between the two GPUs, monitor the performance of your model to ensure that both GPUs are being utilized efficiently. You may need to adjust the workload distribution or experiment with different techniques to optimize performance and ensure that the computational load is evenly distributed between the GPUs.


What are the considerations for splitting a large model between 2 GPUs?

  1. Memory capacity: Each GPU should have enough memory capacity to store the portion of the model it will be responsible for. If the model is too large for either GPU to handle, splitting may not be the best approach.
  2. Communication overhead: Splitting a model between GPUs may introduce communication overhead as the GPUs need to communicate with each other to exchange information. This can impact the speed and efficiency of training.
  3. Load balancing: It is important to evenly distribute the workload between the two GPUs to ensure optimal performance. Uneven workload distribution can lead to one GPU being underutilized while the other is overloaded.
  4. Model architecture: Some models may be inherently difficult to split between GPUs due to dependencies or interconnections between different parts of the model. It is important to consider the structure of the model when deciding how to split it.
  5. Training strategy: Different training strategies, such as data parallelism or model parallelism, may be more suitable for splitting a model between GPUs. The chosen strategy will impact how the model is split and how the GPUs communicate with each other.
  6. Hardware compatibility: Not all GPUs are compatible with splitting a model, especially if they have different architectures or capabilities. It is important to ensure that the GPUs being used are compatible with each other for splitting the model.
  7. Software support: Some deep learning frameworks and libraries may have built-in support for splitting a model between GPUs, while others may require custom implementation. It is important to consider the software tools available for splitting the model and their compatibility with the GPUs being used.


What are the common errors encountered when splitting a model between 2 GPUs in TensorFlow?

Some common errors encountered when splitting a model between 2 GPUs in TensorFlow include:

  1. Incorrect device placement: TensorFlow may not automatically place operations on the appropriate GPU, leading to errors such as "InvalidArgumentError: Cannot assign a device for operation 'x': Could not satisfy explicit device specification '/GPU:0' because no devices matching that specification are registered in this process."
  2. Incompatible data sizes: If the data sizes are not properly split between the two GPUs, it can cause errors like "InvalidArgumentError: Incompatible shapes" or "InvalidArgumentError: Dimensions must be equal, but are 64 and 32 for 'add' (op: 'Add') with input shapes: [64,10], [32,10]."
  3. Undefined tensors: TensorFlow may not be able to properly synchronize the operations on the two GPUs, leading to errors like "InvalidArgumentError: You must feed a value for placeholder tensor 'input' with dtype float and shape [batch_size, input_size]."
  4. Insufficient memory: If the memory usage on one GPU exceeds its capacity, it can cause errors like "ResourceExhaustedError: OOM" or "InternalError: Dst tensor is not initialized."
  5. Lack of proper synchronization: Operations that require synchronization across GPUs, such as gradient updates in a distributed training scenario, may not be properly implemented, leading to errors like "DataLossError: Requested 4 bytes, got 0."


What impact does splitting a model between 2 GPUs have on memory usage?

Splitting a model between 2 GPUs can have both positive and negative impacts on memory usage.


Positive impact: By splitting a model between 2 GPUs, the total memory capacity available for processing the model is effectively doubled. This can be particularly advantageous for training large or complex models that may not fit into the memory of a single GPU. Additionally, splitting a model between 2 GPUs can potentially reduce the time required for training the model, as each GPU can work on a separate portion of the model simultaneously.


Negative impact: Splitting a model between 2 GPUs also means that the memory usage of the model will be divided between the two GPUs. This can lead to potential inefficiencies or communication overhead between the GPUs, which may impact the overall performance of the model. Additionally, splitting a model between 2 GPUs may require additional programming and optimization efforts to ensure that the model is effectively distributed and the GPUs are properly synchronized.

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