memray
dtale
memray | dtale | |
---|---|---|
27 | 46 | |
12,649 | 4,573 | |
1.8% | 1.3% | |
9.0 | 8.1 | |
3 days ago | 17 days ago | |
Python | TypeScript | |
Apache License 2.0 | GNU Lesser General Public License v3.0 only |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
memray
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Memray – A Memory Profiler for Python
I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
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Microservice memory profiling
second time was nastier. I used https://github.com/bloomberg/memray to try to spot it - that's the tool you should try out. You load your service through memray, and it will get you some stats that you can export as a flamegraph. I can't really afford to make it run on production so I ran it in a docker image and repeatedly ran the scenario I thought was responsible. Didn't find anything. I know what I did wrong: I assumed one particular codepath was the problem. If would have find the issue if I had a really complete scenario that covers broadly every possible endpoint and condition. Can't blame memray, that tool is really promising.
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Big Data Is Dead
This is an excellent summary, but it omits part of the problem (perhaps because the author has an obvious, and often quite good solution, namely DuckDB).
The implicit problem is that even if the dataset fits in memory, the software processing that data often uses more RAM than the machine has. It's _really easy_ to use way too much memory with e.g. Pandas. And there's three ways to approach this:
* As mentioned in the article, throw more money at the problem with cloud VMs. This gets expensive at scale, and can be a pain, and (unless you pursue the next two solutions) is in some sense a workaround.
* Better data processing tools: Use a smart enough tool that it can use efficient query planning and streaming algorithms to limit data usage. There's DuckDB, obviously, and Polars; here's a writeup I did showing how Polars uses much less memory than Pandas for the same query: https://pythonspeed.com/articles/polars-memory-pandas/
* Better visibility/observability: Make it easier to actually see where memory usage is coming from, so that the problems can be fixed. It's often very difficult to get good visibility here, partially because the tooling for performance and memory is often biased towards web apps, that have different requirements than data processing. In particular, the bottleneck is _peak_ memory, which requires a particular kind of memory profiling.
In the Python world, relevant memory profilers are pretty new. The most popular open source one at this point is Memray (https://bloomberg.github.io/memray/), but I also maintain Fil (https://pythonspeed.com/fil/). Both can give you visibility into sources of memory usage that was previous painfully difficult to get. On the commercial side, I'm working on https://sciagraph.com, which does memory and also performance profiling for Python data processing applications, and is designed to support running in development but also in production.
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Check Python Memory Usage
bloomberg/memray: Memray is a memory profiler for Python
- What Python library do you wish existed?
- Modules Import and Optimisation
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The hand-picked selection of the best Python libraries and tools of 2022
Memray — a memory profiler
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Python 3.11 delivers.
Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.
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Memory Profiling for Python
I've been using this recently for memory profiling with Python, it works pretty well: https://github.com/bloomberg/memray
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What stack or tools are you using for ensuring code quality and best practices in medium and large codebases ?
great suggestions in this thread. i also recommend performance testing your codebase. these include techniques such as: - creating micro performance benchmarks - using [cProfile] (and learning how to plot / read flame graphs) - memory profiling (e.g. via memray)
dtale
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The free pandas visualizer, D-Tale, has now been integrated with ArcticDB which will allow users to load huge datasets and easily navigate their databases
[D-Tale](https://github.com/man-group/dtale) has recently released version 3.2.0 on pypi & conda-forge: ``` pip install -U dtale conda install dtale -c conda-forge ``` But if you want to take it one step further you can now integrate it with [ArcticDB](https://github.com/man-group/ArcticDB): ``` pip install -U dtale[arcticdb] ``` This allows you the ability to navigate your libraries of datasets saved to your ArcticDB database! But the best part is that all the reads are occuring directly against ArcticDB so some of the memory constraints you may have been hit with before are now a thing of the past. Here's a full write up how to use this functionality along with a quick demo: https://github.com/man-group/dtale/blob/master/docs/arcticdb/ARCTICDB\_INTEGRATION.md Hope this helps & please support open-source by throwing your star on the [repo](https://github.com/man-group/dtale). Thanks! 🙏
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Data Scientists using neovim: how do you explore dataframes?
I've looked into external tooling, libs such as dtale, which feel overly complicated for my use case (but I'm open to alternatives). What I would like to have instead is something akin to Spyder's variable viewer, which allows sorting by column. VSCode goes a step further and also provides the ability to filter the dataframe.
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I need help lol
D-Tale: A Python library that provides an interactive web-based interface for data exploration and analysis.
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Something better than pandas? with interactive graphical UI?
Try this: https://github.com/man-group/dtale
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Mito – Excel-like interface for Pandas dataframes in Jupyter notebook
https://github.com/man-group/dtale
I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it.
If I really want to show everything, I just use:
'''
- Memray is a memory profiler for Python by Bloomberg
- Show HN: D-Tale, easy to use pandas GUI
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Added visualizations of statsmodels time series analysis functions to the free pandas visualizer, D-Tale
Just added "Time Series Analysis" in v1.60.1 of D-Tale on pypi & conda-forge: pip install -U dtale conda install dtale -c conda-forge This feature provides a quick and easy way to visualize the usage of the following time series analysis function in statsmodels:
- Show HN: Open-source pandas dataframe visualizer
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For all the python/pandas users out there I just released a bunch of UI updates to the free visualizer, D-Tale
Your data is stored in memory so the size of your dataframe is limited to the memory of your machine. That being said we’ve allowed users to swap out the machanism which stores the data so you can use something like Redis or Shelve to allieviate memory. Here’s some documentation: https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md
What are some alternatives?
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
PandasGUI - A GUI for Pandas DataFrames
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
MemoryProfiler - memory_profiler for ruby
jupyterlab-autoplot - Magical Plotting in JupyterLab
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
pandastable - Table analysis in Tkinter using pandas DataFrames.
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
sqliteviz - Instant offline SQL-powered data visualisation in your browser
py-spy - Sampling profiler for Python programs
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.