DeepSpeech: Memory leakage in python client
- Have I written custom code (as opposed to running examples on an unmodified clone of the repository): No
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux 4.9.0-11-amd64 x86_64
- TensorFlow version (use command below): 1.14.0-rc1-22-gaf24dc91b5 1.14.0
- Python version: 3.6.9
- Bazel version (if compiling from source):N/A
- GCC/Compiler version (if compiling from source):N/A
- CUDA/cuDNN version:N/A (using CPU for inference)
- GPU model and memory:N/A (using CPU for inference)
- Exact command to reproduce: using python client (deepspeech 0.6.1)
from deepspeech import Model
model = Model(pbmm_local_path, 512)
model.enableDecoderWithLM(
lm_local_path,
trie_local_path,
alpha,
beta,
)
del model
Description: Repeated calls to the above command causes about 4 mb memory leak per 10 calls. I have an application where variously different models are loaded for ASR depending on the context and purpose. After about 2-3 thousand requests or so, the machine runs out of memory. Only restarting the process will free the memory.
I saw that there is this thread https://github.com/mozilla/DeepSpeech/issues/2403, which confirmed leakage on C++, python and .Net client, but only .Net is fixed?
I’m wondering if this is still a known issue on python, and whether there are plans to fix it? If not, any recommendations or workarounds?
About this issue
- Original URL
- State: closed
- Created 4 years ago
- Comments: 48 (6 by maintainers)
Thanks @lissyx for the detailed analysis. Looks like 0.7.3 is pretty clean, and just some allocations in python that makes it seems like there is leakage. I think we can close this issue.
For my use case, things should improve dramatically when I switch to 0.7.3.
Got it, will do this week.
On Tue, Jun 16, 2020 at 3:26 AM lissyx notifications@github.com wrote:
This was taken care of in #3049
Here is the info on setting it up