#HOW TO MAKE TF2 RUN FASTER GENERATOR#
#HOW TO MAKE TF2 RUN FASTER UPDATE#
#HOW TO MAKE TF2 RUN FASTER PATCH#
#HOW TO MAKE TF2 RUN FASTER FULL#

A loss scaling is used in the process to keep training stability.Д«y keeping float32 weights, this process does not lower the accuracy of your models.

#HOW TO MAKE TF2 RUN FASTER UPDATE#

In the end, you use the float16 gradients to update the float32 weights. All the expensive operations to obtain the gradients are performed using float16 elements. The core concept of Mixed Precision Training is to say: we don't need so much precision at all time, let's use 16 bits sometimes.Дuring the Mixed Precision Training process, you keep a float32 version of the weights, but perform forward and backward passes on float16 versions of the weights. This means every element has to be encoded on 32 bits. Step 3: Mixed Precision TrainingД«y default, all variables used in our neural network training are stored on float32. Those small changes make a 5 epochs training time fall from 1000 sec (on an RTX2080) to 616s (full graph is below).

#HOW TO MAKE TF2 RUN FASTER PATCH#

Most operations for data loading are pretty well supported, the only tricky part is to take the same patch on the blurred image and the real one. Making the switch from the original Keras sequences to tf.data was fairly easy.

GPUs will tend to have free time while CPUs are performing multiple operations related to data loading. You can easily spot this phenomenom using the TensorBoard profiling. In my article, I initially used Keras Sequences to load the images. Even if youвЂ™re using tf.data, data loading can still be an issue.

#HOW TO MAKE TF2 RUN FASTER GENERATOR#

If you are using a Python generator or a Keras Sequence, your data loading is probably sub-optimal. To do so, we want to reduce the data loading bottleneck. The first objective is to make the GPU busy 100% of the time. I have activated only 1 of the 2 GPUs on the computer, so total utilization is around 20%.

Here, we can easily spot that GPU is at 40% speed most of the time. The advantage is you donвЂ™t have to monitor a specific batch, but rather have a look on performance over the whole training. Along with your GPU, you can also monitor elements from your motherboard ( CPU, Disks,В .). This is a Jupyterlab extension which gives access to various metrics. This can help identify if your GPU is stalled at some point for lack of data.

#HOW TO MAKE TF2 RUN FASTER FULL#

TensorBoard ProfilerД«y simply setting profile_batch= inside the TensorBoard callback, TF adds a full report on operations performed by either the CPU or GPU for the given batch. Visualization is friendlier than nvidia-smiВ , and you can track metrics over time. If youвЂ™re using an Nvidia card, the simplest solution to monitor GPU utilization over time might probably be nvtopВ . Hence, the first step of this TensorFlow tutorial is to explore these better options. nvidia-smiis nice to make sure your process is running on the GPU, but when it comes to GPU monitoring, there are smarter tools out there.

To optimize training speed, you want your GPUs to be running at 100% speed. It is based on the ResNet architecture and is fully convolutional. The model is a convolutional net which takes the (256, 256, 3) blurred patch and predicts the (256, 256, 3) corresponding sharp patch. In this article, IвЂ™ll train a simpler version of the model (the cnn part only). I thought it would be a nice transition to pass the repository in TF2.0 to understand what has changed and what are the implications on my code. Usecase: Improving TensorFlow training time of an image deblurring CNNДў years ago, I published a blog post on Image Deblurring with GANs in Keras. You can get a 2вЂ“10x training time speed-up depending on your current pipeline. I adapted all these tricks to a custom project on image deblurring, and the result is astonishing. This tutorial explores how you can improve training time performance of your TensorFlow 2.0 model around:

How to make tf2 run faster