Helm charts provided by Allegro AI, ready to launch on Kubernetes using Kubernetes Helm.
The clearml-server is the backend service infrastructure for ClearML. It allows multiple users to collaborate and manage their experiments. By default, ClearML is set up to work with the ClearML demo server, which is open to anyone and resets periodically. In order to host your own server, you will need to install clearml-server and point ClearML to it.
clearml-server contains the following components:
- The ClearML Web-App, a single-page UI for experiment management and browsing
- RESTful API for:
- Documenting and logging experiment information, statistics and results
- Querying experiments history, logs and results
- Locally-hosted file server for storing images and models making them easily accessible using the Web-App
Use this repository to deploy clearml-server on Kubernetes clusters.
ClearML is supported by the team behind allegro.ai, where we build deep learning pipelines and infrastructure for enterprise companies.
We built ClearML to track and control the glorious but messy process of training production-grade deep learning models. We are committed to vigorously supporting and expanding the capabilities of ClearML.
We promise to always be backwardly compatible, making sure all your logs, data and pipelines will always upgrade with you.
Apache License, Version 2.0, (see the LICENSE for more information)
For setting up Kubernetes on various platforms refer to the Kubernetes getting started guide.
For setting up Kubernetes on your laptop/desktop we suggest kind.
Helm is a tool for managing Kubernetes charts. Charts are packages of pre-configured Kubernetes resources.
To install Helm, refer to the Helm install guide and ensure that the helm binary is in the PATH of your shell.
$ helm repo add allegroai https://allegroai.github.io/clearml-helm-charts
$ helm repo update
$ helm search repo allegroai
$ helm install <release-name> allegroai/<chart>More information in the official documentation and on YouTube.
If you have any questions: post on our Slack Channel, or tag your questions on stackoverflow with 'clearml' tag (previously trains tag).
For feature requests or bug reports, please use GitHub issues.
Additionally, you can always find us at [email protected]
PRs are always welcomed ❤️ See more details in the ClearML Guidelines for Contributing.
May the force (and the goddess of learning rates) be with you!