ipex-llm/docs/readthedocs/source/doc/Orca/Overview/known_issues.md

Orca Known Issues

Estimator Issues

OSError: Unable to load libhdfs: ./libhdfs.so: cannot open shared object file: No such file or directory

This error occurs while running Orca TF2 Estimator with spark backend for YARN on Cloudera, where PyArrow fails to locate libhdfs.so in default path of $HADOOP_HOME/lib/native. To solve this issue, you need to set the path of libhdfs.so in Cloudera to the environment variable of ARROW_LIBHDFS_DIR on Spark driver and executors with the following steps:

1. Run locate libhdfs.so on the client node to find libhdfs.so
2. export ARROW_LIBHDFS_DIR=/opt/cloudera/parcels/CDH-5.15.2-1.cdh5.15.2.p0.3/lib64 (replace with the result of locate libhdfs.so in your environment)
3. If you are using init_orca_context(cluster_mode="yarn-client"):

   conf = {"spark.executorEnv.ARROW_LIBHDFS_DIR": "/opt/cloudera/parcels/CDH-5.15.2-1.cdh5.15.2.p0.3/lib64"}
   init_orca_context(cluster_mode="yarn", conf=conf)
   

   If you are using init_orca_context(cluster_mode="spark-submit"):

   # For yarn-client mode
   spark-submit --conf spark.executorEnv.ARROW_LIBHDFS_DIR=/opt/cloudera/parcels/CDH-5.15.2-1.cdh5.15.2.p0.3/lib64
   
   # For yarn-cluster mode
   spark-submit --conf spark.executorEnv.ARROW_LIBHDFS_DIR=/opt/cloudera/parcels/CDH-5.15.2-1.cdh5.15.2.p0.3/lib64 \
                --conf spark.yarn.appMasterEnv.ARROW_LIBHDFS_DIR=/opt/cloudera/parcels/CDH-5.15.2-1.cdh5.15.2.p0.3/lib64
   

Orca Context Issues

Exception: Failed to read dashbord log: [Errno 2] No such file or directory: '/tmp/ray/.../dashboard.log'

This error occurs when initialize an orca context with init_ray_on_spark=True. We have not locate the root cause of this problem, but it might be caused by an atypical python environment.

You could follow below steps to workaround:

1. If you only need to use functions in ray (e.g. bigdl.orca.learn with backend="ray", bigdl.orca.automl for pytorch/tensorflow model, bigdl.chronos.autots for time series model's auto-tunning), we may use ray as the first-class.

   1. Start a ray cluster by ray start --head. if you already have a ray cluster started, please direcetly jump to step 2.
   2. Initialize an orca context with runtime="ray" and init_ray_on_spark=False, please refer to detailed information here.
   3. If you are using bigdl.orca.automl or bigdl.chronos.autots on a single node, please set:

      ray_ctx = OrcaContext.get_ray_context()
      ray_ctx.is_local=True
      

2. If you really need to use ray on spark, please install bigdl-orca under a conda environment. Detailed information please refer to here.