Implement a multi-GPU inference pipeline for a foundation model using DeepSpeed or TensorParallel

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With the help of proper code implementation, can you tell me how to implement a multi-GPU inference pipeline for a foundation model using DeepSpeed or TensorParallel?
Apr 1 in Generative AI by Ashutosh
• 27,850 points
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1 answer to this question.

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You can implement a multi-GPU inference pipeline for a foundation model using DeepSpeed or TensorParallel by partitioning the model across multiple GPUs for efficient parallel execution.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

  • DeepSpeed Inference (deepspeed.init_inference): Distributes model across GPUs.

  • Automatic Kernel Injection (replace_with_kernel_inject=True): Optimizes performance.

  • Half-Precision Inference (dtype=torch.float16): Reduces memory usage.

  • CUDA Execution (.to('cuda')): Enables GPU acceleration.

Hence, DeepSpeed enables efficient multi-GPU inference for foundation models, optimizing speed and memory usage.

Related Post: model training across multiple GPUs

answered Apr 1 by safak

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