I have been looking for a version of Stable-Diffusion which would be able to run on the IPU's. Currently (due to the high availability) so far I can find CUDA based ones only.
Now I wonder if there is a way to run scripts/trainers/learning etc that are Cuda based on IPU? For example a translation program in between.
I doubt there is, and I bet as I cannot find a IPU version I'll have to modify the scripts :(.
There is the HuggingFace optimum library which acts as the interoperability layer for transformers to run on IPUs. You can find Stable Diffusion there.
For other models that are not supported in the library, there's a guide on how you could modify your script to make it IPU-compatible here
I am a big fan of Jax and have wrote a lot of code in it. At the same time, I have a bit of legacy code that I need to run with PyTorch (I am normally using 1.9.0). This hasn’t caused any issues until recently. I was running a rather old version of Jax but decided to update it for some new functionality. This doesn’t seem to work.
Was anyone successful in creating an environment with a version of Jax>2.12 and PyTorch>1.6.0? If so, how?
I think it would be immensely helpful if we could run those together, but whenever Anaconda is trying to solve the environment, I’m getting a very long list of conflicts. I tried several versions of PyTorch, but only Pytorch 0.34 seems to work, which isn’t sufficient for my legacy code.
Any pointers would be appreciated!
I am currently trying to use an eBPF program on a Raspberry Pi 3 Model B V1.2, that has Ubuntu installed. For managing the compilation, system calls and all that, I use the BPF compiler collection.
Whenever BCC tries to compile the program, I get a multitude of errors, one of which is "SMP is not supported on this platform" and another "SMP not supported on pre-ARMv6 CPUs".
This seems really strange to me, because looking at the headers, those should only occur, if __LINUX_ARM_ARCH__ is smaller than 6.
"uname -m" gives me armv7l, which should be sufficient, right?
Looking at the kernel config, CONFIG_SMP is y and CONFIG_CPU_32v7 is y as well. So as far as I understand, everything seems correct.
So why doesn't it work and how can I fix it?
Also if you need more information, I'll gladly supply it. I've never been so deep into this stuff, so I don't know what's more and what's less important.
I have trained the model on a super computer(ubuntu). After the training I used the model with Windows 10 and got this error:
SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
I can't load the model I trained.
pytorch version in ubuntu: 1.1.0a0+9a7bcac
pytorch version in windows: 0.4.1
What is going wrong and how can I fix it?
EDIT after comment discussion:
It seems like yor Windows version is outdated and there is thus a version conflict occurring. I would strongly suggest to update the version on Windows to post 1.0 release, which should fix the problem.
According to this link, you can likely ignore the warning (not an error), as long as your model seems to still work as intended. The usual culprit for such changes is that you have inconsistent versions of PyTorch on your two systems, and therefore might encounter this warning.
Generally, the versions are supposed to be fully backward compatible, but of course there is no guarantee for this. It has nothing to do with the fact that you are on Linux and/or Windows, unless the source code detects changes in the line break character (which is the main difference from what I remember), although I think this is very unlikely to be the case.
Thank dennlinger very much!
Is the problem of version.
After I update the pytorch version, it work!
Background
I use an Anaconda environment in Windows 10, made following this post by Mike Müller:
conda create -n keras python=3.6
conda activate keras
conda install keras
This environment has Python 3.6.8, Keras 2.2.4, TensorFlow 1.12.0, and NumPy 1.16.1.
I was working on optimizing code for a team I just joined when I found I can't even run their code. I reduced it to a test case with an MCVE (at least, for me; apologies for not being able to give a testable example):
class TestEvaluation(unittest.TestCase):
def setUp(self):
# In-house function loads inputs and labels properly.
self.inputs, self.labels = load_data()
# Using a pretrained model, known to work.
self.model = keras.models.load_model('model_name.h5')
# Passes, and is loaded successfully.
self.assertIsNotNone(self.model)
def test_model_evaluation(self):
# Fails on my machine, reporting high loss and 0% accuracy.
scores = self.model.evaluate(self.inputs, self.labels)
accuracy = scores[1] * 100
self.assertAlmostEqual(accuracy, 93, delta=5)
Research
This exact scenario runs perfectly fine from someone else's computer, so we've deduced the following: we have the same code, model, and data. Therefore, it should be the environment, right?
I built more Anaconda environments to reproduce the version numbers that work on their machine. However, this didn't fix it. Moreover, this seems to be an issue that not many other people have had, as far I've found by searching online.
I went through the following other environments:
Python 3.6.4, Keras 2.2.4, TensorFlow 1.12.0, NumPy 1.16.2
(The one that worked for someone else, though admittedly without Anaconda)
Python 3.5.2, Keras 2.2.2, TensorFlow 1.10.0, NumPy 1.15.2
Question
The model is pretrained, the validation set is correctly loaded, but Keras fails to report the ~93% accuracy I'm expecting.
How can I fix this issue of getting 0% accuracy?
Update
I've learned a lot more about the situation. I found that installing a Python 3.6 environment on Ubuntu 18.04 got me to random guessing (~25% accuracy). So, it's no longer 0%! Further, I tried to replicate a machine that's been used for testing a lot, which had Ubuntu 16.04.5. This got me to ~46% accuracy. I wasn't able to perfectly replicate it since Ubuntu forced me to update to 16.04.6 when I installed some packages, and I also don't know how they run things on the machine they test with (I tried myself, and it didn't work).
I also learned that the guy who compiled and saved the model was using MacOS High Sierra, but he also gets it to work in the lab environment. I'll need to follow up on that.
Further, I kept searching online and found others with the same issue:
Keras issue #7676 - An open issue for nearly 2 years. The OP reported his saved model works differently on different machines, which sounds a lot like my problem.
Keras issue #4875 - An open issue for over 2 years. This particular comment seems to be the common solution. I'm not sure if this will solve the problem or not, and I don't actually have the code that compiled this model. However, it seems that many people found issues in how their model was built and saved, so I might need to investigate this further...
I apologize for claiming a solution before, I was ecstactic to see that assertNotEqual(accuracy, 0) passed.
Be Aware
I previously wrote an incorrect answer, and this may very well be another poorly-formed solution. Please be aware I haven't fully tested this hypothesis. Also be aware that this is still an open issue in the Keras community and many people have messed things up in a number of ways to arrive at this problem.
Developing Our Solution
Let Person A be the guy who can run the model okay on our lab computers, as well as his MacBook. Let Person B be the one who can't (i.e. me and everyone else).
I got my team to take this problem more seriously. We got to the point where A has a terminal open at a desktop next to B. A runs the test script and gets 92% accuracy. B runs the script and gets 2%. At this point, we were on the same machine using the exact same Python environment and Keras settings (~/.keras). We were also sure that we had the same script, model, and data. Or, so we thought.
I chose to doubt everything at that point. I scp'd the script, model, and data from A's account to B's account. It worked. Here's what that could mean as a solution:
A Guess at the Problem
The files B had were bad. B got them from team storage on Google Drive, as well as Slack. Further, some were delivered by A through his MacBook. The scripts were genuinely the same. The model and data B had actually differed in binary, but had the same size in bytes, looked "similar" in binary, and could've possibly been an encoding issue.
It wasn't Google Drive. I uploaded and re-downloaded the correct files, and nothing went wrong. However, the wrong file was there to begin with.
Possibly Slack? Perhaps Slack was corrupting the encoding when B downloaded A's files.
Possibly it coming from a MacBook? MacOS generates a lot of .DS_Store-like files, and I don't know much about it. MacOS might've played a role in the model and data being OS-dependent. I wouldn't rule it out simply because I'm ignorant of how that OS operates. I heavily suspect this though because I happen to have a spare MacBook, and I got it to work in that environment before we started testing on the same machine.
Worst Case Scenario
We're accepting that we can get the model to work on a single machine that everyone has access to. Does this mean that the model might still not work on other machines? Unfortunately, yes.
We're not taking the time to test other machines after wasting nearly 2 months on this problem. I hope this research and debugging helps someone else out. I didn't want to leave it at "never mind, fixed it."