Experiment Tracking

All Native imports from segmind.tracking

segmind.tracking.set_project

This must be initialised at the beginning of your code/notebook, in order to use all other segmind.tracking methods. It lets you associate your further work on the code with a Segmind Project ID.

Name

Type

Default

Description

project_id*

string

none

Set the project ID. To learn more about creating a project and getting the project_id, click here.

Python

segmind.tracking.start_run

This must be initialised after running set_project("project_id"). This creates a unique run_uuid in our system and associates all the further tasks/artifacts/params etc with this run_uuid.

Name

Type

Default

Description

project_id*

string

none

Set the project ID. To learn more about creating a project and getting the project_id, click here.



Python



segmind.tracking.end_run

This will end your run, which means, your further run data will not be associated with any run_uuid, and will be lost. Only use this if you don't want to associate your params/metrices/artifacts with any run and you don't want it to be logged.

Name

Type

Default

Description

project_id*

string

none

Set the project ID. To learn more about creating a project and getting the project_id, click here.

Python

segmind.tracking.get_run

This will get the Current Run Info. You can call this method at any-time in your notebook - Required that your Run is initialised and not ended.
Python

segmind.tracking.set_runid

If you have multiple-runs in your notebook and you need your further params/metrices/artifacts to be associated with a particular run. Then you must use this. This will associate your further RUN data to this run_uuid.

Name

Type

Default

Description

run_uuid*

string

none

The unique run_uuid that was generated by start_run OR get_run() method. You can also get this from Segmind Portal.

Python

segmind.tracking.log_param

This will log 1-single param to your associated RUN. Although, params are logged automatically by adding segming-callbacks to your Machine Learning Models while running your .fit or training commands.

But, if you need to manually log params, then this method is very helpful. PS: If you haven't started your run by running start_run() this will automatically create a run for you. PS: key should be unique. And you won't be able to change/update the parameter value, if it's set once. PS: You won't be able to see your params on Segmind Portal, until and unless you have a metric associated with your run - which is done by log_metric() method. PS: You can log UNLIMITED Params, as required in your RUN.

Name

Type

Default

Description

key*

string

none

PARAMETER

value*

string

none

RUN VALUE

Python

segmind.tracking.log_params

This will log multiple params to your associated RUN.

This is similar to log_param method.

Name

Type

Default

Description

key*

string

none

PARAMETER

value*

string

none

RUN VALUE

Python

segmind.tracking.log_metric

Name

Type

Default

Description

key*

string

none



value*

string

none



Python

segmind.tracking.log_metrics

Python

segmind.tracking.log_artifact

Name

Type

Default

Description

key

string

none



value

string

none



Python

segmind.tracking.log_image

This will log an image to your associated Run. You can see your image in your Segmind Portal, in your associated run.

Name

Type

Default

Description

key*

string

none

unique-key for your image

image*

string

none

image name, what is in your notebook

Python

segmind.tracking.log_table

Name

Type

Default

Description

key*

string

string



table*

table

dataframe



Python

Integrations

Tensorflow

segmind.tracking.tensorflow.autolog

segmind.tracking.tensorflow.log_model

Keras

segmind.tracking.keras.autolog

segmind.tracking.keras.log_model

PyTorch

segmind.tracking.pytorch.autolog

segmind.tracking.pytorch.autolog

Sckikit Learn

segmind.sklearn.autolog

segmind.sklearn.log_model

XGBoost

segmind.xgboost.autolog

segmind.xgboost.log_model