Tensorflow trainiert auf CPU anstelle der GPU der RTX 3000-Serie

Nov 28 2020

Ich versuche, mein Tensorflow-Modell auf meiner RTX 3070-GPU zu trainieren. Ich verwende eine virtuelle Anaconda-Umgebung und die Eingabeaufforderung zeigt an, dass die GPU erfolgreich erkannt wurde und keine Fehler oder Warnungen anzeigt. Wenn das Modell jedoch mit dem Training beginnt, wird stattdessen die CPU verwendet.

Meine Anaconda-Eingabeaufforderung:

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.

Mein Modellcode:

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))

Ich benutze:

  • Tensorflow Nightly GPU 2.5.0.dev20201111 (installiert auf einer virtuellen Anaconda-Umgebung)
  • CUDA 11.1 (cuda_11.1.1_456.81)
  • CUDNN v8.0.4.30 (für CUDA 11.1)
  • Python 3.8

Ich weiß, dass meine GPU nicht verwendet wird, da ihre Auslastung bei 1% liegt, während meine CPU bei 60% liegt und der oberste Prozess Python ist.

Kann mir jemand helfen, mein Modelltraining mit der GPU zu erhalten?

Antworten

TarakNathNandi Nov 29 2020 at 01:57

Höchstwahrscheinlich verwenden Sie Tensorflow für die CPU anstelle der GPU. Führen Sie einen "pip uninstall tensorflow" und einen "pip install tensorflow-gpu" durch, um den für die Verwendung der GPU geeigneten zu installieren.