performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks requires new models to be trained from scratch. For models that share the same domain, it is likely that the first few layers learn similar features. Hence training new models from scratch for these tasks is likely wasteful. Regarding the first limitation, we know that model quality can usually be naively expensive, and is unlikely to scale to the level that we want for complex tasks. To achieve a reasonable quality on non-trivial tasks, the amount of labeled data required is large too. For the second limitation

representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

University 2020 • Flink requires a sufficient number of processing slots in order to execute all tasks of an application. • The JobManager cannot restart the application until enough slots become available JobManager failures ??? Vasiliki Kalavri | Boston University 2020 When the JobManager fails all tasks are automatically cancelled. The new JobManager performs the following steps: 1. It requests It requests processing slots. 3. It restarts the application and resets the state of all its tasks to the last completed checkpoint. Highly available Flink setup ??? Vasiliki Kalavri | Boston University

kyuubi.SQLOperationListener: Query [a46ca504-fe3a-4dfb-be1e- ˓→19770af8ac4c]: Stage 3 started with 1 tasks, 1 active stages running 2021-10-28 13:56:27.651 INFO scheduler.DAGScheduler: Job 3 finished: collect script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPAAK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://localhost:10009' Or you can submit tasks directly through local beeline: ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://${hostname}:${port}'

kyuubi.SQLOperationListener: Query [a46ca504-fe3a-4dfb-be1e- ˓→19770af8ac4c]: Stage 3 started with 1 tasks, 1 active stages running 2021-10-28 13:56:27.651 INFO scheduler.DAGScheduler: Job 3 finished: collect script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPAAK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://localhost:10009' Or you can submit tasks directly through local beeline: ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://${hostname}:${port}'

kyuubi.SQLOperationListener: Query [a46ca504-fe3a-4dfb-be1e- ˓→19770af8ac4c]: Stage 3 started with 1 tasks, 1 active stages running 2021-10-28 13:56:27.651 INFO scheduler.DAGScheduler: Job 3 finished: collect script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPAAK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://localhost:10009' Or you can submit tasks directly through local beeline: ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://${hostname}:${port}'

kyuubi.SQLOperationListener: Query [a46ca504-fe3a-4dfb-be1e-19770af8ac4c]: Stage 3 started with 1 tasks, 1 active stages running 2021-10-28 13:56:27.651 INFO scheduler.DAGScheduler: Job 3 finished: collect script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPAAK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://localhost:10009' Or you can submit tasks directly through local beeline: ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://${hostname}:${port}'

kyuubi.SQLOperationListener: Query [a46ca504-fe3a-4dfb-be1e-19770af8ac4c]: Stage 3 started with 1 tasks, 1 active stages running 2021-10-28 13:56:27.651 INFO scheduler.DAGScheduler: Job 3 finished: collect script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPAAK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://localhost:10009' Or you can submit tasks directly through local beeline: ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://${hostname}:${port}'

kyuubi.SQLOperationListener: Query [a46ca504-fe3a-4dfb-be1e-19770af8ac4c]: Stage 3 started with 1 tasks, 1 active stages running 2021-10-28 13:56:27.651 INFO scheduler.DAGScheduler: Job 3 finished: collect script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPAAK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://localhost:10009' Or you can submit tasks directly through local beeline: ${SPARK_HOME}/bin/beeline -u 'jdbc:hive2://${hostname}:${port}'

Any Scale Most of the Kyuubi engine types have a distributed backend or can schedule distributed tasks at runtime. They can process data on single-node machines or clusters, such as YARN and Kubernetes script can also help build external Spark into a Kyuubi image that acts as a client for submitting tasks by -s ${SPARK_HOME}. Of course, if you have an image that contains the Spark binary package, you kyuubi-example -- /bin/bash ${KYUUBI_HOME}/bin/beeline -u 'jdbc:kyuubi://localhost:10009' Or you can submit tasks directly through local beeline: ${KYUUBI_HOME}/bin/beeline -u 'jdbc:kyuubi://${hostname}:${port}'