cog
memray
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cog | memray | |
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20 | 27 | |
7,133 | 12,545 | |
8.2% | 1.8% | |
9.4 | 9.0 | |
7 days ago | 9 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
cog
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AI Grant Traction in OSS Startups
View on GitHub
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Talk-Llama
I'm in the same situation. I found this cog project to dockerise ML https://github.com/replicate/cog : you write just one python class and a yaml file, and it takes care of the "CUDA hell" and deps. It even creates a flask app in front of your model.
That helps keep your system clean, but someone with big $s please rewrite pytorch to golang or rust or even nodejs / typescript.
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Llama 2 – Meta AI
https://github.com/replicate/cog
Our thinking was just that a bunch of folks will want to fine-tune right away, then deploy the fine-tunes, so trying to make that easy... Or even just deploy the models-as-is on their own infra without dealing with CUDA insanity!
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Handling concurrent requests to ML model API
I have used this tool before: https://github.com/replicate/cog/tree/main
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Opinions on Cog: Containers for machine learning
Then I discovered Cog: Containers for Machine Learning. Looks like a way more flexible solution to plug in the existing infrastructure: you write your custom code and Cog plugs it in a Docker image with FastAPI, no extra ecosystem complexity added.
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can someone teach me how to install the new stable diffusion repo?
Highly recommend using cog https://github.com/replicate/cog
- Run Stable Diffusion on Your M1 Mac’s GPU
- replicate/cog: Containers for machine learning
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Why companies move off Heroku (besides the cost)
Dokku Maintainer here.
Dokku also supports Dockerfiles, Docker Images, Tarballs (similar to heroku slugs), and Cloud Native Buildpacks. I'm also actively working on AWS Lambda support (both for simple usage without much config as well as SAM-based usage) and investigating Replicate's Cog[1] and Railways Nixpacks[2] functionalities for building apps.
There are quite a few options in the OSS space (as well as Commercial offerings from new startups and popular incumbents). It's an interesting space to be in, and its always fun to see how new offerings innovate on existing solutions.
[1] https://github.com/replicate/cog
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)
What are some alternatives?
nixpacks - App source + Nix packages + Docker = Image
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
pytorch_wavelets - Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
piku - The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.
MemoryProfiler - memory_profiler for ruby
heroku-review-app-actions - GitHub action to automate managing review apps on your Heroku account
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
sidekiq - Sidekiq worker on Render
py-spy - Sampling profiler for Python programs