checkpointing

问题 There seem to be a few postings on this but none seem to answer what I understand. The following code run on DataBricks: spark.sparkContext.setCheckpointDir("/dbfs/FileStore/checkpoint/cp1/loc7") val checkpointDir = spark.sparkContext.getCheckpointDir.get val ds = spark.range(10).repartition(2) ds.cache() ds.checkpoint() ds.count() ds.rdd.isCheckpointed Added an improvement of sorts: ... val ds2 = ds.checkpoint(eager=true) println(ds2.queryExecution.toRdd.toDebugString) ... returns: (2)

问题 What does checkpointing do for Apache Spark, and does it take any hits on RAM or CPU? 回答1: From Apache Streaming Documentation - Hope it helps: A streaming application must operate 24/7 and hence must be resilient to failures unrelated to the application logic (e.g., system failures, JVM crashes, etc.). For this to be possible, Spark Streaming needs to checkpoint enough information to a fault- tolerant storage system such that it can recover from failures. There are two types of data that are

来源： https://stackoverflow.com/questions/64226597/apache-flink-to-use-s3-for-backend-state-and-checkpoints

来源： https://stackoverflow.com/questions/64226597/apache-flink-to-use-s3-for-backend-state-and-checkpoints

问题 Goal: Read from Kinesis and store data in to S3 in Parquet format via spark streaming. Situation: Application runs fine initially, running batches of 1hour and the processing time is less than 30 minutes on average. For some reason lets say the application crashes, and we try to restart from checkpoint. The processing now takes forever and does not move forward. We tried to test out the same thing at batch interval of 1 minute, the processing runs fine and takes 1.2 minutes for batch to

问题 I'll run some larger models and want to try intermediate results. Therefore, I try to use checkpoints to save the best model after each epoch. This is my code: model = Sequential() model.add(LSTM(700, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(700, return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(700)) model.add(Dropout(0.2)) model.add(Dense(Y_modified.shape[1], activation='softmax')) model.compile(loss=

问题 I'll run some larger models and want to try intermediate results. Therefore, I try to use checkpoints to save the best model after each epoch. This is my code: model = Sequential() model.add(LSTM(700, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(700, return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(700)) model.add(Dropout(0.2)) model.add(Dense(Y_modified.shape[1], activation='softmax')) model.compile(loss=

问题 I am trying to export the weights and biases of a "model" in which I did not originally train the model with "export_weights_and_biases = TRUE" Therefore, I'd like to try to checkpoint the model and try to export_weights_and_biases = TRUE in a new "model2". However, despite not changing any of the parameters - and ensuring that nfolds=10 just as in the original "model", the checkpoint model continues to return a parameter change error almost immediately (h2o version 3.10.4.6): water

问题 I am using pre-trained VGG-16 model for image classification. I am adding custom last layer as the number of my classification classes are 10. I am training the model for 200 epochs. My question is: is there any way if I randomly stop (by closing python window) the training at some epoch, let's say epoch no. 50 and resume from there? I have read about saving and reloading model but my understanding is that works for our custom models only instead of pre-trained models like VGG-16. 回答1: You

问题 I have noticed that spark streaming examples also have code for checkpointing. My question is how important is that checkpointing. If its there for fault tolerance, how often do faults happen in such streaming applications? 回答1: It all depends on your use case. For suppose if you are running a streaming job, which just reads data from Kafka and counts the number of records. What would you do if your application crashes after a year or so? If you don't have a backup/checkpoint, you will have