### Install Task Requirements

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/clearml_agent/executable_exp_containers.md

Navigate to the task's example directory and install its dependencies.

```console
cd clearml/examples/frameworks/keras
pip install -r requirements.txt
```

--------------------------------

### Install ClearML and Run Setup Wizard

Source: https://context7.com/clearml/clearml-docs/llms.txt

Installs the ClearML Python package and runs the interactive setup wizard to connect to a ClearML Server. For Jupyter Notebooks, use credentials pasted directly into notebook cells.

```bash
pip install clearml

clearml-init
```

--------------------------------

### ClearML Setup Wizard Console Output

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_sdk/clearml_sdk_setup.md

This is an example of the console output during the `clearml-init` process, showing credential verification and configuration file creation.

```console
Please create new clearml credentials through the settings page in your `clearml-server` web app (e.g. http://localhost:8080//settings/workspace-configuration), 
or create a free account at https://app.clear.ml/settings/workspace-configuration

In the settings page, press "Create new credentials", then press "Copy to clipboard".

Paste copied configuration here:
```

```console
Detected credentials key="********************" secret="*******"

CLEARML Hosts configuration:
Web App: https://app.<your-domain>
API: https://api.<your-domain>
File Store: https://files.<your-domain>
            
Verifying credentials ...
Credentials verified! 

New configuration stored in /home/<username>/clearml.conf
CLEARML setup completed successfully.
```

--------------------------------

### Checkout Code and Setup Python

Source: https://github.com/clearml/clearml-docs/blob/main/docs/getting_started/video_tutorials/hands-on_mlops_tutorials/ml_ci_cd_using_github_actions_and_clearml.md

Steps to checkout the repository code and set up a specific Python version for the job. Ensure Python is installed before proceeding with package installations.

```yaml
steps:
  - name: Checkout code
    uses: actions/checkout@v3
  - name: Set up Python 3.10
    uses: actions/setup-python@v3
    with:
      python-version: "3.10"
```

--------------------------------

### Install clearml-serving CLI

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_serving/clearml_serving_setup.md

Install the ClearML Serving command-line interface using pip.

```bash
pip3 install clearml-serving
```

--------------------------------

### Install ClearML with GPU Monitoring Support

Source: https://github.com/clearml/clearml-docs/blob/main/docs/release_notes/sdk/open_source/ver_0_10.md

Install ClearML with GPU monitoring capabilities by including the 'gpu' extra.

```bash
pip install trains[gpu]
```

--------------------------------

### Navigate to Examples Directory

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_data/data_management_examples/data_man_simple.md

Change the current directory to the examples folder within the cloned clearml repository. This is a prerequisite for running the subsequent data management commands.

```bash
cd clearml/examples/reporting
```

--------------------------------

### Start ClearML Docker Containers

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/vpc_aws.md

Deploy ClearML services by starting Docker containers using docker-compose and an environment file.

```bash
sudo docker-compose --env-file constants.env up -d
```

--------------------------------

### Project Selection Dropdown Configuration

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/app_custom.md

Sets up a dropdown field for selecting projects, filtering out those whose names start with 'example'. Ideal for project-specific configurations.

```hcl
{
      name: a_project_field
      title: Choose a Project
      info: "The app will count the tasks in this project"
      type: dropdown
      required: true
      autocomplete: true
      choices {
          source: project
          value_field: id
          display_field: name
          filter {
              fields {
                 name: "^\\\(?i)(?!example).*$"
              }
          }
      }
      target {
          field: hyperparams
          section: General
          name: a_project_id
      }
   }
```

--------------------------------

### Runtime Environment File Example

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/appgw_install_compose_hosted.md

Example `runtime.env` file for configuring the AI Gateway. Fill in user-specific details like router name, URL, and API credentials.

```env
# PREFILLED SECTION, PROVIDED BY CLEARML
PROXY_TAG=
ROUTER_TAG=
CLEARML_API_HOST=https://api.
AUTH_COOKIE_NAME=

# TO BE FILLED BY USER
ROUTER_NAME=main-router
ROUTER_URL=http://<ROUTER-HOST-PUBLIC-IP>:8010
CLEARML_API_ACCESS_KEY=
CLEARML_API_SECRET_KEY=
AUTH_SECURE_ENABLED=true
TCP_ROUTER_ADDRESS=<ROUTER-HOST-PUBLIC-IP>
TCP_PORT_START=
TCP_PORT_END=
```

--------------------------------

### Install ClearML Python Package

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_sdk/clearml_sdk_setup.md

Use this command to install the ClearML Python package. This is the first step before connecting to the ClearML Server.

```bash
pip install clearml
```

--------------------------------

### Install clearml-session

Source: https://github.com/clearml/clearml-docs/blob/main/docs/apps/clearml_session.md

Install the clearml-session package using pip. This is a prerequisite for launching ClearML sessions.

```command-line
pip install clearml-session
```

--------------------------------

### Install Docker CE

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/on_prem_ubuntu.md

Follow the official Docker documentation for Ubuntu to install Docker CE.

```bash
https://docs.docker.com/install/linux/docker-ce/ubuntu/
```

--------------------------------

### Install ClearML with Azure Support

Source: https://github.com/clearml/clearml-docs/blob/main/docs/release_notes/sdk/open_source/ver_0_10.md

Install ClearML with Microsoft Azure cloud storage support by including the 'azure' extra.

```bash
pip install trains[azure]
```

--------------------------------

### Install ClearML Serving Package

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_serving/clearml_serving_tutorial.md

Install or upgrade the ClearML Serving package using pip. Ensure you have the latest version for all features.

```bash
~$ pip install -U clearml-serving
```

--------------------------------

### Install ClearML with Google Cloud Storage Support

Source: https://github.com/clearml/clearml-docs/blob/main/docs/release_notes/sdk/open_source/ver_0_10.md

Install ClearML with Google Cloud Storage support by including the 'gs' extra.

```bash
pip install trains[gs]
```

--------------------------------

### Example Console Output

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/clearml-task/clearml_task_tutorial.md

This is an example of the console output you might see after successfully enqueuing a ClearML task for execution.

```console
New task created id=2f96ee95b05d4693b360d0fcbb26b727
Task id=2f96ee95b05d4693b360d0fcbb26b727 sent for execution on queue default
Execution log at: https://app.clear.ml/projects/552d5399112d47029c146d5248570295/experiments/2f96ee95b05d4693b360d0fcbb26b727/output/log
```

--------------------------------

### Install ClearML and Dependencies

Source: https://github.com/clearml/clearml-docs/blob/main/docs/getting_started/video_tutorials/hands-on_mlops_tutorials/ml_ci_cd_using_github_actions_and_clearml.md

Install the ClearML SDK and any other necessary Python packages for your project. This step ensures all required libraries are available in the action environment.

```yaml
      - name: Install ClearML and requirements
        run: |
          pip install clearml
          pip install -r requirements.txt
```

--------------------------------

### Example Task Execution Output

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/pipeline/pipeline_controller.md

Example console output when a ClearML task is created, showing the task ID and links to the results and pipeline pages in the ClearML WebApp.

```text
ClearML Task: created new task id=bc93610688f242ecbbe70f413ff2cf5f
ClearML results page: https://app.clear.ml/projects/462f48dba7b441ffb34bddb783711da7/experiments/bc93610688f242ecbbe70f413ff2cf5f/output/log
ClearML pipeline page: https://app.clear.ml/pipelines/462f48dba7b441ffb34bddb783711da7/experiments/bc93610688f242ecbbe70f413ff2cf5f
```

--------------------------------

### Initialize ClearML SDK with Wizard

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_sdk/clearml_sdk_setup.md

Run the `clearml-init` command to launch the setup wizard for connecting the ClearML SDK to your server. Note that this command will not work if a `clearml.conf` file already exists.

```bash
clearml-init
```

--------------------------------

### Run ClearML Example Script

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/ide/remote_jupyter_tutorial.md

Use this magic command to execute a Python script from the cloned ClearML repository within your Jupyter Notebook. Packages like ClearML, Keras, and TensorFlow are available without explicit installation due to the initial `clearml-session` setup.

```python
%run clearml/examples/frameworks/keras/keras_tensorboard.py
```

--------------------------------

### Install and Initialize ClearML for Hosted Service

Source: https://github.com/clearml/clearml-docs/blob/main/docs/faq.md

Install the ClearML SDK and run the initialization wizard to configure your environment for the ClearML Hosted Service. This process gathers necessary credentials.

```bash
pip install clearml
    
clearml-init
```

--------------------------------

### ClearML Initialization Wizard Output

Source: https://github.com/clearml/clearml-docs/blob/main/docs/faq.md

Example output from the clearml-init wizard after successful configuration.

```text
Verifying credentials ...
Credentials verified!
New configuration stored in /home/<username>/clearml.conf
ClearML setup completed successfully.
```

--------------------------------

### Cleanup Service Console Output Example

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/services/cleanup_service.md

Example console output when the cleanup service is executed. This shows the task creation, results page URL, and the start of the cleanup process.

```console
ClearML Task: created new task id=8126c0af800f4903be07421aa344d7b3
ClearML results page: https://app.clear.ml/projects/608e9039/experiments/81261aa34d7b3/output/log
Cleanup service started
Starting cleanup
Deleting 100 tasks
```

--------------------------------

### AWS Autoscaler Setup Wizard - Pre-execution Script

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/services/aws_autoscaler.md

Provide a bash script to be executed on new EC2 instances before the ClearML Agent starts. This allows for custom setup or environment preparation.

```console
Enter any pre-execution bash script to be executed on the newly created instances []:
```

--------------------------------

### Backup MongoDB Data

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/clearml_server_mongo44_migration.md

Back up the ClearML data folder before starting the migration. Assumes ClearML is installed in default paths.

```bash
sudo tar -zcf mongodb_upgrade.gz -C /opt/clearml/data/mongo .
```

--------------------------------

### Display example.env file

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_serving/clearml_serving_setup.md

Display the contents of the example environment variables file, which contains placeholders for ClearML server API credentials and the Serving Service UID.

```bash
cat docker/example.env
```

--------------------------------

### Configure Fixed User Authentication

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/k8s.md

Enable and configure simple login with username and password in `clearml-values.override.yaml`. This is useful for simple PoC installations and is an optional step if SSO is not configured. Note that this setup is not ideal for multi-tenant setups as fixed users will only be associated with the default tenant.

```yaml
apiserver:
  additionalConfigs:
    apiserver.conf: |
      auth {
        fixed_users {
          enabled: true
          pass_hashed: false
          users: [
            {
              username: "my_user"
              password: "my_password"
              name: "My User"
              admin: true
            },
          ]
        }
      }
```

--------------------------------

### Initialize Accelerator with ClearML Tracker

Source: https://github.com/clearml/clearml-docs/blob/main/docs/integrations/accelerate.md

Instantiate the Accelerator with the 'log_with' parameter set to 'all' or 'clearml'. Ensure 'clearml' is installed and set up in your environment.

```python
from accelerate import Accelerator

accelerator = Accelerator(log_with="all")  # For all available trackers in the environment
accelerator = Accelerator(log_with="clearml") # For the ClearML tracker only
```

--------------------------------

### Create ClearML Data and Log Directories

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/on_prem_ubuntu.md

Set up necessary directories for databases, files, logs, and configuration. Ensure correct ownership for the data directories.

```bash
sudo mkdir -pv ${CLEARML_ROOT}/data/elastic7plus
sudo chown 1000:1000 ${CLEARML_ROOT}/data/elastic7plus
sudo mkdir -pv ${CLEARML_ROOT}/data/mongo_4/configdb
sudo mkdir -pv ${CLEARML_ROOT}/data/mongo_4/db
sudo mkdir -pv ${CLEARML_ROOT}/data/redis
sudo mkdir -pv ${CLEARML_ROOT}/data/fileserver
sudo mkdir -pv ${CLEARML_ROOT}/data/fileserver/tmp
sudo mkdir -pv ${CLEARML_ROOT}/logs/apiserver
sudo mkdir -pv ${CLEARML_ROOT}/documentation
sudo mkdir -pv ${CLEARML_ROOT}/logs/fileserver
sudo mkdir -pv ${CLEARML_ROOT}/logs/fileserver-proxy
sudo mkdir -pv ${CLEARML_ROOT}/data/fluentd/buffer
sudo mkdir -pv ${CLEARML_ROOT}/config/webserver_external_files
sudo mkdir -pv ${CLEARML_ROOT}/config/onprem_poc
```

--------------------------------

### Display Help for upload_apps.py Script

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/app_install_ubuntu_on_prem.md

Run this command to view all available options and usage instructions for the `upload_apps.py` script.

```bash
python3 upload_apps.py --help
```

--------------------------------

### Control Automatic Framework Logging

Source: https://github.com/clearml/clearml-docs/blob/main/docs/model_registry.md

Specify which models to log automatically by providing a pattern for model names. This example logs only PyTorch models with names starting with 'final'.

```python
from clearml import Task

task = Task.init(
	project_name="My Project",
	task_name="My Task",
	auto_connect_frameworks={'pytorch': 'final*.pt',
	}
)
```

--------------------------------

### Launch AWS Auto-Scaler Locally

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/services/aws_autoscaler.md

Execute the AWS autoscaler script locally to initiate the configuration wizard. This command starts the interactive setup process for your AWS environment and ClearML integration.

```bash
python aws_autoscaler.py --run
```

--------------------------------

### Default App Gateway Helm Values

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/appgw_install_k8s.md

Configure essential parameters for the ClearML App Gateway installation using a Helm override values file. This setup enables Ingress for HTTP(S) traffic.

```yaml
imageCredentials:
  password: ""
clearml:
  apiKey: ""
  apiSecret: ""
  apiServerUrlReference: ""
  authCookieName: ""
ingress:
  enabled: true
  hostName: ""
streamSession:
  routerAddress: ""
  service:
    type: LoadBalancer
  portRange:
    start: 
    end:
```

--------------------------------

### Create and Manage Datasets with ClearML

Source: https://context7.com/clearml/clearml-docs/llms.txt

Demonstrates creating a new dataset version, adding local and external files, removing files, reporting statistics, and uploading the dataset. Also shows how to consume a dataset, get local copies (read-only and mutable), and squash multiple versions into a flat version.

```python
dataset = Dataset.create(
    dataset_name='ImageNet subset',
    dataset_project='data/vision',
    parent_datasets=['PARENT_DS_ID'],       # inherit files from parent
    dataset_version='2.0',
    output_uri='s3://my-bucket/datasets/',  # upload destination
    description='Filtered, augmented ImageNet subset'
)

# Add local files or folders
dataset.add_files(path='/data/images/', wildcard='*.jpg', recursive=True)

# Add files from cloud storage (left in place, linked by URL)
dataset.add_external_files(
    source_url='s3://source-bucket/raw-images/',
    dataset_path='/images/raw/',
    wildcard='*.jpg',
    recursive=True,
)

# Remove files matching a pattern
dataset.remove_files(dataset_path='*.tmp', recursive=True)

# Attach a preview table for visibility in the Web UI
dataset.get_logger().report_table(
    title='Dataset Stats', series='Class Distribution', csv='/tmp/stats.csv'
)

# Upload and finalize (makes dataset read-only)
dataset.upload(output_url='s3://my-bucket/datasets/', max_workers=8)
dataset.finalize()

# --- Consume a dataset ---
ds = Dataset.get(
    dataset_project='data/vision',
    dataset_name='ImageNet subset',
    dataset_version='2.0',
    only_completed=True,
    alias='train_dataset',   # stores dataset ID in task hyperparameters for traceability
)
local_path = ds.get_local_copy()            # cached read-only local copy
print(f"Dataset available at: {local_path}")

# For a writable copy (e.g., to add more files)
mutable_path = ds.get_mutable_local_copy(target_folder='/tmp/dataset_work', overwrite=True)

# --- Squash multiple dataset versions into a flat version ---
flat_ds = Dataset.squash(
    dataset_name='ImageNet subset flat',
    dataset_ids=['DS_V1_ID', 'DS_V2_ID', 'DS_V3_ID'],
    output_uri='s3://my-bucket/datasets/flat/',
)
```

--------------------------------

### Initialize ClearML Task in Jupyter Notebook

Source: https://github.com/clearml/clearml-docs/blob/main/docs/faq.md

Create a ClearML task and start training within your Jupyter Notebook. This code snippet is used after installing ClearML and configuring credentials (Option 2 for Jupyter integration).

```python
# create a task and start training
task = Task.init(project_name='jupyter project', task_name='my notebook')
```

--------------------------------

### Customize Agent Container Start Script for Air-Gapped Systems

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/air_gapped_env.md

Configure the ClearML Agent to support air-gapped systems by skipping default APT installations and specifying a custom container image. The custom Bash script must end with `clearml_agent execute`.

```yaml
agentk8sglue:
  # -- Support for air-gapped systems. Skip container APT installations.
  airGappedSupport: true
  # -- Default container image for ClearML Task pod
  defaultContainerImage: "<YOUR_IMAGE_WITH_PYTHON_3.9>"
  # -- Custom Bash script for the Task Pods run by the Agent
  containerCustomBashScript: |
    export HOME=/tmp
    export LOCAL_PYTHON=python3
    {extra_bash_init_cmd}
    [ ! -z $CLEARML_AGENT_NO_UPDATE ] || $LOCAL_PYTHON -m pip install clearml-agent{agent_install_args}
    {extra_docker_bash_script}
    $LOCAL_PYTHON -m clearml_agent execute {default_execution_agent_args} --id {task_id}
```

--------------------------------

### Install ClearML Agent

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_agent/clearml_agent_deployment_bare_metal.md

Install ClearML Agent using pip. It is recommended to install it as a system Python package, not within a virtual environment.

```bash
pip install clearml-agent
```

--------------------------------

### Start clearml-serving with Docker Compose

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_serving/clearml_serving_setup.md

Launch the ClearML Serving containers using Docker Compose, specifying the environment file and the main Docker Compose configuration.

```bash
cd docker && docker-compose --env-file example.env -f docker-compose.yml up
```

--------------------------------

### Remote Environment Setup Status

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/ide/remote_jupyter_tutorial.md

Monitor the progress of your remote environment setup. This output shows the stages from creation to allocation and setup completion.

```console
Creating new session
New session created [id=35c0af81ae6541589dbae1efb747f388]
Waiting for remote machine allocation [id=35c0af81ae6541589dbae1efb747f388]
.Status [queued]
...Status [in_progress]
Remote machine allocated
Setting remote environment [Task id=35c0af81ae6541589dbae1efb747f388]
Setup process details: https://app.clear.ml/projects/60893b87b0b642679fde00db96e90359/experiments/35c0af81ae6541589dbae1efb747f388/output/log
Waiting for environment setup to complete [usually about 20-30 seconds]
```

--------------------------------

### Initialize ClearML Agent Configuration

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_agent/clearml_agent_deployment_bare_metal.md

Run the initialization wizard to configure ClearML Agent. This process guides you through setting up credentials, server URLs, and Git authentication. Follow the prompts to paste your ClearML credentials and configure server connections.

```bash
clearml-agent init
```

```text
Please create new clearml credentials through the settings page in your `clearml-server` web app, 
or create a free account at https://app.clear.ml/settings/webapp-configuration

In the settings > workspace page, press "Create new credentials", then press "Copy to clipboard".

Paste copied configuration here:    
```

```text
Detected credentials key="********************" secret="*******"
```

```text
WEB Host configured to: [https://app.clear.ml] 
```

```text
CLEARML Hosts configuration:
Web App: https://app.clear.ml
API: https://api.clear.ml
File Store: https://files.clear.ml

Verifying credentials ...
Credentials verified!
```

```text
Default Output URI (used to automatically store models and artifacts): (N)one/ClearML (S)erver/(C)ustom [None]
```

```text
Enter git username for repository cloning (leave blank for SSH key authentication): []
```

```text
Enter password for user '<username>':
```

```text
Git repository cloning will be using user=<username> password=<password>        
```

```text
Enter additional artifact repository (extra-index-url) to use when installing python packages (leave blank if not required):
```

```text
New configuration stored in /home/<username>/clearml.conf
CLEARML-AGENT setup completed successfully.        
```

--------------------------------

### Verify Docker Installation

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/clearml_server_linux_mac.md

Run this command to ensure Docker is installed and functioning correctly.

```bash
docker run hello-world
```

--------------------------------

### Create ClearML Dataset Step-by-Step

Source: https://github.com/clearml/clearml-docs/blob/main/docs/integrations/yolov5.md

Creates a ClearML dataset by sequentially creating, adding files, and closing the dataset. Optionally specify a parent dataset to avoid duplicate uploads.

```command
# Optionally add --parent <parent_dataset_id> if you want to base
# this version on another dataset version, so no duplicate files are uploaded!
clearml-data create --name coco128 --project YOLOv5
clearml-data add --files .
clearml-data close
```

--------------------------------

### Configure Environment Variables

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_serving/clearml_serving_setup.md

Example environment variables for ClearML Serving, including server connection details and the Serving Service ID. Ensure you replace placeholder values with your actual credentials.

```console
 CLEARML_WEB_HOST="https://app.clear.ml"
 CLEARML_API_HOST="https://api.clear.ml"
 CLEARML_FILES_HOST="https://files.clear.ml"
 CLEARML_API_ACCESS_KEY="<access_key_here>"
 CLEARML_API_SECRET_KEY="<secret_key_here>"
 CLEARML_SERVING_TASK_ID="<serving_service_id_here>"
```

--------------------------------

### Asset Access Example

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/app_custom.md

Demonstrates how to reference an asset file within the application configuration using the ${ASSET:...} syntax.

```clearml-config
icon: "${ASSET:app-simple-app@2x.png}"
```

--------------------------------

### Install clearml-custom-events Helm Chart

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/extra_configs/custom_billing.md

Steps to prepare values and install the 'clearml-custom-events' umbrella Helm chart.

```APIDOC
### Install custom-events

The `clearml-custom-events` is the Kubernetes "Custom Events" umbrella Helm chart for ClearML. It bundles all necessary services and sets up a dedicated Redis instance.

#### Prepare Values

Create a `clearml-custom-events-values.override.yaml` file with the following content (make sure to replace the `<PLACEHOLDERS>`):

```yaml
global:
  imageCredentials:
    password: "<CLEARML_DOCKERHUB_TOKEN>"
  clearml:
    apiServerUrlReference: "<CLEARML_APISERVER_URL>"
    apiServerKey: "<ACCESSKEY>"
    apiServerSecret: "<SECRETKEY>"

clearml-pods-monitor-exporter:
  checkIntervalSeconds: 60
  # -- *REQUIRED* Universal Unique string to identify Pods Monitor instances across worker clusters. Cannot be empty.
  # Uniqueness is required across different clusters installations to preserve the reported data status.
  podsMonitorUUID: ""

clearml-pods-inventory:
  cronJob:
    schedule: "@daily"

clearml-company-inventory:
  cronJob:
    schedule: "@daily"
```

#### Install

Install the Custom Events umbrella chart:

```bash
helm upgrade -i clearml-custom-events oci://docker.io/clearml/clearml-custom-events -f clearml-custom-events-values.override.yaml
```
```

--------------------------------

### Run Tutorial Script

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/reporting/explicit_reporting.md

Executes the ClearML tutorial script. This command should be run from the terminal after setting up your ClearML environment and script.

```bash
python pytorch_mnist_tutorial.py
```

--------------------------------

### Package Application for Upload

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/enterprise_deploy/app_custom.md

Create a zip archive of your application's configuration and assets. This command packages the 'simple-app.app.conf' file and the 'assets/' directory into 'simple-app.zip'.

```bash
zip -r simple-app.zip simple-app.app.conf assets/
```

--------------------------------

### Install Upgrade Elastic Helm Chart

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/clearml_server_es7_migration.md

Install the upgrade-elastic-helm chart into the upgrade-elastic namespace on the Kubernetes cluster.

```bash
helm install allegroai/upgrade-elastic-helm --namespace=upgrade-elastic --name upgrade
```

--------------------------------

### Start ClearML Agent

Source: https://github.com/clearml/clearml-docs/blob/main/docs/integrations/autokeras.md

Deploy a ClearML Agent on a machine by running this command. The agent listens to specified queues and executes tasks, reporting results back to the ClearML Task Manager.

```commandline
clearml-agent daemon --queue <queues_to_listen_to> [--docker]
```

--------------------------------

### Install Docker Compose on Linux

Source: https://github.com/clearml/clearml-docs/blob/main/docs/deploying_clearml/clearml_server_linux_mac.md

Installs Docker Compose for Linux systems. Ensure you have curl and sudo privileges.

```bash
sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
```

--------------------------------

### Install ClearML with S3 and GPU Support

Source: https://github.com/clearml/clearml-docs/blob/main/docs/release_notes/sdk/open_source/ver_0_10.md

Combine cloud storage and GPU monitoring by specifying multiple extras, such as 's3' and 'gpu'.

```bash
pip install trains[s3,gpu]
```

--------------------------------

### Example Click App with ClearML Integration

Source: https://github.com/clearml/clearml-docs/blob/main/docs/integrations/click.md

This example demonstrates a simple Click command-line interface that initializes a ClearML task and logs its arguments. The ClearML task will automatically capture the values passed to `--count` and `--name`.

```python
import click
from clearml import Task

@click.command()
@click.option('--count', default=1, help='Number of greetings.')
@click.option('--name', prompt='Your name', help='The person to greet.')

def hello(count, name):
    task = Task.init(project_name='examples', task_name='Click single command')

    for x in range(count):
        click.echo("Hello {}!".format(name))


if __name__ == '__main__':
    hello()
```

--------------------------------

### Initialize ClearMLVisBackend

Source: https://github.com/clearml/clearml-docs/blob/main/docs/integrations/mmengine.md

Instantiate ClearMLVisBackend to create a ClearML Task and log environment information. Artifacts with specified suffixes will be uploaded upon task completion. `init_kwargs` can be used to pass arguments to ClearML's Task.init().

```python
from mmengine.visualization import ClearMLVisBackend
vis_backend = ClearMLVisBackend(
    artifact_suffix=('.py', 'pth'), 
    init_kwargs=dict(
        project_name='examples',
        task_name='OpenMMLab cifar10',
        output_uri=True
    )
)
```

--------------------------------

### Install ClearML Packages

Source: https://github.com/clearml/clearml-docs/blob/main/docs/guides/ide/google_colab.md

Installs the necessary ClearML packages for agent functionality. Run this in the first cell of your Colab notebook.

```bash
!pip install git+https://github.com/clearml/clearml
!pip install clearml-agent
```

--------------------------------

### Install CFGI Helm Chart

Source: https://github.com/clearml/clearml-docs/blob/main/docs/clearml_agent/fractional_gpus/cfgi.md

Command to install or upgrade the CFGI Helm chart, using a specified override file.

```bash
helm upgrade -i -n cfgi cfgi oci://docker.io/clearml/clearml-fractional-gpu-injector --create-namespace -f cfgi-values.override.yaml
```