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cloudml | CloudML : Transparent deployment of cloud applications | Continuous Deployment library

 by   SINTEF-9012 Java Version: root-2.0-rc0 License: LGPL-3.0

 by   SINTEF-9012 Java Version: root-2.0-rc0 License: LGPL-3.0

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kandi X-RAY | cloudml Summary

cloudml is a Java library typically used in Devops, Continuous Deployment applications. cloudml has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.
Transparent provisioning of cloud resources and deployment of cloud applications. For more details on how to use CloudML please have a look at our Wiki page. ##License## Licensed under the GNU LESSER GENERAL PUBLIC LICENSE.
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kandi-support Support

  • cloudml has a low active ecosystem.
  • It has 28 star(s) with 8 fork(s). There are 17 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 12 open issues and 39 have been closed. On average issues are closed in 43 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of cloudml is root-2.0-rc0
cloudml Support
Best in #Continuous Deployment
Average in #Continuous Deployment
cloudml Support
Best in #Continuous Deployment
Average in #Continuous Deployment

quality kandi Quality

  • cloudml has 0 bugs and 0 code smells.
cloudml Quality
Best in #Continuous Deployment
Average in #Continuous Deployment
cloudml Quality
Best in #Continuous Deployment
Average in #Continuous Deployment

securitySecurity

  • cloudml has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • cloudml code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
cloudml Security
Best in #Continuous Deployment
Average in #Continuous Deployment
cloudml Security
Best in #Continuous Deployment
Average in #Continuous Deployment

license License

  • cloudml is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
  • Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.
cloudml License
Best in #Continuous Deployment
Average in #Continuous Deployment
cloudml License
Best in #Continuous Deployment
Average in #Continuous Deployment

buildReuse

  • cloudml releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • cloudml saves you 15909 person hours of effort in developing the same functionality from scratch.
  • It has 31688 lines of code, 2670 functions and 427 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
cloudml Reuse
Best in #Continuous Deployment
Average in #Continuous Deployment
cloudml Reuse
Best in #Continuous Deployment
Average in #Continuous Deployment
Top functions reviewed by kandi - BETA

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

  • Create the image frame .
    • Configure the loadbalancer targets .
      • Handle a mouse released event .
        • Create a cloud instance
          • Execute a ssh command .
            • Complete the deployment of CloudBees .
              • Converts a list of external components to kvm .
                • Convert a relationship to a POJO object .
                  • Convert an object to a type .
                    • Populate the configuration .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      cloudml Key Features

                      CloudML: Transparent deployment of cloud applications

                      GCP AI Platform: Error when creating a custom predictor model version ( trained model Pytorch model + torchvision.transform)

                      copy iconCopydownload iconDownload
                      REQUIRED_PACKAGES = [line.strip() for line in open(base/"requirements.txt")] +\
                      ['torchvision==0.5.0', 'torch @ https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl']
                      
                      import json
                      json.dump(your data to send to predictor class)
                      
                      from torch import zeros,load 
                          your code
                      
                      REQUIRED_PACKAGES = [line.strip() for line in open(base/"requirements.txt")] +\
                      ['torchvision==0.5.0', 'torch @ https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl']
                      
                      import json
                      json.dump(your data to send to predictor class)
                      
                      from torch import zeros,load 
                          your code
                      
                      REQUIRED_PACKAGES = [line.strip() for line in open(base/"requirements.txt")] +\
                      ['torchvision==0.5.0', 'torch @ https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl']
                      
                      import json
                      json.dump(your data to send to predictor class)
                      
                      from torch import zeros,load 
                          your code
                      

                      Submit a Keras training job to Google cloud

                      copy iconCopydownload iconDownload
                      gsutil mb -l europe-north1 gs://keras-cloud-tutorial
                      
                      keras-cloud-tutorial/
                      ├── setup.py
                      └── trainer
                          ├── __init__.py
                          ├── cloudml-gpu.yaml
                          └── cnn_with_keras.py
                      
                      trainingInput:
                        scaleTier: CUSTOM
                        # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
                        masterType: standard_gpu
                        runtimeVersion: "1.5"
                      
                      from setuptools import setup, find_packages
                      
                      setup(name='trainer',
                            version='0.1',
                            packages=find_packages(),
                            description='Example on how to run keras on gcloud ml-engine',
                            author='Username',
                            author_email='user@gmail.com',
                            install_requires=[
                                'keras==2.1.5',
                                'h5py'
                            ],
                            zip_safe=False)
                      
                      gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
                      
                      gsutil mb -l europe-north1 gs://keras-cloud-tutorial
                      
                      keras-cloud-tutorial/
                      ├── setup.py
                      └── trainer
                          ├── __init__.py
                          ├── cloudml-gpu.yaml
                          └── cnn_with_keras.py
                      
                      trainingInput:
                        scaleTier: CUSTOM
                        # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
                        masterType: standard_gpu
                        runtimeVersion: "1.5"
                      
                      from setuptools import setup, find_packages
                      
                      setup(name='trainer',
                            version='0.1',
                            packages=find_packages(),
                            description='Example on how to run keras on gcloud ml-engine',
                            author='Username',
                            author_email='user@gmail.com',
                            install_requires=[
                                'keras==2.1.5',
                                'h5py'
                            ],
                            zip_safe=False)
                      
                      gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
                      
                      gsutil mb -l europe-north1 gs://keras-cloud-tutorial
                      
                      keras-cloud-tutorial/
                      ├── setup.py
                      └── trainer
                          ├── __init__.py
                          ├── cloudml-gpu.yaml
                          └── cnn_with_keras.py
                      
                      trainingInput:
                        scaleTier: CUSTOM
                        # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
                        masterType: standard_gpu
                        runtimeVersion: "1.5"
                      
                      from setuptools import setup, find_packages
                      
                      setup(name='trainer',
                            version='0.1',
                            packages=find_packages(),
                            description='Example on how to run keras on gcloud ml-engine',
                            author='Username',
                            author_email='user@gmail.com',
                            install_requires=[
                                'keras==2.1.5',
                                'h5py'
                            ],
                            zip_safe=False)
                      
                      gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
                      
                      gsutil mb -l europe-north1 gs://keras-cloud-tutorial
                      
                      keras-cloud-tutorial/
                      ├── setup.py
                      └── trainer
                          ├── __init__.py
                          ├── cloudml-gpu.yaml
                          └── cnn_with_keras.py
                      
                      trainingInput:
                        scaleTier: CUSTOM
                        # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
                        masterType: standard_gpu
                        runtimeVersion: "1.5"
                      
                      from setuptools import setup, find_packages
                      
                      setup(name='trainer',
                            version='0.1',
                            packages=find_packages(),
                            description='Example on how to run keras on gcloud ml-engine',
                            author='Username',
                            author_email='user@gmail.com',
                            install_requires=[
                                'keras==2.1.5',
                                'h5py'
                            ],
                            zip_safe=False)
                      
                      gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
                      
                      gsutil mb -l europe-north1 gs://keras-cloud-tutorial
                      
                      keras-cloud-tutorial/
                      ├── setup.py
                      └── trainer
                          ├── __init__.py
                          ├── cloudml-gpu.yaml
                          └── cnn_with_keras.py
                      
                      trainingInput:
                        scaleTier: CUSTOM
                        # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
                        masterType: standard_gpu
                        runtimeVersion: "1.5"
                      
                      from setuptools import setup, find_packages
                      
                      setup(name='trainer',
                            version='0.1',
                            packages=find_packages(),
                            description='Example on how to run keras on gcloud ml-engine',
                            author='Username',
                            author_email='user@gmail.com',
                            install_requires=[
                                'keras==2.1.5',
                                'h5py'
                            ],
                            zip_safe=False)
                      
                      gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
                      

                      Community Discussions

                      Trending Discussions on cloudml
                      • After training in AI Platform, where can I find model.bst or other model file?
                      • Could not load dynamic library libcuda.so.1 error on Google AI Platform with custom container
                      • GCP AI Platform: Error when creating a custom predictor model version ( trained model Pytorch model + torchvision.transform)
                      • Triggering a training task on cloud ml when file arrives to cloud storage
                      • Submit a Keras training job to Google cloud
                      Trending Discussions on cloudml

                      QUESTION

                      After training in AI Platform, where can I find model.bst or other model file?

                      Asked 2021-May-28 at 05:48

                      I trained a XGBoost model using AI Platform as here.

                      Now I have the choice in the Console to download the model, as follows (but not Deploy it, since "Only models trained with built-in algorithms can be deployed from this page"). So, I click to download.

                      enter image description here

                      However, in the bucket the only file I see is a tar, as follows.

                      enter image description here

                      That tar (directory tree follows) holds only some training code, and not a model.bst, model.pkl, or model.joblib, or other such model file.

                      enter image description here

                      Where do I find model.bst or the like, which I can deploy?


                      EDIT:

                      Following the answer, below, we see that the "Download model" button is misleading as it sends us to the job directory, not the output directory (which is set arbitrarily in the codel the model is at census_data_20210527_215945/model.bst )

                      bucket = storage.Client().bucket(BUCKET_ID)
                      blob = bucket.blob('{}/{}'.format(
                          datetime.datetime.now().strftime('census_%Y%m%d_%H%M%S'),
                          model))
                      blob.upload_from_filename(model)
                      

                      ANSWER

                      Answered 2021-May-28 at 05:48

                      Only in-build algorithms automatically store the model in Google Cloud storage.

                      In your case, you have a custom training application. You have to take care of saving the model on your own.

                      Referring to your example this is implemented as listed here.

                      The model is uploaded to Google Cloud Storage using the cloud storage client.

                      Source https://stackoverflow.com/questions/67727022

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install cloudml

                      You can download it from GitHub.
                      You can use cloudml 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 cloudml 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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