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

Orca Known Issues

Estimator Issues

UnkownError: Could not start gRPC server

This error occurs while running Orca TF2 Estimator with spark backend, which may because the previous pyspark tensorflow job was not cleaned completely. You can retry later or you can set spark config spark.python.worker.reuse=false in your application.

If you are using init_orca_context(cluster_mode="yarn-client"):

conf = {"spark.python.worker.reuse": "false"}
init_orca_context(cluster_mode="yarn-client", conf=conf)

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

spark-submit --conf spark.python.worker.reuse=false

RuntimeError: Inter op parallelism cannot be modified after initialization

This error occurs if you build your TensorFlow model on the driver rather than on workers. You should build the complete model in model_creator which runs on each worker node. You can refer to the following examples:

Wrong Example

model = ... 

def model_creator(config):
    model.compile(...)
    return model

estimator = Estimator.from_keras(model_creator=model_creator,...)
...

Correct Example

def model_creator(config):
    model = ...
    model.compile(...)
    return model

estimator = Estimator.from_keras(model_creator=model_creator,...)
...

OrcaContext 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.

Other Issues

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

This error is because PyArrow fails to locate libhdfs.so in default path of $HADOOP_HOME/lib/native when you run with YARN on Cloudera. 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-client", 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