python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 8 -a ' --fifo  -a 533 -l 100 ' -s datou_current_533 -M 0 -S 0 -U 95,95,120
 import MySQLdb succeeded 
Import error (python version)
['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/caffe_cuda8_python3/python', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages']
process id : 71703
load datou : 533
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
All sons are already in current list !
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
DataTypes for each output/input checked !
Unexpected type for variable list_input_json
ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0)
 Tried to parse : 
chemin de la photo was removed should we ?
[ (photo_id, hashtag_id_0, score_0), (photo_id, hashtag_id_1, score_1), ...] was removed should we ?
[ (photo_id, hashtag_id_0, score_0), (photo_id, hashtag_id_1, score_1), ...] was removed should we ?
(photo_id, hashtag_id, score_max) was removed should we ?
chemin de la photo was removed should we ?
load thcls
load pdts
Running datou job : batch_current
 TODO datou_current to load to do maybe to take outside batchDatouExec 
no input labels
no input values
updating current state to 1
we have a portfolio with more photos than limit : 217>100
please execute split_portfolio.py -i 24659470 -l 100
size over we load limit photo not treated
list_input_json: {}
Current got : datou_id : 533, datou_cur_ids : ['3243114'] with mtr_portfolio_ids : ['24659470'] and first list_photo_ids : []
new path :  /proc/71703/
Inside batchDatouExec : verbose : 0
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
All sons are already in current list !
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
DataTypes for each output/input checked !
List Step Type Loaded in datou : tfhub_classification2, argmax
list_input_json : {}
origin
We have 1 , 
WARNING: data may be incomplete, need to offset and complete !
BFwe have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
time to download the photos : 0.11994385719299316
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
Calling datou_exec

Inside datou_exec : verbose : 0
number of steps :  2
step1:tfhub_classification2
Mon Jul  7 16:40:28 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec

Beginning of datou_step TFHub with tf2 !
nombre de thcls : 3
we are using the classfication for multi_thcl 
[3513, 3379, 3847]


begin to check gpu status 
inside check gpu memory
2025-07-07 16:40:30.936620: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-07-07 16:40:30.945552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2025-07-07 16:40:30.945835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-07-07 16:40:30.949186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-07-07 16:40:30.952475: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-07-07 16:40:30.952979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-07-07 16:40:30.956657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-07-07 16:40:30.957725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-07-07 16:40:30.962634: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-07-07 16:40:30.963835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-07-07 16:40:30.964184: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-07-07 16:40:30.971476: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493035000 Hz
2025-07-07 16:40:30.972894: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5978000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-07-07 16:40:30.972939: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-07-07 16:40:31.089673: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x405658c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-07-07 16:40:31.089712: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-07-07 16:40:31.090830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2025-07-07 16:40:31.090916: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-07-07 16:40:31.090964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-07-07 16:40:31.090995: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-07-07 16:40:31.091025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-07-07 16:40:31.091055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-07-07 16:40:31.091084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-07-07 16:40:31.091114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-07-07 16:40:31.092700: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-07-07 16:40:31.092769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-07-07 16:40:31.093717: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-07-07 16:40:31.093742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-07-07 16:40:31.093755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-07-07 16:40:31.095414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
l 3637 free memory gpu now : 5665  max_wait_temp :  1 max_wait :  5
1 Physical GPUs, 1 Logical GPUs

tagging for thcl : 3513
To do loadFromThcl(), then load ParamDescType : thcl3513
 thcls : [{'id': 3513, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2_tf', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 4557, 'photo_desc_type': 5767, 'type_classification': 'tf_classification2', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}]
 thcl {'id': 3513, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2_tf', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 4557, 'photo_desc_type': 5767, 'type_classification': 'tf_classification2', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}
 Update svm_hashtag_type_desc : 5767
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5767, 'Rungis_amount_dechets_fall_2018_v2_tf', 2048, 2048, 'Rungis_amount_dechets_fall_2018_v2_tf', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 3, 16, 15, 52, 10), datetime.datetime(2023, 3, 16, 15, 52, 10))
model_name : Rungis_amount_dechets_fall_2018_v2_tf
model_param file didn't exist 
model_name : Rungis_amount_dechets_fall_2018_v2_tf
model_type : tf_classification2
list file need :
['Confusion_Matrix.png', 'Precision_Recall_05102018_Papier_non_papier_dense.jpg', 'Precision_Recall_05102018_Papier_non_papier_peu_dense.jpg', 'Precision_Recall_05102018_Papier_non_papier_presque_vide.jpg', 'Precision_Recall_05102018_Papier_non_papier_tres_dense.jpg', 'Precision_Recall_05102018_Papier_non_papier_tres_peu_dense.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00001', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file exist in s3 :
['Confusion_Matrix.png', 'Precision_Recall_05102018_Papier_non_papier_dense.jpg', 'Precision_Recall_05102018_Papier_non_papier_peu_dense.jpg', 'Precision_Recall_05102018_Papier_non_papier_presque_vide.jpg', 'Precision_Recall_05102018_Papier_non_papier_tres_dense.jpg', 'Precision_Recall_05102018_Papier_non_papier_tres_peu_dense.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00001', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file manque in s3 : 
[]
local folder  : /data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Confusion_Matrix.png
size_local : 67810 size in s3 : 67810
create time local : 2023-10-30 16:21:37 create time in s3 : 2023-10-30 14:09:29
Confusion_Matrix.png already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Precision_Recall_05102018_Papier_non_papier_dense.jpg
size_local : 73949 size in s3 : 73949
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:30
Precision_Recall_05102018_Papier_non_papier_dense.jpg already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Precision_Recall_05102018_Papier_non_papier_peu_dense.jpg
size_local : 85572 size in s3 : 85572
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:39
Precision_Recall_05102018_Papier_non_papier_peu_dense.jpg already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Precision_Recall_05102018_Papier_non_papier_presque_vide.jpg
size_local : 72361 size in s3 : 72361
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:37
Precision_Recall_05102018_Papier_non_papier_presque_vide.jpg already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Precision_Recall_05102018_Papier_non_papier_tres_dense.jpg
size_local : 83567 size in s3 : 83567
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:48
Precision_Recall_05102018_Papier_non_papier_tres_dense.jpg already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Precision_Recall_05102018_Papier_non_papier_tres_peu_dense.jpg
size_local : 71611 size in s3 : 71611
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:29
Precision_Recall_05102018_Papier_non_papier_tres_peu_dense.jpg already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/Result_Summary.txt
size_local : 1058 size in s3 : 1058
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:30
Result_Summary.txt already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/checkpoint
size_local : 99 size in s3 : 99
create time local : 2023-10-30 16:21:38 create time in s3 : 2023-10-30 14:09:36
checkpoint already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/model_checkpoint.ckpt.data-00000-of-00001
size_local : 188538519 size in s3 : 188538519
create time local : 2023-10-30 16:21:41 create time in s3 : 2023-10-30 14:09:40
model_checkpoint.ckpt.data-00000-of-00001 already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/model_checkpoint.ckpt.data-00000-of-00002
size_local : 216572 size in s3 : 216572
create time local : 2023-10-30 16:21:41 create time in s3 : 2023-03-16 14:52:09
model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/model_checkpoint.ckpt.data-00001-of-00002
size_local : 32279708 size in s3 : 32279708
create time local : 2023-10-30 16:21:42 create time in s3 : 2023-03-16 14:52:07
model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/model_checkpoint.ckpt.index
size_local : 28001 size in s3 : 28001
create time local : 2023-10-30 16:21:42 create time in s3 : 2023-10-30 14:09:30
model_checkpoint.ckpt.index already exist and didn't need to update


/data/models_weight/Rungis_amount_dechets_fall_2018_v2_tf/model_weights.h5
size_local : 94501976 size in s3 : 94501976
create time local : 2023-10-30 16:21:44 create time in s3 : 2023-10-30 14:09:37
model_weights.h5 already exist and didn't need to update
desc size : 2048
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
module (KerasLayer)          (None, 2048)              23561152  
_________________________________________________________________
Rungis_amount_dechets_fall_2 (None, 5)                 10245     
=================================================================
Total params: 23,571,397
Trainable params: 10,245
Non-trainable params: 23,561,152
_________________________________________________________________
Loading Weights...
time used to create the model : 8.00935983657837
time used to load_weights : 0.20369529724121094

0it [00:00, ?it/s]
1it [00:00, 2121.55it/s]2025-07-07 16:40:41.107547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-07-07 16:40:41.112839: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2025-07-07 16:40:41.141655: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-07-07 16:40:41.149706: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2025-07-07 16:40:41.153965: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
temp/1751899228_71703_1371043157_ea0fe30aa4160e2e4381291e5d4a01ac.jpg
Found 1 images belonging to 1 classes.

 begin to do the prediction :
ERROR in datou_step_exec, will save and exit !
 Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/conv1/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34490]

Function call stack:
predict_function

  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec
    output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec
    return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3147, in datou_step_tfhub2
    classes, outputs, features = this_model.predict_image_paths(list_paths, keep_aspect_ratio=keep_aspect_ratio,
  File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 288, in predict_image_paths
    Y_pred, F_pred = self.model.predict(valid_generator, validation_steps)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 88, in _method_wrapper
    return method(self, *args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 1268, in predict
    tmp_batch_outputs = predict_function(iterator)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
    result = self._call(*args, **kwds)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 650, in _call
    return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds)  # pylint: disable=protected-access
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1661, in _filtered_call
    return self._call_flat(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1745, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 593, in call
    outputs = execute.execute(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,

[1371043157]


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
time used for this insertion : 0.01418757438659668
 save_final 
ERROR in last step tfhub_classification2,  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/conv1/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34490]

Function call stack:
predict_function


time spend for datou_step_exec : 13.045167922973633
time spend to save output : 0.017518281936645508
total time spend for step 0 : 13.062686204910278
need to delete datou_research and reload, so keep current state 1
caffe_path_current : 

About to save ! 2
After save, about to update current !
12.79user 3.55system 0:17.14elapsed 95%CPU (0avgtext+0avgdata 1720464maxresident)k
6528inputs+1152outputs (151major+558606minor)pagefaults 0swaps