Tuning leads to All-NaN axis encountered after long time #1032
Description
Describe the bug
After running a bunch of tunings, I eventually get the following error:
Search: Running Trial #68
Value |Best Value So Far |Hyperparameter
4 |1 |num_layers
32 |96 |lstm_units_0
64 |64 |dense_units
1e-05 |3.9608e-05 |learning_rate
32 |32 |lstm_units_1
32 |128 |lstm_units_2
96 |32 |lstm_units_3
Epoch 1/100
I0000 00:00:1734570907.031189 1848349 service.cc:146] XLA service 0x76d5d4004ae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1734570907.031210 1848349 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060, Compute Capability 8.6
2024-12-19 12:15:07.104318: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-12-19 12:15:07.423862: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 90300
I0000 00:00:1734570908.886075 1848349 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 46s 33ms/step - loss: 187201.1562 - mae: 192.5935 - val_loss: 64136.8945 - val_mae: 117.8278
Epoch 2/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 40s 31ms/step - loss: 53528.4414 - mae: 98.4317 - val_loss: 30422.4727 - val_mae: 68.2864
Epoch 3/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 21780.5391 - mae: 56.0624 - val_loss: 12904.9023 - val_mae: 43.0659
Epoch 4/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 10389.4004 - mae: 40.3216 - val_loss: 7285.5630 - val_mae: 36.6022
Epoch 5/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 7087.9570 - mae: 35.0648 - val_loss: 6534.5283 - val_mae: 34.3760
Epoch 6/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 5333.1699 - mae: 31.5071 - val_loss: 4140.5713 - val_mae: 28.6819
Epoch 7/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 31ms/step - loss: 4137.9907 - mae: 28.6213 - val_loss: 2916.1416 - val_mae: 23.9534
Epoch 8/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 2356.5020 - mae: 22.2533 - val_loss: 1953.6709 - val_mae: 20.1413
Epoch 9/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 1712.8751 - mae: 19.4396 - val_loss: 1503.3846 - val_mae: 18.8406
Epoch 10/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 1390.0425 - mae: 17.8179 - val_loss: 1104.1555 - val_mae: 16.9162
Epoch 11/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 31ms/step - loss: 1076.9076 - mae: 16.1942 - val_loss: 922.5842 - val_mae: 15.8216
Epoch 12/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 1164.0901 - mae: 16.3073 - val_loss: 1144.8092 - val_mae: 16.7875
Epoch 13/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 1166.4641 - mae: 16.5099 - val_loss: 994.0040 - val_mae: 15.5358
Epoch 14/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 31ms/step - loss: 972.1603 - mae: 15.1948 - val_loss: 1331.9351 - val_mae: 17.0246
Epoch 15/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 30ms/step - loss: 1171.1146 - mae: 15.8957 - val_loss: 1663.4587 - val_mae: 19.4891
Epoch 16/100
1275/1275 ━━━━━━━━━━━━━━━━━━━━ 39s 31ms/step - loss: 1868.6052 - mae: 20.3558 - val_loss: 2075.2771 - val_mae: 23.2808
/home/ghostdog/mambaforge/lib/python3.10/site-packages/keras_tuner/src/engine/metrics_tracking.py:111: RuntimeWarning: All-NaN axis encountered
np.nanmin(values) if self.direction == "min" else np.nanmax(values)
Traceback (most recent call last):
File "/home/ghostdog/hypertuner.py", line 103, in <module>
tuner.search(train_dataset, epochs=100, validation_data=val_dataset, callbacks=[early_stopping])
File "/home/ghostdog/mambaforge/lib/python3.10/site-packages/keras_tuner/src/engine/base_tuner.py", line 235, in search
self.on_trial_end(trial)
File "/home/ghostdog/mambaforge/lib/python3.10/site-packages/keras_tuner/src/engine/base_tuner.py", line 339, in on_trial_end
self.oracle.end_trial(trial)
File "/home/ghostdog/mambaforge/lib/python3.10/site-packages/keras_tuner/src/engine/oracle.py", line 108, in wrapped_func
ret_val = func(*args, **kwargs)
File "/home/ghostdog/mambaforge/lib/python3.10/site-packages/keras_tuner/src/engine/oracle.py", line 588, in end_trial
self._check_consecutive_failures()
File "/home/ghostdog/mambaforge/lib/python3.10/site-packages/keras_tuner/src/engine/oracle.py", line 545, in _check_consecutive_failures
raise RuntimeError(
RuntimeError: Number of consecutive failures exceeded the limit of 3.
Even if i restart the application to resume from my last checkpoint, it leads to this.
To Reproduce
My relevent code is as such:
# Define the model-building function for KerasTuner
def build_model(hp):
model = Sequential()
# Hyperparameter tuning for the number of LSTM layers and units
for i in range(hp.Int('num_layers', 1, 4)):
model.add(LSTM(units=hp.Int(f'lstm_units_{i}', min_value=32, max_value=128, step=32),
activation='relu', return_sequences=True if i < hp.Int('num_layers', 1, 4) - 1 else False))
# Dense layer
model.add(Dense(units=hp.Int('dense_units', min_value=32, max_value=128, step=32), activation='relu'))
# Hyperparameter tuning for output size (e.g., prediction horizon)
output_size = 4 # You can set this dynamically if needed
model.add(Dense(output_size)) # Output size should be set here
# Compile the model
model.compile(optimizer=Adam(learning_rate=hp.Float('learning_rate', min_value=1e-5, max_value=1e-2, sampling='LOG')),
loss='mean_squared_error', metrics=['mae'])
return model
# Initialize the tuner
tuner = kt.BayesianOptimization(build_model,
objective='val_loss',
max_trials=1000,
directory='kerastuner_results',
project_name='prediction',
)
# Define early stopping callback
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
# Create tf.data.Dataset for training and validation
batch_size = 128 # Adjust batch size as needed, may lead to less stable searches... defaults to 32
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
val_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
# Perform hyperparameter search
tuner.search(train_dataset, epochs=100, validation_data=val_dataset, callbacks=[early_stopping])Expected behavior
The tuner continues to tune over time without crashing.
Additional context
My Last successful trials trial.json (67):
{"trial_id": "0067", "hyperparameters": {"space": [{"class_name": "Int", "config": {"name": "num_layers", "default": null, "conditions": [], "min_value": 1, "max_value": 4, "step": 1, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "lstm_units_0", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "dense_units", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Float", "config": {"name": "learning_rate", "default": 1e-05, "conditions": [], "min_value": 1e-05, "max_value": 0.01, "step": null, "sampling": "log"}}, {"class_name": "Int", "config": {"name": "lstm_units_1", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "lstm_units_2", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "lstm_units_3", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}], "values": {"num_layers": 4, "lstm_units_0": 32, "dense_units": 64, "learning_rate": 1e-05, "lstm_units_1": 32, "lstm_units_2": 32, "lstm_units_3": 96}}, "metrics": {"metrics": {"loss": {"direction": "min", "observations": [{"value": [NaN], "step": 0}]}, "mae": {"direction": "min", "observations": [{"value": [NaN], "step": 0}]}, "val_loss": {"direction": "min", "observations": [{"value": [NaN], "step": 0}]}, "val_mae": {"direction": "min", "observations": [{"value": [NaN], "step": 0}]}}}, "score": null, "best_step": 0, "status": "RUNNING", "message": null}And a trial from the middle of the training, just in case:
{"trial_id": "0020", "hyperparameters": {"space": [{"class_name": "Int", "config": {"name": "num_layers", "default": null, "conditions": [], "min_value": 1, "max_value": 4, "step": 1, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "lstm_units_0", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "dense_units", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Float", "config": {"name": "learning_rate", "default": 1e-05, "conditions": [], "min_value": 1e-05, "max_value": 0.01, "step": null, "sampling": "log"}}, {"class_name": "Int", "config": {"name": "lstm_units_1", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "lstm_units_2", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}, {"class_name": "Int", "config": {"name": "lstm_units_3", "default": null, "conditions": [], "min_value": 32, "max_value": 128, "step": 32, "sampling": "linear"}}], "values": {"num_layers": 2, "lstm_units_0": 64, "dense_units": 96, "learning_rate": 0.0012899513615134586, "lstm_units_1": 32, "lstm_units_2": 128, "lstm_units_3": 32}}, "metrics": {"metrics": {"loss": {"direction": "min", "observations": [{"value": [0.04608340561389923], "step": 9}]}, "mae": {"direction": "min", "observations": [{"value": [0.1634569764137268], "step": 9}]}, "val_loss": {"direction": "min", "observations": [{"value": [0.027314456179738045], "step": 9}]}, "val_mae": {"direction": "min", "observations": [{"value": [0.12323067337274551], "step": 9}]}}}, "score": 0.027314456179738045, "best_step": 9, "status": "COMPLETED", "message": null}All of my installed packages and versions (click to expand)
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_kmp_llvm conda-forge
absl-py 2.1.0 pyhd8ed1ab_0 conda-forge
aiobotocore 1.2.2 pyhd3eb1b0_0 anaconda
aiohappyeyeballs 2.4.4 pypi_0 pypi
aiohttp 3.11.9 pypi_0 pypi
aioitertools 0.11.0 pyhd8ed1ab_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
alabaster 0.7.16 pyhd8ed1ab_0 conda-forge
alembic 1.14.0 pypi_0 pypi
alsa-lib 1.2.12 h4ab18f5_0 conda-forge
anaconda-anon-usage 0.4.4 py310hfc0e8ea_100
anaconda-client 1.12.3 pyhd8ed1ab_0 conda-forge
anaconda-cloud-auth 0.4.1 pyhd8ed1ab_0 conda-forge
anaconda-project 0.11.1 pyhd8ed1ab_0 conda-forge
androguard 4.1.0 pypi_0 pypi
annotated-types 0.6.0 pyhd8ed1ab_0 conda-forge
anthropic 0.40.0 pypi_0 pypi
antlr4-python3-runtime 4.9.3 pypi_0 pypi
anyio 4.3.0 pyhd8ed1ab_0 conda-forge
aom 3.8.2 h59595ed_0 conda-forge
apkinspector 1.2.1 pypi_0 pypi
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
archspec 0.2.3 pyhd8ed1ab_0 conda-forge
argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py310h2372a71_4 conda-forge
arrow 1.3.0 pyhd8ed1ab_0 conda-forge
asn1crypto 1.5.1 pypi_0 pypi
astor 0.8.1 pyh9f0ad1d_0 conda-forge
astroid 3.0.3 py310hff52083_0 conda-forge
astropy-iers-data 0.2024.3.18.0.29.47 pyhd8ed1ab_0 conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-lru 2.0.4 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge
atomicwrites 1.4.1 pyhd8ed1ab_0 conda-forge
atpublic 4.0 pypi_0 pypi
attr 2.5.1 h166bdaf_1 conda-forge
attrs 23.2.0 pyh71513ae_0 conda-forge
automat 22.10.0 pyhd8ed1ab_0 conda-forge
autopep8 2.0.4 pyhd8ed1ab_0 conda-forge
aws-c-auth 0.8.0 h56a2c13_4 conda-forge
aws-c-cal 0.8.0 hd3f4568_0 conda-forge
aws-c-common 0.9.31 hb9d3cd8_0 conda-forge
aws-c-compression 0.3.0 hf20e7d7_0 conda-forge
aws-c-event-stream 0.5.0 h68c3b0c_2 conda-forge
aws-c-http 0.9.0 hfad4ed3_3 conda-forge
aws-c-io 0.15.0 h17eb868_2 conda-forge
aws-c-mqtt 0.11.0 h407ecb8_2 conda-forge
aws-c-s3 0.7.0 hadeddc1_5 conda-forge
aws-c-sdkutils 0.2.0 hf20e7d7_0 conda-forge
aws-checksums 0.2.0 hf20e7d7_0 conda-forge
aws-crt-cpp 0.29.0 h73f0fd4_6 conda-forge
aws-sdk-cpp 1.11.407 h6a6dca0_6 conda-forge
azure-core-cpp 1.14.0 h5cfcd09_0 conda-forge
azure-identity-cpp 1.10.0 h113e628_0 conda-forge
azure-storage-blobs-cpp 12.13.0 h3cf044e_1 conda-forge
azure-storage-common-cpp 12.8.0 h736e048_1 conda-forge
azure-storage-files-datalake-cpp 12.12.0 ha633028_1 conda-forge
babel 2.14.0 pyhd8ed1ab_0 conda-forge
banal 1.0.6 pypi_0 pypi
base58 2.1.1 pyhd8ed1ab_0 conda-forge
bcrypt 4.1.2 py310hcb5633a_0 conda-forge
beautifulsoup4 4.12.3 pyha770c72_0 conda-forge
binaryornot 0.4.4 py_1 conda-forge
black 24.3.0 py310hff52083_0 conda-forge
blas 2.116 mkl conda-forge
blas-devel 3.9.0 16_linux64_mkl conda-forge
bleach 6.1.0 pyhd8ed1ab_0 conda-forge
blinker 1.7.0 pyhd8ed1ab_0 conda-forge
blosc 1.21.6 hef167b5_0 conda-forge
boltons 23.1.1 pyhd8ed1ab_0 conda-forge
boto3 1.34.68 pyhd8ed1ab_0 conda-forge
botocore 1.34.68 pyge310_1234567_0 conda-forge
brotli 1.1.0 hd590300_1 conda-forge
brotli-bin 1.1.0 hd590300_1 conda-forge
brotli-python 1.1.0 py310hc6cd4ac_1 conda-forge
brotlicffi 1.1.0.0 py310hc6cd4ac_1 conda-forge
brunsli 0.1 h9c3ff4c_0 conda-forge
build 1.2.1 pypi_0 pypi
bzip2 1.0.8 hd590300_5 conda-forge
c-ares 1.34.1 heb4867d_0 conda-forge
c-blosc2 2.13.2 hb4ffafa_0 conda-forge
ca-certificates 2024.12.14 hbcca054_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 5.3.3 pyhd8ed1ab_0 conda-forge
cairo 1.18.0 hebfffa5_3 conda-forge
certifi 2024.12.14 pyhd8ed1ab_0 conda-forge
cffi 1.16.0 py310h2fee648_0 conda-forge
chardet 5.2.0 py310hff52083_1 conda-forge
charls 2.4.2 h59595ed_0 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
chess 1.10.0 pypi_0 pypi
click 8.1.7 unix_pyh707e725_0 conda-forge
cloudpickle 3.0.0 pyhd8ed1ab_0 conda-forge
clyent 1.2.2 pyhd8ed1ab_2 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
colorcet 3.1.0 pyhd8ed1ab_0 conda-forge
colorlog 6.9.0 pypi_0 pypi
comm 0.2.2 pyhd8ed1ab_0 conda-forge
conda 24.11.1 py310hff52083_0 conda-forge
conda-content-trust 0.2.0 pyhd8ed1ab_0 conda-forge
conda-libmamba-solver 24.1.0 pyhd8ed1ab_0 conda-forge
conda-pack 0.7.1 pyhd8ed1ab_0 conda-forge
conda-package-handling 2.2.0 pyh38be061_0 conda-forge
conda-package-streaming 0.9.0 pyhd8ed1ab_0 conda-forge
conda-repo-cli 1.0.88 py310h06a4308_0
conda-token 0.4.0 pyhd3eb1b0_0 anaconda
configspace 1.2.1 pypi_0 pypi
constantly 15.1.0 py_0 conda-forge
contourpy 1.2.0 py310hd41b1e2_0 conda-forge
cookiecutter 2.6.0 pyhca7485f_0 conda-forge
cryptography 42.0.5 py310h75e40e8_0 conda-forge
cssselect 1.2.0 pyhd8ed1ab_0 conda-forge
cuda-crt-tools 12.6.77 ha770c72_0 conda-forge
cuda-cudart 12.4.127 0 nvidia
cuda-cudart_linux-64 12.6.77 h3f2d84a_0 conda-forge
cuda-cupti 12.4.127 0 nvidia
cuda-libraries 12.4.1 0 nvidia
cuda-nvcc-tools 12.6.77 he02047a_0 conda-forge
cuda-nvrtc 12.4.127 0 nvidia
cuda-nvtx 12.4.127 0 nvidia
cuda-nvvm-tools 12.6.77 he02047a_0 conda-forge
cuda-opencl 12.6.77 0 nvidia
cuda-python 12.6.0 pypi_0 pypi
cuda-runtime 12.4.1 0 nvidia
cuda-version 12.6 h7480c83_3 conda-forge
cudf-cu12 24.12.0 pypi_0 pypi
cudnn 9.3.0.75 h93bb076_0 conda-forge
cupy-cuda12x 13.3.0 pypi_0 pypi
curl 8.10.1 hbbe4b11_0 conda-forge
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
cython 3.0.11 py310h5b1441d_3 conda-forge
cytoolz 0.12.3 py310h2372a71_0 conda-forge
dask-core 2024.3.1 pyhd8ed1ab_0 conda-forge
dask-jobqueue 0.9.0 pypi_0 pypi
dataset 1.6.2 pypi_0 pypi
dav1d 1.2.1 hd590300_0 conda-forge
dbus 1.13.18 hb2f20db_0 anaconda
debugpy 1.8.1 py310hc6cd4ac_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
diff-match-patch 20230430 pyhd8ed1ab_0 conda-forge
dill 0.3.8 pyhd8ed1ab_0 conda-forge
distributed 2024.3.1 pyhd8ed1ab_0 conda-forge
distro 1.9.0 pyhd8ed1ab_0 conda-forge
dnspython 2.6.1 pypi_0 pypi
docstring-to-markdown 0.15 pyhd8ed1ab_0 conda-forge
docutils 0.20.1 py310hff52083_3 conda-forge
emcee 3.1.6 pypi_0 pypi
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
et_xmlfile 1.1.0 pyhd8ed1ab_0 conda-forge
exceptiongroup 1.2.0 pyhd8ed1ab_2 conda-forge
executing 2.0.1 pyhd8ed1ab_0 conda-forge
expat 2.6.3 h5888daf_0 conda-forge
fastrlock 0.8.2 pypi_0 pypi
ffmpeg 4.4.0 h6987444_4 conda-forge
ffmpeg-python 0.2.0 pypi_0 pypi
filelock 3.13.1 pyhd8ed1ab_0 conda-forge
flake8 7.0.0 pyhd8ed1ab_0 conda-forge
flask 3.0.2 pyhd8ed1ab_0 conda-forge
flask-cors 5.0.0 pypi_0 pypi
flatbuffers 24.3.25 h59595ed_0 conda-forge
flufl-lock 8.0.2 pypi_0 pypi
fmt 10.2.1 h00ab1b0_0 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 h77eed37_1 conda-forge
fontconfig 2.14.2 h14ed4e7_0 conda-forge
fonts-anaconda 1 h8fa9717_0 anaconda
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.49.0 pypi_0 pypi
fpstimer 0.0.1 pypi_0 pypi
fqdn 1.5.1 pyhd8ed1ab_0 conda-forge
freeglut 3.2.2 ha6d2627_3 conda-forge
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NVCC Version:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Sep_12_02:18:05_PDT_2024
Cuda compilation tools, release 12.6, V12.6.77
Build cuda_12.6.r12.6/compiler.34841621_0
Nvidia driver 565.77 on a 3060
Arch linux on 6.6.64-1-lts x86_64 GNU/Linux
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