Comments (1)
The recommended way to run distributed training with TensorFlow is by using kubeflow. The Docker and Kubernetes scripts in this repo are outdated. https://github.com/kubeflow/kubeflow
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Related Issues (20)
- About spark-tensorflow-distributor---I have the same problem as you. When I use a spark cluster with two or more nodes, the algorithm needs the same time as if I have only one node. I don't understand why. Please, someone answer this doubt
- Does this connector work with TF 2.x? HOT 4
- Error when deserializing tfrecord's in TF 2.x: Only integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices
- Failed to build spark-tensorflow-connector_2.12 HOT 3
- Functionality to save the trained model HOT 4
- Spark TensorFlow Distributor: Spark custom resource scheduling - when and how?
- Get error when I run spark-tensorflow-connector example... HOT 1
- Cannot convert field to unsupported data type StructType([StructField("user_flush_num", ArrayType(IntegerType(), True)),StructField("field2", ArrayType(IntegerType(), True))]) HOT 2
- Task failed while writing rows HOT 2
- spark-tensorflow-distributor: RAM overflow when running ResNet152 HOT 1
- ByteType not supported, while IntegerType is too space-consuming
- Is it possible to use the spark tensorflow distributor with Parameter server strategy?
- How to make the “YOLOv4 object detection” or “YOLOv5 object detection” or “U-Net segmentation” under the Spark cluster?
- Build new Spark-Tensorflow-Connector for scala 2.12 HOT 5
- Failing to save the model pickle. and how to check for the predictions? HOT 1
- java.lang.NullPointerException when save as TFRecords
- How to avoid aggregate(shuffle) in processing the tfrecord file?
- mirrored_strategy_runner.py for multiple executors / workers
- Limitation on Spark "task_gpu_amount" cannot be less than 1
- why spark-tensorflow-connector can't support double and integer data type.
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