RTX 3000 시리즈 GPU 대신 CPU에서 Tensorflow 학습

Question

RTX 3070 GPU에서 tensorflow 모델을 훈련 시키려고합니다. 아나콘다 가상 환경을 사용하고 있으며 프롬프트에 GPU가 성공적으로 감지되고 오류나 경고가 표시되지 않지만 모델이 훈련을 시작할 때마다 CPU가 대신 사용됩니다.

내 Anaconda 프롬프트 :

2020-11-28 19:38:17.373117: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2020-11-28 19:38:17.378626: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2020-11-28 19:38:17.378679: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2020-11-28 19:38:17.381802: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2020-11-28 19:38:17.382739: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2020-11-28 19:38:17.389401: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2020-11-28 19:38:17.391830: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2020-11-28 19:38:17.392332: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2020-11-28 19:38:17.392422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1866] Adding visible gpu devices: 0
2020-11-28 19:38:26.072912: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-11-28 19:38:26.073904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1724] Found device 0 with properties:
pciBusID: 0000:08:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.725GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2020-11-28 19:38:26.073984: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2020-11-28 19:38:26.074267: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2020-11-28 19:38:26.074535: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2020-11-28 19:38:26.074775: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2020-11-28 19:38:26.075026: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2020-11-28 19:38:26.075275: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2020-11-28 19:38:26.075646: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2020-11-28 19:38:26.075871: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2020-11-28 19:38:26.076139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1866] Adding visible gpu devices: 0
2020-11-28 19:38:26.738596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1265] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-28 19:38:26.738680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1271]      0
2020-11-28 19:38:26.739375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1284] 0:   N
2020-11-28 19:38:26.740149: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1410] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6589 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3070, pci bus id: 0000:08:00.0, compute capability: 8.6)
2020-11-28 19:38:26.741055: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2020-11-28 19:38:28.028828: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:126] None of the MLIR optimization passes are enabled (registered 2)
2020-11-28 19:38:32.428408: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2020-11-28 19:38:33.305827: I tensorflow/stream_executor/cuda/cuda_dnn.cc:344] Loaded cuDNN version 8004
2020-11-28 19:38:33.753275: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2020-11-28 19:38:34.603341: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2020-11-28 19:38:34.610934: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.

내 모델 코드 :

inputs = keras.Input(shape=(None,), dtype="int32")
x = layers.Embedding(max_features, 128)(inputs)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
x = layers.Bidirectional(layers.LSTM(64))(x)
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs, outputs)

model.compile("adam", "binary_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val))

나는 사용하고있다 :

• tensorflow nightly gpu 2.5.0.dev20201111 (아나콘다 가상 환경에 설치됨)
• CUDA 11.1 (cuda_11.1.1_456.81)
• CUDNN v8.0.4.30 (CUDA 11.1 용)
• 파이썬 3.8

사용률이 1 %이고 CPU가 60 %이고 최상위 프로세스가 python이기 때문에 GPU가 사용되지 않는다는 것을 알고 있습니다.

누구든지 GPU를 사용하여 모델 훈련을 받도록 도와 줄 수 있습니까?

Answer 1

아마도 GPU 대신 CPU에 Tensorflow를 사용하고있을 것입니다. "pip uninstall tensorflow"및 "pip install tensorflow-gpu"를 수행하여 GPU 활용에 적합한 것을 설치합니다.