django-silk VS Ray

Compare django-silk vs Ray and see what are their differences.


Silky smooth profiling for Django (by jazzband)


Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads. (by ray-project)
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django-silk Ray
13 37
3,710 23,900
1.2% 2.8%
8.7 10.0
7 days ago 4 days ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.


Posts with mentions or reviews of django-silk. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-10.


Posts with mentions or reviews of Ray. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-02.
  • Elixir Livebook now as a desktop app
    12 projects | | 2 Aug 2022
    I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.

    By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.

    Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding ( and, tldr 20+ seconds down to 3).


  • preprocessing millions of records - how to speed up the processing
    2 projects | | 3 Jun 2022
    Dask, Ray(, or pyspark(if you have a cluster)
  • 3% of 666 Python codebases we checked had a silently failing unit test
    20 projects | | 15 Feb 2022
  • Rust OpenCV - Simple Guide
    3 projects | | 14 Feb 2022
    I'd really want use Rust+OpenCV instead of Python+OpenCV to process a lot of images (xxxxxx pieces on a central NAS). I would want to do it by also splitting the work over multiple worker nodes for speed. Unfortunately, I've so far not had the time to figure this out... Meanwhile, a Rust API for Ray is being worked on!
  • Blazer - HPC python library for MPI workflows
    2 projects | | 10 Feb 2022 doesn't support MPI natively. And thus is not "supercomputer" friendly. Blazer runs on MPI which runs across the NUMA (non-unified memory architecture) setup of a supercomputer. The compute interconnect is 100's of times faster than network remoting, which uses.
  • JORLDY: OpenSource Reinforcement Learning Framework
    2 projects | | 8 Nov 2021
    Distributed RL algorithms are provided using ray
  • Python stands to lose its GIL, and gain a lot of speed
    5 projects | | 20 Oct 2021
    I had a similar use case and ended up using ray.
  • How to deploy a rllib-trained model?
    3 projects | | 16 Oct 2021
    Currently, rllib's "--export-formats" does nothing; I have folders of checkpoints, but no models. Looks like currently the internal export_model function isn't implemented:
    3 projects | | 16 Oct 2021
  • Show HN: SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances
    6 projects | | 3 Oct 2021
    Neat. Congratulations on the launch!

    Apart from the fact that it could deploy to both GCP and AWS, what does it do differently than AWS Batch [0]?

    When we had a similar problem, we ran jobs on spots with AWS Batch and it worked nicely enough.

    Some suggestions (for a later date):

    1. Add built-in support for Ray [1] (you'd essentially be then competing with Anyscale, which is a VC funded startup, just to contrast it with another comment on this thread) and dbt [2].

    2. Support deploying coin miners (might be good to widen the product's reach; and stand it up against the likes of consensys).

    3. Get in front of many cost optimisation consultants out there, like the Duckbill Group.

    If I may, where are you building this product from? And how many are on the team?





What are some alternatives?

When comparing django-silk and Ray you can also consider the following projects:

optuna - A hyperparameter optimization framework

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

Faust - Python Stream Processing

stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

gevent - Coroutine-based concurrency library for Python

SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)

Thespian Actor Library - Python Actor concurrency library

django-debug-toolbar - A configurable set of panels that display various debug information about the current request/response.

Dask - Parallel computing with task scheduling

pymarl - Python Multi-Agent Reinforcement Learning framework

ElegantRL - Cloud-native Deep Reinforcement Learning. 🔥

eventlet - Concurrent networking library for Python