viztracer
h5py
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viztracer | h5py | |
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5 | 5 | |
4,363 | 2,000 | |
- | 1.1% | |
7.7 | 8.8 | |
3 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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viztracer
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Ask HN: C/C++ developer wanting to learn efficient Python
* https://github.com/gaogaotiantian/viztracer get a timeline of execution vs call-stack (great to discover what's happening deep inside pandas)
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GCC Profiler Internals
Do not use bad instrumenting profilers. A good modern tracing-based instrumenting profiler provides so much more actionable information and insights into where problems are than a sampling profiler it is ridiculous.
As a example consider viztracer [1] for Python. By using a aggregate visualizer such as a flame graph you can figure out what is taking the most time then you can use a tracing visualizer to figure out the exact call stacks and system execution and state that caused it. Not only that, a tracing visualizer lets you diagnose whole system performance and makes it trivial to identify 1 in 1000 anomalous execution patterns (with a 4k screen a anomalous execution pattern stands out like a 4 pixel dead spot). In addition you also get vastly less biased information for parallel execution and get easy insights into parallel execution slowdowns, interference, contention, and blocking behaviors.
The only advantages highlighted in your video that still apply to a good instrumenting profiler are:
1. Multi-language support.
2. Performance counters (though that is solved by doing manual tracking after you know the hotspots and causes).
3. Overhead (if you are using low sampling frequency). Even then a good tracing instrumentation implementation should only incur low double-digit percent overhead and maybe 100% overhead in truly pathological cases involving only small functions where the majority of the execution time is literally spent in function call overhead.
4. No need for recompilation, but you are already looking to make performance changes and test so you already intend to rebuild frequently to test those experiments. In addition, the relative difference in information is so humongous that this is not even worth contemplating unless it is a hard requirement like evaluating something in the field.
[1] https://github.com/gaogaotiantian/viztracer
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Memray is a memory profiler for Python by Bloomberg
Actually it has explicit support for async task based reporting:
https://github.com/gaogaotiantian/viztracer#async-support
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Tracing and visualizing the Python GIL with perf and VizTracer
Let us run perf on this, similarly to what we did to example0.py. However, we add the argument -k CLOCK_MONOTONIC so that we use the same clock as VizTracer and ask VizTracer to generate a JSON, instead of an HTML file:
h5py
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Working with data files too large for RAM
There's some good answers here, but another option I haven't seen suggested: Convert your txt file to HDF5 (Regardless if you follow my approach here, you should really consider converting your data to anything but a txt file). There's a nice library for working with it in python called h5py. The HDF format is designed specifically with working with very large sets of data (it even has compression options), often scientific in nature, but it's not a database. As far as how this fixes the specific issue you you've described, you can utilize numpy slicing to load one chunk your data at a time. Here's a stackoverflow answer which discusses a solution.
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How to combine multiple numpy arrays stored on disk which are too big to fit in RAM?
If it is a dataset, it should consist of individual instances. You could store these instances in separate files. Otherwise, HDF5 is a very convenient storage format. It allows random read/write access to elements of arrays stored on disk and has excellent Python support in form of the h5py package.
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Is Python really this slow?
If possible, try to monitor your memory usage during execution and if you see that you are consistently exceeding ~50% (my own rule of thumb, though you may want to discuss this with others as well) of what's available. If you are consistently using most of the available memory, then it's likely worth taking a moment to evaluate whether you can operate on subsets of the data from start to finish, and leave the rest of the data on disk until you are almost ready to use it. Tools like h5py are very helpful in these kinds of situations.
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Python packages as API end points.
Yea - I really struggled with getting the correct version on h5py to work with both tensorflow and allenai nlp modules. May be its about finding the right version of libraries. Github Issue. I ended up using pickle to save stuff, like John who commented on 26/03/2020 on the same(closed) issue.
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Tracing and visualizing the Python GIL with perf and VizTracer
Apply these to more issues, like in https://github.com/h5py/h5py/issues/1516
What are some alternatives?
pytest-austin - Python Performance Testing with Austin
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
Numba - NumPy aware dynamic Python compiler using LLVM
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
gil_load - Utility for measuring the fraction of time the CPython GIL is held
CPython - The Python programming language
memray - Memray is a memory profiler for Python
external-Merge-Sort - external Merge Sort in python.
per4m - Profiling and tracing information for Python using viztracer and perf, the GIL exposed.