add a multigpu support to a keras/tensorflow python script (ai deep learning)

Bonjour! I'd like to deliver an accelerated style transfer model inference. I'm familiar with computer vision and theory of probabilities. I'll do the job blazingly fast. Please, give me a try!

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Hello Sir. I'm very interesting in your project. I've acquired skills in machine learning with python such as tensorflow, keras, LSTM and pytorch... Also I can implement model training using parallel processing with CPU or GPU. I can deliver this running with multiple GPU immediately. I'd appreciate the opportunity to show you how I can help you with this task. Looking forward your reply. Regards, Steven

Hi, I can help you complete this project using the built in multi gpu function from keras, it's just a matter of adapting it to your script. Message me and I'll provide you more details on how to complete your project or I can share some of my previous work with. Either way I'll be happy to hear from you. Regards.

I am a new freelancer, but I can.. I am ML expert. I have rich experience in ML using tensorflow and keras and yolo. I have a GPU computer I can work for a long time and I can work your time zone. Hello, I am glad to meet you. I have rich experience in c, c++, open CV, python for 4 years. Your project is very fit for my skills. if you would award it to me, I am sure that I can complete this project with High Speed and quality in a few days. Hoping for your positive reply. Thank you.

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Hi, I am a DL/ML expert, currently working as a deep learning specialist researcher at a mathematics research center. I don't have experience with the Theano backend, but if you're using the TensorFlow backend I can help you with this quickly and efficiently, because I use a multi-GPU environment for training daily with Keras+TensorFlow, and I've converted several models so they work with that optimally. So if you're using the TensorFlow backend please let me know, and based on your resources (what type of / how many GPUs etc.), I can help you find and implement the optimal way of multi-GPU usage for the style transfer script. Likely, all I'll have to do is replicate the computation graph on each GPU and compute the gradients on each in paralell. I can also use the Keras device_lib to detect how many GPUs the machine has, and make sure to utilise all of them, so the script could be used on different machines without modification. Any questions, please let me know in chat! Thank you

Looks like a fairly straightforward project. However, please keep in my depending on your network the speed up might be less than you expect. I've done deep learning research at multiple universities in the UK. I have experience with Keras, PyTorch and Tensorflow. We used up to 4 GPUs at a time to train networks, so adding multi GPU support shouldn't be an issue. I'm available through your preferred form of communication to discuss this further. It would be great to have a chat to manage expectations. Looking forward to hearing from you.