colabtools | Python libraries for Google Colaboratory | Machine Learning library

 by   googlecolab Python Version: Current License: Apache-2.0

kandi X-RAY | colabtools Summary

colabtools is a Python library typically used in Artificial Intelligence, Machine Learning, Numpy, Pandas applications. colabtools has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.
Python libraries for Google Colaboratory
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                        summary
                        colabtools has a medium active ecosystem.
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                        It has 1709 star(s) with 603 fork(s). There are 87 watchers for this library.
                        summary
                        It had no major release in the last 6 months.
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                        There are 977 open issues and 2325 have been closed. On average issues are closed in 264 days. There are 4 open pull requests and 0 closed requests.
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                        It has a neutral sentiment in the developer community.
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                        The latest version of colabtools is current.
                        colabtools Support
                          Best in #Machine Learning
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                            colabtools Support
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                                  kandi-Quality Quality

                                    summary
                                    colabtools has 0 bugs and 0 code smells.
                                    colabtools Quality
                                      Best in #Machine Learning
                                        Average in #Machine Learning
                                        colabtools Quality
                                          Best in #Machine Learning
                                            Average in #Machine Learning

                                              kandi-Security Security

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

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

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                                                                        colabtools releases are not available. You will need to build from source code and install.
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                                                                        Build file is available. You can build the component from source.
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                                                                                  Top functions reviewed by kandi - BETA
                                                                                  kandi has reviewed colabtools and discovered the below as its top functions. This is intended to give you an instant insight into colabtools implemented functionality, and help decide if they suit your requirements.
                                                                                  • Authenticate the service account
                                                                                    • Return True if the credentials are valid
                                                                                    • Return the path to adc json files
                                                                                    • Activate a service account key
                                                                                  • Authenticate the user
                                                                                    • Enable metrics server for GCE
                                                                                  • Enables the connection asynchronously
                                                                                    • Start the HTTP server
                                                                                  • Create a grid from row data
                                                                                    • Populate the widget
                                                                                  • Add an event listener
                                                                                  • Run a shell command
                                                                                  • Get output of a shell command
                                                                                  • Returns a JSON representation of the executed cells
                                                                                  • Remove an event listener
                                                                                  • Upload a single file
                                                                                  • Implements pip magic magic
                                                                                  • Run a shell command magic
                                                                                  • Run a shell cell magic
                                                                                  • Create a resource
                                                                                  • Get the memory usage of the kernel manager
                                                                                  • Compute metadata for the completions
                                                                                  • Return a JS representation of a table
                                                                                  • Completes a request
                                                                                  • Return the HTML representation of the element
                                                                                  • Format the data
                                                                                  Get all kandi verified functions for this library.
                                                                                  Get all kandi verified functions for this library.

                                                                                  colabtools Key Features

                                                                                  Python libraries for Google Colaboratory

                                                                                  colabtools Examples and Code Snippets

                                                                                  No Code Snippets are available at this moment for colabtools.
                                                                                  Community Discussions

                                                                                  Trending Discussions on colabtools

                                                                                  Cannot copy, cut or paste code in Google Colab (Google Colab + iOS bug)
                                                                                  chevron right
                                                                                  How to build a docker image with tensorflow-nightly and the tensorflow object detection research models
                                                                                  chevron right

                                                                                  QUESTION

                                                                                  Cannot copy, cut or paste code in Google Colab (Google Colab + iOS bug)
                                                                                  Asked 2021-Sep-18 at 01:26

                                                                                  I am using Google Colab through Safari (or Google Chrome) on an iPad.

                                                                                  I noticed I cannot cut, copy or paste code from one cell to another on this device. This does not happen on desktop-based operating systems.

                                                                                  This question addresses the same issue but is still unsolved and hasn't show any activity in a while. This GitHub issue addresses the problem too, but all the proposed solutions are workarounds.

                                                                                  Is there a way to fix this? Does anyone know if there's a browser where Colab works as expected?

                                                                                  ANSWER

                                                                                  Answered 2021-Sep-18 at 01:26

                                                                                  It seems that the bug has been fixed as of 09-17-2021. I can now copy code from one cell to another in Safari on my iPad 8 running iOS 14.7.1.

                                                                                  In case someone needs it, here's the issue on GitHub.

                                                                                  Source https://stackoverflow.com/questions/68916034

                                                                                  QUESTION

                                                                                  How to build a docker image with tensorflow-nightly and the tensorflow object detection research models
                                                                                  Asked 2020-Dec-02 at 22:19

                                                                                  Since GPU support with tensorflow-nightly is currently broken on Google Colab I'm trying to build my own docker image for development. However, when I install the object_detection package from tensorflow/models my nightly tensorflow package is overwritten by the version pulled in as a dependency from the object_detection setup.py.

                                                                                  I'm following essentially the same steps in Google Colab but my tensorflow nightly isn't overwritten there, so I'm not sure what I'm missing...

                                                                                  Here's my Dockerfile:

                                                                                  FROM tensorflow/tensorflow:nightly-gpu-jupyter
                                                                                  
                                                                                  RUN python -c "import tensorflow as tf; print(f'Tensorflow version: {tf.__version__}')"
                                                                                  
                                                                                  RUN apt-get install -y \
                                                                                          curl \
                                                                                          git \
                                                                                          less \
                                                                                          zip
                                                                                  
                                                                                  RUN curl -L -O https://github.com/protocolbuffers/protobuf/releases/download/v3.11.4/protoc-3.11.4-linux-x86_64.zip && unzip protoc-3.11.4-linux-x86_64.zip
                                                                                  
                                                                                  RUN cp bin/protoc /usr/local/bin
                                                                                  
                                                                                  RUN git clone --depth 1 https://github.com/tensorflow/models
                                                                                  RUN cd models/research && \
                                                                                          protoc object_detection/protos/*.proto --python_out=. && \
                                                                                          cp object_detection/packages/tf2/setup.py . && \
                                                                                          python -m pip install .
                                                                                  
                                                                                  RUN python -c "import tensorflow as tf; print(f'Tensorflow version: {tf.__version__}')"
                                                                                  

                                                                                  which I'm building with:

                                                                                  docker pull tensorflow/tensorflow:nightly-gpu-jupyter
                                                                                  docker build --no-cache . -f models-tf-nightly.Dockerfile -t tf-nightly-models
                                                                                  

                                                                                  The first print() shows:

                                                                                  Tensorflow version: 2.5.0-dev20201129
                                                                                  

                                                                                  but the second one shows:

                                                                                  Tensorflow version: 2.3.1
                                                                                  

                                                                                  In Google Colab I'm doing essentially the same steps:

                                                                                  # Install the Object Detection API
                                                                                  %%bash
                                                                                  pip install tf-nightly-gpu
                                                                                  [[ -d models ]] || git clone --depth 1 https://github.com/tensorflow/models
                                                                                  cd models/research/
                                                                                  protoc object_detection/protos/*.proto --python_out=.
                                                                                  cp object_detection/packages/tf2/setup.py .
                                                                                  python -m pip install .
                                                                                  

                                                                                  After which

                                                                                  import tensorflow as tf
                                                                                  print(tf.__version__)
                                                                                  

                                                                                  prints 2.5.0-dev20201201

                                                                                  So somehow my Google Colab steps are preserving my nightly Tensorflow install, whereas on Docker it gets overwritten with 2.3.0.

                                                                                  ANSWER

                                                                                  Answered 2020-Dec-02 at 22:19

                                                                                  If you look at pip list before installing the object detection package, you will see that tf-nightly-gpu is installed but tensorflow is not. When you install the object detection package, the tensorflow package is pulled in as a dependency. pip thinks it is not installed, so it installs it.

                                                                                  One way around this is to trick pip install thinking that the tensorflow package is installed. One can do this by symlinking the tf_nightly_gpu-VERSION.dist-info directory in dist-packages. I have added the lines to do this in the Dockerfile below. At the bottom of this post, I have also included a Dockerfile which implements some best practices to minimize image size.

                                                                                  FROM tensorflow/tensorflow:nightly-gpu-jupyter
                                                                                  
                                                                                  RUN python -c "import tensorflow as tf; print(f'Tensorflow version: {tf.__version__}')"
                                                                                  
                                                                                  RUN apt-get install -y \
                                                                                          curl \
                                                                                          git \
                                                                                          less \
                                                                                          zip
                                                                                  
                                                                                  # Trick pip into thinking that the 'tensorflow' package is installed.
                                                                                  # Installing `object_detection` attempts to install the 'tensorflow' package.
                                                                                  # Name the symlink with the suffix from tf_nightly_gpu.
                                                                                  WORKDIR /usr/local/lib/python3.6/dist-packages
                                                                                  RUN ln -s tf_nightly_gpu-* tensorflow-$(ls -d1 tf_nightly_gpu* | sed 's/tf_nightly_gpu-\(.*\)/\1/')
                                                                                  
                                                                                  WORKDIR /tf
                                                                                  RUN curl -L -O https://github.com/protocolbuffers/protobuf/releases/download/v3.11.4/protoc-3.11.4-linux-x86_64.zip && unzip protoc-3.11.4-linux-x86_64.zip
                                                                                  
                                                                                  RUN cp bin/protoc /usr/local/bin
                                                                                  
                                                                                  RUN git clone --depth 1 https://github.com/tensorflow/models
                                                                                  RUN cd models/research && \
                                                                                          protoc object_detection/protos/*.proto --python_out=. && \
                                                                                          cp object_detection/packages/tf2/setup.py . && \
                                                                                          python -m pip install .
                                                                                  
                                                                                  RUN python -c "import tensorflow as tf; print(f'Tensorflow version: {tf.__version__}')"
                                                                                  

                                                                                  Here is a Dockerfile that leads to a slightly smaller image (0.22 GB uncompressed). Notable changes are clearing the apt lists and using --no-cache-dir in pip install.

                                                                                  FROM tensorflow/tensorflow:nightly-gpu-jupyter
                                                                                  
                                                                                  RUN python -c "import tensorflow as tf; print(f'Tensorflow version: {tf.__version__}')"
                                                                                  
                                                                                  RUN apt-get install -y --no-install-recommends \
                                                                                          ca-certificates \
                                                                                          curl \
                                                                                          git \
                                                                                          less \
                                                                                          zip && \
                                                                                      rm -rf /var/lib/apt/lists/*
                                                                                  
                                                                                  # Trick pip into thinking that the 'tensorflow' package is installed.
                                                                                  # Installing `object_detection` attempts to install the 'tensorflow' package.
                                                                                  # Name the symlink with the suffix from tf_nightly_gpu.
                                                                                  WORKDIR /usr/local/lib/python3.6/dist-packages
                                                                                  RUN ln -s tf_nightly_gpu-* tensorflow-$(ls -d1 tf_nightly_gpu* | sed 's/tf_nightly_gpu-\(.*\)/\1/')
                                                                                  
                                                                                  WORKDIR /tf
                                                                                  RUN curl -L -O https://github.com/protocolbuffers/protobuf/releases/download/v3.11.4/protoc-3.11.4-linux-x86_64.zip && \
                                                                                      unzip protoc-3.11.4-linux-x86_64.zip && \
                                                                                      cp bin/protoc /usr/local/bin && \
                                                                                      rm -r protoc-3.11.4-linux-x86_64.zip bin/
                                                                                  
                                                                                  # Upgrade pip.
                                                                                  RUN python -m pip install --no-cache-dir --upgrade pip
                                                                                  
                                                                                  RUN git clone --depth 1 https://github.com/tensorflow/models
                                                                                  WORKDIR models/research
                                                                                  RUN protoc object_detection/protos/*.proto --python_out=. && \
                                                                                      cp object_detection/packages/tf2/setup.py . && \
                                                                                      python -m pip install  --no-cache-dir .
                                                                                  
                                                                                  RUN python -c "import tensorflow as tf; print(f'Tensorflow version: {tf.__version__}')"
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/65098672

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

                                                                                  Vulnerabilities

                                                                                  No vulnerabilities reported

                                                                                  Install colabtools

                                                                                  You can download it from GitHub.
                                                                                  You can use colabtools 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 support or help using Colab, please submit questions tagged with google-colaboratory on StackOverflow. For any product issues, you can either submit an issue or "Help" -> "Send Feedback" in Colab.
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