Comments (2)
Writing checkpoints normally shouldn't take that long to write to HDFS. Normally, it should be on the order of seconds or maybe minutes (for really large models), so there may be some other issues in your particular setup. As for keeping checkpoints. It's not absolutely necessary, but for Hadoop environments, it's recommended in case a node goes down for any reason, so you wouldn't have to restart your training from scratch. A lot of this depends on how long your training takes, e.g. if it only takes 5 min, then restarting from scratch is not a big deal. If it takes 3 days, then restarting from scratch after a failure 2.5 days into training would be a major pain.
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Closing due to inactivity, feel free to re-open if this is still an issue.
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Related Issues (20)
- 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
- 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
- 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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