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TensorPool CLI

TensorPool CLI is a Python command-line interface for running machine-learning training jobs and accessing on-demand multi-node GPU clusters. It also includes command groups for cluster management, storage volumes, NFS, SSH access to instances, and user information or SSH-key management. The public repository identifies the project as MIT-licensed and provides links to CLI, API, installation, resource and quick-start

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

TensorPool CLI — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use TensorPool CLI when a machine-learning workflow needs command-line control over training jobs and the multi-node GPU infrastructure around them.

Who it is for

  • Machine-learning developers launching training jobs from a command line.
  • Teams managing GPU clusters and related storage or access workflows.
  • Python users building repeatable training-infrastructure procedures around documented CLI commands.

Documented uses

  • Submit and manage machine-learning training jobs.
  • Access and manage on-demand multi-node GPU clusters.
  • Manage storage volumes and NFS resources for ML workloads.
  • Connect to GPU instances through SSH.
  • Display user information and manage SSH keys through the CLI.
Before you choose

Requirements & limits

The project is available as a public GitHub repository, documents installation through Python's package installer, and is released under the MIT license. The supplied material does not specify account, pricing or service-availability requirements.

  • Python 3.8 or newer is stated by the repository's installation badge.
  • The interface is organized as a CLI with separate command groups for jobs, clusters, storage, NFS, SSH and user information.
  • The documented scope includes both training-job workflows and supporting infrastructure operations.
  • An API reference is listed, but the supplied material does not provide endpoint details.
  • A team configuration layer is marked Likely compatible, while the evidence does not show an extension API or plugin mechanism.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install TensorPool CLI

Install the TensorPool command-line interface and inspect its available command groups.

Before you start

  • Python 3.8 or later.
  • A shell with pip available.

Steps

  1. Install the package with the command below.

    Documented command
    pip install tensorpool

  2. Run tp --help to display the CLI command groups and options.

Expected outcome

The TensorPool CLI is installed, and its help output lists cluster, storage, job, SSH, and user-information commands.

Guide limitations

  • These steps cover installation and command discovery only; they do not configure access or submit a job.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
4 October 2026
Original website title
tensorpool/tensorpool · Official homepage title · retained in its source language
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