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extensible-choice-parameter-plugin | Jenkins plugin to define choice build parameters | Machine Learning library

 by   jenkinsci Java Version: Current License: No License

 by   jenkinsci Java Version: Current License: No License

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kandi X-RAY | extensible-choice-parameter-plugin Summary

extensible-choice-parameter-plugin is a Java library typically used in Artificial Intelligence, Machine Learning applications. extensible-choice-parameter-plugin has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Older versions of this plugin may not be safe to use. Please review the following warnings before using an older version:. This plugin adds "Extensible Choice" as a build parameter.You can select how to retrieve choices, including the way to share choices among all jobs.
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kandi-support Support

  • extensible-choice-parameter-plugin has a low active ecosystem.
  • It has 15 star(s) with 32 fork(s). There are 120 watchers for this library.
  • It had no major release in the last 12 months.
  • extensible-choice-parameter-plugin has no issues reported. There are 3 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of extensible-choice-parameter-plugin is current.
This Library - Support
Best in #Machine Learning
Average in #Machine Learning
This Library - Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • extensible-choice-parameter-plugin has 0 bugs and 0 code smells.
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • extensible-choice-parameter-plugin has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • extensible-choice-parameter-plugin code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Machine Learning
Average in #Machine Learning
This Library - Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • extensible-choice-parameter-plugin does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
This Library - License
Best in #Machine Learning
Average in #Machine Learning
This Library - License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • extensible-choice-parameter-plugin releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • extensible-choice-parameter-plugin saves you 2911 person hours of effort in developing the same functionality from scratch.
  • It has 6399 lines of code, 277 functions and 65 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

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

  • Get file list .
    • Run Groovy script .
      • On queueing .
        • Add a new edited value to the given parameter definition .
          • Check whether the build has been completed .
            • Compares this global textarea with the given name .
              • Returns the default value for the choice .
                • Called when a value is triggered .
                  • Creates a list of strings from a textarea .
                    • Get the global config page for the choice list provider .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      extensible-choice-parameter-plugin Key Features

                      Arbitrary code execution vulnerability

                      When building, the value can be selected with a dropdown like a built-in Choice parameter.

                      The choices can be provided in several ways: Global Choice Parameter: defines choices in the Configure System page. Choices can be shared by multiple jobs. Updating the choices in the Configure System, every job can immediately refer the updated choices. Textarea Choice Parameter: writes choices in a textarea, just like a built-in Choice parameter. System Groovy Script Choice Parameter: runs a System Groovy script to determine the list of choices File Choice Parameter: lists files in a directory.

                      Checking Editable checkbox allows you to specify any value, even one not in the choices. Edited values can be added to the choice used next time automatically by checking "Add Edited Value".

                      You can add a new way to provide choices with Extension Points.

                      "Extensible Choice" is added as a type of build parameters.

                      You can select the way to define choices of the parameter. A new way to provide choices can be added with Extension Points.

                      Selecting "Textarea Choice Parameter", you can define choices like the built-in Choice parameter.

                      "Global Choice Parameter" enables you to select a set of choices from the ones defined in System Configuration page. Defining in System Configuration page: Select which set of choices to use:

                      "System Groovy Choice Parameter" generate choices with a Groovy script:

                      "File Choice Parameter" enables select a file in a specified directory:

                      You can specify its default value. This is useful with Global Choice Parameter to specify different default values in jobs:

                      Checking "Editable" enables you to input a value not in choices at build time: Textarea Choice Parameter and Global Choice Parameter provides "Add Edited Value", which automatically adds a value not in the choice list:

                      Bug report

                      Request or propose an improvement of existing feature

                      Request or propose a new feature

                      Community Discussions

                      Trending Discussions on Machine Learning
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                      • How to compare baseline and GridSearchCV results fair?
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                      • Ordinal Encoding or One-Hot-Encoding
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                      • 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 extensible-choice-parameter-plugin

                      You can download it from GitHub.
                      You can use extensible-choice-parameter-plugin like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the extensible-choice-parameter-plugin component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      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 .

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