Multigpu precompute dataset map function and share between processes
Here’ codes that precompute data usingmain_process_first()which use 1st gpu to caculate all precomputation and shares results across  all processes. However, using one gpu to precompute on big data is slow, can we use all gpu multi-processing-ly and finally merge data in one piece which shares  across all processes.github.comhuggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning script for Stable Diffusion XL for text2image."""

import argparse
import functools
import gcThis file has been truncated.show originalwith accelerator.main_process_first():
        from datasets.fingerprint import Hasher

        # fingerprint used by the cache for the other processes to load the result
        # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
        new_fingerprint = Hasher.hash(args)
        new_fingerprint_for_vae = Hasher.hash(vae_path)
        train_dataset_with_embeddings = train_dataset.map(
            compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint