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

Question

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?

Answer 1

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.

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