💫 Industrial-strength Natural Language Processing (NLP) in Python

<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>

# spaCy: Industrial-strength NLP

spaCy is a library for **advanced Natural Language Processing** in Python and
Cython. It's built on the very latest research, and was designed from day one to
be used in real products.

spaCy comes with
[pretrained pipelines](https://spacy.io/models) and
currently supports tokenization and training for **70+ languages**. It features
state-of-the-art speed and **neural network models** for tagging,
parsing, **named entity recognition**, **text classification** and more,
multi-task learning with pretrained **transformers** like BERT, as well as a
production-ready [**training system**](https://spacy.io/usage/training) and easy
model packaging, deployment and workflow management. spaCy is commercial
open-source software, released under the MIT license.

💫 **Version 3.4 out now!**
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)

[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
[![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github)](https://github.com/explosion/spaCy/releases)
[![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/)
[![conda Version](https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/spacy)
[![Python wheels](https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&style=flat-square&logo=python&logoColor=white)](https://github.com/explosion/wheelwright/releases)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)
<br />
[![PyPi downloads](https://static.pepy.tech/personalized-badge/spacy?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads)](https://pypi.org/project/spacy/)
[![Conda downloads](https://img.shields.io/conda/dn/conda-forge/spacy?label=conda%20downloads)](https://anaconda.org/conda-forge/spacy)
[![spaCy on Twitter](https://img.shields.io/twitter/follow/spacy_io.svg?style=social&label=Follow)](https://twitter.com/spacy_io)

## 📖 Documentation

| Documentation                                                                                                                                                                                                             |                                                                                                                                                                                                                                                                                                                              |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ⭐️ **[spaCy 101]**                                                                                                                                                                                                       | New to spaCy? Here's everything you need to know!                                                                                                                                                                                                                                                                            |
| 📚 **[Usage Guides]**                                                                                                                                                                                                     | How to use spaCy and its features.                                                                                                                                                                                                                                                                                           |
| 🚀 **[New in v3.0]**                                                                                                                                                                                                      | New features, backwards incompatibilities and migration guide.                                                                                                                                                                                                                                                               |
| 🪐 **[Project Templates]**                                                                                                                                                                                                | End-to-end workflows you can clone, modify and run.                                                                                                                                                                                                                                                                          |
| 🎛 **[API Reference]**                                                                                                                                                                                                     | The detailed reference for spaCy's API.                                                                                                                                                                                                                                                                                      |
| 📦 **[Models]**                                                                                                                                                                                                           | Download trained pipelines for spaCy.                                                                                                                                                                                                                                                                                        |
| 🌌 **[Universe]**                                                                                                                                                                                                         | Plugins, extensions, demos and books from the spaCy ecosystem.                                                                                                                                                                                                                                                               |
| 👩‍🏫 **[Online Course]**                                                                                                                                                                                                    | Learn spaCy in this free and interactive online course.                                                                                                                                                                                                                                                                      |
| 📺 **[Videos]**                                                                                                                                                                                                           | Our YouTube channel with video tutorials, talks and more.                                                                                                                                                                                                                                                                    |
| 🛠 **[Changelog]**                                                                                                                                                                                                         | Changes and version history.                                                                                                                                                                                                                                                                                                 |
| 💝 **[Contribute]**                                                                                                                                                                                                       | How to contribute to the spaCy project and code base.                                                                                                                                                                                                                                                                        |
| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)** |

[spacy 101]: https://spacy.io/usage/spacy-101
[new in v3.0]: https://spacy.io/usage/v3
[usage guides]: https://spacy.io/usage/
[api reference]: https://spacy.io/api/
[models]: https://spacy.io/models
[universe]: https://spacy.io/universe
[videos]: https://www.youtube.com/c/ExplosionAI
[online course]: https://course.spacy.io
[project templates]: https://github.com/explosion/projects
[changelog]: https://spacy.io/usage#changelog
[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md

## 💬 Where to ask questions

The spaCy project is maintained by the [spaCy team](https://explosion.ai/about).
Please understand that we won't be able to provide individual support via email.
We also believe that help is much more valuable if it's shared publicly, so that
more people can benefit from it.

| Type                            | Platforms                               |
| ------------------------------- | --------------------------------------- |
| 🚨 **Bug Reports**              | [GitHub Issue Tracker]                  |
| 🎁 **Feature Requests & Ideas** | [GitHub Discussions]                    |
| 👩‍💻 **Usage Questions**          | [GitHub Discussions] · [Stack Overflow] |
| 🗯 **General Discussion**        | [GitHub Discussions]                    |

[github issue tracker]: https://github.com/explosion/spaCy/issues
[github discussions]: https://github.com/explosion/spaCy/discussions
[stack overflow]: https://stackoverflow.com/questions/tagged/spacy

## Features

- Support for **70+ languages**
- **Trained pipelines** for different languages and tasks
- Multi-task learning with pretrained **transformers** like BERT
- Support for pretrained **word vectors** and embeddings
- State-of-the-art speed
- Production-ready **training system**
- Linguistically-motivated **tokenization**
- Components for named **entity recognition**, part-of-speech-tagging, dependency parsing, sentence segmentation, **text classification**, lemmatization, morphological analysis, entity linking and more
- Easily extensible with **custom components** and attributes
- Support for custom models in **PyTorch**, **TensorFlow** and other frameworks
- Built in **visualizers** for syntax and NER
- Easy **model packaging**, deployment and workflow management
- Robust, rigorously evaluated accuracy

📖 **For more details, see the
[facts, figures and benchmarks](https://spacy.io/usage/facts-figures).**

## ⏳ Install spaCy

For detailed installation instructions, see the
[documentation](https://spacy.io/usage).

- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
  Studio)
- **Python version**: Python 3.6+ (only 64 bit)
- **Package managers**: [pip] · [conda] (via `conda-forge`)

[pip]: https://pypi.org/project/spacy/
[conda]: https://anaconda.org/conda-forge/spacy

### pip

Using pip, spaCy releases are available as source packages and binary wheels.
Before you install spaCy and its dependencies, make sure that
your `pip`, `setuptools` and `wheel` are up to date.

```bash
pip install -U pip setuptools wheel
pip install spacy
```

To install additional data tables for lemmatization and normalization you can
run `pip install spacy[lookups]` or install
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
separately. The lookups package is needed to create blank models with
lemmatization data, and to lemmatize in languages that don't yet come with
pretrained models and aren't powered by third-party libraries.

When using pip it is generally recommended to install packages in a virtual
environment to avoid modifying system state:

```bash
python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install spacy
```

### conda

You can also install spaCy from `conda` via the `conda-forge` channel. For the
feedstock including the build recipe and configuration, check out
[this repository](https://github.com/conda-forge/spacy-feedstock).

```bash
conda install -c conda-forge spacy
```

### Updating spaCy

Some updates to spaCy may require downloading new statistical models. If you're
running spaCy v2.0 or higher, you can use the `validate` command to check if
your installed models are compatible and if not, print details on how to update
them:

```bash
pip install -U spacy
python -m spacy validate
```

If you've trained your own models, keep in mind that your training and runtime
inputs must match. After updating spaCy, we recommend **retraining your models**
with the new version.

📖 **For details on upgrading from spaCy 2.x to spaCy 3.x, see the
[migration guide](https://spacy.io/usage/v3#migrating).**

## 📦 Download model packages

Trained pipelines for spaCy can be installed as **Python packages**. This
means that they're a component of your application, just like any other module.
Models can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
command, or manually by pointing pip to a path or URL.

| Documentation              |                                                                  |
| -------------------------- | ---------------------------------------------------------------- |
| **[Available Pipelines]**  | Detailed pipeline descriptions, accuracy figures and benchmarks. |
| **[Models Documentation]** | Detailed usage and installation instructions.                    |
| **[Training]**             | How to train your own pipelines on your data.                    |

[available pipelines]: https://spacy.io/models
[models documentation]: https://spacy.io/usage/models
[training]: https://spacy.io/usage/training

```bash
# Download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_sm

# pip install .tar.gz archive or .whl from path or URL
pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
```

### Loading and using models

To load a model, use [`spacy.load()`](https://spacy.io/api/top-level#spacy.load)
with the model name or a path to the model data directory.

```python
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a sentence.")
```

You can also `import` a model directly via its full name and then call its
`load()` method with no arguments.

```python
import spacy
import en_core_web_sm

nlp = en_core_web_sm.load()
doc = nlp("This is a sentence.")
```

📖 **For more info and examples, check out the
[models documentation](https://spacy.io/docs/usage/models).**

## ⚒ Compile from source

The other way to install spaCy is to clone its
[GitHub repository](https://github.com/explosion/spaCy) and build it from
source. That is the common way if you want to make changes to the code base.
You'll need to make sure that you have a development environment consisting of a
Python distribution including header files, a compiler,
[pip](https://pip.pypa.io/en/latest/installing/),
[virtualenv](https://virtualenv.pypa.io/en/latest/) and
[git](https://git-scm.com) installed. The compiler part is the trickiest. How to
do that depends on your system.

| Platform    |                                                                                                                                                                                                                                                                     |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Ubuntu**  | Install system-level dependencies via `apt-get`: `sudo apt-get install build-essential python-dev git` .                                                                                                                                                            |
| **Mac**     | Install a recent version of [XCode](https://developer.apple.com/xcode/), including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled.                                                                                        |
| **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. |

For more details
and instructions, see the documentation on
[compiling spaCy from source](https://spacy.io/usage#source) and the
[quickstart widget](https://spacy.io/usage#section-quickstart) to get the right
commands for your platform and Python version.

```bash
git clone https://github.com/explosion/spaCy
cd spaCy

python -m venv .env
source .env/bin/activate

# make sure you are using the latest pip
python -m pip install -U pip setuptools wheel

pip install -r requirements.txt
pip install --no-build-isolation --editable .
```

To install with extras:

```bash
pip install --no-build-isolation --editable .[lookups,cuda102]
```

## 🚦 Run tests

spaCy comes with an [extensive test suite](spacy/tests). In order to run the
tests, you'll usually want to clone the repository and build spaCy from source.
This will also install the required development dependencies and test utilities
defined in the [`requirements.txt`](requirements.txt).

Alternatively, you can run `pytest` on the tests from within the installed
`spacy` package. Don't forget to also install the test utilities via spaCy's
[`requirements.txt`](requirements.txt):

```bash
pip install -r requirements.txt
python -m pytest --pyargs spacy
```


spacy

ClearML Agent - ML-Ops made easy. ML-Ops scheduler & orchestration solution

<div align="center">

<img src="https://github.com/allegroai/clearml-agent/blob/master/docs/clearml_agent_logo.png?raw=true" width="250px">

**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)
</div>

---

### 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)
<a href="https://app.clear.ml"><img src="https://github.com/allegroai/clearml-agent/blob/master/docs/screenshots.gif?raw=true" width="100%"></a>

### 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

<img src="https://github.com/allegroai/clearml-agent/blob/master/docs/clearml_architecture.png" width="100%" alt="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` <br>
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? <a name="from-scratch"></a>

* 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 <a name="services"></a>

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 <a name="automl-pipes"></a>

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)




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