Python Library
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.
If you are new to Segmind, it might be worthwhile to check out the rest of the documentation, particularly the Platform, before continuing with the Python API.
- Install Segmind
2. Login to Segmind
Python Library (PIP package) is already installed for sessions on Segmind platform.
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.
Usage
segmind [OPTIONS] COMMAND [ARGS] ...
Command | Description |
login | Login to Segmind account |
logout | Log out of Segmind account |
whoami | Display current user information |
Command | Description |
job | Submit a new job to execute |
stats | Display compute and storage performance statistics for a job |
info | Displays information about a job, Eg. Start time, running time, instance details. |
stop | Stop a job |
ps | Display job statuses |
Works is in progress to add this. Check back later to know more about the cluster management endpoints.