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

Last update: Nov 01, 2022

Overview

TF Watcher

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

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
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...

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dependencies python
opened by dependabot[bot] 3
[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

21db4b1 Bump to 4.2.0 final

2390ce6 CHANGES: Merge 4.1.3 (unreleased) to 4.2.0

b67624e Merge pull request #9627 from sphinx-doc/bot/pull-translations

50d7bfa Update message catalogs

260f217 Merge pull request #9594 from hkuno/pr/no_empty_desc_4.x

fb141c3 Update CHANGES for PR #9594

5719299 Add a testcase for #9694

c44ee0e Merge pull request #9611 from tk0miya/9560_NewType_module

8416813 Merge pull request #9616 from jdufresne/fix-url

3a67b49 Update CHANGES for PR #9617

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dependencies python
opened by dependabot[bot] 2
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opened by imgbot[bot] 1
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

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dependencies javascript
opened by dependabot[bot] 1
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:

| | | |-----------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|

cc: @shreyaparadkar @harshgeek4coder
priority-high
opened by Rishit-dagli 1
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?

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
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

added info about delete functionallity in storage by @m-charchit in nhorvath/Pyrebase4#34

updated get_url() docs to mention user token by @m-charchit in nhorvath/Pyrebase4#36

Feature/AuthUpdateProfile by @el-vishal in nhorvath/Pyrebase4#47

ETag + current value in case of operation's failure by @BlackOrder in nhorvath/Pyrebase4#54

New Contributors

@m-charchit made their first contribution in nhorvath/Pyrebase4#34

@el-vishal made their first contribution in nhorvath/Pyrebase4#47

@BlackOrder made their first contribution in nhorvath/Pyrebase4#54

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)

v0.1.2(Sep 29, 2021)

✅ Bug Fixes / Improvements

This patch introduces support for TensorFlow 2.6.x in the pip package (#100)

Thanks to @RomanFoell for pointing this out.
Source code(tar.gz)
Source code(zip)
v0.1.1(Aug 12, 2021)

✅ Bug Fixes / Improvements

Fixes an error with the callbacks subpackage not being installed.
Source code(tar.gz)
Source code(zip)
tfwatcher_docs.pdf(205.11 KB)
tfwatcher_docs_html.tar.gz(14.40 MB)
v0.1.0(Aug 12, 2021)
This is the initial version of TF Watcher Python package and web app. Find more info and examples in the documentation.

🔮 Features

Python Package

Easy structure

Support easily logging training, validation, or testing metrics

Write data to Firebase realtime database

Tests

Web App

Google Account Authentication

Connecting with Firebase Realtime Database

Read data and format it to display charts

Deployed to Azure Static Webapp

Source code(tar.gz)
Source code(zip)
tfwatcher.pdf(205.11 KB)

Owner

Rishit Dagli

High School,TEDx,2xTED-Ed speaker | International Speaker | Microsoft Student Ambassador | Mentor, @TFUGMumbai | Organize @KotlinMumbai

GitHub Repository https://rishit-dagli.github.io/TF-Watcher/

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

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Overview

TF Watcher

📄 Project Documentation

💡 About the Project

🔹 TF Watcher Python Package

🔹 TF Watcher Web App

Want to Contribute 🙋‍♂️ ?

Want to discuss? 💬

Contributors

License

Comments

TensorFlow 2.6.0

Release 2.6.0

Breaking Changes

Known Caveats

Major Features and Improvements

Release 2.6.0

Breaking Changes

Known Caveats

Release 4.2.0 (released Sep 12, 2021)

Features added

Bugs fixed

Beep boop. Your images are optimized!

Beep boop. Your images are optimized!

Beep boop. Your images are optimized!

Beep boop. Your images are optimized!

Perform Tests on Package tfwatcher.callbacks

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

v4.6.0

Breaking Changes

What's Changed

New Contributors

Releases(v0.1.2)

v0.1.2(Sep 29, 2021)

✅ Bug Fixes / Improvements

v0.1.1(Aug 12, 2021)

✅ Bug Fixes / Improvements

v0.1.0(Aug 12, 2021)

🔮 Features

Python Package

Web App

Owner

Rishit Dagli

Convnet transfer - Code for paper How transferable are features in deep neural networks?

Mmdetection3d Noted - MMDetection3D is an open source object detection toolbox based on PyTorch

PyTorch implementation of Pay Attention to MLPs

Official code for the publication "HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder".

Code for the paper: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection

InsCLR: Improving Instance Retrieval with Self-Supervision

《Dual-Resolution Correspondence Network》(NeurIPS 2020)

Diverse Image Generation via Self-Conditioned GANs

How to train a CNN to 99% accuracy on MNIST in less than a second on a laptop

[CVPR 2022] "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy" by Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang

Instance-conditional Knowledge Distillation for Object Detection

PyTorch implementation of DreamerV2 model-based RL algorithm

Python scripts using the Mediapipe models for Halloween.

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

Categorical Depth Distribution Network for Monocular 3D Object Detection

A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Implementation of the SUMO (Slim U-Net trained on MODA) model

Snscrape-jsonl-urls-extractor - Extracts urls from jsonl produced by snscrape

Save-restricted-v-3 - Save restricted content Bot For telegram

Perform Tests on Package `tfwatcher.callbacks`