I was playing with the AWS instances and trying to deploy some locally trained Keras models, but I find no documentation on that. Has anyone already been able to do it?
I tried to use a similar approach to https://aws.amazon.com/pt/blogs/machine-learning/bring-your-own-pre-trained-mxnet-or-tensorflow-models-into-amazon-sagemaker/, but I had no success. I also found some examples for training keras models in the cloud, but I was not able to get the entry_point + artifacts right.
Thanks for your time!
Yes, it is possible, and yes, the official documentation is not much of help.
However, I wrote an article on that, and I hope it will help you.
Let me know if you need more details. Cheers!
AWS recently released a tutorial on this exact same topic which I find very easy and quicker than the docker image route. Hope this helps.
Related
Please i need you help concerning my yolov5 training process for object detection!
I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.
The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.
This is what i get when the training process starts
This the last message error when the training finish
I think the problem comes maybe from here but i don't know how to fix it, i used the code of yolov5 team as it's in the tuto
The training continue without any problem but the map and precision remains 0 all the process !!
Ps : Here is the link of tuto i followed : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
This is what I would do to troubleshoot it. - Run your code on collab because the environment is proven to work well - Confirm that your labels look good and are setup correctly. Can you checked to ensure the classes look right? In one of the screenshots it looks like you have no labels
Running my code in colab worked successfully and the resulats were good. I think that the problem was in my personnel laptop environment maybe the version of pytorch i was using '1.10.0+cu113', or something else ! If you have any advices to set up my environnement for yolov5 properly i would be happy to take from you guys. many Thanks again to #alexheat
I'm using Yolov5 for my custom dataset too. This problem might be due to the directory misplacement.
And using different version of Pytorch will not be a problem. Anyway you can try using the version they mentioned in 'requirements.txt'
It's better if you run
cd yolov5
pip3 install -r requirements.txt
Let me know if this helps.
In Tensorflow documentation, it is shown how to tune several hyperparameters but not the learning rate.I have searched how to tune learning rate using HParams dashboard but could not find much. The only example is another question on github but it does not work.Can you please give me some suggestions on this?Should I use a callback function?Or provide different learning rates in hp_optimizer as in the question in github? Or something else?
Thank you,
I wanted to quantize (change all the floats into INT8) a ssd-mobilenet model and then want to deploy it onto my raspberry-pi. So far, I have not yet found any thing which can help me with it. Any help would be highly appreciated.
I saw tensorflow-lite but it seems it only supports android and iOS.
Any library/framweork is acceptable.
Thanks in advance.
Tensorflow Lite now has support for the Raspberry Pi via Makefiles. Here's the shell script. Regarding Mobilenet-SSD, you can get details on how to use it with TensorFlow Lite in this blog post (and here)
You can try using TensorRT library.
One of the features of the library is quantization.
In general mobilenets are difficult to quantize (see https://arxiv.org/pdf/2004.09602.pdf) but the library should do a good work
I'm following the RNN tutorial on Tensorflow site. However, I couldn't find the rnn file named ptb_word_lm.py.
In my ptb folder, there is only reader.py there. Where I can find ptb_word_lm.py?
Many thanks.
I believe this is a bug, which we're tracking at https://github.com/tensorflow/tensorflow/issues/6196
The short answer is that the code is now here: https://github.com/tensorflow/models/tree/master/tutorials/rnn/ptb
I found it here, by searching "ptb_word_lm.py" in Google.
It seems it was removed from the official repository in version recently, one can still find it in 0.12 revision branch here.
Are there instructions or some documentation somewhere or could somebody describe how to deploy the models available as "Parsey's Cousins" (see https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md) with SyntaxNet under Tensorflow Serving? Even deploying just Parsey is a rather complex undertaking that is not really documented anywhere, but how to do this for the additional 40 languages?
This pull request partially addresses your request, but it still has some issues: https://github.com/tensorflow/models/pull/250.
We do have some tentative plans to provide easier integration between SyntaxNet and Tensorflow Serving, but no precise timeline.
Just for the benefit of anyone else who finds this question, after some digging around on GitHub, one can find the following issue started by Johann Petrak:
https://github.com/dsindex/syntaxnet/issues/7
a model from parsey's cousin is not able to export by that patch due to version mismatch
So whilst some people have been able to modify syntaxnet so that it works with Tensorflow Serving, this seems to be at the cost of using a version which is not compatible with Parsey's Cousins.
Currently the only way to get Tensorflow Serving working with languages other than English is to use something like dsindex's code and train your own models.