Comments (3)
@jaideepjoshi can you try modifying the ipynb script to set num_executors = 6
? In that particular script, we aren't inferring the number of executors from the environment, so you'll have to tell the app how many executors are actually present.
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At the moment, yes, since we're mostly targeting YARN use cases internally. In these use cases, you would typically use spark-submit --num-executors N
to start a TF cluster of size N. It would be a bit weird to start 10 spark executors to run 2 TF nodes. And even if we made this work, then data feeding via InputMode.SPARK would require another network hop to send data from a non-TF node to a TF node.
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Related Issues (20)
- MNIST SPARK on Standalone Cluster inside Docker Container HOT 11
- Writing checkpoints to HDFS takes long HOT 2
- when using mnist_spark.py , serializer.dump_stream Timeout while feeding partition HOT 2
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- 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
- tensorflow.python.framework.errors_impl.UnimplementedError: File system scheme 'cosn' not implemented HOT 2
- Model Saved with TF-2.5.0 HOT 3
- How to integrate a model into Spark cluster HOT 12
- 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
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