Are there any methods to use pandas, numpy for doing transformations in google cloud data flow?
https://cloud.google.com/blog/big-data/2016/03/google-announces-cloud-dataflow-with-python-support
In the above link it says having support for numpy, scipy and pandas, But there are no examples available
Dataflow or Beam do not currently have transforms that use Numpy or Pandas. Nonetheless, you must be able to use them without much trouble.
If you give more info about your use case, we can help you figure it out.
Related
I am currently facing a situation where I have a vast amount of images coming in from time to time and on which I will have to run some models for inferencing.
To solve this scaling issue, I came across 2 ways:
Using Tensorflow with Spark
Using TF-Serving
Most blogs about using TensorFlow with spark date to the 2017-20 period(except Databricks blogs). So has TF-serving replaced the idea of using Tensorflow with Spark? Or do they cater to different use cases?
If they cater to different use cases then how should I decide which one fits my use case?
Im using a Sklearn for my machine learning and my question is how can i see my process of my taining?
If i use Tensoflow i can see my loading process with Tensorboard. But does Sklearn have something like this?
As pointed out in the comments, you can use matplotlib. There are plenty of tutorials of how to create a plot updating in real-time during your training.
However, personally I found these options pretty cumbersome. I instead chose to use the PyTorch interface to tensorboard.
That works like a charm and you can just pass in numpy loss values.
Here's how to get started: https://pytorch.org/docs/stable/tensorboard.html
Please could you tell me if it is feasible to transform a torch model (torch.save) into algebraic matrices/ equations that can be operated with numpy or basic Python, without the need to install torch and other related libraries (that occupy a lot of space)? In an afirmative case, could you please give me some hints or a link with explanations? Thank you very much.
I'm not aware of any way to do this without a lot of your own work. Basically you'd have to port most of the pytorch library to numpy, which would be a huge project. If space is an issue check if you can save some space by e.g using earlier torch versions or using only the CPU-versions of pytorch.
I know spectrogram can be plotted using different functions of the different libraries in python. In matplotlib, plyplot plots spectrogram directly using time-series audio data but librosa first applies short Fourier transform on data before plotting spectrogram.
But I am still confused between two.
Please tell me the detailed difference between
1.librosa.dispay.specshow()
2.matplotlib.pyplot.specgram()
I have searched the internet a lot but couldn't find any relevant information though.
According to librosa documentation, all librosa plotting functions are depends on matplotlib.
All of librosa’s plotting functions rely on matplotlib. To demonstrate everything we can do, it will help to import matplotlib’s pyplot API here.
I was amazed at the results of DASK and JOBLIB when testing our different algorithms in a multi-node dash cluster with SciKit Learn and XGBoost.
I would like to know how to combine tensorflow 2.0 with Dask and Joblib to accomplish parallel computing with neural networks.
I don't think there are currently any "out of the box" solutions for this, though I believe there is chatter about building this functionality into dask or dask-ml. In early development too, but check out ray, they have some info in their docs about distributed TensorFlow: https://docs.ray.io/en/master/raysgd/raysgd_tensorflow.html