So I have a tensorflow model in python 3.5 registered with the ML engine and I want to run a batch prediction job using it. My API request body looks like:
{
"versionName": "XXXXX/v8_0QSZ",
"dataFormat": "JSON",
"inputPaths": [
"XXXXX"
],
"outputPath": "XXXXXX",
"region": "us-east1",
"runtimeVersion": "1.12",
"accelerator": {
"count": "1",
"type": "NVIDIA_TESLA_P100"
}
}
Then the batch prediction job runs and returns "Job completed successfully.", however, it was completely unsuccessful and consistently threw the following error for each input:
Exception during running the graph: 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 convolution_layer/conv1d/conv1d/Conv2D (defined at /usr/local/lib/python2.7/dist-packages/google/cloud/ml/prediction/frameworks/tf_prediction_lib.py:210) = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](convolution_layer/conv1d/conv1d/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, convolution_layer/conv1d/conv1d/ExpandDims_1)]] [[{{node Cast_6/_495}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_789_Cast_6", tensor_type=DT_INT64, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
My questions are:
Response from batch prediction dev: "we don't officially support Python 3 yet. However, the issue you're encountering is a known bug affecting our GPU runtimes for TF 1.11 and 1.12
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