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Allow HubSpot to contact me about my documentation feedback. Email address Only used if we need clarification on your feedback. Thank you for your feedback, it means a lot to us. A challenge might be to get distributed training working with multiple GPUs. I believe that was the biggest obstacle with the optimizer wrapper solution.

The text was updated successfully, but these errors were encountered:. andreped Thank you for reporting this issue! Could you please specify the use cases for this feature. Thank you! Sorry, something went wrong. What do you mean by "use cases"?

Do you mean scenarios on which having a simple way to perform gradient accumulation, would be beneficial?

Are you familiar with the concept of using gradient accumulation to "artificially" increase batch size while holding memory usage fixed?

Essentially splitting a batch into smaller micro-batches, calculating the gradients for each, before averaging across, without having the entire batch in memory.

It is a generic concept for "approximating" batch training - that is the use case. Or am I misunderstanding something? andreped Thanks for reporting the issue!

Currently users would need to write their own custom training loop to handle the gradient accumulation, which is not too hard, so we have not yet made this an API. I would like to understand more here - do you see gradients accumulation widely used? If it's a popular feature, we will design an API for that.

It's doable to implement it in a custom training loop. But at the same time, it would be feasible to have an API to do this with the model.

Implementing a custom loop to have gradient accumulation is cumbersome IMHO. Yes, it's widely used when it's required. It's one of the techniques to enable larger batch training with limited computational resources. FYI, it's mentioned in pytorch-lighting as one of the effective training techniques.

Currently users would need to write their own custom training loop to handle the gradient accumulation. Actually, you don't even need to write your own custom training loops anymore in TF2. py L However, it is definitely a very commonly used method and it will surely be a popular feature to add.

Perhaps having an API to do just what I did there, is a good idea? Not sure. Note that it is important that this works in multi-GPU strategies, as that is one of its core usages.

That is not something I have explored that much myself, but that is a popular use case for it. Also note that if you introduce gradient accumulation naively, like I did above, then some layers will not be directly compatible.

You will haven suboptimal behaviour on BatchNormalization , for instance, as it will update for every single micro-batch and not when the gradient accumulation is done for a given mini-batch. Has anyone made an attempt to fix BN for this use case? innat dathudeptrai.

Hence, you might lose the effect of using gradient accumulation if you are use BN in your model, which is an extremely popular layer. Hence, it might be a good idea to solve that issue simulaneously. Also note, that it appear common to just SUM the gradients in gradient accumulation instead of doing MEAN reduction.

I think the latter makes more sense, but might be situations where SUM reduction is more correct. Discussed in the same thread as mentioned. Thanks for tagging me! I'll take a look. For context: I've implemented a version that works with keras and model.

andreped Thanks for raising the BN issue, yes, it's something we should support. Actually I am curious about the performanceloss if we don't handle the accumulation for BN layer - in your experiments, was there a big performance loss caused by the suboptimal treatment to BN?

For how to handle accumulation in distributed training, I believe the current Mirrored strategy can handle both SUM and MEAN. I need to double check with our distributed training experts on 1 if GPU distributed training could support aggregating over sub-batches and also across devices.

Actually I am curious about the performanceloss if we don't handle the accumulation for BN layer. I have also observed this myself, where the benefit of increased batch size through GA is lost when BN was used to the model.

But that is surely task and data dependent. It has been suggested to change the default momentum hyperparameter based on the number of accumulations. Essentially, reducing momentum, the too-frequent-updates of BN would be introduce less noise, and therefore should result in more smooth behaviour.

However, I have not seen an actual benchmark on this topic before, nor am I aware of best practice. Perhaps anyone else knows?

But it would surely be better to be able to accumulate the parameters in BN similarly as done for the gradients when using GA, instead of playing around with BN parameters. Is primarily made for multi-GPU training, but I guess it could also be used on single GPU in GA scenario?

What is lacking is having multi-GPU support. However, for my use case it is not that critical. My main use case is to artificially increase batch size using a single-GPU. I came across here looking for gradient accumulation where I will train using: 1- Multiple GPUs.

My specific case is to train large language models e. BART mentions that in Section 5, sentence 1 training language models with very large batch sizes improve performance so I second that keras fit function should support this natively. meliksahturker Have you tested the tool I mentioned above?

I have not added multi-GPU support yet, but all other stuff you mention should work. Can try to add multi-GPU support tomorrow, if you'd like. At least using a single GPU, you can artificially increase the batch size to whichever size you want.

But for such a large batch size be sure to use the right optimizer. I have looked into it but seeing that it does not support multi-GPUs, I haven't tested your tool.

I thought of trying this which has an example that seems to work with multi-gpu setting but, it seems you are aware of its existence and you have developed your tool despite that. So my question is, have you experienced an issue with fsx 's implementation? Moreover I have seen some mention of gradient accumulation causing issues with batch normalization layer especially in existence of FP Have you looked into that, too?

meliksahturker If you follow the commit history of the tool you will see that I used the code you mentioned as baseline. However, I did not reach the same results as regular batch training, which is why I also went with a different approach.

In TF 2. This enabled me to trivially add GA support as well as have full control on what happened with it when mixed precision was added. This is a much simpler solution than re-doing the optimizer itself, which proved to be very challenging. Adding multi-GPU support should be easy. I know what it takes.

I just rarely use multi-GPU setups myself and thus have not had the time to add it. But I know of someone who has been successful in a project, so I will consult with him. However I have benchmarked my tool and I achieve approximately the same results as regular batch training as close as it gets with expected deviations due to floating point errors.

So I believe it is working. I also run unit tests to check exactly this for each new update, and have run several benchmarks which I intend to make public when I get the time. BN is not compatible with GA. It requires you to modify how and when it updates which in Keras is not trivial.

However, for some of my use cases it has worked fine to use batch size 8 and accum steps 4, essentially boosting overall batch size to I have also added support for adaptive gradient clipping as a suggregate for BN in GA, but I have yet to see much benefit for it compared to using BN with GA.

Might need to tune some params in AGC, which you can do through the tool. EDIT: It is a fundamental issue with using BN with GA. With or without mixed precision. They are not compatible.

What I have observed are NaNs, especially using Adam. But if you know of someone who have seen other issues with BN with GA, let me know. I'm currently making a benchmark and could add some more experiments, if of interest :.

This is great insight, especially regarding testing fsx 's implementation thoroughly! Since LMs are Transformer-based, which use BN heavily, then I think I will choose skipping GA for now.

The issue with BN and GA make it even more crucial for this feature to be added to fit as a complete and noob friendly parameter, e. It should just be an argument to set in Model. fit or similar. GA is already available in pytorch-lightning , and others have added support for GA in their framework.

About time Keras does the same. But note that making BN compatible with GA is definitely not easy. There was some people in the PyTorch forum that tried, but I have yet to see a working solution. Not tempted to try myself, but perhaps I will have to make a go soon.

I also actively use BN, so I have the same problem as you. A temporary fix could be to adjust the momentum term in all BN layers, which should make it more robust with GA. If I was you I would at least try that : Let me know how it goes. Always happy to contribute! Hello chenmoneygithub , any update on this issue?

If anyone is interested, I have now implemented a custom AccumBatchNormalization layer which can be used as a direct drop-in replacement in gradient accumulation scenarios.

For more information, see here or the dedicated documentations. Model wrapping also now reaches sufficiently comparable results to regular batch training, benchmarked on various scenarios.

To get started:. Still remains to have multi-GPU support in a seemless manner. Any update regarding this, chenmoneygithub? Hi andreped , thank you for all your work on this - would you be interested in contributing a PR adding gradient accumulation as a parameter to fit?

Multi-GPU support should be easier from Keras internally. would you be interested in contributing a PR adding gradient accumulation as a parameter to fit? Hello, grasskin! Just came back from vacation. Sorry, for not replying earlier. I'm for sure interesting in contributing.

To summarize for you, and whoever might be interested, in the gradient-accumulator package, I have developed the following solutions:. Note that regarding multi-GPU training, I have not yet had the time to properly benchmark this, as I do not myself use multiple GPUs in my research simultaneously, and I am finalizing my PhD so little time for open-source funstuff.

grasskin Regarding adding GA support for the model. fit directly, I am not sure that is a good idea. There was an attempt made to do this for tf-keras but there was an issue in tensorflow hindering us from this working in a distributed setting. However, by wrapping the optimizer instead, this seemed to work as intended.

So perhaps wrapping the optimizer, or adding support for GA there directly, should be the way to go. What do you think? I can also add GA support to the BN layer and where appropriate, but it would be good if we could discuss this further.

Could you contact me per e-mail: andrped94 gmail. IvanUkhov This only addresses the Adam optimizer. If you want to wrap "any" optimizer, use this instead.

I have made a simple example below on how to do this:. If you dont want to wrap the optimizer itself, you can also wrap the model. You can see how to do that from the link above.

The approach was illustrated on Adam, but it is not limited to that. The code is very general.

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The VCP 11 has 14 elevation angles and completes 16 azimuthal scans in five minutes, while VCP 21 has nine elevation angles and completes 11 azimuthal scans in six minutes. This VCP has the same number of elevation angles as VCP However, denser vertical sampling at lower elevation angles provides better vertical definition of storms, improves the detection capability of radars impacted by terrain blockage for better rainfall and snowfall estimates, results in the identification of more storms, and provides quicker updates.

This VCP has 14 elevation angles and completes 17 azimuthal scans in about 4. The Clear-Air Mode, which optimizes the sensitivity of the WSRD, uses both of these VCPs. The VCP 31 long pulse provides a better signal-to-noise ratio, permitting lower reflectivity returns to be detected, while VCP 32 short pulse provides a higher unambiguous velocity.

Both VCPs have five elevation angles, seven azimuthal scans, and take 10 minutes to complete. This VCP has the same elevation angles nine as VCP 21, but completes 20 azimuthal scans in five minutes.

These VCPs have the same elevation angles as VCPs 11, 12, and 21 respectively. The "split cuts" generally elevation angles below 1. See chapter five, Part C, of the Federal Meteorological Handbook No. The Sachidananda-Zrnic Algorithm reduces range ambiguity for Doppler data. This VCP scans the same elevation angles as VCP 12 and below 10 degrees, the same angles as VCP 11 above 10 degrees, and like VCP it employs the Sachidananda-Zrnic Algorithm as VCP on the lowest elevations.

It provides good vertical definition of storms at longer range and also when convective precipitation is within 60 nautical miles nmi of the antenna, and it reduces range ambiguity for Doppler data.

VCP scans 15 elevation angles in 6 minutes. This Clear-Air Mode VCP provides improved sensitivity by scanning slowly to increase signal-to-noise ratio, permitting lower reflectivity returns to be detected.

Like VCP 32, it uses the short pulse to provide a higher unambiguous velocity, it scans the same elevation angles as VCP 12 up through 6. It scans nine elevation angles in about 5. This VCP has the same elevation angles 14 as VCP 12, but completes 20 azimuthal scans in under eight minutes.

It also employs the Sachidananda-Zrnic Algorithm on the lowest elevation angles. Many of these VCPs may also include scanning features such as AVSET, Base Tilt, and SAILS or MRLE.

Automated Volume Scan Evaluation and Termination AVSET concludes a VCP at angles 6. Base Tilt adds an additional elevation scan lower than 0. Supplemental Adaptive Intra-Volume Low-Level Scan SAILS repeats the lowest elevation angle up to 3 times during a VCP, while Mid-Volume Rescan of Low-Level Elevations MRLE repeats up to the lowest 4 elevation angles during a VCP.

NEXRAD system details are available in Federal Meteorological Handbook No. The official Interface Control Documents ICDs contain detailed information on the binary data formats and product specifications.

Home Products Radar Next Generation Weather Radar NEXRAD. Access Methods Digital data is available for free, and paper copies can be purchased and certified. NEXRAD Inventory A search and discovery hub for NEXRAD data that hosts a variety of data access tools, statistics, and documentation.