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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.
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> |
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 |
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.
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.
Use the following command(s) from a system with podman installed
Use the following command(s) from a system with docker service installed and running
Use the following instructions to get images from a Red Hat container registry using your Red Hat login.
For best practices, it is recommended to use registry tokens when pulling content for OpenShift deployments.
Use the following command(s) from a system with podman installed
Use the following command(s) from a system with docker service installed and running