| Library | Description | Links |
|---|---|---|
| JAX | High-performance array computing |
Understanding Randomization in TF Datasets (including TFRecords) https://colab.research.google.com/github/christianmerkwirth/colabs/blob/master/Understanding_Randomization_in_TF_Datasets.ipynb
How to apply transfer learning and fine-tuning. https://www.tensorflow.org/guide/keras/transfer_learning
I tried different installation methods via
curlwhich failed because of a non existing URLapt-getwhich worked but the installed version does not GPU accesspipwhich worked and the installed version does GPU access
Below the tried methods in detail.
Following the instructions from https://docs.docker.com/compose/install/ resulted in the following error when trying to run docker-compose afterwards.
Newer NVIDA GPUs like the GeForce RTX 3090 require CUDA 11.
TensorFlow provides pre-built Docker images on dockerhub - tensorflow/tensorflow. The underlying Docker files are availble in the TensorFlow GitHub repository.
Currently latest TF version is 2.7. In table gives an overview of CUDA version used within the pre-built Docker images for the specified TF versions. Versions were retrieved from the TensorFlow GitHub repository (date 2022-01-10).