'add_slot' failing in Windows 11 installation

[pdduggan]

'add_slot' failing in Windows 11 installation

[drubinstein]

Hi. This is a problem we discovered recently as well. It's due to the tensorflow 2.16 release where keras 3 is now used for the saved model format. Currently the main branch has tensorflow pinned to <2.16 which should work until we push the new minor release (once we're sure there are no bugs in main).

[drubinstein]

Hi @Dragonfore . I'm gonna keep the issue open until he responds. I'm fairly confident the issue is with tensorflow 2.16.1 (released last week) as I ran into this error earlier this week when working on and merging #100.

Regardless, there's no reason to be a stickler here and close and reopen the issue. He provided enough detail to get started and I could always ask for more e.g. how he's invoking it. What could be useful is an issue template and I can make one or base one off of what you wrote. Thanks for your comments.

FYI your packages may have a slight conflict given that they install tensorflow-cpu<2.11 and tf-nightly 2.17.

[Dragonfore]

Hi @pdduggan ,

In the future, it is better to provide at least the following specifications as this can be a few possible issues. A few examples of what should be provided is (but exclusively)

• System Specifications (CPU + Architecture, GPU + Architecture, RAM, and possibly other requirements depending)
• Operating System (and terminal environment Win10 CMD or MacOS TERM, etc)
• Python Environment + Pip Environment (VirtualEnv or Pyenv recommended)
• Related pip module versions (Tensorflow, basic-audio)
• Development Environment (Jupyter Lab, VSCode, Visual Studio Community/Professional, vim, ...)
• Reproduction Steps
• Screenshots / Terminal logs
• Other related information

A few things that I have previously considered when setting this (or other Machine Learning projects is the following)
OS: Windows 11 - Command Line Terminal VIA VSCode
CPU & GPU: AMD Ryzen 5 PRO 7540U + AMD Radeon 740M Graphics (CPU-only, GPU disabled)
Python Version: 3.10.11 through Pyenv
PIP Versions:
(tensorflow-cpu<2.11 | basic-pitch | setuptools) -> installations required
Package Version

---

absl-py 2.1.0
asttokens 2.4.1
astunparse 1.6.3
audioread 3.0.1
basic-pitch 0.2.6
cachetools 5.3.3
certifi 2024.2.2
cffi 1.16.0
charset-normalizer 3.3.2
colorama 0.4.6
comm 0.2.2
debugpy 1.8.1
decorator 5.1.1
dm-tree 0.1.8
exceptiongroup 1.2.0
executing 2.0.1
flatbuffers 24.3.7
future 1.0.0
gast 0.4.0
google-auth 2.28.2
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
grpcio 1.62.1
h5py 3.10.0
idna 3.6
ipykernel 6.29.3
ipython 8.22.2
jedi 0.19.1
joblib 1.3.2
jupyter_client 8.6.1
jupyter_core 5.7.2
keras 2.10.0
keras-nightly 3.1.0.dev2024031503
Keras-Preprocessing 1.1.2
lazy_loader 0.3
libclang 16.0.6
librosa 0.10.1
llvmlite 0.42.0
Markdown 3.6
markdown-it-py 3.0.0
MarkupSafe 2.1.5
matplotlib-inline 0.1.6
mdurl 0.1.2
mido 1.3.2
mir-eval 0.7
ml-dtypes 0.3.2
msgpack 1.0.8
namex 0.0.7
nest-asyncio 1.6.0
numba 0.59.0
numpy 1.23.5
oauthlib 3.2.2
opt-einsum 3.3.0
packaging 23.2
parso 0.8.3
pip 23.0.1
platformdirs 4.2.0
pooch 1.8.1
pretty-midi 0.2.10
prompt-toolkit 3.0.43
protobuf 3.19.6
psutil 5.9.8
pure-eval 0.2.2
pyasn1 0.5.1
pyasn1-modules 0.3.0
pycparser 2.21
Pygments 2.17.2
python-dateutil 2.9.0.post0
pywin32 306
pyzmq 25.1.2
requests 2.31.0
requests-oauthlib 1.4.0
resampy 0.4.3
rich 13.7.1
rsa 4.9
scikit-learn 1.4.1.post1
scipy 1.12.0
setuptools 65.5.0
six 1.16.0
soundfile 0.12.1
soxr 0.3.7
stack-data 0.6.3
tb-nightly 2.17.0a20240315
tensorboard 2.10.1
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorflow-cpu 2.10.1
tensorflow-directml-plugin 0.2.0.dev221020
tensorflow-estimator 2.10.0
tensorflow-intel 2.10.1
tensorflow-io-gcs-filesystem 0.31.0
termcolor 2.4.0
tf-nightly 2.17.0.dev20240306
tf_nightly_intel 2.17.0.dev20240306
threadpoolctl 3.3.0
tornado 6.4
traitlets 5.14.2
typing_extensions 4.10.0
urllib3 2.2.1
wcwidth 0.2.13
Werkzeug 3.0.1
wheel 0.43.0
wrapt 1.16.0
Other information: Jupyter on Visual Studio Code Insiders (Daily build)

Here are a few steps I took to solve it:

• Unsure that your python version is within the supported set (3.7...3.10)
• Use pyenv to perform the previous step
• Try using this if you run into issues https://github.com/microsoft/tensorflow-directml-plugin
• If you are running into an error related to Multiple OpKernels, keep deleting Tensorflow until all have been deleted (there may be multiple versions) and then reinstall (tensorflow-cpu XOR tensorflow XOR tf-nightly)
• Reference: https://stackoverflow.com/questions/72994470/error-when-using-directml-multiple-opkernel-registrations-match-nodedef-at-the
• Ensure you are using the correct python kernel for jupyter, or for your terminal/IDE
• If using jupyter, ensure that the kernel is reset after EVERY PACKAGE CHANGE (CTRL-SHIFT-P -> Restart Kernel) or ensure that you reactivate your virtual environment (WSL, MacOS, Ubuntu) or any other steps related to your OS
• If you are unable to install from pip, I would recommend installing "setuptools" package which uses a deprecated package called "distutils" which is a requirement for numpy as I recall..
• Disable the GPU if you are running into issues, and run cpu-bound to get started, which I added in the attached code.
• Load the model directly through TensorFlow instead of through this library as you can add additional configuration easier
• If you run into a CUDART_64 error try this https://stackoverflow.com/questions/66206835/importerror-could-not-load-dynamic-library-cudart64-110-dll
• Try this if you want to only use CPU https://stackoverflow.com/questions/37660312/how-to-run-tensorflow-on-cpu

# Uses Spotify Open-Source Basic Pitch audio-to-midi conversion
from basic_pitch.inference import predict
from basic_pitch import ICASSP_2022_MODEL_PATH
from os.path import join as pathjoin
from os import sep
import tensorflow as tf

# Set variables
input_file_path = pathjoin("C:" + sep, "repo", "JupyterNotebooks", "cached_data", "blob_data", "nuvoleBianche.mp3")
output_file_path = pathjoin("C:" + sep, "repo", "JupyterNotebooks", "cached_data", "processed_data", "")

# Check version and load model (used as workaround in Windows)
print(tf.__version__)
tf.config.set_visible_devices([], "GPU")
basic_pitch_model = tf.saved_model.load(ICASSP_2022_MODEL_PATH)

# Perform prediction
(model_output, midi_data, note_events) = predict(
    input_file_path,
    basic_pitch_model,
)

# Do something with the data
print(note_events)

[Dragonfore]

@drubinstein I would recommend that this issue is set to closed based on the initial description and may be re-opened once the author provides more information or goes through the necessary steps based on input I provided.

[Dragonfore]

Hi @drubinstein Thanks for the FYI on the package conflicts! I do think that an issue template would be beneficial if you have some time or I might be able to create a PR to do this sometime in the next week or so.

Sorry about the recommendation to close, I should have waited until after a response was provided as there was a reasonable amount of detail. After re-reading, I did come across as a stickler, my goal only goal was trying to help out for the reporting of possible issues. I'll keep that in mind for the future.

Additionally, @pdduggan if you have any other questions please feel free to provide more info and sorry for one of my previous comment about closing prematurely.

[whythomedia]

I had the same issue and rolling back from tensorflow 2.16 to 2.15 fixed the problem for me.

I'm on OSX using python 3.11

[drubinstein]

Until I can find a way to reserialize the model or find what transitive dependency is breaking model loading, I've pinned tensorflow to <2.15.1. I hope this doesn't impede any of your work.