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* Default spark compute resource to spark workloads
* Add spark compute to model config and remove default
* Update model config documentation
* Refactoring variable and deleting unnecessary function
* Remove duplicate tag field in doc
* Declare spark compute validation object const as ptr
* Update models.md
Copy file name to clipboardExpand all lines: docs/applications/resources/models.md
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@@ -41,11 +41,21 @@ Train custom TensorFlow models at scale.
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start_delay_secs: <int> # start evaluating after waiting for this many seconds (default: 120)
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throttle_secs: <int> # do not re-evaluate unless the last evaluation was started at least this many seconds ago (default: 600)
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compute:
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compute:# Resources for training and evaluations steps (TensorFlow)
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cpu: <string> # CPU request (default: Null)
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mem: <string> # memory request (default: Null)
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gpu: <string> # GPU request (default: Null)
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dataset_compute: # Resources for constructing training dataset (Spark)
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executors: <int> # number of spark executors (default: 1)
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driver_cpu: <string> # CPU request for spark driver (default: 1)
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driver_mem: <string> # memory request for spark driver (default: 500Mi)
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driver_mem_overhead: <string> # off-heap (non-JVM) memory allocated to the driver (overrides mem_overhead_factor) (default: min[driver_mem * 0.4, 384Mi])
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executor_cpu: <string> # CPU request for each spark executor (default: 1)
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executor_mem: <string> # memory request for each spark executor (default: 500Mi)
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executor_mem_overhead: <string> # off-heap (non-JVM) memory allocated to each executor (overrides mem_overhead_factor) (default: min[executor_mem * 0.4, 384Mi])
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mem_overhead_factor: <float> # the proportion of driver_mem/executor_mem which will be additionally allocated for off-heap (non-JVM) memory (default: 0.4)
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tags:
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<string>: <scalar> # arbitrary key/value pairs to attach to the resource (optional)
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