Skip to main content

First Steps

note

This tutorial assumes that you've already signed up to ClearML

ClearML provides tools for automation, orchestration, and tracking, all key in performing effective MLOps.

Effective MLOps relies on the ability to scale work beyond one's own computer. Moving from your own machine can be time-consuming. Even assuming that you have all the drivers and applications installed, you still need to manage multiple python environments for different packages / package versions, or worse - manage different Dockers for different package versions.

Not to mention, when working on remote machines, executing experiments, tracking what's running where, and making sure machines are fully utilized at all times become daunting tasks.

This can create overhead that derails you from your core work!

ClearML Agent was designed to deal with such issues and more! It is a tool responsible for executing experiments on remote machines: on-premises or in the cloud! ClearML Agent provides the means to reproduce and track experiments in your machine of choice through the ClearML WebApp with no need for additional code.

The agent will set up the environment for a specific Task’s execution (inside a Docker, or bare-metal), install the required python packages, and execute & monitor the process.

Set up an Agent​

  1. Let's install the agent!

    pip install clearml-agent

  2. Connect the agent to the server by creating credentials, then run this:

    clearml-agent init
    note

    If you've already created credentials, you can copy-paste the default agent section from here (this is optional. If the section is not provided the default values will be used)

  3. Start the agent's daemon and assign it to a queue. 

    clearml-agent daemon --queue default

    A queue is an ordered list of Tasks that are scheduled for execution. The agent will pull Tasks from its assigned queue (default in this case), and execute them one after the other. Multiple agents can listen to the same queue (or even multiple queues), but only a single agent will pull a Task to be executed.

Agent Deployment Modes

ClearML Agents can be deployed in Virtual Environment Mode or Docker Mode. In virtual environment mode, the agent creates a new venv to execute an experiment. In Docker mode, the agent executes an experiment inside a Docker container. See all running mode options here.

Clone an Experiment​

Experiments already in the system can be reproduced for validation, or used as a baseline for further experimentation. Cloning a task duplicates the task’s configuration, but not its outputs.

To clone an experiment in the ClearML WebApp:

  1. Click on any project card to open its experiments table
  2. Right-click one of the experiments on the table
  3. Click Clone in the context menu, which will open a CLONE EXPERIMENT window.
  4. Click CLONE in the window.

The newly cloned experiment will appear and its info panel will slide open. The cloned experiment is in draft mode, so it can be modified. You can edit the Git / code references, control the python packages to be installed, specify the Docker container image to be used, or change the hyperparameters and configuration files. See Modifying Experiments for more information about editing experiments in the UI.

Enqueue an Experiment​

Once you have set up an experiment, it is now time to execute it.

To execute an experiment through the ClearML WebApp:

  1. Right-click your draft experiment (the context menu is also available through the Menu button on the top right of the experiment’s info panel)
  2. Click ENQUEUE, which will open the ENQUEUE EXPERIMENT window
  3. In the window, select default in the queue menu
  4. Click ENQUEUE

This action pushes the experiment into the default queue. The experiment's status becomes Pending until an agent assigned to the queue fetches it, at which time the experiment’s status becomes Running. The agent executes the experiment, and the experiment can be tracked and its results visualized.

Programmatic Interface​

The cloning, modifying, and enqueuing actions described above can also be performed programmatically.

First Steps​

Access Previously Executed Experiments​

All Tasks in the system can be accessed through their unique Task ID, or based on their properties using the Task.get_task method. For example:

from clearml import Task
executed_task = Task.get_task(task_id='aabbcc')

Once a specific Task object has been obtained, it can be cloned, modified, and more. See Advanced Usage.

Clone an Experiment​

To duplicate an experiment, use the Task.clone method, and input either a Task object or the Task’s ID as the source_task argument. 

cloned_task = Task.clone(source_task=executed_task)

Enqueue an Experiment​

To enqueue the task, use the Task.enqueue method, and input the Task object with the task argument, and the queue to push the task into with queue_name. 

Task.enqueue(task=cloned_task, queue_name='default')

Advanced Usage​

Before execution, use a variety of programmatic methods to manipulate a task object.

Modify Hyperparameters​

Hyperparameters are an integral part of Machine Learning code as they let you control the code without directly modifying it. Hyperparameters can be added from anywhere in your code, and ClearML supports multiple ways to obtain them!

Users can programmatically change cloned experiments' parameters.

For example:

from clearml import Task
cloned_task = Task.clone(task_id='aabbcc')
cloned_task.set_parameter(name='internal/magic', value=42)

Report Artifacts​

Artifacts are files created by your task. Users can upload multiple types of data, objects and files to a task anywhere from code. 

import numpy as np
from clearml import Task
Task.current_task().upload_artifact(name='a_file', artifact_object='local_file.bin')
Task.current_task().upload_artifact(name='numpy', artifact_object=np.ones(4,4))

Artifacts serve as a great way to pass and reuse data between tasks. Artifacts can be retrieved by accessing the Task that created them. These artifacts can be modified and uploaded to other tasks.

from clearml import Task
executed_task = Task.get_task(task_id='aabbcc')
# artifact as a file
local_file = executed_task.artifacts['file'].get_local_copy()
# artifact as object
a_numpy = executed_task.artifacts['numpy'].get()

By facilitating the communication of complex objects between tasks, artifacts serve as the foundation of ClearML's Data Management and pipeline solutions.

Log Models​

Logging models into the model repository is the easiest way to integrate the development process directly with production. Any model stored by a supported framework (Keras / TensorFlow / PyTorch / Joblib etc.) will be automatically logged into ClearML.

ClearML also supports methods to explicitly log models. Models can be automatically stored on a preferred storage medium (s3 bucket, google storage, etc.).

Log Metrics​

Log as many metrics as you want from your processes using the Logger module. This improves the visibility of your processes’ progress.

from clearml import Logger
Logger.current_logger().report_scalar(
   graph='metric',
   series='variant',
   value=13.37,
   iteration=counter
)

You can also retrieve reported scalars for programmatic analysis:

from clearml import Task
executed_task = Task.get_task(task_id='aabbcc')
# get a summary of the min/max/last value of all reported scalars
min_max_values = executed_task.get_last_scalar_metrics()
# get detailed graphs of all scalars
full_scalars = executed_task.get_reported_scalars()

Query Experiments​

You can also search and query Tasks in the system. Use the Task.get_tasks method to retrieve Task objects and filter based on the specific values of the Task - status, parameters, metrics and more!

from clearml import Task
tasks = Task.get_tasks(
   project_name='examples',
   task_name='partial_name_match',
   task_filter={'status': 'in_progress'}
)

Manage Your Data​

Data is probably one of the biggest factors that determines the success of a project. Associating a model’s data with the model's configuration, code, and results (such as accuracy) is key to deducing meaningful insights into model behavior.

ClearML Data lets you version your data, so it's never lost, fetch it from every machine with minimal code changes, and associate data to experiment results.

Logging data can be done via command line, or programmatically. If any preprocessing code is involved, ClearML logs it as well! Once data is logged, it can be used by other experiments.