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clearml-agent | ClearML Agent | Machine Learning library

 by   allegroai Python Version: v1.2.3 License: Apache-2.0

 by   allegroai Python Version: v1.2.3 License: Apache-2.0

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kandi X-RAY | clearml-agent Summary

clearml-agent is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. clearml-agent has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install clearml-agent' or download it from GitHub, PyPI.
The ClearML Agent is a job scheduler that listens on job queue(s), pulls jobs, sets the job environments, executes the job and monitors its progress. Any 'Draft' experiment can be scheduled for execution by a ClearML agent.
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  • clearml-agent has a low active ecosystem.
  • It has 162 star(s) with 54 fork(s). There are 15 watchers for this library.
  • There were 4 major release(s) in the last 12 months.
  • There are 44 open issues and 41 have been closed. On average issues are closed in 35 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of clearml-agent is v1.2.3
clearml-agent Support
Best in #Machine Learning
Average in #Machine Learning
clearml-agent Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • clearml-agent has 0 bugs and 0 code smells.
clearml-agent Quality
Best in #Machine Learning
Average in #Machine Learning
clearml-agent Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

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

license License

  • clearml-agent 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.
clearml-agent License
Best in #Machine Learning
Average in #Machine Learning
clearml-agent License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • clearml-agent releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
clearml-agent Reuse
Best in #Machine Learning
Average in #Machine Learning
clearml-agent Reuse
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Top functions reviewed by kandi - BETA

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

  • Execute the worker .
    • Load requirements from requirements file .
      • Parse the configuration file .
        • Monitor for hanging pods
          • Add an entry to the cache .
            • Clone the remote repository locally .
              • Create files from configuration .
                • Determine the driver version .
                  • Send a batch request .
                    • Daemonize a child process .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      clearml-agent Key Features

                      Using the 'Reset' action from the experiment right-click context menu in the ClearML UI - This will clear any results and artifacts the previous run had created.

                      Using the 'Clone' action from the experiment right-click context menu in the ClearML UI - This will create a new 'Draft' experiment with the same configuration as the original experiment.

                      Workers Tab: Monitor you cluster Review available resources Monitor machines statistics (CPU / GPU / Disk / Network)

                      Queues Tab: Control the scheduling order of jobs Cancel or abort job execution Move jobs between execution queues

                      Create a new virtual environment (or launch the selected docker image)

                      Clone the code into the virtual-environment (or inside the docker)

                      Install python packages based on the package requirements listed for the experiment Special note for PyTorch: The ClearML Agent will automatically select the torch packages based on the CUDA_VERSION environment variable of the machine

                      Execute the code, while monitoring the process

                      Log all stdout/stderr in the ClearML UI, including the cloning and installation process, for easy debugging

                      Monitor the execution and allow you to manually abort the job using the ClearML UI (or, in the unfortunate case of a code crash, catch the error and signal the experiment has failed)

                      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 clearml-agent

                      You can install using 'pip install clearml-agent' or download it from GitHub, PyPI.
                      You can use clearml-agent 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 .

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