Welcome to the documentation for the Segmind Python API! This library allows you to programmatically interact with Segmind from within a Python script or application.
2. Login to Segmind
Instances (used for Jobs, Jupyter Lab and VS Code sessions) on Segmind come pre-installed and pre-configured with your settings.
The library provides segmind.tracking API written in python, running on top of the MLFLow tracking. Use the library to track machine learning experiments. For Tensorflow, Keras, PyTorch and Scikit Learn, we have special lightweight integrations to make it fast and easy to setup experiment tracking.
To learn more about Tracking module API endpoints, check outExperiment TrackingAPI reference docs.
Use segmind.jobs API to initiate a single instance of job on Segmind. It takes inputs such as base docker (environment definition), dataset to be mounted, code and compute requirements to execute it on the cluster.
Segmind provides command line interface to interact with the platform. Below you will find a description of each command, including the various options and arguments.
segmind [OPTIONS] COMMAND [ARGS] ...
Login to Segmind account
Log out of Segmind account
Display current user information
Submit a new job to execute
Display compute and storage performance statistics for a job
Displays information about a job, Eg. Start time, running time, instance details.
Stop a job
Display job statuses
Works is in progress to add this. Check back later to know more about the cluster management endpoints.