gc.collect()
A Python garbage collection function used to reclaim objects involved in reference cycles and return their memory to the allocator. The operating system does not necessarily release that memory back immediately.
Core Facts
The entire system runs on a server with 1 vCPU, 2 GB of memory, and no GPU; the author recommends running only one process at a time, calling gc.collect() between assets, measuring peak memory with resource.getrusage, and rendering at the resolution actually needed
Calling gc.collect() once after processing each asset can actively reclaim circular references and avoid memory buildup; on a 2GB machine, actively reclaiming once per processed item can turn the memory curve from a sawtooth buildup into a flat staircase
The author runs the entire pipeline on a server with 1 vCPU, 2 GB of memory, and no GPU, and recommends running only one process at a time on a single core and calling gc.collect() after processing each asset.
Timeline (last 90 days)
Calling gc.collect() after processing each asset can reclaim cyclic references and bring memory back down to baseline; however, memory freed by CPython often remains in pymalloc's internal pools and is not returned to the operating system, so RSS only truly drops when the child process exits.
When batch processing images on low-memory machines, calling gc.collect() per asset can actively trigger garbage collection and release image buffers.
Python's gc.collect() interface can be called manually to promptly free objects no longer in use and avoid memory accumulation.
Calling gc.collect() after processing each asset prevents peak RSS from climbing continuously; otherwise the process may be killed by the kernel while processing the 40th file.
The author frees memory between projects with gc.collect() and measures peak RSS rather than average using resource.getrusage(RUSAGE_SELF).ru_maxrss.
The author runs the entire pipeline on a server with 1 vCPU, 2 GB of memory, and no GPU, and recommends running only one process at a time on a single core and calling gc.collect() after processing each asset.
Python's `gc.collect()` can only reclaim objects in reference cycles and return the memory to the allocator; the operating system does not necessarily reclaim it immediately; heavy steps should be placed in a separate subprocess, so that the memory can be fully reclaimed by the operating system when the process exits.
gc.collect() is used to actively trigger garbage collection after processing each asset, promptly releasing objects that are no longer referenced, and handling circular references that Python reference counting cannot reclaim.
The entire system runs on a server with 1 vCPU, 2 GB of memory, and no GPU; the author recommends running only one process at a time, calling gc.collect() between assets, measuring peak memory with resource.getrusage, and rendering at the resolution actually needed
Calling gc.collect() once after processing each asset can actively reclaim circular references and avoid memory buildup; on a 2GB machine, actively reclaiming once per processed item can turn the memory curve from a sawtooth buildup into a flat staircase
2 more timeline events
All Facts (12)
The entire system runs on a server with 1 vCPU, 2 GB of memory, and no GPU; the author recommends running only one process at a time, calling gc.collect() between assets, measuring peak memory with resource.getrusage, and rendering at the resolution actually needed
70%VerifiedCalling gc.collect() once after processing each asset can actively reclaim circular references and avoid memory buildup; on a 2GB machine, actively reclaiming once per processed item can turn the memory curve from a sawtooth buildup into a flat staircase
70%Partially VerifiedThe author runs the entire pipeline on a server with 1 vCPU, 2 GB of memory, and no GPU, and recommends running only one process at a time on a single core and calling gc.collect() after processing each asset.
65%Partially VerifiedPython's `gc.collect()` can only reclaim objects in reference cycles and return the memory to the allocator; the operating system does not necessarily reclaim it immediately; heavy steps should be placed in a separate subprocess, so that the memory can be fully reclaimed by the operating system when the process exits.
65%Partially Verifiedgc.collect() is used to actively trigger garbage collection after processing each asset, promptly releasing objects that are no longer referenced, and handling circular references that Python reference counting cannot reclaim.
65%UnverifiedCalling gc.collect() after processing each asset can reclaim cyclic references and bring memory back down to baseline; however, memory freed by CPython often remains in pymalloc's internal pools and is not returned to the operating system, so RSS only truly drops when the child process exits.
50%UnverifiedWhen batch processing images on low-memory machines, calling gc.collect() per asset can actively trigger garbage collection and release image buffers.
50%UnverifiedPython's gc.collect() interface can be called manually to promptly free objects no longer in use and avoid memory accumulation.
50%UnverifiedCalling gc.collect() after processing each asset prevents peak RSS from climbing continuously; otherwise the process may be killed by the kernel while processing the 40th file.
50%UnverifiedThe author frees memory between projects with gc.collect() and measures peak RSS rather than average using resource.getrusage(RUSAGE_SELF).ru_maxrss.
50%UnverifiedOn a machine with 2GB of memory, running multiple parallel processes for image tasks reduces efficiency due to resource contention; calling gc.collect() after processing each asset helps free memory between assets.
50%UnverifiedOn a 1 vCPU / 2 GB instance, not explicitly calling gc.collect() may cause Python's cyclic-reference garbage collection to lag, with peak memory exceeding 2 GB and being terminated by the OOM Killer.
50%