memory_profiler | memory_profiler for ruby

 by   SamSaffron Ruby Version: v1.0.1 License: MIT

kandi X-RAY | memory_profiler Summary

kandi X-RAY | memory_profiler Summary

memory_profiler is a Ruby library. memory_profiler has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

memory_profiler for ruby
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              memory_profiler has a medium active ecosystem.
              It has 1575 star(s) with 84 fork(s). There are 21 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 10 open issues and 35 have been closed. On average issues are closed in 38 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of memory_profiler is v1.0.1

            kandi-Quality Quality

              memory_profiler has 0 bugs and 0 code smells.

            kandi-Security Security

              memory_profiler has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              memory_profiler code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              memory_profiler is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              memory_profiler releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              memory_profiler saves you 469 person hours of effort in developing the same functionality from scratch.
              It has 1128 lines of code, 137 functions and 22 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of memory_profiler
            Get all kandi verified functions for this library.

            memory_profiler Key Features

            No Key Features are available at this moment for memory_profiler.

            memory_profiler Examples and Code Snippets

            No Code Snippets are available at this moment for memory_profiler.

            Community Discussions

            QUESTION

            Dask Array.compute() peak memory in Jupyterlab
            Asked 2022-Mar-04 at 22:18

            I am working with dask on a distributed cluster, and I noticed a peak memory consumption when getting the results back to the local process.

            My minimal example consists in instanciating the cluster and creating a simple array of ~1.6G with dask.array.arange.

            I expected the memory consumption to be around the array size, but I observed a memory peak around 3.2G.

            Is there any copy done by Dask during the computation ? Or does Jupyterlab needs to make a copy ?

            ...

            ANSWER

            Answered 2022-Mar-04 at 22:18

            What happens when you do compute():

            • the graph of your computation is constructued (this is small) and send to the scheduler
            • the scheduler gets workers to produce the pieces of the array, which should be a total of about 1.6GB on the workers
            • the client constructs an empty array for the output you are asking for, knowing its type and size
            • the client receives bunches of bytes across the network or IPC from each worker which has pieces of the output. These are copied into the output of the client
            • the complete array is returned to you

            You can see that the penultimate step here necessarily requires duplication of data. The original bytes buffers may eventually be garbage collected later.

            Source https://stackoverflow.com/questions/71354899

            QUESTION

            executing multiline terminal python commands during code execution from within code body
            Asked 2022-Jan-27 at 05:10

            I would like to use some code line speed indicator related libraries such as scalene and tamppa to evaluate which code lines consume more times. We will need to run some command lines in the terminal, before and after the code execution, for seeing the results. For example using tamppa library, if we have the following code (test.py) and execute it in PyCharm:

            ...

            ANSWER

            Answered 2022-Jan-11 at 20:47

            Don't worry about all these fancy python tools. It's all already built into bash. Install bash on Ubuntu on Windows here. And I will give you the script to run.

            https://devblogs.microsoft.com/commandline/bash-on-ubuntu-on-windows-download-now-3/

            Source https://stackoverflow.com/questions/70656362

            QUESTION

            Applying dask dataframe to 3D bar chart
            Asked 2021-Aug-12 at 06:50

            I'm trying to load a dask dataframe from a 30gb csv file into a 3D barchart using matplotlib.

            The problem is the task has been running for days with no end in sight as soon as it gets to the 'color settings' portion of the code.

            I have tried to make it use only a limited number of rows from the dataframe but dask doesn't seem to allow for row indexing, only column indexing.

            So I split the partition and used the partition size to limit the row size. However even with only 100 rows it takes days.

            I have my doubts that it would take days for 100 rows of color settings to be calculated (not even to the plotting portion yet)

            So clearly I am doing something wrong.

            Here is what the dataframe looks like

            Here is the code

            ...

            ANSWER

            Answered 2021-Aug-12 at 06:50

            These lines use the original df, you can check the size of these lists:

            Source https://stackoverflow.com/questions/68747112

            QUESTION

            Amount of memory used in a function?
            Asked 2021-Jul-19 at 13:25

            Which python module can I use to calculate the amount of memory spent executing a function? I'm using memory_profiler, but it shows the amount of memory spent by each line of the algorithm, in this case I want one that shows the total amount spent.

            ...

            ANSWER

            Answered 2021-Jul-19 at 13:03

            You can use tracemalloc to do what memory_profiller does automatically. It's a little unfriendly but I think it does what you want to do pretty well.
            Just follow the code snippet below.

            Source https://stackoverflow.com/questions/68440690

            QUESTION

            Python memory_profiler: @profile not working on multithreading
            Asked 2021-Jun-02 at 15:32

            I have the following code from the example folder with the exception that I added @profile. I am just trying to make this example run because in my code which is more complex I have the same error and I would like to know how much memory is used on each line.

            SYSTEM:

            Python: 3.9

            memory-profiler: 0.58

            OS: Manjaro

            CODE:

            ...

            ANSWER

            Answered 2021-Jun-02 at 15:32

            The docs for memory_profiler : https://pypi.org/project/memory-profiler/ say the following if you use the decorator (@profile):

            In this case the script can be run without specifying -m memory_profiler in the command line.

            So I think you just need to run python MyScript.py

            Source https://stackoverflow.com/questions/67804505

            QUESTION

            Why Does My Package Take up so Much Memory
            Asked 2021-Apr-22 at 01:46

            I am working on Ptera Software, an open-source aerodynamics solver. This is the first package I have distributed, and I'm having some issues related to memory management.

            Specifically, importing my package takes up an absurd amount of memory. The last time I checked, it took around 136 MB of RAM. PyPI lists the package size as 118 MB, which also seems crazy high. For reference, NumPy is only 87 MB.

            At first, I thought that maybe I had accidentally included some huge file in the package. So I downloaded every version's tar.gz files from PyPI and extracted them. None was over 1 MB unzipped.

            This leads me to believe that there's something wrong with how I am importing my requirements. My REQUIREMENTS.txt file looks like this:

            ...

            ANSWER

            Answered 2021-Apr-22 at 01:46

            See Importing a python module takes too much memory. Importing your module requires the memory to store your bytecode (i.e. .pyc files) as well as to store the compiled form of referenced objects.

            So for what, exactly, is all that memory being allocated?

            We can check whether the memory is being allocated for your package or for your dependencies by running your memory profiler. We'll import your package's dependencies first to see how much memory they take up.

            Since no memory will be allocated the next time(s) you import those libraries (you can try this yourself), when we import your package, we will see only the memory usage of that package and not its dependencies.

            Source https://stackoverflow.com/questions/67157553

            QUESTION

            Dask: Running out of memory during filtering (MRE)
            Asked 2021-Mar-23 at 18:37
            tl;dr

            I want to filter a Dask dataframe based on a value of a column, i.e.

            ...

            ANSWER

            Answered 2021-Mar-23 at 08:26

            The problem arises very early in the processing - during the reading of the data. If you use the memory profiler in Jupyter Lab (for Python scripts use pip install memory_profiler), then you will see that simply loading a file with pandas uses memory that is multiples of the file size. In my experiments using csv and parquet files, the memory multiplier was around 3 to 10 times of the underlying file sizes (I'm using pandas version 1.2.3).

            Googling shows that high memory usage of pd.read_csv and pd.read_parquet is a recurring issue... So unless you can find a memory-efficient way of loading the data, the workers have to be given a lot more memory (or a lot smaller load in terms of the file size). Note this is an issue that arises before any of the dask operations, so something that is outside of control of resources option.

            Source https://stackoverflow.com/questions/66751854

            QUESTION

            How do you fix a memory leak within Django tests?
            Asked 2021-Mar-23 at 13:01

            Recently I started having some problems with Django (3.1) tests, which I finally tracked down to some kind of memory leak. I normally run my suite (roughly 4000 tests at the moment) with --parallel=4 which results in a high memory watermark of roughly 3GB (starting from 500MB or so). For auditing purposes, though, I occasionally run it with --parallel=1 - when I do this, the memory usage keeps increasing, ending up over the VM's allocated 6GB.

            I spent some time looking at the data and it became clear that the culprit is, somehow, Webtest - more specifically, its response.html and response.forms: each call during the test case might allocate a few MBs (two or three, generally) which don't get released at the end of the test method and, more importantly, not even at the end of the TestCase.

            I've tried everything I could think of - gc.collect() with gc.DEBUG_LEAK shows me a whole lot of collectable items, but it frees no memory at all; using delattr() on various TestCase and TestResponse attributes and so on resulted in no change at all, etc.

            I'm quite literally at my wits' end, so any pointer to solve this (beside editing the thousand or so tests which use WebTest responses, which is really not feasible) would be very much appreciated.

            (please note that I also tried using guppy and tracemalloc and memory_profiler but neither gave me any kind of actionable information.)

            Update

            I found that one of our EC2 testing instances isn't affected by the problem, so I spent some more time trying to figure this out. Initially, I tried to find the "sensible" potential causes - for instance, the cached template loader, which was enabled on my local VM and disabled on the EC2 instance - without success. Then I went all in: I replicated the EC2 virtualenv (with pip freeze) and the settings (copying the dotenv), and checked out the same commit where the tests were running normally on the EC2.

            Et voilà! THE MEMORY LEAK IS STILL THERE!

            Now, I'm officially giving up and will use --parallel=2 for future tests until some absolute guru can point me in the right directions.

            Second update

            And now the memory leak is there even with --parallel=2. I guess that's somehow better, since it looks increasingly like it's a system problem rather than an application problem. Doesn't solve it but at least I know it's not my fault.

            Third update

            Thanks to Tim Boddy's reply to this question I tried using chap to figure out what's making memory grow. Unfortunately I can't "read" the results properly but it looks like some non-python library is actually causing the problem. So, this is what I've seen analyzing the core after a few minutes running the tests that I know cause the leak:

            ...

            ANSWER

            Answered 2021-Mar-23 at 13:01

            First of all, a huge apology: I was mistaken in thinking WebTest was the cause of this, and the reason was indeed in my own code, rather than libraries or anything else.

            The real cause was a mixin class where I, unthinkingly, added a dict as class attribute, like

            Source https://stackoverflow.com/questions/66720626

            QUESTION

            Small Flask-SQLAlchemy Query taking 900Mb of RAM within Celery Task (Kubernetes Cluster)
            Asked 2021-Feb-13 at 00:15

            I have a very simple query, in a very simple table:

            ...

            ANSWER

            Answered 2021-Feb-13 at 00:15

            For anyone that's going through something similar, I fixed my problem with lazy=True in the backref model declaration.

            This wasn't a problem until a completely different table in the database started to grow fast - we were using lazy='joined' which would automatically join every table that had relationships declared with BsaeDefaults.

            By using lazy=True you only load the table you've queried, so memory consumption in the pod dropped from 1.2Gb to 140Mb.

            Source https://stackoverflow.com/questions/66173740

            QUESTION

            How to measure time and RAM usage for recursive function?
            Asked 2021-Jan-31 at 14:23

            I want to benchmark QuickSort, and BubbleSort. My task it's measure time, CPU usage, and memory usage for both. I wrote code, and in BubbleSort everything works fine. But I have a problem with QuickSort. Example script result:

            ...

            ANSWER

            Answered 2021-Jan-31 at 14:23

            Use context managers when you want control over which function call gets measured.

            Source https://stackoverflow.com/questions/65980054

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install memory_profiler

            Add this line to your application's Gemfile:.

            Support

            Fork itCreate your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am 'Add some feature')Push to the branch (git push origin my-new-feature)Create new Pull Request
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/SamSaffron/memory_profiler.git

          • CLI

            gh repo clone SamSaffron/memory_profiler

          • sshUrl

            git@github.com:SamSaffron/memory_profiler.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link