mirror of
https://github.com/clearml/clearml-agent
synced 2025-05-30 09:59:01 +00:00
Fix README
This commit is contained in:
allegroai
parent
d96b8ff906
commit
1d1ffd17fb
196
README.md
196
README.md
@ -9,14 +9,14 @@ ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows**
|
||||
[](https://img.shields.io/pypi/pyversions/clearml-agent.svg)
|
||||
[](https://img.shields.io/pypi/v/clearml-agent.svg)
|
||||
[](https://pypi.org/project/clearml-agent/)
|
||||
[](https://artifacthub.io/packages/search?repo=allegroai)
|
||||
[](https://artifacthub.io/packages/search?repo=allegroai)
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
### ClearML-Agent
|
||||
#### *Formerly known as Trains Agent*
|
||||
|
||||
#### *Formerly known as Trains Agent*
|
||||
|
||||
* Run jobs (experiments) on any local or cloud based resource
|
||||
* Implement optimized resource utilization policies
|
||||
@ -24,23 +24,31 @@ ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows**
|
||||
* 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)
|
||||
*
|
||||
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-))
|
||||
|
||||
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)
|
||||
**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
|
||||
@ -56,20 +64,23 @@ It is a zero configuration fire-and-forget execution agent, providing a full ML/
|
||||
|
||||
*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.
|
||||
|
||||
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
|
||||
|
||||
#### 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
|
||||
- Web UI for customization, scheduling & prioritization of jobs
|
||||
|
||||
**Two K8s integration flavours**
|
||||
|
||||
**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))
|
||||
@ -77,57 +88,66 @@ Find Dockerfiles in the [docker](./docker) dir and a helm Chart in https://githu
|
||||
- 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
|
||||
- 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)
|
||||
- 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.
|
||||
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.
|
||||
* 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
|
||||
|
||||
- Workers Tab: Monitor you cluster
|
||||
- Review available resources
|
||||
- Monitor machines statistics (CPU / GPU / Disk / Network)
|
||||
- Queues Tab:
|
||||
- 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)
|
||||
|
||||
- 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
|
||||
@ -137,6 +157,7 @@ pip install clearml-agent
|
||||
#### ClearML Agent Usage Examples
|
||||
|
||||
Full Interface and capabilities are available with
|
||||
|
||||
```bash
|
||||
clearml-agent --help
|
||||
clearml-agent daemon --help
|
||||
@ -148,7 +169,8 @@ clearml-agent daemon --help
|
||||
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`
|
||||
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`
|
||||
|
||||
@ -158,29 +180,36 @@ They are designed to share the same configuration file, see example [here](docs/
|
||||
#### 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`).
|
||||
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` is an empty string (""), no gpu will be allocated for the `clearml-agent`
|
||||
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` is an empty string (""), 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
|
||||
@ -189,23 +218,29 @@ 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:
|
||||
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:
|
||||
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
|
||||
@ -216,55 +251,61 @@ clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda
|
||||
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)
|
||||
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:
|
||||
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
|
||||
- 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.
|
||||
* 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...
|
||||
- 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 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.
|
||||
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
|
||||
@ -272,22 +313,27 @@ 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>
|
||||
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.
|
||||
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)
|
||||
|
||||
- [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
|
||||
- [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)
|
||||
|
||||
- [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)
|
||||
- [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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user