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Error occurs in combine_audio_files() during concurrent FFmpeg operations.
Root Cause: File descriptor exhaustion — the worker process has too many files open and can't open any more, which is controlled by a resource limit (RLIMIT_NOFILE). When combining multiple audio clips concurrently via MoviePy/FFmpeg, file handles aren't closed between clips quickly enough, hitting the system limit.
Solution (v1.14.0): Worker concurrency is configurable via OPEN_NOTEBOOK_WORKER_MAX_TASKS and is read at worker launch, so restart the worker after changing it.
Set in .env or deployment config:
OPEN_NOTEBOOK_WORKER_MAX_TASKS=2
Then restart the worker. This reduces concurrent audio processing from default 5 to 2, preventing file descriptor exhaustion.
Trade-off: Podcasts generate slower but complete successfully.
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because i am having an issue doing that,
AI summary: Issue Summary: Podcast Generation Fails with [Errno 11] EAGAIN on >4 Segments
Environment:
Open Notebook v1.14.0 (Docker)
Railway deployment (8GB RAM, 8 vCPU)
FFmpeg + MoviePy audio processing
Problem: Podcast generation fails when creating podcasts with more than 4 audio segments:
RuntimeError: Podcast audio generation failed: ERROR: Failed to write output audio - [Errno 11] Resource temporarily unavailable
Error occurs in combine_audio_files() during concurrent FFmpeg operations.
Root Cause: File descriptor exhaustion — the worker process has too many files open and can't open any more, which is controlled by a resource limit (RLIMIT_NOFILE). When combining multiple audio clips concurrently via MoviePy/FFmpeg, file handles aren't closed between clips quickly enough, hitting the system limit.
Solution (v1.14.0): Worker concurrency is configurable via OPEN_NOTEBOOK_WORKER_MAX_TASKS and is read at worker launch, so restart the worker after changing it.
Set in .env or deployment config:
OPEN_NOTEBOOK_WORKER_MAX_TASKS=2
Then restart the worker. This reduces concurrent audio processing from default 5 to 2, preventing file descriptor exhaustion.
Trade-off: Podcasts generate slower but complete successfully.
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