kandi background
Explore Kits

clubber.ml | Artificial Intelligence & Machine Learning CLUB | Machine Learning library

 by   BUPT Python Version: Current License: Apache-2.0

 by   BUPT Python Version: Current License: Apache-2.0

Download this library from

kandi X-RAY | clubber.ml Summary

clubber.ml is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. clubber.ml has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However clubber.ml build file is not available. You can download it from GitHub.
Welcome to Artificial Intelligence & Machine Learning CLUB!. Here are all of friends for Code, Paper, and Beers! 🍻. We have weekly offline meetups, you will be welcome to join if you are interested. 第二季第10次俱乐部 After Party: Beers at Lakers Bar.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • clubber.ml has a low active ecosystem.
  • It has 122 star(s) with 41 fork(s). There are 31 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 138 open issues and 36 have been closed. On average issues are closed in 17 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of clubber.ml is current.
clubber.ml Support
Best in #Machine Learning
Average in #Machine Learning
clubber.ml Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • clubber.ml has no bugs reported.
clubber.ml Quality
Best in #Machine Learning
Average in #Machine Learning
clubber.ml Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • clubber.ml has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
clubber.ml Security
Best in #Machine Learning
Average in #Machine Learning
clubber.ml Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • clubber.ml is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
clubber.ml License
Best in #Machine Learning
Average in #Machine Learning
clubber.ml License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • clubber.ml releases are not available. You will need to build from source code and install.
  • clubber.ml has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
clubber.ml Reuse
Best in #Machine Learning
Average in #Machine Learning
clubber.ml Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed clubber.ml and discovered the below as its top functions. This is intended to give you an instant insight into clubber.ml implemented functionality, and help decide if they suit your requirements.

  • Resizes an image
    • Get resize resolution

Get all kandi verified functions for this library.

Get all kandi verified functions for this library.

clubber.ml Key Features

Paper Index: https://ai-ml.club/papers

Meetup Announcements & Minutes: https://ai-ml.club/categories/#events

Join Us 💖 by reading the Newcomer Manual: https://ai-ml.club/manuals/newcomer/

Git

copy iconCopydownload iconDownload
GIT_LFS_SKIP_SMUDGE=1 git clone git@github.com:BUPT/ai-ml.club.git

Install

copy iconCopydownload iconDownload
# Setup ruby environment first
sudo apt install ruby
sudo gem install bundle

# Install the blog requirements & NPM
make install

Serve

copy iconCopydownload iconDownload
make serve

Local Test

copy iconCopydownload iconDownload
make test
# If ERROR occurs please check out
# 1. node version >= 10
# 2. you have installed the lastest typescript

Image Resizing

copy iconCopydownload iconDownload
# Mac
brew install imagemagick

# Linux
apt install imagemagick

./scripts/fit-image.sh

VsCode Markdown Linting

copy iconCopydownload iconDownload
code --install-extension DavidAnson.vscode-markdownlint

Rules

copy iconCopydownload iconDownload
header:
  teaser: /assets/2019/my-awesome-post-teaser-500x300.jpg

Community Discussions

Trending Discussions on Machine Learning
  • Using RNN Trained Model without pytorch installed
  • Flux.jl : Customizing optimizer
  • How can I check a confusion_matrix after fine-tuning with custom datasets?
  • CUDA OOM - But the numbers don't add upp?
  • How to compare baseline and GridSearchCV results fair?
  • Getting Error 524 while running jupyter lab in google cloud platform
  • TypeError: brain.NeuralNetwork is not a constructor
  • Ordinal Encoding or One-Hot-Encoding
  • How to increase dimension-vector size of BERT sentence-transformers embedding
  • How to identify what features affect predictions result?
Trending Discussions on Machine Learning

QUESTION

Using RNN Trained Model without pytorch installed

Asked 2022-Feb-28 at 20:17

I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random

torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')

class RNN(nn.Module):
  def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
    super(RNN, self).__init__()
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.output_size = output_size
    self.num_layers = num_layers
    self.batch_size = batch_size
    self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
    self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
    self.hidden2out = nn.Linear(hidden_size, output_size)
    self.hidden = self.init_hidden()
  def forward(self, feature_list):
    feature_list=torch.tensor(feature_list)
    
    if self.matching_in_out:
      lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1))
      output_space = self.hidden2out(lstm_out.view(len( feature_list), -1))
      output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoid
      return output_scores #output_scores
    else:
      for i in range(len(feature_list)):
        cur_ft_tensor=feature_list[i]#.view([1,1,self.input_size])
        cur_ft_tensor=cur_ft_tensor.view([1,1,self.input_size])
        lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden)
        outs=self.hidden2out(lstm_out)
      return outs
  def init_hidden(self):
    #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
    return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device),
            torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device))

I am aware of this question, but I'm willing to go as low level as possible. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.

Based on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function:

  • nn.LSTM
  • nn.Linear
  • torch.sigmoid

I think I can easily implement the sigmoid function using numpy. However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class?

So, the question is, how can I "translate" this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it? Alternatively, is there a "light" version of pytorch, that I can use just to run the model and yield a result?

EDIT

I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. Specifically, a numpy equivalent for the following would be great:

rnn = nn.LSTM(10, 20, 2)
input = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
c0 = torch.randn(2, 3, 20)
output, (hn, cn) = rnn(input, (h0, c0))

and also for linear:

m = nn.Linear(20, 30)
input = torch.randn(128, 20)
output = m(input)

ANSWER

Answered 2022-Feb-17 at 10:47

You should try to export the model using torch.onnx. The page gives you an example that you can start with.

An alternative is to use TorchScript, but that requires torch libraries.

Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

A running example

Just modifying a little your example to go over the errors I found

Notice that via tracing any if/elif/else, for, while will be unrolled

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random

torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')

class RNN(nn.Module):
  def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
    super(RNN, self).__init__()
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.output_size = output_size
    self.num_layers = num_layers
    self.batch_size = batch_size
    self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
    self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
    self.hidden2out = nn.Linear(hidden_size, output_size)
  def forward(self, x, h0, c0):
    lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0))
    outs=self.hidden2out(lstm_out)
    return outs, (hidden_a, hidden_b)
  def init_hidden(self):
    #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
    return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(),
            torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach())

# convert the arguments passed during onnx.export call
class MWrapper(nn.Module):
    def __init__(self, model):
        super(MWrapper, self).__init__()
        self.model = model;
    def forward(self, kwargs):
        return self.model(**kwargs)

Run an example

rnn = RNN(10, 10, 10, 3)
X = torch.randn(3,1,10)
h0,c0  = rnn.init_hidden()
print(rnn(X, h0, c0)[0])

Use the same input to trace the model and export an onnx file


torch.onnx.export(MWrapper(rnn), {'x':X,'h0':h0,'c0':c0}, 'rnn.onnx', 
                  dynamic_axes={'x':{1:'N'},
                               'c0':{1: 'N'},
                               'h0':{1: 'N'}
                               },
                  input_names=['x', 'h0', 'c0'],
                  output_names=['y', 'hn', 'cn']
                 )

Notice that you can use symbolic values for the dimensions of some axes of some inputs. Unspecified dimensions will be fixed with the values from the traced inputs. By default LSTM uses dimension 1 as batch.

Next we load the ONNX model and pass the same inputs

import onnxruntime
ort_model = onnxruntime.InferenceSession('rnn.onnx')
print(ort_model.run(['y'], {'x':X.numpy(), 'c0':c0.numpy(), 'h0':h0.numpy()}))

Source https://stackoverflow.com/questions/71146140

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install clubber.ml

You can download it from GitHub.
You can use clubber.ml like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

DOWNLOAD this Library from

Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items
Find more libraries
Reuse Solution Kits and Libraries Curated by Popular Use Cases
Explore Kits

Save this library and start creating your kit

Share this Page

share link
Reuse Pre-built Kits with clubber.ml
Consider Popular Machine Learning Libraries
Try Top Libraries by BUPT
Compare Machine Learning Libraries with Highest Support
Compare Machine Learning Libraries with Highest Quality
Compare Machine Learning Libraries with Highest Security
Compare Machine Learning Libraries with Permissive License
Compare Machine Learning Libraries with Highest Reuse
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items
Find more libraries
Reuse Solution Kits and Libraries Curated by Popular Use Cases
Explore Kits

Save this library and start creating your kit

  • © 2022 Open Weaver Inc.