Skip to main content

dlib is not compatibile with numpy 2.x


I spent way too long trying to figure out this problem with dlib while using the Python face_recognition library that wraps it, and since I couldn't find anyone giving the correct diagnosis and solution online, I'm posting it as a public service to the next person who hits it.

Here's the error I was getting:

RuntimeError: Error while calling cudaMallocHost(&data, new_size*sizeof(float)) in file /home/jonathan/Projects/dlib/dlib/cuda/gpu_data.cpp:211. code: 2, reason: out of memory

Eventually I gave up and switched from the GPU model ("cnn") to the CPU one ("hog").  Then I started getting errors about 

RuntimeError: Unsupported image type, must be 8bit gray or RGB image.

The errors persisted after adding PIL code to convert to RGB.

This one was easier to track down on Google: it happens when you have numpy 2.x installed, which is not compatible with dlib.  Seems like something along the way should give a warning about that!

At any rate, with numpy downgraded to the latest 1.x version, the cudaMallocHost error also went away.  I guess something in numpy 2.x is getting interpreted as a Very Large image size value.

Postscript: 

Later on I started getting cudaMallocHost errors again.  These came from using an Image that had not been converted to RGB.  So the unifying theme seems to be "cnn mode doesn't have the same sanity checks enabled that hog does, if you get weird errors you should switch to hog and once it works try cnn again."

Comments

Post a Comment

Popular posts from this blog

The Missing Piece in AI Coding: Automated Context Discovery

I recently switched tasks from writing the ColBERT Live! library and related benchmarking tools to authoring BM25 search for Cassandra . I was able to implement the former almost entirely with "coding in English" via Aider . That is: I gave the LLM tasks, in English, and it generated diffs for me that Aider applied to my source files. This made me easily 5x more productive vs writing code by hand, even with AI autocomplete like Copilot. It felt amazing! (Take a minute to check out this short thread on a real-life session with Aider , if you've never tried it.) Coming back to Cassandra, by contrast, felt like swimming through molasses. Doing everything by hand is tedious when you know that an LLM could do it faster if you could just structure the problem correctly for it. It felt like writing assembly without a compiler -- a useful skill in narrow situations, but mostly not a good use of human intelligence today. The key difference in these two sce...
Post a Comment
Read more

A week of Windows Subsystem for Linux

I first experimented with WSL2 as a daily development environment two years ago. Things were still pretty rough around the edges, especially with JetBrains' IDEs, and I ended up buying a dedicated Linux workstation so I wouldn't have to deal with the pain.  Unfortunately, the Linux box developed a heat management problem, and simultaneously I found myself needing a beefier GPU than it had for working on multi-vector encoding , so I decided to give WSL2 another try. Here's some of the highlights and lowlights. TLDR, it's working well enough that I'm probably going to continue using it as my primary development machine going forward. The Good NVIDIA CUDA drivers just work. I was blown away that I ran conda install cuda -c nvidia and it worked the first try. No farting around with Linux kernel header versions or arcane errors from nvidia-smi. It just worked, including with PyTorch. JetBrains products work a lot better now in remote development mod...
Post a Comment
Read more

Python at Mozy.com

At my day job, I write code for a company called Berkeley Data Systems. (They found me through this blog, actually. It's been a good place to work.) Our first product is free online backup at mozy.com . Our second beta release was yesterday; the obvious problems have been fixed, so I feel reasonably good about blogging about it. Our back end, which is the most algorithmically complex part -- as opposed to fighting-Microsoft-APIs complex, as we have to in our desktop client -- is 90% in python with one C extension for speed. We (well, they, since I wasn't at the company at that point) initially chose Python for speed of development, and it's definitely fulfilled that expectation. (It's also lived up to its reputation for readability, in that the Python code has had 3 different developers -- in serial -- with very quick ramp-ups in each case. Python's succinctness and and one-obvious-way-to-do-it philosophy played a big part in this.) If you try it out, pleas...
9 comments
Read more