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GPU.LAND might be a bit more popular than TensorFlow. We know about 8 links to it since March 2021 and only 7 links to TensorFlow. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I'm just going to mention here the experience of someone who ran gpu.land (doesn't exist any more). He did something similar, monetized it (very cheap) and then had to shut down because people were running crypto miners on it. I hope you have a plan to avoid that type of abuse. Source: about 2 years ago
RIP to gpu.land... I was hoping they would take off because they seemed to have a cool product with great pricing. Source: almost 3 years ago
There's also https://gpu.land (which has their own comparison page). Source: almost 3 years ago
Heya, I'm also so just keeping in touch. After liek 1 month of non redditing, someone replied who claimed to be the developer of gpu.land Apparently it is cloud computing for full Linux rather than the Jupyter notebook like what we tried before. Can I ask what is the update on the cloud computing site? I messaged the gpu.land person to see if we can get some free trial ($1 per hour on cheapest one but I don't know... Source: about 3 years ago
There are also more affordable GPU-for-DL-lending options like gpu.land, although I have never used them so I can't vouch for them -- just something I saw on PH. Source: about 3 years ago
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 1 year ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 2 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: almost 2 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 2 years ago
I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 2 years ago
Banana.dev - Banana provides inference hosting for ML models in three easy steps and a single line of code.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Apple Core ML - Integrate a broad variety of ML model types into your app
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
TensorFlow Lite - Low-latency inference of on-device ML models
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.