Comments (1)
nerdcha
commented on July 17, 2024
Yes. After changes are made to the Dockerfile, the image needs to be rebuilt and deployed. Usually that's pretty fast, but I wanted to include a nightly build of TensorFlow in this batch and that took longer than hoped. You should be all set to use those packages now. Thanks for raising this, and please let us know if you run into any other problems!
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Related Issues (20)
- TF TensorRT misconfigured
- NameError: name 'io' is not defined HOT 1
- Check the version of python and cuda with each image of cpu and gpu. HOT 1
- ImportError: cannot import name 'QhullError' from 'scipy.spatial' HOT 3
- Unable to fully install xFormers in auxilliary notebook HOT 1
- bump pyTorch to 2.1+
- Interrupt code breaks sometimes HOT 1
- Please Update CUDA version to 12.1 or 11.8
- Updating torch ecosystem HOT 1
- Please Install new package to be used in Kaggel Notebook HOT 1
- Ultralytics YOLOv8 Kaggle CPU notebook running deprecated Python 3.7 HOT 1
- v143 - GPU Latest, The latest update version upgrading CUDA seems to have caused many errors related to DeepSpeed
- CHAOS AT CURRENT CUDF WITH RAPIDS DRIVERS HOT 2
- update to glibc 2.35 0r 2.32
- Keras Issue for TensorFlow Hub model HOT 1
- Can't Install Krippendorff HOT 3
- Can't install vllm, llama.cpp
- NotFoundError: Graph execution error: TPU HOT 8
- Could not establish connection to "Kaggle"
- Not Able to run google generative ai models on kaggle notebook [bug] HOT 2
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