How severe does this issue affect your experience of using Ray?
- Medium: It contributes to significant difficulty to complete my task, but I can work around it.
I have a pipeline in which files are downloaded from a remote server and processed on an AWS cluster to derive the output. The configuration of the cluster is 4 Cores, 32G Ram. The whole system runs in a batch where 3 workflows run parallelly.
Use Case
Workflows include: downloading 2 files from the remote server. Processing them. store it on s3.
The whole pipeline seems to work fine for batch size 3. Meaning 6 files can be downloaded parallelly, processed parallelly, and stored on s3.
The problem starts when we increase the batch size if we go to 4. it throws RaySystemError
. The workflow failed during execution and it says
S3 subsystem not initialized;
I also want to draw your attention that, the workflows storage path is of an S3 bucket.
ray.init(storage=<path-to-s3>)
Error Tail Logs
File "pyarrow/_s3fs.pyx", line 214, in pyarrow._s3fs.S3FileSystem._reconstruct
File "pyarrow/_s3fs.pyx", line 204, in pyarrow._s3fs.S3FileSystem.__init__
File "pyarrow/error.pxi", line 141, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: S3 subsystem not initialized; please call InitializeS3() before carrying out any S3-related operation
Although I can work around this by setting up the batch size limited to 3, but I am looking for some concrete explanation why it worked for batch size 3 and not for more than that.
It might help me fix the issue.
Thanks in Advance.