Configuration class for tf.data service dispatchers.
tf.data.experimental.service.DispatcherConfig( port=0, protocol='grpc', work_dir=None, fault_tolerant_mode=False, job_gc_check_interval_ms=None, job_gc_timeout_ms=None )
port
: Specifies the port to bind to. A value of 0 indicates that the server may bind to any available port.protocol
: The protocol to use for communicating with the tf.data service. Defaults to "grpc"
.work_dir
: A directory to store dispatcher state in. This argument is required for the dispatcher to be able to recover from restarts.fault_tolerant_mode
: Whether the dispatcher should write its state to a journal so that it can recover from restarts. Dispatcher state, including registered datasets and created jobs, is synchronously written to the journal before responding to RPCs. If True
, work_dir
must also be specified.job_gc_check_interval_ms
: How often the dispatcher should scan through to delete old and unused jobs, in milliseconds. If not set, the runtime will select a reasonable default. A higher value will reduce load on the dispatcher, while a lower value will reduce the time it takes for the dispatcher to garbage collect expired jobs.job_gc_timeout_ms
: How long a job needs to be unused before it becomes a candidate for garbage collection, in milliseconds. If not set, the runtime will select a reasonable default. A higher value will cause jobs to stay around longer with no consumers. This is useful if there is a large gap in time between when consumers read from the job. A lower value will reduce the time it takes to reclaim the resources from expired jobs.Attributes | |
---|---|
port | |
protocol | |
work_dir | |
fault_tolerant_mode | |
job_gc_check_interval_ms | |
job_gc_timeout_ms |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/data/experimental/service/DispatcherConfig