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:
If you only need to use functions in ray (e.g.
bigdl.orca.learn
withbackend="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.Start a ray cluster by
ray start --head
. if you already have a ray cluster started, please direcetly jump to step 2.Initialize an orca context with
runtime="ray"
andinit_ray_on_spark=False
, please refer to detailed information here.If you are using
bigdl.orca.automl
orbigdl.chronos.autots
on a single node, please set:ray_ctx = OrcaContext.get_ray_context() ray_ctx.is_local=True
If you really need to use ray on spark, please install bigdl-orca under a conda environment. Detailed information please refer to here.
Ray Issues#
ValueError: Ray component worker_ports is trying to use a port number … that is used by other components.#
This error is because that some port in worker port list is occupied by other processes. To handle this issue, you can set range of the worker port list by using the parameters min-worker-port
and max-worker-port
in init_orca_context
as follows:
init_orca_context(extra_params={"min-worker-port": "30000", "max-worker-port": "30033"})
ValueError: Failed to bind to 0.0.0.0:8265 because it’s already occupied. You can use ray start --dashboard-port ...
or ray.init(dashboard_port=...)
to select a different port.#
This error is because that ray dashboard port is occupied by other processes. To handle this issue, you can end the process that occupies the port or you can manually set the ray dashboard port by using the parameter dashboard-port
in init_orca_context
as follows:
init_orca_context(extra_params={"dashboard-port": "50005"})
Note that, the similar error can happen to ray redis port as well, you can also set the ray redis port by using the parameter redis_port
in init_orca_context
as follows:
init_orca_context(redis_port=50006)
Other Issues#
Spark Dynamic Allocation#
By design, BigDL does not support Spark Dynamic Allocation mode, and needs to allocate fixed resources for deep learning model training. Thus if your environment has already configured Spark Dynamic Allocation, or stipulated that Spark Dynamic Allocation must be used, you may encounter the following error:
requirement failed: Engine.init: spark.dynamicAllocation.maxExecutors and spark.dynamicAllocation.minExecutors must be identical in dynamic allocation for BigDL
Here we provide a workaround for running BigDL under Spark Dynamic Allocation mode.
For spark-submit
cluster mode, the first solution is to disable the Spark Dynamic Allocation mode in SparkConf
when you submit your application as follows:
spark-submit --conf spark.dynamicAllocation.enabled=false
Otherwise, if you can not set this configuration due to your cluster settings, you can set spark.dynamicAllocation.minExecutors
to be equal to spark.dynamicAllocation.maxExecutors
as follows:
spark-submit --conf spark.dynamicAllocation.enabled=true \
--conf spark.dynamicAllocation.minExecutors 2 \
--conf spark.dynamicAllocation.maxExecutors 2
For other cluster modes, such as yarn
and k8s
, our program will initiate SparkContext
for you, and the Spark Dynamic Allocation mode is disabled by default. Thus, generally you wouldn’t encounter such problem.
If you are using Spark Dynamic Allocation, you have to disable barrier execution mode at the very beginning of your application as follows:
from bigdl.orca import OrcaContext
OrcaContext.barrier_mode = False
For Spark Dynamic Allocation mode, you are also recommended to manually set num_ray_nodes
and ray_node_cpu_cores
equal to spark.dynamicAllocation.minExecutors
and spark.executor.cores
respectively. You can specify num_ray_nodes
and ray_node_cpu_cores
in init_orca_context
as follows:
init_orca_context(..., num_ray_nodes=2, ray_node_cpu_cores=4)