Analyzing Model Performance Tf Profiler

Model performance is always a important aspect while training or tuning deep learning models. It is often left to experience or referred to certain previous benchmarks on different data and made analysis upon.

[TF Blog] Performance is a key consideration of successful ML research and production solutions. Faster model training leads to faster iterations and reduced overhead. It is sometimes an essential requirement to make a particular ML solution feasible. However it is not clear what needs to be optimized, whether the model or the input pipeline needs tweaking or a call to some operation.

This talk will focus on identifying the areas that are maybe slowing down model performance using a tool from tensorflow called Tensorflow profiler.

Learn more about tensorflow profiler.

This talk will focus on :

1. General ML model performance roadblocks faced.
2. Understanding efficient ways to analyze model performance.
---Introduction to TF Profiler
---Brief on how’s and what’s of profiler.
---TF profiler as a tool.
3. Do keras calls to profiler itself affect performance?
4. TF Profiler demo using Google colab and tensorboard.

👉 Speaker: Ashwin Phadke
AI and Deep Learning Engineer at Cynapto Technologies

[sameekshakrishnan]

can u plz tell me what is the use of tfds.disable_progress_bar() function?