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e479c46 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from __future__ import annotations
import logging
import os
import pathlib
import shutil
import pytest
from test_support.readme import extract_code_blocks, find_block, replace_once, run_bash_blocks
from test_support.runtime import TEST_CACHE_PATH, get_root, timed
logger = logging.getLogger(__name__)
REPO_ROOT = get_root()
TRAINING_STEPS = 2
README = REPO_ROOT / "examples/SO100/README.md"
DATASET_ROOT = REPO_ROOT / "examples/SO100/finish_sandwich_lerobot"
DATASET_PATH = DATASET_ROOT / "izuluaga/finish_sandwich"
MODALITY_SRC = REPO_ROOT / "examples/SO100/modality.json"
MODALITY_DST = DATASET_PATH / "meta/modality.json"
MODEL_CHECKPOINT = pathlib.Path(f"/tmp/so100_finetune/checkpoint-{TRAINING_STEPS}")
SHARED_DATASET_ROOT = TEST_CACHE_PATH / "datasets/so100_finish_sandwich"
def _dataset_ready(dataset_root: pathlib.Path) -> bool:
"""Return True when the converted SO100 dataset is present and non-empty."""
inner = dataset_root / "izuluaga/finish_sandwich"
info = inner / "meta/info.json"
videos = inner / "videos"
if not info.is_file() or not videos.is_dir():
return False
return next(videos.rglob("*.mp4"), None) is not None
def _point_to_shared() -> None:
"""Symlink DATASET_ROOT → SHARED_DATASET_ROOT."""
if DATASET_ROOT.is_symlink():
if DATASET_ROOT.resolve() == SHARED_DATASET_ROOT.resolve():
return
DATASET_ROOT.unlink()
elif DATASET_ROOT.exists():
return # real local dataset — don't replace it
DATASET_ROOT.parent.mkdir(parents=True, exist_ok=True)
DATASET_ROOT.symlink_to(SHARED_DATASET_ROOT, target_is_directory=True)
def _prepare_so100_dataset(convert_block: str, convert_env: dict) -> None:
"""Download + convert the SO100 dataset, preferring shared cache when available."""
if _dataset_ready(SHARED_DATASET_ROOT):
_point_to_shared()
return
# Direct convert output to shared storage when the cache mount is present.
convert_code = convert_block
if TEST_CACHE_PATH.exists():
convert_code = convert_code.replace(
"examples/SO100/finish_sandwich_lerobot",
str(SHARED_DATASET_ROOT),
)
run_bash_blocks([convert_code], cwd=REPO_ROOT, env=convert_env)
if _dataset_ready(SHARED_DATASET_ROOT):
_point_to_shared()
return
assert _dataset_ready(DATASET_ROOT), f"Expected SO100 dataset at {DATASET_ROOT}"
def _cleanup_dataset_path() -> None:
"""Remove the dataset directory created by the SO100 workflow."""
try:
if DATASET_ROOT.is_symlink():
DATASET_ROOT.unlink()
elif DATASET_ROOT.exists():
shutil.rmtree(DATASET_ROOT)
except OSError as exc:
print(f"[so100] cleanup_warning path={DATASET_PATH} error={exc}", flush=True)
@pytest.mark.gpu
@pytest.mark.timeout(1800)
def test_so100_readme_workflow_executes_via_subprocess() -> None:
"""Run the README's bash commands in order, with minor test-only substitutions."""
env = {**os.environ, "GIT_LFS_SKIP_SMUDGE": "1"}
print(f"[so100] uv_env={env.get('UV_PROJECT_ENVIRONMENT', '<unset>')}", flush=True)
blocks = extract_code_blocks(README)
try:
# Step 1: Convert dataset (README: Handling the dataset)
# The lerobot_conversion sub-project has its own dependencies (different
# numpy/pyarrow versions). Remove UV_PROJECT_ENVIRONMENT so uv creates
# an isolated venv for it instead of contaminating the main one.
convert_env = {k: v for k, v in env.items() if k != "UV_PROJECT_ENVIRONMENT"}
with timed("step 1: dataset conversion"):
_prepare_so100_dataset(
find_block(blocks, "convert_v3_to_v2.py", language="bash").code,
convert_env,
)
# Step 2: Copy modality.json (README cp command)
MODALITY_DST.parent.mkdir(parents=True, exist_ok=True)
with timed("step 2: modality.json copy"):
run_bash_blocks(
[find_block(blocks, "modality.json", language="bash")],
cwd=REPO_ROOT,
env=env,
)
assert MODALITY_DST.is_file(), f"Expected modality file after copy: {MODALITY_DST}"
# Step 3: Finetune (README: Finetuning) — env overrides keep the run short
finetune_code = (
find_block(
blocks,
"--modality-config-path examples/SO100/so100_config.py",
language="bash",
).code.rstrip()
+ " -- --skip_weight_loading"
)
with timed("step 3: finetune"):
run_bash_blocks(
[finetune_code],
cwd=REPO_ROOT,
env={
**env,
"SAVE_STEPS": str(TRAINING_STEPS),
"MAX_STEPS": str(TRAINING_STEPS),
"USE_WANDB": "0",
"DATALOADER_NUM_WORKERS": "0",
"GLOBAL_BATCH_SIZE": "2",
"SHARD_SIZE": "64",
"NUM_SHARDS_PER_EPOCH": "1",
"EPISODE_SAMPLING_RATE": "0.02",
},
)
assert MODEL_CHECKPOINT.exists(), (
f"Expected model checkpoint after finetune: {MODEL_CHECKPOINT}"
)
# Step 4: Open-loop eval — replace README defaults with test-specific values
eval_cmd = replace_once(
replace_once(
find_block(blocks, "open_loop_eval.py", language="bash").code,
"/tmp/so100_finetune/checkpoint-10000",
str(MODEL_CHECKPOINT),
),
"--steps 400",
"--steps 5",
)
with timed("step 4: open-loop eval"):
run_bash_blocks([eval_cmd], cwd=REPO_ROOT, env=env)
assert pathlib.Path("/tmp/open_loop_eval/traj_0.jpeg").exists(), (
"Expected eval plot at /tmp/open_loop_eval/traj_0.jpeg"
)
finally:
_cleanup_dataset_path()
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