**ClearML Agent - ML-Ops made easy ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows** [![GitHub license](https://img.shields.io/github/license/allegroai/clearml-agent.svg)](https://img.shields.io/github/license/allegroai/clearml-agent.svg) [![PyPI pyversions](https://img.shields.io/pypi/pyversions/clearml-agent.svg)](https://img.shields.io/pypi/pyversions/clearml-agent.svg) [![PyPI version shields.io](https://img.shields.io/pypi/v/clearml-agent.svg)](https://img.shields.io/pypi/v/clearml-agent.svg) [![PyPI Downloads](https://pepy.tech/badge/clearml-agent/month)](https://pypi.org/project/clearml-agent/) [![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/allegroai)](https://artifacthub.io/packages/search?repo=allegroai)
--- ### ClearML-Agent #### *Formerly known as Trains Agent* * Run jobs (experiments) on any local or cloud based resource * Implement optimized resource utilization policies * Deploy execution environments with either virtualenv or fully docker containerized with zero effort * Launch-and-Forget service containers * [Cloud autoscaling](https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler) * [Customizable cleanup](https://clear.ml/docs/latest/docs/guides/services/cleanup_service) * Advanced [pipeline building and execution](https://clear.ml/docs/latest/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline) It is a zero configuration fire-and-forget execution agent, providing a full ML/DL cluster solution. **Full Automation in 5 steps** 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: 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-)) 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" **Try ClearML now** [Self Hosted](https://github.com/allegroai/clearml-server) or [Free tier Hosting](https://app.clear.ml) ### Simple, Flexible Experiment Orchestration **The ClearML Agent was built to address the DL/ML R&D DevOps needs:** * Easily add & remove machines from the cluster * Reuse machines without the need for any dedicated containers or images * **Combine GPU resources across any cloud and on-prem** * **No need for yaml / json / template configuration of any kind** * **User friendly UI** * Manageable resource allocation that can be used by researchers and engineers * Flexible and controllable scheduler with priority support * Automatic instance spinning in the cloud **Using the ClearML Agent, you can now set up a dynamic cluster with \*epsilon DevOps** *epsilon - Because we are :triangular_ruler: and nothing is really zero work ### Kubernetes Integration (Optional) We think Kubernetes is awesome, but it should be a choice. We designed `clearml-agent` so you can run bare-metal or inside a pod with any mix that fits your environment. Find Dockerfiles in the [docker](./docker) dir and a helm Chart in https://github.com/allegroai/clearml-helm-charts #### Benefits of integrating existing K8s with ClearML-Agent - ClearML-Agent adds the missing scheduling capabilities to K8s - Allowing for more flexible automation from code - A programmatic interface for easier learning curve (and debugging) - Seamless integration with ML/DL experiment manager - Web UI for customization, scheduling & prioritization of jobs **Two K8s integration flavours** - Spin ClearML-Agent as a long-lasting service pod - use [clearml-agent](https://hub.docker.com/r/allegroai/clearml-agent) docker image - map docker socket into the pod (soon replaced by [podman](https://github.com/containers/podman)) - allow the clearml-agent to manage sibling dockers - benefits: full use of the ClearML scheduling, no need to worry about wrong container images / lost pods etc. - downside: Sibling containers - Kubernetes Glue, map ClearML jobs directly to K8s jobs - Run the [clearml-k8s glue](https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py) on a K8s cpu node - The clearml-k8s glue pulls jobs from the ClearML job execution queue and prepares a K8s job (based on provided yaml template) - Inside the pod itself the clearml-agent will install the job (experiment) environment and spin and monitor the experiment's process - benefits: Kubernetes full view of all running jobs in the system - downside: No real scheduling (k8s scheduler), no docker image verification (post-mortem only) ### Using the ClearML Agent **Full scale HPC with a click of a button** The ClearML Agent is a job scheduler that listens on job queue(s), pulls jobs, sets the job environments, executes the job and monitors its progress. Any 'Draft' experiment can be scheduled for execution by a ClearML agent. A previously run experiment can be put into 'Draft' state by either of two methods: * Using the **'Reset'** action from the experiment right-click context menu in the ClearML UI - This will clear any results and artifacts the previous run had created. * Using the **'Clone'** action from the experiment right-click context menu in the ClearML UI - This will create a new ' Draft' experiment with the same configuration as the original experiment. An experiment is scheduled for execution using the **'Enqueue'** action from the experiment right-click context menu in the ClearML UI and selecting the execution queue. See [creating an experiment and enqueuing it for execution](#from-scratch). Once an experiment is enqueued, it will be picked up and executed by a ClearML agent monitoring this queue. The ClearML UI Workers & Queues page provides ongoing execution information: - Workers Tab: Monitor you cluster - Review available resources - Monitor machines statistics (CPU / GPU / Disk / Network) - Queues Tab: - Control the scheduling order of jobs - Cancel or abort job execution - Move jobs between execution queues #### What The ClearML Agent Actually Does The ClearML Agent executes experiments using the following process: - Create a new virtual environment (or launch the selected docker image) - Clone the code into the virtual-environment (or inside the docker) - Install python packages based on the package requirements listed for the experiment - Special note for PyTorch: The ClearML Agent will automatically select the torch packages based on the CUDA_VERSION environment variable of the machine - Execute the code, while monitoring the process - Log all stdout/stderr in the ClearML UI, including the cloning and installation process, for easy debugging - Monitor the execution and allow you to manually abort the job using the ClearML UI (or, in the unfortunate case of a code crash, catch the error and signal the experiment has failed) #### System Design & Flow clearml-architecture #### Installing the ClearML Agent ```bash pip install clearml-agent ``` #### ClearML Agent Usage Examples Full Interface and capabilities are available with ```bash clearml-agent --help clearml-agent daemon --help ``` #### Configuring the ClearML Agent ```bash clearml-agent init ``` Note: The ClearML Agent uses a cache folder to cache pip packages, apt packages and cloned repositories. The default ClearML Agent cache folder is `~/.clearml` See full details in your configuration file at `~/clearml.conf` Note: The **ClearML agent** extends the **ClearML** configuration file `~/clearml.conf` They are designed to share the same configuration file, see example [here](docs/clearml.conf) #### Running the ClearML Agent For debug and experimentation, start the ClearML agent in `foreground` mode, where all the output is printed to screen ```bash clearml-agent daemon --queue default --foreground ``` For actual service mode, all the stdout will be stored automatically into a temporary file (no need to pipe) Notice: with `--detached` flag, the *clearml-agent* will be running in the background ```bash clearml-agent daemon --detached --queue default ``` GPU allocation is controlled via the standard OS environment `NVIDIA_VISIBLE_DEVICES` or `--gpus` flag (or disabled with `--cpu-only`). If no flag is set, and `NVIDIA_VISIBLE_DEVICES` variable doesn't exist, all GPU's will be allocated for the `clearml-agent`
If `--cpu-only` flag is set, or `NVIDIA_VISIBLE_DEVICES="none"`, no gpu will be allocated for the `clearml-agent` Example: spin two agents, one per gpu on the same machine: Notice: with `--detached` flag, the *clearml-agent* will be running in the background ```bash clearml-agent daemon --detached --gpus 0 --queue default clearml-agent daemon --detached --gpus 1 --queue default ``` Example: spin two agents, pulling from dedicated `dual_gpu` queue, two gpu's per agent ```bash clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu ``` ##### Starting the ClearML Agent in docker mode For debug and experimentation, start the ClearML agent in `foreground` mode, where all the output is printed to screen ```bash clearml-agent daemon --queue default --docker --foreground ``` For actual service mode, all the stdout will be stored automatically into a file (no need to pipe) Notice: with `--detached` flag, the *clearml-agent* will be running in the background ```bash clearml-agent daemon --detached --queue default --docker ``` Example: spin two agents, one per gpu on the same machine, with default nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 docker: ```bash clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 clearml-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 ``` Example: spin two agents, pulling from dedicated `dual_gpu` queue, two gpu's per agent, with default nvidia/cuda: 10.1-cudnn7-runtime-ubuntu18.04 docker: ```bash clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 ``` ##### Starting the ClearML Agent - Priority Queues Priority Queues are also supported, example use case: High priority queue: `important_jobs` Low priority queue: `default` ```bash clearml-agent daemon --queue important_jobs default ``` The **ClearML Agent** will first try to pull jobs from the `important_jobs` queue, only then it will fetch a job from the `default` queue. Adding queues, managing job order within a queue and moving jobs between queues, is available using the Web UI, see example on our [free server](https://app.clear.ml/workers-and-queues/queues) ##### Stopping the ClearML Agent To stop a **ClearML Agent** running in the background, run the same command line used to start the agent with `--stop` appended. For example, to stop the first of the above shown same machine, single gpu agents: ```bash clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 --stop ``` ### How do I create an experiment on the ClearML Server? * Integrate [ClearML](https://github.com/allegroai/clearml) with your code * Execute the code on your machine (Manually / PyCharm / Jupyter Notebook) * As your code is running, **ClearML** creates an experiment logging all the necessary execution information: - Git repository link and commit ID (or an entire jupyter notebook) - Git diff (we’re not saying you never commit and push, but still...) - Python packages used by your code (including specific versions used) - Hyper-Parameters - Input Artifacts You now have a 'template' of your experiment with everything required for automated execution * In the ClearML UI, Right-click on the experiment and select 'clone'. A copy of your experiment will be created. * You now have a new draft experiment cloned from your original experiment, feel free to edit it - Change the Hyper-Parameters - Switch to the latest code base of the repository - Update package versions - Select a specific docker image to run in (see docker execution mode section) - Or simply change nothing to run the same experiment again... * Schedule the newly created experiment for execution: Right-click the experiment and select 'enqueue' ### ClearML-Agent Services Mode ClearML-Agent Services is a special mode of ClearML-Agent that provides the ability to launch long-lasting jobs that previously had to be executed on local / dedicated machines. It allows a single agent to launch multiple dockers (Tasks) for different use cases. To name a few use cases, auto-scaler service (spinning instances when the need arises and the budget allows), Controllers (Implementing pipelines and more sophisticated DevOps logic), Optimizer (such as Hyper-parameter Optimization or sweeping), and Application (such as interactive Bokeh apps for increased data transparency) ClearML-Agent Services mode will spin **any** task enqueued into the specified queue. Every task launched by ClearML-Agent Services will be registered as a new node in the system, providing tracking and transparency capabilities. Currently clearml-agent in services-mode supports cpu only configuration. ClearML-agent services mode can be launched alongside GPU agents. ```bash clearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only ``` **Note**: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue. ### AutoML and Orchestration Pipelines The ClearML Agent can also be used to implement AutoML orchestration and Experiment 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. AutoML 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) - This example will create multiple copies of the Keras experiment-template, with different hyper-parameter combinations Experiment Pipeline examples - [First step experiment](https://github.com/allegroai/clearml/blob/master/examples/automation/task_piping_example.py) - This example will "process data", and once done, will launch a copy of the 'second step' experiment-template - [Second step experiment](https://github.com/allegroai/clearml/blob/master/examples/automation/toy_base_task.py) - In order to create an experiment-template in the system, this code must be executed once manually ### License Apache License, Version 2.0 (see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0.html) for more information)