Tune experiments and edit an experiment's execution details, then execute the tuned experiments on local or remote machines.
To Tune an Experiment and Execute it Remotely:
Locate the experiment. Open the experiment's Project page from the Dashboard or the main Projects page.
- On the Dashboard,
- Click on an experiment from RECENT EXPERIMENTS
- In RECENT PROJECTS > click on a project card > click experiment
- In RECENT PROJECTS > click VIEW ALL > click the project card > click experiment
- On the Projects page, click project card, or the All projects card > click experiment
- On the Dashboard,
Clone the experiment. In the experiments table:
- Click Clone, and a Clone experiment box will pop up.
- In the Project textbox, select or create a project. To search for another project, start typing the project name. To create a new project, type new experiment name and click Create New.
- Enter an optional description.
- Click CLONE.
The cloned experiment's status is now Draft.
Edit the experiment. See modifying experiments.
Enqueue the experiment for execution. Right-click the experiment > Enqueue > Select a queue > ENQUEUE.
The experiment's status becomes Pending. When the worker assigned to the queue fetches the Task (experiment), the status becomes Running. The experiment can now be tracked and its results visualized.
Experiments whose status is Draft are editable (see the user properties exception). In the ClearML Web UI, edit any of the following
- Source code
- Output destination for artifacts
- Base Docker image
- Log level
- Hyperparameters - Parameters, TensorFlow Definitions, command line options, environment variables, and user-defined properties
User parameters are editable in any experiment, except experiments whose status is Published (read-only).
- Configuration objects - Task model description
- Initial weight input model
- Output destination for artifacts storage
Select source code by changing any of the following:
- Repository, commit (select by ID, tag name, or choose the last commit in the branch), script, and /or working directory.
- Installed Python packages and/or versions - Edit or clear (remove) them all.
- Uncommitted changes - Edit or clear (remove) them all.
To select different source code:
- In the EXECUTION tab, hover over a section > EDIT or (DISCARD DIFFS for UNCOMMITTED CHANGES) > edit > SAVE.
Base Docker Image
Select a pre-configured Docker that ClearML Agent will use to remotely execute this experiment (see Building Docker containers).
To add, change, or delete a base Docker image:
- In EXECUTION > AGENT CONFIGURATION > BASE DOCKER IMAGE > hover > EDIT > Enter the base Docker image.
Set an output destination for model checkpoints (snapshots) and other artifacts. Examples of supported types of destinations and formats for specifying locations include:
- A shared folder:
- Google Cloud Storage:
- Azure Storage:
To add, change, or delete an artifact output destination:
- In EXECUTION > OUTPUT > DESTINATION > hover > EDIT > edit > SAVE.
Also set the output destination for artifacts in code (see the
output_uri parameter of the
method), and in the ClearML configuration file
for all experiments (see
on the ClearML Configuration Reference page).
Set a logging level for the experiment (see the standard Python logging levels).
To add, change, or delete a log level:
- In EXECUTION > OUTPUT > LOG LEVEL > hover > EDIT > Enter the log level.
Add, change, or delete hyperparameters, which are organized in the ClearML Web UI in the following sections:
Args - Command line arguments and all older experiments parameters, except TensorFlow definitions (logged from code,
argparseargument automatic logging).
TF_DEFINE - TensorFlow definitions (from code, TF_DEFINEs automatic logging).
General - Parameter dictionaries (from code, connected to the Task by calling the Task.connect method).
Environment variables - Tracked if the
CLEARML_LOG_ENVIRONMENTenvironment variable was set (see this FAQ).
Custom named parameter groups (see the
nameparameter in Task.connect).
To add, change, or delete hyperparameters:
- In the CONFIGURATION tab > HYPERPARAMETERS > General > hover > EDIT > add, change, or delete keys and /or values > SAVE.
User properties allow storing any descriptive information in key-value pair format. They are editable in any experiment, except experiments whose status is Published (read-only).
To add, change, or delete user properties:
- In CONFIGURATION > USER PROPERTIES > Properties > hover > EDIT > add, change, or delete keys and /or values > SAVE.
To add, change, or delete the Task model configurations:
- In CONFIGURATION > CONFIGURATION OBJECTS > GENERAL > hover > EDIT or CLEAR (if the configuration is not empty).
Initial Weights Input Model
Edit model configuration and label enumeration, choose a different initial input weight model for the same project or any other project, or remove the model.
The models are editable in the MODELS tab, not the EXPERIMENTS tab. Clicking the model name hyperlink shows the model in the MODELS tab.
To select a different model:
- In ARTIFACTS > Input Model > Hover and click EDIT.
- If a model is associated with the experiment, click .
- In the SELECT MODEL dialog, select a model from the current project or any other project.
To edit a model's configuration or label enumeration:
Click the model name hyperlink. The model details appear in the MODELS tab.
Edit the model configuration or label enumeration.
Model configuration - In the NETWORK tab > Hover and click EDIT. > CLick EDIT or CLEAR (to remove the configuration).
Users can also search for the configuration (hover over the configuration textbox, the search box appears) and copy the configuration to the clipboard (hover and click ).
Label enumeration - In the LABELS tab > Hover and click EDIT > Add, change, or delete label enumeration key-value pairs.
To remove a model from an experiment:
- Hover and click EDIT > Click