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简介

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按 episode 把一份 LeRobot v3.0 数据集切成两份互不重叠的独立数据集（默认 train + val），源数据集不动。包 lerobot datasets/dataset_tools.py:144 split_dataset，外面加 crash-safe 与 skip-if-done。

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功能描述

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算子参数

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输入

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输入含义
dataset_path源 v3.0 数据集根目录（只读）
output_path两份 split 的父目录，split 落在 <output_path>/<split_name>
train_episode_indices第一份 split 的显式 episode 索引列表（list[int]）；为 null 时改用构造参数 train_fraction
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输出

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输出含义
result.statusSUCCESS / SKIPPED: output splits already exist / Failed: dataset_path and output_path are required / Failed: [<dataset_path>] <异常类型>: <信息>
result.splitslist，每份 split 一条：name = split 名，path = 该 split 的数据集根目录
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参数

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参数名称类型默认值描述
train_fractionfloat0.8第一份 split 占的 episode 比例（按索引顺序）；行里给了 train_episode_indices 时忽略
split_namestuple[str, str] 或 list[str](\"train\", \"val\")两份 split 的名字，必须正好两个，否则构造算子时抛 ValueError
forceboolFalseTrue 表示两份 split 都已是 v3.0 也重新生成
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注意事项

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调用示例

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\n Python\n \n
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1from __future__ import annotations\n2\n3import os\n4\n5import daft\n6from daft import col\n7\n8from daft.aihc.common.udf import aihc_udf\n9from daft.aihc.functions.embodied.lerobot.split_dataset_episodes_udf import (\n10    SplitDatasetEpisodes,\n11)\n12\n13if __name__ == \"__main__\":\n14    if os.getenv(\"DAFT_RUNNER\", \"native\") == \"ray\":\n15        import ray\n16        ray.init(dashboard_host=\"0.0.0.0\", ignore_reinit_error=True)\n17        daft.set_runner_ray()\n18    daft.set_execution_config(actor_udf_ready_timeout=6000, min_cpu_per_task=0)\n19\n20# 夹具可用 test/_helpers/lerobot_edit_fixture.py:build_tabular_dataset 生成\n21    # train_episode_indices 给 [0, 2] 即切到索引模式，train_fraction 失效\n22    samples = {\n23        \"dataset_path\": [\"/path/to/lerobot_dataset\"],\n24        \"output_path\": [\"/tmp/lerobot_split_out\"],\n25        \"train_episode_indices\": [None],\n26    }\n27    ds = daft.from_pydict(samples)\n28    ds = ds.with_column(\n29        \"result\",\n30        aihc_udf(\n31            SplitDatasetEpisodes,\n32            construct_args={\n33                \"train_fraction\": 0.5,\n34                \"split_names\": (\"train\", \"val\"),\n35                \"force\": False,\n36            },\n37            num_cpus=1,\n38            concurrency=1,\n39            batch_size=1,\n40        )(col(\"dataset_path\"), col(\"output_path\"), col(\"train_episode_indices\")),\n41    )\n42    ds.show()
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