title: Tensorflow trains on CPU instead of RTX 3000 series GPU
body: <p>I am trying to train my tensorflow model on my RTX 3070 GPU. I am using an anaconda virtual environment and the prompt shows that the GPU is successfully detected and doesn't show any errors or warnings but whenever the model starts training it uses the CPU instead.</p>
  <p><strong>My Anaconda Prompt:</strong></p>
  <pre><code>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) -&gt; 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.
  </code></pre>
  <p><strong>My Model Code:</strong></p>
  <pre><code>inputs = keras.Input(shape=(None,), dtype=&quot;int32&quot;)
  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=&quot;sigmoid&quot;)(x)
  model = keras.Model(inputs, outputs)
  
  model.compile(&quot;adam&quot;, &quot;binary_crossentropy&quot;, metrics=[&quot;accuracy&quot;])
  model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val))
  </code></pre>
  <p><strong>I am using:</strong></p>
  <ul>
  <li>tensorflow nightly gpu 2.5.0.dev20201111 (installed on a anaconda virtual env)</li>
  <li>CUDA 11.1 (cuda_11.1.1_456.81)</li>
  <li>CUDNN v8.0.4.30 (for CUDA 11.1)</li>
  <li>python 3.8</li>
  </ul>
  <p>I know that my GPU is not being used because its utilization is at 1% while my CPU is at 60% with its top process being python.</p>
  <p>Can anyone help me get my model training using the GPU?</p>