whylogs VS memray

whylogs — model monitoring

Have a look at whylogs. Nice profiling functionality incl. definition of constraints on profiles: https://github.com/whylabs/whylogs

WhyLabs has an open-source first approach. We maintain an open standard for data and ML logging https://github.com/whylabs/whylogs, which allows anybody to begin logging statistical properties of data in their data pipeline, ML inference, feature stores, etc. These statistical profiles capture all the key signals to enable observability in a given component. This unique approach means that we can run a fully SaaS service, which allows for huge scalability (in both the size of models and their number), and ensures that our customers are able to maintain their data autonomy. We maintain a huge array of integrations for whylogs, including Python, Spark, Kafka, Ray, Flask, MLflow, Kubeflow, etc… Once the profiles are captured systematically, they are centralized in the WhyLabs platform, where we organize them, run forecasting and anomaly detection on each metric, and surface alerts to users. The platform itself has a zero-config design philosophy, meaning all monitoring configurations can be set up using smart baselines and require no manual configuration. The TL;DR here is the focus on open source integrations, working with data at massive/streaming scale, and removing manual effort from maintaining configuration.

This is why we've been trying to encourage people to think about lightweight data logging as a mitigation for data quality problems. Similar to how we monitor applications with Prometheus, we should approach ML monitoring with the same rigor.

Disclaimer: I'm one of the authors. We spend a lot of effort to build the standard for data logging here: https://github.com/whylabs/whylogs. It's meant to be a lightweight and open standard for collecting statistical signatures of your data without having to run SQL/expensive analysis.

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)

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.

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.

bloomberg/memray: Memray is a memory profiler for Python

Memray — a memory profiler

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.

I've been using this recently for memory profiling with Python, it works pretty well: https://github.com/bloomberg/memray

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)