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@@ -3,8 +3,8 @@ title: ClearML Modules
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---
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- [**ClearML Python Package**](../getting_started/ds/ds_first_steps.md#install-clearml) (`clearml`) for integrating ClearML into your existing code-base.
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- [**ClearML Server**](../deploying_clearml/clearml_server.md) (`clearml-server`) for storing experiment, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the MLOps.
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- [**ClearML Agent**](../clearml_agent.md) (`clearml-agent`), the MLOps orchestration agent. Enabling experiment and workflow reproducibility, and scalability.
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- [**ClearML Server**](../deploying_clearml/clearml_server.md) (`clearml-server`) for storing task, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the MLOps.
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- [**ClearML Agent**](../clearml_agent.md) (`clearml-agent`), the MLOps orchestration agent. Enabling task and workflow reproducibility, and scalability.
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- [**ClearML Data**](../clearml_data/clearml_data.md) (`clearml-data`) data management and versioning on top of file-systems/object-storage.
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- [**ClearML Serving**](../clearml_serving/clearml_serving.md) (`clearml-serving`) for model deployment and orchestration.
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- [**ClearML Session**](../apps/clearml_session.md) (`clearml-session`) for launching remote instances of Jupyter Notebooks and VSCode.
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@@ -44,7 +44,7 @@ pip install clearml
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CLEARML_CONFIG_FILE = MyOtherClearML.conf
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```
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For more information about running experiments inside Docker containers, see [ClearML Agent Deployment](../../clearml_agent/clearml_agent_deployment.md)
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For more information about running tasks inside Docker containers, see [ClearML Agent Deployment](../../clearml_agent/clearml_agent_deployment.md)
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and [ClearML Agent Reference](../../clearml_agent/clearml_agent_ref.md).
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</Collapsible>
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@@ -2,14 +2,14 @@
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title: Next Steps
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---
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So, you've already [installed ClearML's Python package](ds_first_steps.md) and run your first experiment!
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So, you've already [installed ClearML's Python package](ds_first_steps.md) and run your first task!
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Now, you'll learn how to track Hyperparameters, Artifacts, and Metrics!
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## Accessing Experiments
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## Accessing Tasks
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Every previously executed experiment is stored as a Task.
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A Task's project and name can be changed after the experiment has been executed.
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A Task's project and name can be changed after it has been executed.
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A Task is also automatically assigned an auto-generated unique identifier (UUID string) that cannot be changed and always locates the same Task in the system.
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Retrieve a Task object programmatically by querying the system based on either the Task ID,
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@@ -23,8 +23,8 @@ Once you have a Task object you can query the state of the Task, get its model(s
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## Log Hyperparameters
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For full reproducibility, it's paramount to save hyperparameters for each experiment. Since hyperparameters can have substantial impact
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on model performance, saving and comparing these between experiments is sometimes the key to understanding model behavior.
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For full reproducibility, it's paramount to save each task's hyperparameters. Since hyperparameters can have substantial impact
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on model performance, saving and comparing them between tasks is sometimes the key to understanding model behavior.
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ClearML supports logging `argparse` module arguments out of the box, so once ClearML is integrated into the code, it automatically logs all parameters provided to the argument parser.
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@@ -40,7 +40,7 @@ See [Configuration](../../clearml_sdk/task_sdk.md#configuration) for all hyperpa
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## Log Artifacts
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ClearML lets you easily store the output products of an experiment - Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
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ClearML lets you easily store the output products of a task: Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
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Essentially, artifacts are files (or Python objects) uploaded from a script and are stored alongside the Task.
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These artifacts can be easily accessed by the web UI or programmatically.
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@@ -107,9 +107,9 @@ task = Task.init(
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)
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```
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Now, whenever the framework (TensorFlow/Keras/PyTorch etc.) stores a snapshot, the model file is automatically uploaded to the bucket to a specific folder for the experiment.
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Now, whenever the framework (TensorFlow/Keras/PyTorch etc.) stores a snapshot, the model file is automatically uploaded to the bucket to a specific folder for the task.
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Loading models by a framework is also logged by the system; these models appear in an experiment's **Artifacts** tab,
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Loading models by a framework is also logged by the system; these models appear in a task's **Artifacts** tab,
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under the "Input Models" section.
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Check out model snapshots examples for [TensorFlow](https://github.com/clearml/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py),
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@@ -149,7 +149,7 @@ You can log everything, from time series data and confusion matrices to HTML, Au
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Once everything is neatly logged and displayed, use the [comparison tool](../../webapp/webapp_exp_comparing.md) to find the best configuration!
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## Track Experiments
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## Track Tasks
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The task table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development.
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@@ -163,13 +163,13 @@ You can filter and sort based on parameters and metrics, so creating custom view
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Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.
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It can also be used as a live leaderboard, showing the best performing experiments' status, updated in real time.
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It can also be used as a live leaderboard, showing the best performing tasks' status, updated in real time.
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This is helpful to monitor your projects' progress, and to share it across the organization.
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Any page is sharable by copying the URL from the address bar, allowing you to bookmark leaderboards or to send an exact view of a specific experiment or a comparison page.
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Any page is sharable by copying the URL from the address bar, allowing you to bookmark leaderboards or to send an exact view of a specific task or a comparison page.
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You can also tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment.
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Later you can search based on task name in the search bar, and filter experiments based on their tags, parameters, status, and more.
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You can also tag Tasks for visibility and filtering allowing you to add more information on the execution of the task.
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Later you can search based on task name in the search bar, and filter tasks based on their tags, parameters, status, and more.
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## What's Next?
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@@ -181,7 +181,7 @@ or check these pages out:
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- Scale you work and deploy [ClearML Agents](../../clearml_agent.md)
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- Develop on remote machines with [ClearML Session](../../apps/clearml_session.md)
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- Structure your work and put it into [Pipelines](../../pipelines/pipelines.md)
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- Improve your experiments with [Hyperparameter Optimization](../../fundamentals/hpo.md)
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- Improve your tasks with [Hyperparameter Optimization](../../fundamentals/hpo.md)
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- Check out ClearML's integrations with your favorite ML frameworks like [TensorFlow](../../integrations/tensorflow.md),
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[PyTorch](../../integrations/pytorch.md), [Keras](../../integrations/keras.md),
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and more
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@@ -109,10 +109,10 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
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- [Track and upload](../fundamentals/task.md) metrics and models with only 2 lines of code
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- [Reproduce](../webapp/webapp_exp_reproducing.md) tasks with 3 mouse clicks
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- [Create bots](../guides/services/slack_alerts.md) that send you Slack messages based on experiment behavior (for example,
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- [Create bots](../guides/services/slack_alerts.md) that send you Slack messages based on task behavior (for example,
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alert you whenever your model improves in accuracy)
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- Manage your [data](../clearml_data/clearml_data.md) - store, track, and version control
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- Remotely execute experiments on any compute resource you have available with [ClearML Agent](../clearml_agent.md)
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- Remotely execute tasks on any compute resource you have available with [ClearML Agent](../clearml_agent.md)
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- Automatically scale cloud instances according to your resource needs with ClearML's
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[AWS Autoscaler](../webapp/applications/apps_aws_autoscaler.md) and [GCP Autoscaler](../webapp/applications/apps_gcp_autoscaler.md)
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GUI applications
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