slackker 1.0.0

Python package for reporting your ML training status in realtime on slack channel.

Introducing slackker! :fire:

slackker is a python package for monitoring your ML training status in real-time on slack channel. It can send you update for your ML model training progress and send final report with graphs when the training finishes. So now you don't have to sit in front of the machine all the time. You can quickly go and grab coffee :coffee: downstairs or run some errands and still keep tracking the progress while on the move.

Star this repo if this has been useful in your ML training workflows! :star2:

Table of contents :notebook:

• Requirements
• Installation
• Getting started with slackker callbacks
  • Setup Slack to work with slackker
  • Using slackker callbacks with keras callbacks
  • Create slackker object
  • Call slackker object into callbacks during model.fit()
  • Final code
• Support
• Citation
• Maintainer

Requirements :clipboard:

• slackker utilises slack_sdk>=3.19.0 for communicating with slack API.
• To use the slackker.callbacks method in keras.callbackss keras>=2.0.0 is required.

Installation :arrow_down:

• Install slackker from PyPi is recommended. slackker is compatible with Python >= 3.6 and runs on Linux, MacOS X and Windows.
• Installing slackker in your environment is easy. Just use below pip command:

pip install slackker

Getting started with slackker callbacks

Setup Slack to work with slackker

• First create an slack app from scratch in your workspace.
• we must give below mentioned permissions for slackker to be able to send status update and report to your channel:
  • chat:write
  • chat:write.public
  • files:read
  • files:write
• Now install the app to your workspace and copy our apps Bot & OAuth Token. it should be in following format:

 xoxb-123234234235-123234234235-123234234235-adedce74748c3844747aed48499bb

• Now go to slack and add this slack app to the channel where you wish to receive al the update. Now we are ready to use slackker in your training flow!:smiling_imp:

Using slackker callbacks with keras callbacks method

Import slackker.callbacks with following line:

from slackker.callbacks import SLKerasUpdate

Create slackker object

create slackker object with SLKerasUpdate

slack_update = SLKerasUpdate(token="xoxb-123234234235-123234234235-123234234235-adedce74748c3844747aed48499bb",
    channel="A04AAB77ABC",
    modelName='SampleModel',
    export='png',
    sendPlot=True,
    verbose=0)

SLKerasUpdate takes following arguments:

• token: (string) Slack app token
• channel: (string) Slack channel where you want to receive updates (make sure you have added slack app to this same channel)
• modelName: (string) Name for your model. This same name will be used in future for title of the generated plots.
• export: (string) default "png": Format for plots to be exported. (supported formats: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff)
• sendPlots: (Bool) default True: If set to True it will export history of model, both training and validation, save it in the format given in export argument and send graphs to slack channel when training ends. If set to False it will not send exported graphs to slack channel.
• verbose: (int) default 0: You can sent the verbose level up to 3.
  • verbose = 0 No logging
  • verbose = 1 Basic/minimal logging
  • verbose = 2 Info logging
  • verbose = 3 Debug/In-depth logging

Call slackker object into callbacks during model fit

Now you can call slackker object into callbacks argument just like any other callbacks object.

history = model.fit(x_train, 
                    y_train,
                    epochs = 3,
                    batch_size = 16,
                    verbose=1,
                    validation_data=(x_val,y_val),
                    callbacks=[slack_update])

Final code

# Import library for keras
from slackker.callbacks import SLKerasUpdate

# Train-Test split for your keras model
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, train_size=0.8)

# Build keras model
model = Sequential()
model.add(Dense(8,activation='relu',input_shape = (IMG_WIDTH, IMG_HEIGHT, DEPTH)))
model.add(Dense(3,activation='softmax'))
model.compile(optimizer = 'rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# Create Slackker object
slack_update = SLKerasUpdate(token="xoxb-123234234235-123234234235-123234234235-adedce74748c3844747aed48499bb",
    channel="A04AAB77ABC",
    modelName='SampleModel',
    export='png',
    sendPlot=True)

# Call Slackker object in model.fit() callbacks
history = model.fit(x_train, 
                    y_train,
                    epochs = 3,
                    batch_size = 16,
                    verbose=1,
                    validation_data=(x_val,y_val),
                    callbacks=[slack_update])

Support :sparkles:

If you get stuck, we’re here to help. The following are the best ways to get assistance working through your issue:

• Use our Github Issue Tracker for reporting bugs or requesting features. Contribution are the best way to keep slackker amazing :muscle:
• If you want to contribute please refer Contributor's Guide for how to contribute in a helpful and collaborative way :innocent:

Citation :page_facing_up:

Please cite slackker in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{siddheshgunjal2023slackker,
  title={slackker},
  author={Siddhesh Gunjal},
  year={2023},
  howpublished={\url{https://github.com/siddheshgunjal/slackker}},
}

Maintainer :sunglasses:

• Siddhesh Gunjal :sunglasses:
  • GitHub: Siddhesh Gunjal
  • LinkedIn: LinkedIn