如何在Windows上为RTX 3070设置Tensorflow？

2021-01-25 21:36:01.042433: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2) Epoch 1/500 2021-01-25 21:36:03.304809: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll 2021-01-25 21:36:03.880223: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll 2021-01-25 21:36:03.911531: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll 2021-01-25 21:36:04.515409: E tensorflow/stream_executor/cuda/cuda_dnn.cc:336] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED 2021-01-25 21:36:04.515498: E tensorflow/stream_executor/cuda/cuda_dnn.cc:340] Error retrieving driver version: Unimplemented: kernel reported driver version not implemented on Windows 2021-01-25 21:36:04.515607: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at cudnn_rnn_ops.cc:1514 : Unknown: Fail to find the dnn implementation. Traceback (most recent call last): File "<input>", line 1, in <module> File "C:\Users\Aleksander\.IntelliJIdea2018.3\config\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "C:\Users\Aleksander\.IntelliJIdea2018.3\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:/Workspace_GpwScan/dnn/sandbox/reproduce_issue.py", line 110, in <module> callbacks=[checkpoint, tensorboard]) File "C:\Workspace_GpwScan\stubs\tensorflow\python\keras\engine\training.py", line 1100, in fit tmp_logs = self.train_function(iterator) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\def_function.py", line 828, in __call__ result = self._call(*args, **kwds) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\def_function.py", line 888, in _call return self._stateless_fn(*args, **kwds) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\function.py", line 2943, in __call__ filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\function.py", line 1919, in _call_flat ctx, args, cancellation_manager=cancellation_manager)) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\function.py", line 560, in call ctx=ctx) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\execute.py", line 60, in quick_execute inputs, attrs, num_outputs) tensorflow.python.framework.errors_impl.UnknownError: Fail to find the dnn implementation. [[{{node CudnnRNN}}]] [[sequential/lstm/PartitionedCall]] [Op:__inference_train_function_8782] Function call stack: train_function -> train_function -> train_function

2021-01-25 21:31:05.429799: E tensorflow/stream_executor/dnn.cc:618] CUDNN_STATUS_EXECUTION_FAILED in tensorflow/stream_executor/cuda/cuda_dnn.cc(1975): 'cudnnRNNBackwardData( cudnn.handle(), rnn_desc.handle(), model_dims.max_seq_length, output_desc.handles(), output_data.opaque(), output_desc.handles(), output_backprop_data.opaque(), output_h_desc.handle(), output_h_backprop_data.opaque(), output_c_desc.handle(), output_c_backprop_data.opaque(), rnn_desc.params_handle(), params.opaque(), input_h_desc.handle(), input_h_data.opaque(), input_c_desc.handle(), input_c_data.opaque(), input_desc.handles(), input_backprop_data->opaque(), input_h_desc.handle(), input_h_backprop_data->opaque(), input_c_desc.handle(), input_c_backprop_data->opaque(), workspace.opaque(), workspace.size(), reserve_space_data->opaque(), reserve_space_data->size())' 2021-01-25 21:31:05.430291: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at cudnn_rnn_ops.cc:1926 : Internal: Failed to call ThenRnnBackward with model config: [rnn_mode, rnn_input_mode, rnn_direction_mode]: 2, 0, 0 , [num_layers, input_size, num_units, dir_count, max_seq_length, batch_size, cell_num_units]: [1, 1, 128, 1, 128, 256, 128] Traceback (most recent call last): File "<input>", line 1, in <module> File "C:\Users\Aleksander\.IntelliJIdea2018.3\config\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "C:\Users\Aleksander\.IntelliJIdea2018.3\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:/Workspace_GpwScan/dnn/sandbox/reproduce_issue.py", line 108, in <module> callbacks=[checkpoint, tensorboard]) File "C:\Workspace_GpwScan\stubs\tensorflow\python\keras\engine\training.py", line 1134, in fit tmp_logs = self.train_function(iterator) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\def_function.py", line 818, in __call__ result = self._call(*args, **kwds) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\def_function.py", line 846, in _call return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\function.py", line 2994, in __call__ filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\function.py", line 1939, in _call_flat ctx, args, cancellation_manager=cancellation_manager)) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\function.py", line 569, in call ctx=ctx) File "C:\Workspace_GpwScan\stubs\tensorflow\python\eager\execute.py", line 60, in quick_execute inputs, attrs, num_outputs) tensorflow.python.framework.errors_impl.InternalError: Failed to call ThenRnnBackward with model config: [rnn_mode, rnn_input_mode, rnn_direction_mode]: 2, 0, 0 , [num_layers, input_size, num_units, dir_count, max_seq_length, batch_size, cell_num_units]: [1, 1, 128, 1, 128, 256, 128] [[{{node gradients/CudnnRNN_grad/CudnnRNNBackprop}}]] [[Adam/gradients/PartitionedCall_2]] [Op:__inference_train_function_8936] Function call stack: train_function -> train_function -> train_function

import datetime import os import pandas as pd from numpy import reshape import tensorflow as tf EPOCHS = 500 BATCH_SIZE = 256 TEST_SET_RATIO = 0.2 LEARNING_RATE = 0.001 DECAY = 3e-5 LOSS_FUNC = 'categorical_crossentropy' DROPOUT = 0.2 OUTPUT_PATH = "e:\\ml" RNN_SEQ_LEN = 128 # number of RNN/LSTM sequence features L_AMOUNT = 2 # number of labels MIN_ACC_TO_SAVE_MODEL = 0.6 def create_model(): new_model = tf.keras.models.Sequential() # NETWORK INPUT new_model.add(tf.keras.layers.LSTM(RNN_SEQ_LEN, input_shape=TR_FEATURES.shape[1:], return_sequences=True)) new_model.add(tf.keras.layers.Dropout(DROPOUT)) new_model.add(tf.keras.layers.BatchNormalization()) new_model.add(tf.keras.layers.LSTM(RNN_SEQ_LEN, return_sequences=True)) new_model.add(tf.keras.layers.Dropout(DROPOUT / 2)) new_model.add(tf.keras.layers.BatchNormalization()) new_model.add(tf.keras.layers.LSTM(RNN_SEQ_LEN)) new_model.add(tf.keras.layers.Dropout(DROPOUT)) new_model.add(tf.keras.layers.BatchNormalization()) # NETWORK OUTPUT new_model.add(tf.keras.layers.Dense(L_AMOUNT, activation=tf.keras.activations.softmax)) opt = tf.keras.optimizers.Adam(LEARNING_RATE, decay=DECAY) new_model.compile(optimizer=opt, loss=LOSS_FUNC, metrics=['accuracy']) print(new_model.summary()) return new_model class CustomModelCheckpoint(tf.keras.callbacks.ModelCheckpoint): def __init__(self, fp, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', save_freq='epoch', **kwargs): super().__init__(fp, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, **kwargs) def on_epoch_end(self, epoch, logs=None): print("\n-------------------------------------------------------------------------------------------------------") print(f"epoch: {epoch}, training_acc: {round(float(logs['accuracy']), 4)}, validation_acc: {round(float(logs['val_accuracy']), 4)}") print("-------------------------------------------------------------------------------------------------------\n") if MIN_ACC_TO_SAVE_MODEL <= logs['accuracy']: super().on_epoch_end(epoch, logs) if __name__ == '__main__': data_filename = 'train_2020-02-07_pp_x128_3_2_all.csv' print("Loading data file: %s" % data_filename) dataset = pd.read_csv(data_filename, delimiter=',', header=None) dataset = dataset.drop(columns=[0, 1, 2, 3, 4, 5, 6]).values # drop columns with additional information test_set_size = int(len(dataset) * TEST_SET_RATIO) print("Test set split at: %d" % test_set_size) train_data = dataset[:-test_set_size] test_data = dataset[-test_set_size:] # use most recent data for validation (extract before shuffle) TR_F = train_data[:, 0:RNN_SEQ_LEN] TS_F = test_data[:, 0:RNN_SEQ_LEN] TR_L = train_data[:, RNN_SEQ_LEN:RNN_SEQ_LEN + L_AMOUNT] TS_L = test_data[:, RNN_SEQ_LEN:RNN_SEQ_LEN + L_AMOUNT] TR_FEATURES = reshape(TR_F, (len(TR_F), RNN_SEQ_LEN, 1)) TS_FEATURES = reshape(TS_F, (len(TS_F), RNN_SEQ_LEN, 1)) model = create_model() TRAINING_TIMESTAMP = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") model_name = "sscce_%s" % TRAINING_TIMESTAMP os.mkdir("%s\\models\\%s" % (OUTPUT_PATH, model_name)) filepath = "%s\\models\\%s\\%s--{epoch:02d}-{val_accuracy:.3f}.model" % (OUTPUT_PATH, model_name, model_name) checkpoint = CustomModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max') log_dir = "%s\\logs\\fit\\%s.model" % (OUTPUT_PATH, model_name) tensorboard = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, profile_batch=0) model.fit(x=TR_FEATURES, y=TR_L, epochs=EPOCHS, batch_size=BATCH_SIZE, shuffle=True, validation_data=(TS_FEATURES, TS_L), callbacks=[checkpoint, tensorboard])

Path=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin;C:\cudnn-11.1-v8.0.5.39\bin;C:\Python36\Scripts\;C:\Python36;C:\ProgramData\DockerDesktop\version-bin;C:\Program Files\Docker\Docker\Resources\bin;c:\Java\jdk1.8.0_144_x86;C:\gradle-6.0.1\bin;C:\SVN\bin;C:\MinGW\bin;C:\WinAVR-20100110\;c:\avrdude\;c:\Android\sdk\platform-tools;C:\adb\;C:\TortoiseGit\bin;C:\Git4Windows\cmd;c:\sqlite-tools-win32-x86-3130000\;C:\WINDOWS\System32;C:\WINDOWS;C:\WINDOWS\System32\wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\Program Files (x86)\Bitvise SSH Client;C:\Program Files (x86)\Windows Live\Shared;C:\WINDOWS\system32;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\OpenSSH\;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Program Files\NVIDIA Corporation\Nsight Compute 2020.3.0\;C:\Program Files\NVIDIA Corporation\NVIDIA NvDLISR

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1楼 · 发布于 2024-10-03 21:32:40

我回滚到CUDA 11.0和匹配的CUDNN 8.0.2与tensorflow 2.4.1只是为了再次检查它和这个组合

cudnn-11.0-windows-x64-v8.0.2.39.zip
cuda_11.0.2_451.48_win10.exe
latest stable tensorflow 2.4.1
updated nVidia GPU drivers to 461.40 as 451.48 packaged with above CUDA installer won't work with rtx 3070

。。。给出：

2021-02-04 19:36:59.700433: E tensorflow/stream_executor/cuda/cuda_dnn.cc:336] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED
2021-02-04 19:36:59.700523: E tensorflow/stream_executor/cuda/cuda_dnn.cc:340] Error retrieving driver version: Unimplemented: kernel reported driver version not implemented on Windows
2021-02-04 19:36:59.700630: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at cudnn_rnn_ops.cc:1514 : Unknown: Fail to find the dnn implementation.

最终，它开始与最近与2.5-nightly一起发布的最新cudnn-11.2-windows-x64-v8.1.0.77.zip一起工作，但显然只与cuda_11.2.0_460.89_win10.exe一起发布

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