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:
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DeepSpeed Inference (deepspeed.init_inference): Distributes model across GPUs.
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Automatic Kernel Injection (replace_with_kernel_inject=True): Optimizes performance.
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Half-Precision Inference (dtype=torch.float16): Reduces memory usage.
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CUDA Execution (.to('cuda')): Enables GPU acceleration.
Hence, DeepSpeed enables efficient multi-GPU inference for foundation models, optimizing speed and memory usage.