Comments (2)
Yes, you should be able to use Python3. However, for that, I'd recommend using the publicly available pip packages for installing tensorflow instead of building our RDMA-enabled version (especially since we've now merged that RDMA support into tensorflow.contrib.iverbs
).
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Thanks! I will try it
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Related Issues (20)
- Writing checkpoints to HDFS takes long HOT 2
- when using mnist_spark.py , serializer.dump_stream Timeout while feeding partition HOT 2
- pkg_resources.DistributionNotFound: The 'tensorflow' distribution was not found and is required by the application HOT 3
- MNIST example - Exception in TF background thread HOT 2
- the doubt about the data policy HOT 1
- Performance issues in the program HOT 2
- Performance issues in examples/mnist/estimator (by P3) HOT 3
- Retaining original columns after inference HOT 2
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- Get stuck at "Added broadcast_0_piece0 in memory on" while runing Spark standalone cluster HOT 1
- ExitCode: 13 executing mnist_data_setup.py on a yarn cluster HOT 3
- can it run on tensorflow-cpu? HOT 1
- can it run use ParameterServerStrategy HOT 3
- do we support scala & java code write tensorflow model with tenorflow-core-api ? HOT 3
- Evalator hangs while training HOT 1
- yarn mode error HOT 1
- error while running mnist_tf_ds.py HOT 1
- I have been trying to use TensorFlowOnSpark in Azure Synapse Analytics and I would like to ask if you have any information about its compatibility in this environment
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