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@ -33,12 +33,12 @@ It is a zero configuration fire-and-forget execution agent, providing a full ML/
1. ClearML Server [self-hosted](https://github.com/allegroai/clearml-server)
or [free tier hosting](https://app.clear.ml)
2. `pip install clearml-agent` ([install](#installing-the-clearml-agent) the ClearML Agent on any GPU machine:
2. `pip install clearml-agent` ([install](#installing-the-clearml-agent) the ClearML Agent on any CPU/GPU machine:
on-premises / cloud / ...)
3. Create a [job](https://github.com/allegroai/clearml/docs/clearml-task.md) or
Add [ClearML](https://github.com/allegroai/clearml) to your code with just 2 lines
4. Change the [parameters](#using-the-clearml-agent) in the UI & schedule for [execution](#using-the-clearml-agent) (or
automate with an [AutoML pipeline](#automl-and-orchestration-pipelines-))
automate with a [pipelines](#automl-and-orchestration-pipelines-))
5. :chart_with_downwards_trend: :chart_with_upwards_trend: :eyes: :beer:
"All the Deep/Machine-Learning DevOps your research needs, and then some... Because ain't nobody got time for that"
@ -313,21 +313,24 @@ clearml-agent daemon --services-mode --detached --queue services --create-queue
**Note**: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue.
### AutoML and Orchestration Pipelines <a name="automl-pipes"></a>
### Orchestration and Pipelines <a name="automl-pipes"></a>
The ClearML Agent can also be used to implement AutoML orchestration and Experiment Pipelines in conjunction with the
The ClearML Agent can also be used to orchestrate and automate Pipelines in conjunction with the
ClearML package.
Sample AutoML & Orchestration examples can be found in the
ClearML [example/automation](https://github.com/allegroai/clearml/tree/master/examples/automation) folder.
Sample automation examples can be found in the
ClearML [pipelines](https://github.com/allegroai/clearml/tree/master/examples/pipeline) / [automation](https://github.com/allegroai/clearml/tree/master/examples/automation) folder.
AutoML examples
HPO examples
- [Toy Keras training experiment](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py)
- In order to create an experiment-template in the system, this code must be executed once manually
- [Random Search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
- [Manual Search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
- This example will create multiple copies of the Keras experiment-template, with different hyper-parameter
combinations
- [Optimized Bayesian search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py)
- This example will create multiple copies of the Keras experiment-template, with different hyper-parameter combinations launch them on remote machines, monitor the metric (i.e. loss) decide which one has the best potential and abort the others
Experiment Pipeline examples