TLDR: It depends on your function and whether you are in production or development. Don’t use tf.function if you want to be able to debug your function easily, or if it falls under the limitations of AutoGraph or tf.v1 code compatibility. I would highly recommend watching the Inside TensorFlow talks about AutoGraph and Functions, not … Read more
Update: TF2.2 forced me to clean up all implementations – so now they can be used as a reference for TF best practices. Also added a section below on _get_hyper vs. _set_hyper. I’ve implemented Keras AdamW in all major TF & Keras versions – I invite you to examine optimizers_v2.py. Several points: You should inherit … Read more
UPDATE For newer TF versions (tensorflow>=1.14.0 & tensorflow != 2.0.0a0 – newer than TF2.0-alpha) load the extension like this %load_ext tensorboard OLD ANSWER The extension needs to be loaded first: %load_ext tensorboard.notebook %tensorboard –logdir {logs_base_dir}
You can set the fraction of GPU memory to be allocated when you construct a tf.Session by passing a tf.GPUOptions as part of the optional config argument: # Assume that you have 12GB of GPU memory and want to allocate ~4GB: gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) The per_process_gpu_memory_fraction acts as a hard upper bound … Read more