Virtual computing environments that are running on a cluster with CPU, Memory and GPU as basic resources. Instance can be used to run Jupyter Lab, VS Code and Jobs Sessions. Instance state is always saved before stopping a session, except for Jobs.


Segmind Datastore provides high-performance versioned datasets and artifacts storage. Datastore can be attached to an instance whenever you need the data while running your code.


Projects can be connected to instances to track their costs and activities.


A session is a single stretch of an instance used for Jupyter Lab or VS Code.


Storage attached to instances. Storage volumes provide high-speed (High IOPS) throughput to run your workloads. You will lose your storage when you delete an instance. Use Datastore to store your datasets and other files that need to be stored for longer.

Spot Instance

Instances that can be terminated at any time without notice. Spot instances are much cheaper (~ 50%) than normal instances and can be used to run non-critical workloads.


Docker image that is used to create an instance. Your environment is saved whenever a session ends so that you can continue from where you left off in your previous session.


Service that is used to track all your experiment runs. You can log metrics, parameters, and artifact files via tracking. You can use the Python Library to log your experiments.

For self-hosted

Cloud Service Provider (CSP)

You can choose your IaaS (AWS, GCP, Azure). Segmind will create your cluster used to run your workloads and other MLOps services.


Segmind cluster is a set of computation resources and cloud services that your team can use to run their workloads and other MLOps related services. Segmind uses Kubernetes to create and manage compute resources.

Cluster Region

You can choose which region to run your workloads in. They differ based on CSP.

Updated 24 May 2022
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