trains-server

0.15

Overview

Description

The Allegro Trains is a full system open source ML / DL experiment manager, versioning and ML-Ops solution. It is composed of a Python SDK, server, Web UI, and execution agents. Allegro Trains enables data scientists and data engineers to effortlessly track, manage, compare, and collaborate on their experiments as well as easily manage their training workloads on remote machines. Allegro Trains is designed for effortless integration so that teams can preserve their existing methods and practices. Use it on a daily basis to boost collaboration and visibility, or use it to automatically collect your experimentation logs, outputs, and data to one centralized server.

Security

Technical information

General information

The following information was extracted from the containerfile and other sources.

Summary	The Allegro Trains is a full system open source ML / DL experiment manager, versioning and ML-Ops solution. It is composed of a Python SDK, server, Web UI, and execution agents. Allegro Trains enables data scientists and data engineers to effortlessly track, manage, compare, and collaborate on their experiments as well as easily manage their training workloads on remote machines. Allegro Trains is designed for effortless integration so that teams can preserve their existing methods and practices. Use it on a daily basis to boost collaboration and visibility, or use it to automatically collect your experimentation logs, outputs, and data to one centralized server.
Description	Python 3.6 available as container is a base platform for building and running various Python 3.6 applications and frameworks. Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python's elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
Provider	allegroai
Maintainer	SoftwareCollections.org <sclorg@redhat.com>

Technical information

The following information was extracted from the containerfile and other sources.

Repository name	trains-server
Image version	0.15.1
Architecture	amd64
Usage	s2i build https://github.com/sclorg/s2i-python-container.git --context-dir=3.6/test/setup-test-app/ rhscl/python-36-rhel7 python-sample-app
Exposed ports	["8008/tcp" "8080/tcp" "8081/tcp"]
User	1000
Working directory	/opt/app-root/src

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Using registry tokens

Registry

Repository

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Use the following instructions to get images from a Red Hat container registry using registry service account tokens. You will need to create a registry service account to use prior to completing any of the following tasks.

Using OpenShift secrets

First, you will need to add a reference to the appropriate secret and repository to your Kubernetes pod configuration via an imagePullSecrets field.

Then, use the following from the command line or from the OpenShift Dashboard GUI interface.

Using podman login

Use the following command(s) from a system with podman installed

Using docker login

Use the following command(s) from a system with docker service installed and running

Registry

Repository

Manifest List Digest

Image Digest

Use the following instructions to get images from a Red Hat container registry using your Red Hat login.

Using OpenShift

For best practices, it is recommended to use registry tokens when pulling content for OpenShift deployments.

Using podman login

Use the following command(s) from a system with podman installed

Using docker login

Use the following command(s) from a system with docker service installed and running

Description

General information

The following information was extracted from the containerfile and other sources.

Summary	The Allegro Trains is a full system open source ML / DL experiment manager, versioning and ML-Ops solution. It is composed of a Python SDK, server, Web UI, and execution agents. Allegro Trains enables data scientists and data engineers to effortlessly track, manage, compare, and collaborate on their experiments as well as easily manage their training workloads on remote machines. Allegro Trains is designed for effortless integration so that teams can preserve their existing methods and practices. Use it on a daily basis to boost collaboration and visibility, or use it to automatically collect your experimentation logs, outputs, and data to one centralized server.
Description	Python 3.6 available as container is a base platform for building and running various Python 3.6 applications and frameworks. Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python's elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
Provider	allegroai
Maintainer	SoftwareCollections.org <sclorg@redhat.com>

Technical information

The following information was extracted from the containerfile and other sources.

Repository name	trains-server
Image version	0.15.1
Architecture	amd64
Usage	s2i build https://github.com/sclorg/s2i-python-container.git --context-dir=3.6/test/setup-test-app/ rhscl/python-36-rhel7 python-sample-app
Exposed ports	["8008/tcp" "8080/tcp" "8081/tcp"]
User	1000
Working directory	/opt/app-root/src

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Using OpenShift secrets

Using podman login

Using docker login

Using OpenShift

Using podman login

Using docker login

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trains-server

Description

Published

Release category

Size

Digest

General information

Technical information

Using OpenShift secrets

Using podman login

Using docker login

Using OpenShift

Using podman login

Using docker login