Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

Related tags

Deep Learningreactpythonmachine-learningfirebasedeep-learningtensorflowpypidevtoolssphinxrechartspython-packagespython-packagepyrebasechakra-uitf-watcher
Overview

TF Watcher Twitter URL

PyPI Test Python package Upload Python Package Build Docs Lint Code Base

DOI GitHub license Code style: black GitHub stars GitHub forks GitHub watchers

TF Watcher is a simple to use Python package and web app which allows you to monitor 👀 your Machine Learning training or testing process on mobile devices 📱 built specially for Google Colab, Azure ML and Kaggle. Dedicated to all ML Developers with ❤️ .

📄 Project Documentation

Visit the documentation of this project to get more information in detail.

💡 About the Project

This project includes two subprojects:

🔹 TF Watcher Python Package

This is a Python Package built using TensorFlow and Pyrebase (a python wrapper for the Firebase API). This allows to eaisly monitor the metrics you want and writes them to Firebase realtime database.

Navigate to /tfwatcher directory to browse and know more about the TF Watcher Python Package.

🔹 TF Watcher Web App

This is a Web Application built using React, Chakra-UI, Recharts and Firebase deployed on Azure Static Web Apps. It reads from the Firebase realtime database and displays the logs using charts.

Navigate to /webapp directory to browse and know more about TF Watcher Web App.

Want to Contribute 🙋‍♂️ ?

Awesome! If you want to contribute to this project, you're always welcome! See Contributing Guidelines. You can also take a look at TF Watcher's Project Status Tracker for getting more information about current or upcoming tasks.

Want to discuss? 💬

Have any questions, doubts or want to present your opinions, views? You're always welcome. You can start discussions.

Contributors

License

Copyright 2020 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Comments
  • Similar to Tensorgram

    Similar to Tensorgram

    Hello @Rishit-dagli @ayushmaang @shreyaparadkar @harshgeek4coder

    I have seen your project but it is almost similar to my project which is licensed.

    TensorGram: https://github.com/ksdkamesh99/TensorGram

    I request you to please check this project. You can connect me via [email protected].

    opened by ksdkamesh99 6
  • [pip](deps): Update tensorflow requirement from ~=2.5.0 to >=2.5,<2.7

    [pip](deps): Update tensorflow requirement from ~=2.5.0 to >=2.5,<2.7

    Updates the requirements on tensorflow to permit the latest version.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.6.0

    Release 2.6.0

    Breaking Changes

    • tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead.

    • tf.lite:

      • Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they're no longersupported. It's recommended to just use keras lstm instead.

    • tf.keras:

      • Keras been split into a separate PIP package (keras), and its code has been moved to the GitHub repositorykeras-team/keras. The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. The existing code in tensorflow/python/keras is a staled copy and will be removed in future release (2.7). Please remove any imports to tensorflow.python.keras and replace them with public tf.keras API instead.
      • The methods Model.to_yaml() and keras.models.model_from_yaml have been replaced to raise a RuntimeError as they can be abused to cause arbitrary code execution. It is recommended to use JSON serialization instead of YAML, or, a better alternative, serialize to H5.

    Known Caveats

    • TF Core:
      • A longstanding bug in tf.while_loop, which caused it to execute sequentially, even when parallel_iterations>1, has now been fixed. However, the increased parallelism may result in increased memory use. Users who experience unwanted regressions should reset their while_loop's parallel_iterations value to 1, which is consistent with prior behavior.

    Major Features and Improvements

    • tf.keras:

      • Keras has been split into a separate PIP package (keras), and its code has been moved to the GitHub repository keras-team/keras. The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. All Keras-related PRs and issues should now be directed to the GitHub repository keras-team/keras.
      • tf.keras.utils.experimental.DatasetCreator now takes an optional tf.distribute.InputOptions for specific options when used with distribution.
      • tf.keras.experimental.SidecarEvaluator is now available for a program intended to be run on an evaluator task, which is commonly used to supplement a training cluster running with tf.distribute.experimental.ParameterServerStrategy (see `https://www.tensorflow.org/tutorials/distribute/parameter_server_training). It can also be used with single-worker training or other strategies. See docstring for more info.
      • Preprocessing layers moved from experimental to core.
        • Import paths moved from tf.keras.layers.preprocessing.experimental to tf.keras.layers.
      • Updates to Preprocessing layers API for consistency and clarity:
        • StringLookup and IntegerLookup default for mask_token changed to None. This matches the default masking behavior of Hashing and Embedding layers. To keep existing behavior, pass mask_token="" during layer creation.
        • Renamed "binary" output mode to "multi_hot" for CategoryEncoding, StringLookup, IntegerLookup, and TextVectorization. Multi-hot encoding will no longer automatically uprank rank 1 inputs, so these layers can now multi-hot encode unbatched multi-dimensional samples.
        • Added a new output mode "one_hot" for CategoryEncoding, StringLookup, IntegerLookup, which will encode each element in an input batch individually, and automatically append a new output dimension if necessary. Use this mode on rank 1 inputs for the old "binary" behavior of one-hot encoding a batch of scalars.
        • Normalization will no longer automatically uprank rank 1 inputs, allowing normalization of unbatched multi-dimensional samples.

    • tf.lite:

      • The recommended Android NDK version for building TensorFlow Lite has been changed from r18b to r19c.
      • Supports int64 for mul.
      • Supports native variable builtin ops - ReadVariable, AssignVariable.
      • Converter:
        • Experimental support for variables in TFLite. To enable through conversion, users need to set experimental_enable_resource_variables on tf.lite.TFLiteConverter to True. Note: mutable variables is only available using from_saved_model in this release, support for other methods is coming soon.
        • Old Converter (TOCO) is getting removed from next release. It's been deprecated for few releases already.

    • tf.saved_model:

      • SavedModels can now save custom gradients. Use the option tf.saved_model.SaveOption(experimental_custom_gradients=True) to enable this feature. The documentation in Advanced autodiff has been updated.
      • Object metadata has now been deprecated and no longer saved to the SavedModel.

    • TF Core:

      • Added tf.config.experimental.reset_memory_stats to reset the tracked peak memory returned by tf.config.experimental.get_memory_info.

    • tf.data:

      • Added target_workers param to data_service_ops.from_dataset_id and data_service_ops.distribute. Users can specify "AUTO", "ANY", or "LOCAL" (case insensitive). If "AUTO", tf.data service runtime decides which workers to read from. If "ANY", TF workers read from any tf.data service workers. If "LOCAL", TF workers will only read from local in-processs tf.data service workers. "AUTO" works well for most cases, while users can specify other targets. For example, "LOCAL" would help avoid RPCs and data copy if every TF worker colocates with a tf.data service worker. Currently, "AUTO" reads from any tf.data service workers to preserve existing behavior. The default value is "AUTO".

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.6.0

    Breaking Changes

    • tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead.

    • tf.lite:

      • Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they're no longer supported. It's recommended to just use keras lstm instead.

    • Keras been split into a separate PIP package (keras), and its code has been moved to the GitHub repositorykeras-team/keras. The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. The existing code in tensorflow/python/keras is a staled copy and will be removed in future release (2.7). Please remove any imports to tensorflow.python.keras and replace them with public tf.keras API instead.

    • Modular File System Migration

      • S3 and HDFS file system supports have been migrated to modular file systems and is now available in https://github.com/tensorflow/io. The tensorflow-io python package should be installed for S3 and HDFS support with tensorflow.

    * *

    Known Caveats

    * * *

    • TF Core:
      • A longstanding bug in tf.while_loop, which caused it to execute sequentially, even when parallel_iterations>1, has now been fixed. However, the increased parallelism may result in increased memory use.

    ... (truncated)

    Commits
    • 919f693 Merge pull request #51398 from tensorflow-jenkins/version-numbers-2.6.0-30580
    • 9752e10 Update version numbers to 2.6.0
    • 421ef70 Merge pull request #51397 from tensorflow/update-version-numbers
    • 662740b Update keras and estimator deps
    • 093800c Merge pull request #51396 from bmd3k/cherrypicks_4ENL2
    • baa3136 Update tensorboard dependency to 2.6.x and and tb-nightly dependency to 2.7.x.
    • 274c83b Merge pull request #51360 from tensorflow/mm-update-relnotes-on-r2.6
    • 6f80b7d Put CVE numbers for fixes in parentheses
    • 2743ff9 Update release notes with the security updates.
    • a10858d Merge pull request #51293 from tensorflow/mm-cherrypick-23d6383eb6c14084a8fc3...
    • Additional commits viewable in compare view

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    dependencies python 
    opened by dependabot[bot] 3
  • [pip](deps): Bump sphinx from 4.1.2 to 4.2.0

    [pip](deps): Bump sphinx from 4.1.2 to 4.2.0

    Bumps sphinx from 4.1.2 to 4.2.0.

    Changelog

    Sourced from sphinx's changelog.

    Release 4.2.0 (released Sep 12, 2021)

    Features added

    • #9445: autodoc: Support class properties
    • #9479: autodoc: Emit a warning if target is a mocked object
    • #9560: autodoc: Allow to refer NewType instances with module name in Python 3.10 or above
    • #9447: html theme: Expose the version of Sphinx in the form of tuple as a template variable sphinx_version_tuple
    • #9594: manpage: Suppress the title of man page if description is empty
    • #9445: py domain: :py:property: directive supports :classmethod: option to describe the class property
    • #9524: test: SphinxTestApp can take builddir as an argument
    • #9535: C and C++, support more fundamental types, including GNU extensions.

    Bugs fixed

    • #9608: apidoc: apidoc does not generate a module definition for implicit namespace package
    • #9504: autodoc: generate incorrect reference to the parent class if the target class inherites the class having _name attribute
    • #9537, #9589: autodoc: Some objects under typing module are not displayed well with the HEAD of 3.10
    • #9487: autodoc: typehint for cached_property is not shown
    • #9509: autodoc: AttributeError is raised on failed resolving typehints
    • #9518: autodoc: autodoc_docstring_signature does not effect to __init__() and __new__()
    • #9522: autodoc: PEP 585 style typehints having arguments (ex. list[int]) are not displayed well
    • #9481: autosummary: some warnings contain non-existing filenames
    • #9568: autosummary: summarise overlined sectioned headings correctly
    • #9600: autosummary: Type annotations which contain commas in autosummary table are not removed completely
    • #9481: c domain: some warnings contain non-existing filenames
    • #9481: cpp domain: some warnings contain non-existing filenames
    • #9456: html search: abbreation marks are inserted to the search result if failed to fetch the content of the page
    • #9617: html search: The JS requirement warning is shown if browser is slow
    • #9267: html theme: CSS and JS files added by theme were loaded twice
    • #9585: py domain: :type: option for :rst:dir:py:property directive does not create a hyperlink
    • #9576: py domain: Literal typehint was converted to a cross reference
    • #9535 comment: C++, fix parsing of defaulted function parameters that are function pointers.
    • #9564: smartquotes: don't adjust typography for text with language-highlighted :code: role.

    ... (truncated)

    Commits

    Dependabot compatibility score

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    dependencies python 
    opened by dependabot[bot] 2
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    opened by imgbot[bot] 1
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    opened by imgbot[bot] 1
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    opened by imgbot[bot] 1
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    Beep boop. Your images are optimized!

    Your image file size has been reduced by 18% 🎉

    Details

    | File | Before | After | Percent reduction | |:--|:--|:--|:--| | /docs/source/_static/add-key.png | 374.92kb | 304.48kb | 18.79% | | /docs/source/_static/webapp-screenshot.png | 364.17kb | 303.52kb | 16.66% | | /webapp/src/assets/tf-watcher.png | 4.13kb | 3.86kb | 6.59% | | | | | | | Total : | 743.23kb | 611.86kb | 17.68% |

    Black Lives Matter | 💰 donate | 🎓 learn | ✍🏾 sign

    📝 docs | :octocat: repo | 🙋🏾 issues | 🏅 swag | 🏪 marketplace

    opened by imgbot[bot] 1
  • Bump tar from 6.1.0 to 6.1.6 in /webapp

    Bump tar from 6.1.0 to 6.1.6 in /webapp

    Bumps tar from 6.1.0 to 6.1.6.

    Commits
    • 9bc1729 6.1.6
    • bdf4f51 fix: properly prefix hard links
    • 94b2a74 chore: remove benchmarks from git repo
    • ecaafb4 update deps
    • bd4691c 6.1.5
    • d694c4f ci: test on node 16
    • 84acbd3 fix(unpack): fix hang on large file on open() fail
    • 97c46fc fix(unpack): always resume parsing after an entry error
    • 488ab8c chore: WriteEntry cleaner write() handling
    • be89aaf WriteEntry backpressure
    • Additional commits viewable in compare view

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    dependencies javascript 
    opened by dependabot[bot] 1
  • Push to main

    Push to main

    This PR pushes our existing work to main which adds the:

    • Python Package

    The Python package file can be present at ./tfwatcher, the two Firebase files in this directory help connect and write data to Firebase. The ./tfwatcher/callbacks subpackage creates new TensorFlow callback classes which can be used very easily as showed in docs.

    This package could be run locally using:

    # Use pip install -e .[dev] to install with dev dependencies
pip install -e .
    • Documentation

    The documentation is created with Sphinx and is to be hosted on https://readthedocs.org/ . Currently, the documentation isn't hosted but you could view the latest version locally by downloading the HTML or the PDF version (generated by GitHub Actions and uploaded as an artifact).

    All the documentation code can be found at ./docs which could easily be developed locally as HTML by running:

    cd docs
pip install sphinx
pip install -r requirements.txt
make html

    The documentation can also be generated as a LaTeX PDF by running:

    sudo apt-get update -y && sudo apt-get install -y latexmk texlive-latex-recommended texlive-latex-extra texlive-fonts-recommended imagemagick
make latexpdf
    • A web app

    The web app, created with React, Chakra-UI and Recharts is present at ./webapp. There are two main screens in the app(./screen), one for getting the key generated by the python package and another to show the logs for it. The Firebase instance to authenticate the user and get realtime data is in ./firebase, and the screens folder mentioned above.

    To run locally:

    cd webapp
yarn install
yarn run start

    Here are some screenshots of the web-app in action:

    | image | image | |-----------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|

    cc: @shreyaparadkar @harshgeek4coder

    priority-high 
    opened by Rishit-dagli 1
  • Tests Script

    Tests Script

    Perform Tests on Package tfwatcher.callbacks

    • [x] Performed Test on tfwatcher.callbacks.epoch -> EpochEnd
    • [x] Performed Test on tfwatcher.callbacks.predict-> PredictEnd
    • [x] Performed Test on tfwatcher.callbacks.test_batch -> TestBatchEnd
    • [x] Performed Test on tfwatcher.callbacks.train_batch -> TrainBatchEnd

    Closes #43

    tests 
    opened by harshgeek4coder 1
  • Does it work with the latest tensorflow version 2.6?

    Does it work with the latest tensorflow version 2.6?

    Discussed in https://github.com/Rishit-dagli/TF-Watcher/discussions/99

    Originally posted by RomanFoell September 26, 2021 Hello,

    when I want to install the package via pip tensorflow 2.5 gets downloaded. Does this mean, there is no support for tensorflow 2.6?

    Best, Roman

    bug python-package dependencies 
    opened by Rishit-dagli 0
  • [pip](deps): Update pyrebase4 requirement from ~=4.5.0 to >=4.5,<4.7

    [pip](deps): Update pyrebase4 requirement from ~=4.5.0 to >=4.5,<4.7

    Updates the requirements on pyrebase4 to permit the latest version.

    Release notes

    Sourced from pyrebase4's releases.

    v4.6.0

    Breaking Changes

    • get_etag now returns an object with ETag and value, where value is the whole object. The previous response was just the ETag header.
    • conditional_set and conditional_remove now expect the new result of get_etag as the input to the etag parameter. Additionally, if the response code was 412 they now return the new ETag format to be reused.

    What's Changed

    New Contributors

    Full Changelog: https://github.com/nhorvath/Pyrebase4/compare/v4.5.0...v4.6.0

    Commits
    • bfc5a8e bump version
    • 6834b62 Merge pull request #54 from BlackOrder/ETag
    • 3e00233 ETag new data
    • 916d13f Merge pull request #47 from el-vishal/feature/auth_updateProfile
    • d3f1d86 fixed auth update profile link + added readme
    • 300965f added function for Auth - Update Profile
    • b4c9b7d Create CODESHELTER.md
    • 192ef82 Merge pull request #36 from Charchit-beginner/patch-2
    • d938435 updated get_url() docs to mention user token
    • 529204b Merge pull request #34 from Charchit-beginner/patch-1
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    dependencies python 
    opened by dependabot[bot] 0
Releases(v0.1.2)
Owner
Rishit Dagli
High School,TEDx,2xTED-Ed speaker | International Speaker | Microsoft Student Ambassador | Mentor, @TFUGMumbai | Organize @KotlinMumbai
Rishit Dagli
GitHub Repository https://rishit-dagli.github.io/TF-Watcher/
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Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

Tensor Component Analysis for Interpreting the Latent Space of GANs [ paper | project page ] Code to reproduce the results in the paper "Tensor Compon

James Oldfield 4 Jun 17, 2022
Reproducing Results from A Hybrid Approach to Targeting Social Assistance

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A curated list of awesome Deep Learning tutorials, projects and communities.

Awesome Deep Learning Table of Contents Books Courses Videos and Lectures Papers Tutorials Researchers Websites Datasets Conferences Frameworks Tools

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QT Py Media Knob using rotary encoder & neopixel ring

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Repository for the paper "PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation", CVPR 2021.

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Implementation of the "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos" paper.

Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos Introduction Point cloud videos exhibit irregularities and lack of or

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