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Home
Getting Started
Getting Started using the Free ClearML Hosted Service
Getting Started
Your Own ClearML Server
ClearML Server Deployment Formats
AWS EC2 AMIs
Google Cloud Platform
Linux and macOS
Windows 10
Kubernetes
Kubernetes using Helm
Upgrading from Trains Server 0.15 or older to ClearML Server
Configuring Your Own ClearML Server
ClearML Server Deployment Configuration
ClearML Server Feature Configurations
Configuration procedures
Configuring ClearML for Your ClearML Server
Add ClearML to a configuration file
Securing Your Own ClearML Server
Network Security
User Access Security
Server Credentials and Secrets
Fundamentals
Tasks (Experiments)
Creating new Tasks
Data stored in Tasks
Tasks types
Task states and state transitions
Logging and Debug Samples
Automatic logging
Explicit reporting
Debug Samples
Artifacts
Storage
Configuring ClearML for artifact storage
Configuring ClearML for debug samples storage
Workers and Queues
Architecture
ClearML Agent
ClearML Python Package
Modules
Examples
ClearML Server
ClearML Agent services container
MLOps
Installing and Configuring Your ClearML Agent
Adding ClearML Agent to a configuration file
ClearML Agent Use Case Examples
Running workers
The default queue
User-created queues
Prioritizing queues
Docker mode
Specifying GPUs
Debugging
Explicit Task execution
Execute a Task without queue
Clone a Task and execute the cloned Task
Docker mode
Building Docker containers
Containerized Tasks
Base Docker image
Launching ClearML Agent in services mode
Tutorials
Explicit Reporting Tutorial
Prerequisites
Before you begin
Step 1. Setting an output destination for model checkpoints
Step 2. Logger class reporting methods
Get a logger
Plot scalar metrics
Plot other (not scalar) data
Log text
Step 3. Registering artifacts
Register the artifact
Reference the registered artifact
Step 4. Uploading artifacts
Additional information
Tuning Experiments Tutorial
Prerequisites
Step 1. Run the experiment
Step 2. Clone the experiment
Step 3. Tune the cloned experiment
Step 4. Run a worker daemon listening to a queue
Step 5. Enqueue the tuned experiment
Step 6. Compare the experiments
Tracking Leaderboards Tutorial
Step 1. Select a project
Step 2. Filter the experiments
Step 3. Hide the defaults column
Step 4. Show metrics or hyperparameters
Step 5. Enable auto refresh
Step 6. Save the tracking leaderboard
Examples
Automation
Manual Random Parameter Search
Task Piping
Distributed
PyTorch Distributed
Artifacts
Scalars
Hyperparameters
Log
Subprocess
Hyperparameters
Log
Frameworks
AutoKeras
Scalars
Hyperparameters
Log
Artifacts
Fastai
Scalars
Plots
Logs
Keras
Keras with TensorBoard - Jupyter Notebook
Keras with Matplotlib - Jupyter Notebook
Keras with TensorBoard
Manual Model Upload
Matplotlib
Matplotlib - Jupyter Notebook
Matplotlib
PyTorch
Manual Model Upload
PyTorch Distributed
PyTorch with Matplotlib
PyTorch MNIST
PyTorch with TensorBoard
PyTorch TensorBoardX
PyTorch TensorBoard Toy
Pytorch Notebooks
Audio Classification - Jupyter Notebooks
Audio Preprocessing - Jupyter Notebook
Image Hyperparameter Optimization - Jupyter Notebook
Image Classification - Jupyter Notebook
Tabular Data Downloading and Preprocessing - Jupyter Notebook
Text Classification - Jupyter Notebook
scikit-learn
scikit-learn with Joblib
scikit-learn with Matplotlib
TensorBoardX
Scalars
Hyperparameters
Log
Artifacts
TensorFlow
Manual Model Upload
TensorBoard PR Curve
TensorBoard Toy
TensorFlow MNIST
XGBoost
Plots
Log
Artifacts
Explicit Reporting
Explicit Reporting - Jupyter Notebook
Scalars
Plots
Media
Text
2D Plots Reporting
Histograms
Confusion Matrices
2D scatter plots
3D Plots Reporting
Surface plot
3D scatter plot
Artifacts Reporting
Dynamically tracked artifacts
Artifacts without tracking
Configuring Models
Configuring models
Label enumeration
HTML Reporting
Reporting HTML URLs
Reporting HTML local files
Hyperparameters Reporting
argparse command line options
TensorFlow Definitions
Parameter dictionaries
Manual Matplotlib Reporting
Images Reporting
Media Reporting
Reporting (uploading) media from a source by URL
Reporting (uploading) media from a local file
Plotly Reporting
Scalars Reporting
Tables Reporting (Pandas and CSV Files)
Reporting Pandas DataFrames as tables
Reporting CSV files as tables
Text Reporting
Optimization
Hyperparameter Optimization
Set the search strategy for optimization
Define a callback
Initialize the optimization Task
Setup the arguments
Instantiate the optimizer object
Running as a service
Optimize
Pipelines
Simple Pipeline - Serialized Data
The pipeline controller
Step 1 - Downloading the data
Step 2 - Processing the data
Step 3 - Training the network
Running the pipeline
Tabular Data Pipeline with Concurrent Steps - Jupyter Notebook
Pipeline controller and steps
Running the pipeline
Services
ClearML AWS Autoscaler Service
Running the ClearML AWS autoscaler
Cleanup Service
Prerequisites
Running the cleanup service
The cleanup service code
Jupyter Notebook Server Service
Running the Jupyter Notebook server service
Logging the Jupyter Notebook server
Monitoring Service Posting Slack Alerts
Prerequisites
Creating a Slack Bot
Running the service
Additional information about slack_alerts.py
Storage
Storage Examples
StorageManager
Web UI
Home Page
Projects Page
Experiments
The Experiments Table
Tracking Experiments and Visualizing Results
Reproducing experiments
Tuning experiments
Comparing Experiments
Sharing Experiments and Models
Models
The Models Table
Viewing Model Details
Modifying Models
Archiving
Workers and Queues Page
Resources utilization
Worker utilization
Queue utilization
Queue management
Profile Page
Setting user preferences
Providing Cloud Storage Access for the ClearML Web UI
Creating ClearML credentials
Switching workspaces
Inviting new teammates
Leaving a workspace
References
ClearML Python Package
Task
task.Task
DeleteError
args
with_traceback
TaskStatusEnum
add_requirements
add_tags
artifacts
cache_dir
clone
close
completed
connect
connect_configuration
connect_label_enumeration
create
create_function_task
current_task
debug_simulate_remote_task
delete
delete_parameter
delete_user_properties
dequeue
enqueue
execute_remotely
export_task
flush
get_all
get_archived
get_base_docker
get_configuration_object
get_initial_iteration
get_label_num_description
get_labels_enumeration
get_last_iteration
get_last_scalar_metrics
get_logger
get_model_config_dict
get_model_config_text
get_model_design
get_models
get_num_of_classes
get_offline_mode_folder
get_output_destination
get_output_log_web_page
get_parameter
get_parameters
get_parameters_as_dict
get_project_id
get_projects
get_registered_artifacts
get_reported_console_output
get_reported_scalars
get_status
get_task
get_tasks
get_user_properties
import_offline_session
import_task
init
input_model
is_current_task
is_main_task
is_offline
labels_stats
logger
mark_failed
mark_started
mark_stopped
metrics_manager
models
output_model
publish
register_artifact
reload
reset
running_locally
save_exec_model_design_file
set_archived
set_artifacts
set_base_docker
set_comment
set_configuration_object
set_credentials
set_initial_iteration
set_input_model
set_model_config
set_model_label_enumeration
set_name
set_offline
set_parameter
set_parameters
set_parameters_as_dict
set_parent
set_resource_monitor_iteration_timeout
set_task_type
set_user_properties
started
status
stopped
unregister_artifact
update_model_desc
update_output_model
update_output_model_and_upload
update_parameters
update_task
upload_artifact
wait_for_status
Logger
logger.Logger
capture_logging
current_logger
flush
get_default_upload_destination
get_flush_period
report_confusion_matrix
report_histogram
report_image
report_image_and_upload
report_line_plot
report_matplotlib_figure
report_matrix
report_media
report_plotly
report_scalar
report_scatter2d
report_scatter3d
report_surface
report_table
report_text
report_vector
set_default_upload_destination
set_flush_period
tensorboard_auto_group_scalars
tensorboard_single_series_per_graph
Dataset
Dataset
add_files
create
delete
file_entries_dict
finalize
get
get_default_storage
get_dependency_graph
get_local_copy
get_logger
get_mutable_local_copy
is_dirty
is_final
list_added_files
list_datasets
list_files
list_modified_files
list_removed_files
publish
remove_files
squash
sync_folder
upload
verify_dataset_hash
Storage
storage.manager.StorageManager
download_folder
get_local_copy
set_cache_file_limit
upload_file
upload_folder
Automation
automation.controller.PipelineController
add_step
elapsed
get_pipeline_dag
get_processed_nodes
get_running_nodes
is_running
start
stop
wait
automation.optimization.HyperParameterOptimizer
elapsed
get_active_experiments
get_num_active_experiments
get_optimizer
get_optimizer_top_experiments
get_time_limit
get_top_experiments
is_active
is_running
reached_time_limit
set_default_job_class
set_report_period
set_time_limit
start
stop
wait
automation.parameters.DiscreteParameterRange
from_dict
get_random_seed
get_value
set_random_seed
to_dict
to_list
automation.parameters.ParameterSet
from_dict
get_random_seed
get_value
set_random_seed
to_dict
to_list
automation.parameters.UniformIntegerParameterRange
from_dict
get_random_seed
get_value
set_random_seed
to_dict
to_list
automation.parameters.UniformParameterRange
from_dict
get_random_seed
get_value
set_random_seed
to_dict
to_list
automation.optimization.GridSearch
create_job
get_created_jobs_ids
get_created_jobs_tasks
get_objective_metric
get_running_jobs
get_top_experiments
helper_create_job
monitor_job
process_step
set_job_class
set_job_default_parent
set_job_naming_scheme
set_optimizer_task
start
stop
automation.optimization.RandomSearch
create_job
get_created_jobs_ids
get_created_jobs_tasks
get_objective_metric
get_running_jobs
get_top_experiments
helper_create_job
monitor_job
process_step
set_job_class
set_job_default_parent
set_job_naming_scheme
set_optimizer_task
start
stop
automation.optuna.optuna.OptimizerOptuna
create_job
get_created_jobs_ids
get_created_jobs_tasks
get_objective_metric
get_running_jobs
get_top_experiments
helper_create_job
monitor_job
process_step
set_job_class
set_job_default_parent
set_job_naming_scheme
set_optimizer_task
start
stop
automation.hpbandster.bandster.OptimizerBOHB
create_job
get_created_jobs_ids
get_created_jobs_tasks
get_objective_metric
get_random_seed
get_running_jobs
get_top_experiments
helper_create_job
monitor_job
process_step
set_job_class
set_job_default_parent
set_job_naming_scheme
set_optimization_args
set_optimizer_task
set_random_seed
start
stop
Model
model.InputModel
comment
config_dict
config_text
connect
empty
get_local_copy
get_weights
get_weights_package
id
import_model
labels
load_model
name
publish
system_tags
tags
task
url
model.Model
comment
config_dict
config_text
get_local_copy
get_weights
get_weights_package
id
labels
name
publish
system_tags
tags
task
url
model.OutputModel
comment
config_dict
config_text
connect
get_weights
get_weights_package
id
labels
name
publish
published
set_upload_destination
system_tags
tags
task
update_design
update_labels
update_weights
update_weights_package
url
wait_for_uploads
ClearML Python Package Extras
Step 1. Install ClearML extras for storage
Step 2. Initializing a new ClearML configuration file
Step 3. Add storage credentials to your ClearML configuration file
AWS S3
Azure Storage
Google Cloud Storage
ClearML Agent
build
Syntax
Arguments
config
Syntax
daemon
Syntax
Arguments
execute
Syntax
Arguments
list
Syntax
ClearML Server API (self-hosted)
auth.login
auth.logout
auth.get_token_for_user
auth.validate_token
auth.create_user
auth.create_credentials
auth.get_credentials
auth.revoke_credentials
auth.edit_user
auth.fixed_users_mode
debug.ping
events.add
events.add_batch
events.delete_for_task
events.debug_images
events.get_debug_image_sample
events.next_debug_image_sample
events.get_task_metrics
events.get_task_log
events.get_task_events
events.download_task_log
events.get_task_plots
events.get_multi_task_plots
events.get_vector_metrics_and_variants
events.vector_metrics_iter_histogram
events.scalar_metrics_iter_histogram
events.multi_task_scalar_metrics_iter_histogram
events.get_task_latest_scalar_values
events.get_scalar_metrics_and_variants
events.get_scalar_metric_data
login.supported_modes
models.get_by_id
models.get_by_task_id
models.get_by_id_ex
models.get_all_ex
models.get_all
models.get_frameworks
models.update_for_task
models.create
models.edit
models.update
models.set_ready
models.delete
models.make_public
models.make_private
models.move
organization.get_tags
organization.get_user_companies
projects.create
projects.get_by_id
projects.get_all
projects.get_all_ex
projects.update
projects.move
projects.merge
projects.delete
projects.get_unique_metric_variants
projects.get_hyperparam_values
projects.get_hyper_parameters
projects.get_task_tags
projects.get_model_tags
projects.make_public
projects.make_private
projects.get_task_parents
queues.get_by_id
queues.get_all_ex
queues.get_all
queues.get_default
queues.create
queues.update
queues.delete
queues.add_task
queues.get_next_task
queues.remove_task
queues.move_task_forward
queues.move_task_backward
queues.move_task_to_front
queues.move_task_to_back
queues.get_queue_metrics
server.get_stats
server.config
server.info
server.endpoints
server.report_stats_option
tasks.get_by_id
tasks.get_by_id_ex
tasks.get_all_ex
tasks.get_all
tasks.get_types
tasks.clone
tasks.add_or_update_model
tasks.delete_models
tasks.create
tasks.validate
tasks.update
tasks.update_batch
tasks.edit
tasks.reset
tasks.delete
tasks.archive
tasks.started
tasks.stop
tasks.stopped
tasks.failed
tasks.close
tasks.publish
tasks.enqueue
tasks.dequeue
tasks.set_requirements
tasks.completed
tasks.ping
tasks.add_or_update_artifacts
tasks.delete_artifacts
tasks.make_public
tasks.make_private
tasks.get_hyper_params
tasks.edit_hyper_params
tasks.delete_hyper_params
tasks.get_configurations
tasks.get_configuration_names
tasks.edit_configuration
tasks.delete_configuration
tasks.move
users.get_by_id
users.get_current_user
users.get_all_ex
users.get_all
users.delete
users.create
users.update
users.get_preferences
users.set_preferences
workers.get_all
workers.register
workers.unregister
workers.status_report
workers.get_metric_keys
workers.get_stats
workers.get_activity_report
ClearML Configuration
Editing your configuration file
agent section
agent.default_docker
agent.package_manager
agent.pip_download_cache
agent.vcs_cache
agent.venv_update
api section
api.credentials
sdk section
sdk.aws
sdk.azure
sdk.development
sdk.google.storage
sdk.log
sdk.metrics
sdk.network
sdk.storage
Integrations
AutoKeras
Adding ClearML to code
Keras Tuner
ClearMLTunerLogger
Scalars
Summary of hyperparameter optimization
Artifacts
Configuration objects
Hyperparameters
Configuration
PyTorch Ignite
Ignite ClearMLLogger
ClearMLLogger parameters
Visualizing experiment results
Scalars
Model snapshots
Logging
Ignite engine output and / or metrics
Optimizer parameters
Model weights
Model snapshots
MNIST example
Git for Jupyter Notebook
Installation
Using the plugin
Screenshots
Acknowledgements
Integration for PyCharm
Installation
Optional: ClearML configuration parameters
FAQ
General Information
Models
Experiments
Graphs and Logs
GIT and Storage
Jupyter
Remote Debugging (ClearML PyCharm Plugin)
scikit-learn
ClearML Configuration
ClearML Hosted Service
ClearML Server Deployment
ClearML Server Configuration
ClearML Server Troubleshooting
ClearML Agent
ClearML API
Community Resources
Join the ClearML Conversation
Allegro AI resources
Guidelines for Contributing
Reporting Issues
Suggesting New Features and Enhancements
Pull Requests
Release Notes
Version 0.17
ClearML Agent 0.17.2
Version 0.17.4
ClearML
Version 0.17.3
ClearML
Version 0.17.2
ClearML
Version 0.17.1
ClearML
Version 0.17.0
ClearML
Open Source ClearML Server
ClearML Hosted Service only
ClearML Agent
Version 0.16
Version 0.16.4
Trains
Version 0.16.3
Trains
Version 0.16.2
Trains
Trains-Agent
Version 0.16.1
Trains
Trains Server
Trains Agent
Version 0.16.0
Trains
Trains Server
Trains Agent
Version 0.15
Version 0.15.1
Trains
Trains Server
Trains Agent
Version 0.15.0
Trains
Trains Server
Trains Agent
Version 0.14
Version 0.14.3
Trains
Version 0.14.2
Trains
Trains Server
Version 0.14.1
Trains
Trains Server
Trains Agent
Version 0.14.0
Trains
Trains Server
Trains Agent
Version 0.13
Version 0.13.3
Trains
Trains Agent
Version 0.13.2
Trains
Version 0.13.1
Trains
Version 0.13.0
Trains
Trains Server
Trains Agent
Version 0.12
Version 0.12.2
Trains
Trains Agent
Version 0.12.1
Trains
Trains Server
Trains Agent
Version 0.12.0
Trains
Trains Server
Trains Agent
Version 0.11
Version 0.11.3
Trains
Version 0.11.2
Trains
Version 0.11.1
Trains
Version 0.11.0
Trains
Trains Server
Version 0.10
Version 0.10.7
Trains
Version 0.10.6
Trains
Version 0.10.5
Trains
Version 0.10.4
Trains
Version 0.10.3
Trains
Version 0.10.2
Trains
Version 0.10.1
Trains
Trains Server
Version 0.10.0
Trains
Trains Server
Version 0.9
Version 0.9.3
Trains
Version 0.9.2
Trains
Version 0.9.1
Trains
ClearML Documentation
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Experiments
Experiments
¶
The Experiments Table
Tracking Experiments and Visualizing Results
Reproducing experiments
Tuning experiments
Comparing Experiments
Sharing Experiments and Models