How to do Multi-GPU inference

[redagavin]

Hi, How can I do multi-gpu inference with paddleocr? I cannot find instructions about this in documentations or github.

[GreatV]

To perform multi-GPU inference with PaddleOCR, it requires some additional configurations, as PaddleOCR does not provide direct multi-GPU inference support out-of-the-box. While there is no explicit instruction in the official documentation or GitHub, you can achieve this using PaddlePaddle's distributed computing capabilities. Here's a detailed guide:

1. Understanding PaddleOCR's GPU Usage

By default, PaddleOCR uses a single GPU for inference. You can specify which GPU to use by setting the CUDA_VISIBLE_DEVICES environment variable or through PaddlePaddle's runtime configuration.

2. Enabling Multi-GPU Inference

Multi-GPU inference is typically implemented by distributing the workload (e.g., batches of images) across multiple GPUs. Here’s how you can set it up:

a. Divide the Input Data

Split your input data (e.g., images) into smaller batches, each of which can be processed by a different GPU.

b. Assign GPUs

Assign each batch to a specific GPU using CUDA_VISIBLE_DEVICES or PaddlePaddle's device placement APIs. For example:

import paddle

# Set the device for each process
paddle.set_device('gpu:0')  # For GPU 0
# Similarly, you can set 'gpu:1', 'gpu:2', etc. for other GPUs

c. Parallelize Processing

Use Python's multiprocessing or threading modules to run inference on multiple GPUs in parallel. Each process/thread should handle one batch of data on a specific GPU.

Example:

import paddle
import threading

def run_inference_on_gpu(gpu_id, data_batch):
    paddle.set_device(f'gpu:{gpu_id}')
    # Load your PaddleOCR model here
    # Perform inference on data_batch
    pass

data_batches = [...]  # Split your data into batches
threads = []
for i, batch in enumerate(data_batches):
    t = threading.Thread(target=run_inference_on_gpu, args=(i, batch))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

d. Aggregate Results

Once the inference on all GPUs is complete, combine the results into a single output.

3. Consider PaddlePaddle Distributed APIs

PaddlePaddle provides APIs for distributed training and inference, such as paddle.distributed. Though primarily designed for training, these APIs can also be adapted for inference in advanced use cases. Refer to the PaddlePaddle distributed training documentation for more insights.

4. Relevant Discussions

While the linked discussion (#13862) focuses on specifying a single GPU and adjusting batch size, the approach can be extended to handle multi-GPU setups by splitting the workload as described above.

If you need further clarification or examples, feel free to ask!

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Response generated by feifei-bot | chatgpt-4o-latest