Optimize Tensorflow Pipeline Performance: prefetch & cache | Deep Learning Tutorial 45 (Tensorflow)

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[@nguyenminhluong7571]

Thank u so much, I have been stuck on dataset. prefetch() for 2 days until I found your Video!! Many Thanks!

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Glad it helped

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I have just finished the whole series. The way you teach makes everything astoundingly intuitive and simple. You and this channel are treasures. Thank you for the time and effort you've expended doing this. I know you already have a lot of requests, and I would not want to impose more upon you, but, whenever time permits, I would love for you to explain forecasting models with multiple features both theoretically and with TF/Keras. Now, on to your other playlists.

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yes I am in the process of making more videos. there are many more exciting videos coming up.. next one is going to be on BERT :) Give me some time I am working on them.

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Glad it was helpful!

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[@_Ahmed_O]

I am so grateful for your help and clear explanation.

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First off, you're channel is so amazing, you're videos are excellent quality and they've helped me learn so much about deep learning, so thank you so much for your hard work and quality content. :D Now lately I've been developing a large Tensorflow model, and I'm getting out of memory errors, from what I've learned it seems the best solution to this road block is gradient checkpointing, however there is little to no resources online about it. Could you make a video covering gradient checkpointing?

[@ArpitYadav-ws5xe]

Great. Excellent video

[@mateusbalotin7247]

Amazing content!!! Thank you very much.

[@kasivisweswarreddy1472]

00:00 Optimizing Tensorflow pipeline performance with prefetch and cache 00:04 Optimizing Tensorflow input pipeline performance using prefetch and caching 02:17 Optimize TensorFlow pipeline performance with prefetch & cache 04:46 Optimize TensorFlow performance using cache function 06:59 Optimizing Tensorflow pipeline performance through prefetching and caching 09:28 Optimizing Tensorflow pipeline performance by prefetching and caching. 12:21 Understanding Tensorflow pipeline performance optimization 14:53 Benchmarking the performance of file dataset class with prefetch API 17:29 Using prefetch and cache to optimize Tensorflow pipeline performance 19:29 Optimizing Tensorflow Pipeline Performance with Cache 21:51 Caching the map function in Tensorflow pipeline prevents recomputation 24:21 Improving performance using cache in Tensorflow pipeline

[@תומרמיכאלוביץ]

You are amazing ! TY

[@canoksuzoglu6540]

thank you for great explainatition

[@sweetykatam3036]

Please do vedios on digital image processing

[@LaurentLaborde]

4:00 I'm very confused at this diagram, you're not calling "batch()" ?

[@kishanikandasamy]

Sir Can you please make a video on how to detect facial expressions using Deep learning

[@isitabhoye4081]

Please , upload video on BERT model also.

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yes it is coming up soon

[@mawkuri5496]

this is the last video. can you create a tuts on transformers and deep minds Fictitious Co-Play (FCP)

[@anandakhan4160]

helpful.

[@juanete69]

When do we need to use .cache() and when .cache().repeat()? Some tutorials say we need the second if we have multiple epochs.

[@ThePietro1234]

Hi, thanks for the video, it is very clear! Only one thing I didn't understand: with .cache optimization the data remains in the CPU RAM right? Or the data remains in GPU DRAM? Because even though we talk about caching I doubt that the data remains in cache memory (trivially because cache memory is too small to fit a medium size dataset). Thanks!

[@shubhamtalks9718]

What if there is some randomization step in between our pipeline? Should we use prefetch and cache then? Because that randomization step should ideally generate some different result for each epoch.

[@suleymanerim2119]

You should use randomization after you cache . If you do caching after randomization step, then you will be fixing the last output of randomization step and getting the same results for each epoch.

[@shubhamtalks9718]

What is the difference between tf.data.Dataset.map(...) vs tf.data.Dataset.apply(...) ?

[@PriyankaDarshanam]

My benchmark(FileDataset()), prefetch and autotune are not showing much difference. Its just 278, 282 and 280

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Plz have a video on chatbot with flask

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[@pafiwachitake3318]

using autotune is giving back an attribute error, claiming that

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