**ClearML - Auto-Magical Suite of tools to streamline your ML workflow
Experiment Manager, ML-Ops and Data-Management**
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---
**Note regarding Apache Log4j2 Remote Code Execution (RCE) Vulnerability - CVE-2021-44228 - ESA-2021-31**
According to [ElasticSearch's latest report](https://discuss.elastic.co/t/apache-log4j2-remote-code-execution-rce-vulnerability-cve-2021-44228-esa-2021-31/291476),
supported versions of Elasticsearch (6.8.9+, 7.8+) used with recent versions of the JDK (JDK9+) **are not susceptible to either remote code execution or information leakage**
due to Elasticsearch’s usage of the Java Security Manager.
**As the latest version of ClearML Server uses Elasticsearch 7.10+ with JDK15, it is not affected by these vulnerabilities.**
As a precaution, we've upgraded the ES version to 7.16.2 and added the mitigation recommended by ElasticSearch to our latest [docker-compose.yml](https://github.com/allegroai/clearml-server/blob/cfccbe05c158b75e520581f86e9668291da5c70a/docker/docker-compose.yml#L42) file.
While previous Elasticsearch versions (5.6.11+, 6.4.0+ and 7.0.0+) used by older ClearML Server versions are only susceptible to the information leakage vulnerability
(which in any case **does not permit access to data within the Elasticsearch cluster**),
we still recommend upgrading to the latest version of ClearML Server. Alternatively, you can apply the mitigation as implemented in our latest
[docker-compose.yml](https://github.com/allegroai/clearml-server/blob/cfccbe05c158b75e520581f86e9668291da5c70a/docker/docker-compose.yml#L42) file.
**Update 15 December**: A further vulnerability (CVE-2021-45046) was disclosed on December 14th.
ElasticSearch's guidance for Elasticsearch remains unchanged by this new vulnerability, thus **not affecting ClearML Server**.
**Update 22 December**: To keep with ElasticSearch's recommendations, we've upgraded the ES version to the newly released 7.16.2
---
## ClearML Server
#### *Formerly known as Trains Server*
The **ClearML Server** is the backend service infrastructure for [ClearML](https://github.com/allegroai/clearml).
It allows multiple users to collaborate and manage their experiments.
**ClearML** offers a [free hosted service](https://app.clear.ml/), which is maintained by **ClearML** and open to anyone.
In order to host your own server, you will need to launch the **ClearML Server** and point **ClearML** to it.
The **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
You can quickly [deploy](#launching-the-clearml-server) your **ClearML Server** using Docker, AWS EC2 AMI, or Kubernetes.
## System design
The **ClearML Server** has two supported configurations:
- Single IP (domain) with the following open ports
- Web application on port 8080
- API service on port 8008
- File storage service on port 8081
- Sub-Domain configuration with default http/s ports (80 or 443)
- Web application on sub-domain: app.\*.\*
- API service on sub-domain: api.\*.\*
- File storage service on sub-domain: files.\*.\*
## Launching The ClearML Server
### Prerequisites
The ports 8080/8081/8008 must be available for the **ClearML Server** services.
For example, to see if port `8080` is in use:
* Linux or macOS:
sudo lsof -Pn -i4 | grep :8080 | grep LISTEN
* Windows:
netstat -an |find /i "8080"
### Launching
Launch The **ClearML Server** in any of the following formats:
- Pre-built [AWS EC2 AMI](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_aws_ec2_ami)
- Pre-built [GCP Custom Image](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_gcp)
- Pre-built Docker Image
- [Linux](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_linux_mac)
- [macOS](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_linux_mac)
- [Windows 10](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_win)
- Kubernetes
- [Kubernetes Helm](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_kubernetes_helm)
- Manual [Kubernetes installation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_kubernetes)
## Connecting ClearML to your ClearML Server
In order to set up the **ClearML** client to work with your **ClearML Server**:
- Run the `clearml-init` command for an interactive setup.
- Or manually edit `~/clearml.conf` file, making sure the server settings (`api_server`, `web_server`, `file_server`) are configured correctly, for example:
api {
# API server on port 8008
api_server: "http://localhost:8008"
# web_server on port 8080
web_server: "http://localhost:8080"
# file server on port 8081
files_server: "http://localhost:8081"
}
**Note**: If you have set up your **ClearML Server** in a sub-domain configuration, then there is no need to specify a port number,
it will be inferred from the http/s scheme.
After launching the **ClearML Server** and configuring the **ClearML** client to use the **ClearML Server**,
you can [use](https://github.com/allegroai/clearml) **ClearML** in your experiments and view them in your **ClearML Server** web server,
for example http://localhost:8080.
For more information about the ClearML client, see [**ClearML**](https://github.com/allegroai/clearml).
## ClearML-Agent Services
As of version 0.15 of **ClearML Server**, dockerized deployment includes a **ClearML-Agent Services** container running as
part of the docker container collection.
ClearML-Agent Services is an extension of ClearML-Agent that provides the ability to launch long-lasting jobs
that previously had to be executed on local / dedicated machines. It allows a single agent to
launch multiple dockers (Tasks) for different use cases. To name a few use cases, auto-scaler service (spinning instances
when the need arises and the budget allows), Controllers (Implementing pipelines and more sophisticated DevOps logic),
Optimizer (such as Hyper-parameter Optimization or sweeping), and Application (such as interactive Bokeh apps for
increased data transparency)
ClearML-Agent Services container will spin **any** task enqueued into the dedicated `services` queue.
Every task launched by ClearML-Agent Services will be registered as a new node in the system,
providing tracking and transparency capabilities.
You can also run the ClearML-Agent Services manually, see details in [ClearML-agent services mode](https://github.com/allegroai/clearml-agent#clearml-agent-services-mode-)
**Note**: It is the user's responsibility to make sure the proper tasks are pushed into the `services` queue.
Do not enqueue training / inference tasks into the `services` queue, as it will put unnecessary load on the server.
## Advanced Functionality
The **ClearML Server** provides a few additional useful features, which can be manually enabled:
* [Web login authentication](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config#web-login-authentication)
* [Non-responsive experiments watchdog](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config#non-responsive-task-watchdog)
## Restarting ClearML Server
To restart the **ClearML Server**, you must first stop the containers, and then restart them.
```bash
docker-compose down
docker-compose -f docker-compose.yml up
```
## Upgrading
**ClearML Server** releases are also reflected in the [docker compose configuration file](https://github.com/allegroai/trains-server/blob/master/docker/docker-compose.yml).
We strongly encourage you to keep your **ClearML Server** up to date, by keeping up with the current release.
**Note**: The following upgrade instructions use the Linux OS as an example.
To upgrade your existing **ClearML Server** deployment:
1. Shut down the docker containers
```bash
docker-compose down
```
1. We highly recommend backing up your data directory before upgrading.
Assuming your data directory is `/opt/clearml`, to archive all data into `~/clearml_backup.tgz` execute:
```bash
sudo tar czvf ~/clearml_backup.tgz /opt/clearml/data
```
Restore instructions:
To restore this example backup, execute:
```bash
sudo rm -R /opt/clearml/data
sudo tar -xzf ~/clearml_backup.tgz -C /opt/clearml/data
```
1. Download the latest `docker-compose.yml` file.
```bash
curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker/docker-compose.yml -o docker-compose.yml
```
1. Configure the ClearML-Agent Services (not supported on Windows installation).
If `CLEARML_HOST_IP` is not provided, ClearML-Agent Services will use the external
public address of the **ClearML Server**. If `CLEARML_AGENT_GIT_USER` / `CLEARML_AGENT_GIT_PASS` are not provided,
the ClearML-Agent Services will not be able to access any private repositories for running service tasks.
```bash
export CLEARML_HOST_IP=server_host_ip_here
export CLEARML_AGENT_GIT_USER=git_username_here
export CLEARML_AGENT_GIT_PASS=git_password_here
```
1. Spin up the docker containers, it will automatically pull the latest **ClearML Server** build
```bash
docker-compose -f docker-compose.yml pull
docker-compose -f docker-compose.yml up
```
**\* If something went wrong along the way, check our FAQ: [Common Docker Upgrade Errors](https://clear.ml/docs/latest/docs/faq/).**
## Community & Support
If you have any questions, look to the ClearML [FAQ](https://clear.ml/docs/latest/docs/faq), or
tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/clearml) with '**clearml**' tag.
For feature requests or bug reports, please use [GitHub issues](https://github.com/allegroai/clearml-server/issues).
Additionally, you can always find us at *clearml@allegro.ai*
## License
[Server Side Public License v1.0](https://github.com/mongodb/mongo/blob/master/LICENSE-Community.txt)
The **ClearML Server** relies on both [MongoDB](https://github.com/mongodb/mongo) and [ElasticSearch](https://github.com/elastic/elasticsearch).
With the recent changes in both MongoDB's and ElasticSearch's OSS license, we feel it is our responsibility as a
member of the community to support the projects we love and cherish.
We believe the cause for the license change in both cases is more than just,
and chose [SSPL](https://www.mongodb.com/licensing/server-side-public-license) because it is the more general and flexible of the two licenses.
This is our way to say - we support you guys!