A production-speed performance and memory profiler for data batch processing applications.

Project description

Sciagraph: the performance and memory profiler for Python data processing

Whether it's detecting disease, modeling the electric grid, or whatever data processing you do with Python, inefficient code is a cost you can't afford to pay:

If it takes 30 minutes for your code to run, debugging minor changes can waste your whole afternoon.
If your program runs out of memory—it's dead, and you're not getting any results until you fix that.
Once you're running in production at scale, inefficient software means throwing money at your cloud provider. You probably need that money more than they do.

On the other hand, the faster your software, the easier it will be for you to iterate and improve. And the faster your software, the happier your users (and accountant) will be.

That's where profilers come in: tools that will help you find speed and memory bottlenecks in your code, so that instead of guessing, you can quickly fix the problem. Unfortunately, profilers that work well for web applications don't necessary work as well when it comes to data processing. You need a profiler designed for your kind of software.

Sciagraph is a profiler that gives you deep visibility into your Python code's speed and memory usage—with a focus on data science, scientific computing, and data processing. It's designed specifically for the needs of people like you, from measurements to visualizations to integrations (Jupyter, MLFlow, Celery, and more.)

Learn more at https://sciagraph.com.

Project details

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This version

2023.6.1

Jun 30, 2023

2023.6.0

Jun 21, 2023

2023.4.2

Apr 27, 2023

2023.4.1

Apr 16, 2023

2023.4.0

Apr 4, 2023

2023.3.4

Mar 29, 2023

2023.3.3

Mar 18, 2023

2023.3.2

Mar 14, 2023

2023.3.1

Mar 13, 2023

2023.3.0

Mar 13, 2023

2023.2.2

Feb 25, 2023

2023.2.1

Feb 17, 2023

2023.2.0

Feb 15, 2023

2023.1.3

Jan 21, 2023

2023.1.2

Jan 13, 2023

2023.1.1

Jan 6, 2023

2023.1.0

Jan 5, 2023

2022.12.0

Dec 14, 2022

2022.11.1

Nov 28, 2022

2022.11.0

Nov 10, 2022

2022.10.0

Oct 19, 2022

2022.9.1

Sep 30, 2022

2022.9.0

Sep 26, 2022

2022.7.3

Jul 19, 2022

2022.7.2

Jul 19, 2022

2022.7.1

Jul 14, 2022

2022.7.0

Jul 6, 2022

2022.6.2

Jun 21, 2022

2022.6.1

Jun 9, 2022

2022.6.0

Jun 8, 2022

2022.5.4

Jun 1, 2022

2022.5.3

May 25, 2022

2022.5.2

May 20, 2022

2022.5.1

May 16, 2022

2022.5.0

May 9, 2022

2022.4.1

Apr 27, 2022

2022.4.0

Apr 14, 2022

2022.3.2

Mar 29, 2022

2022.3.1

Mar 21, 2022

2022.3.0

Mar 12, 2022

2022.1.2

Feb 28, 2023

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sciagraph-2023.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.7 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.11 manylinux: glibc 2.17+ x86-64

sciagraph-2023.6.1-cp311-cp311-macosx_11_0_universal2.whl (26.6 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.11 macOS 11.0+ universal2 (ARM64, x86-64)

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Uploaded Jun 30, 2023 CPython 3.11 macOS 10.15+ x86-64

sciagraph-2023.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.7 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.10 manylinux: glibc 2.17+ x86-64

sciagraph-2023.6.1-cp310-cp310-macosx_11_0_universal2.whl (26.6 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.10 macOS 11.0+ universal2 (ARM64, x86-64)

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Uploaded Jun 30, 2023 CPython 3.10 macOS 10.15+ x86-64

sciagraph-2023.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.7 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.9 manylinux: glibc 2.17+ x86-64

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Uploaded Jun 30, 2023 CPython 3.9 macOS 11.0+ universal2 (ARM64, x86-64)

sciagraph-2023.6.1-cp39-cp39-macosx_10_15_x86_64.whl (13.2 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.9 macOS 10.15+ x86-64

sciagraph-2023.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.7 MB view hashes)

Uploaded Jun 30, 2023 CPython 3.8 manylinux: glibc 2.17+ x86-64

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Uploaded Jun 30, 2023 CPython 3.7m manylinux: glibc 2.17+ x86-64

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