问题描述
观察到 gluonTS 深度状态时间序列模型在不同的操作系统中表现不同。在 Mac OS（四核、物理 VM、intel i7 @2.3 GHz）上运行 docker 容器时，预测需要大约 3 秒；当它在 Linux 操作系统上的 docker 容器上运行时，这个函数需要大约 8 秒（仅 4 个 cpu，VM，intel broadwell @2.19 GHz）；同样的函数在 kubernetes 集群上运行时大约需要 20 秒（分配有 16 个 cpu，VM，BM.standard2.52）。在此期间监视 cpu / 内存时，可以观察到该程序试图消耗所有分配的 cpu，同时最少占用内存和页面文件。我在 Linux 环境下运行 cProfile，下面是分析器的简短输出。我知道 mxnet 带有不同的库来支持不同的操作系统。是否存在有关 mxnet 性能的已知问题？我如何能最好地确定性能问题。
1 0.000 0.000 7.819 7.819 /usr/local/lib/python3.7/site-packages/xxxxxx/forecast/forecast_client.py:26(predict)
1 0.000 0.000 7.801 7.801 /usr/local/lib/python3.7/site-packages/xxxxxx/forecaster.py:8(predict)
2 0.000 0.000 7.801 3.900 /usr/local/lib/python3.7/site-packages/gluonts/mx/model/predictor.py:157(predict)
2 0.000 0.000 7.800 3.900 /usr/local/lib/python3.7/site-packages/gluonts/model/forecast_generator.py:113(__call__)
1 0.000 0.000 7.786 7.786 /usr/local/lib/python3.7/functools.py:835(wrapper)
1 0.000 0.000 7.786 7.786 /usr/local/lib/python3.7/site-packages/gluonts/mx/model/predictor.py:61(_)
5901/1 0.029 0.000 7.785 7.785 /usr/local/lib/python3.7/site-packages/mxnet/gluon/block.py:688(__call__)
5901/1 0.075 0.000 7.785 7.785 /usr/local/lib/python3.7/site-packages/mxnet/gluon/block.py:1127(forward)
1 0.001 0.001 7.764 7.764 /usr/local/lib/python3.7/site-packages/gluonts/model/deepstate/_network.py:216(hybrid_forward)
89852 3.323 0.000 4.976 0.000 /usr/local/lib/python3.7/site-packages/mxnet/_ctypes/ndarray.py:80(_imperative_invoke)
2 0.000 0.000 4.301 2.151 /usr/local/lib/python3.7/site-packages/gluonts/model/deepstate/_network.py:102(compute_lds)
2 0.002 0.001 4.239 2.119 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:803(unroll)
4 0.010 0.003 4.226 1.057 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:195(unroll)
5880/2352 0.021 0.000 4.162 0.002 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:287(forward)
2352 0.019 0.000 3.939 0.002 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:958(hybrid_forward)
1 0.001 0.001 2.847 2.847 /usr/local/lib/python3.7/site-packages/gluonts/mx/distribution/lds.py:201(log_prob)
1 0.024 0.024 2.840 2.840 /usr/local/lib/python3.7/site-packages/gluonts/mx/distribution/lds.py:238(kalman_filter)
2352 0.085 0.000 2.313 0.001 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:527(hybrid_forward)
672 0.041 0.000 2.290 0.003 /usr/local/lib/python3.7/site-packages/gluonts/mx/distribution/lds.py:581(kalman_filter_step)
1176 0.007 0.000 1.383 0.001 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:988(hybrid_forward)
2352 0.019 0.000 1.092 0.000 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:969(<listcomp>)
672 0.026 0.000 0.790 0.001 /usr/local/lib/python3.7/site-packages/gluonts/mx/distribution/multivariate_gaussian.py:73(log_prob)
89852 0.403 0.000 0.760 0.000 /usr/local/lib/python3.7/site-packages/mxnet/ndarray/register.py:75(_verify_all_legacy_ndarrays)
4704 0.020 0.000 0.740 0.000 /usr/local/lib/python3.7/site-packages/mxnet/gluon/rnn/rnn_cell.py:961(<lambda>)
10249 0.052 0.000 0.666 0.000 <string>:2(linalg_gemm2)
关于环境：
- python 3.7
- gluonts 0.6.1
- mxnet 1.6.0
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
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