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Merge pull request #1853 from Giskard-AI/doc/gsk-2877-handle-kernel-migration-for-existing-projects
[GSK-2877] Update doc about ML worker to adapt kernel and multi-ML worker
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‎docs/cli/ngrok/index.rst‎

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@@ -31,7 +31,13 @@ Now you can run :code:`giskard hub expose --ngrok-token <ngrok_API_key>` which s
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token=...
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client = giskard.GiskardClient("<ngrok_external_server_link>", token)
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# To run your model with the Giskard Hub, execute these three lines on Google Colab:
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# To run your model with the current Python environment on Google Colab, execute these lines:
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%env GSK_API_KEY=...
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!giskard worker start -d -u <ngrok_external_server_link>
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!giskard worker start -d -u <ngrok_external_server_link> --name <your-project-worker-id>
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# To let Giskard Hub run your model in a managed Python environment, execute these lines:
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%env GSK_API_KEY=...
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!giskard worker start -s -u <ngrok_external_server_link> --name <your-project-worker-id>
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‎docs/giskard_hub/installation_hub/index.md‎

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@@ -13,7 +13,7 @@ The Giskard Hub is the app adapted for an enterprise use of Giskard. Extending t
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You have 3 ways to install the Hub:
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* **Hugging Face Space installation**: This is adapted for an **easy installation** in the cloud for prototyping purposes. If you don't want to upload your own model and just want to check some Giskard demo projects, the HF public space is perfect for you.
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* **On-premise installation**: This is adapted if your data and model are **private** and you don't have the possibility to use the cloud (for instance, because of privacy and connectivity issues).
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* **Private cloud**: This is adapted if you can easily run a **Cloud instance** by your favourite Cloud provider (AWS, GCP or Azure) and want to easily use Giskard collaborative features (collect feedback from business, share results, etc.). Make sure that you have the rights to open ports of your Cloud machine because Giskard needs to open a connection with an ML Worker running on your Python environment.
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* **Private cloud**: This is adapted if you can easily run a **Cloud instance** by your favourite Cloud provider (AWS, GCP or Azure) and want to easily use Giskard collaborative features (collect feedback from business, share results, etc.). Make sure that you have the rights to open ports of your Cloud machine, if you want to connect your own ML Worker to the Giskard Hub.
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```{toctree}
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:caption: Table of Contents

‎docs/giskard_hub/installation_hub/install_cloud/install_aws/index.md‎

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@@ -44,72 +44,8 @@ You can stop the instance and restart it when you need to save AWS compute costs
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## 5. Start the ML worker
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Giskard executes your model using a worker that runs the model directly in **your Python environment**, with all the dependencies required by your model. You can either execute the ML worker:
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Giskard executes your model using an ML worker that runs the model. The worker is created along with your project, using the dependencies in your current environment. You can start the worker on Giskard Hub, if it is not started automatically.
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- From your **local notebook** within the kernel that contains all the dependencies of your model
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- From **Google Colab** within the kernel that contains all the dependencies of your model
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- Or from **your terminal** within the Python environment that contains all the dependencies of your model
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:::{note}
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If you plan to use LLM-assisted tests or transformations, don’t forget to set the ``OPENAI_API_KEY`` environment
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variable before starting the Giskard worker.
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:::
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:::::::{tab-set}
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::::::{tab-item} From your local notebook
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To start the ML worker from your notebook, run the following code in your notebook:
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```
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!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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For advanced and flexible usages, please check [our doc for ML worker](../../mlworker/index.md).
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::
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::::::{tab-item} From Colab notebook
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To start the ML worker from your Colab notebook, run in your Colab cell:
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```
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!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::{tab-item} From your terminal
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* Run the following command **within the Python environment that contains all the dependencies of your model**:
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```
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giskard worker start -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::
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:::::::

‎docs/giskard_hub/installation_hub/install_cloud/install_azure/index.md‎

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@@ -57,72 +57,8 @@ You can stop the instance and restart it when you need to save your Azure comput
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## 4. Start the ML worker
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Giskard executes your model using a worker that runs the model directly in **your Python environment**, with all the dependencies required by your model. You can either execute the ML worker:
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Giskard executes your model using an ML worker that runs the model. The worker is created along with your project, using the dependencies in your current environment. You can start the worker on Giskard Hub, if it is not started automatically.
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- From your **local notebook** within the kernel that contains all the dependencies of your model
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- From **Google Colab** within the kernel that contains all the dependencies of your model
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- Or from **your terminal** within the Python environment that contains all the dependencies of your model
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:::{note}
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If you plan to use LLM-assisted tests or transformations, don’t forget to set the ``OPENAI_API_KEY`` environment
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variable before starting the Giskard worker.
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:::
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:::::::{tab-set}
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::::::{tab-item} From your local notebook
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To start the ML worker from your notebook, run the following code in your notebook:
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```
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!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::{tab-item} From Colab notebook
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To start the ML worker from your Colab notebook, run in your Colab cell:
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```
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!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::{tab-item} From your terminal
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* Run the following command **within the Python environment that contains all the dependencies of your model**:
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```
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giskard worker start -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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For advanced and flexible usages, please check [our doc for ML worker](../../mlworker/index.md).
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::
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:::::::

‎docs/giskard_hub/installation_hub/install_cloud/install_gcp/index.md‎

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&#x20;`giskard hub start`
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:::
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## 4. Start the ML worker
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Giskard executes your model using a worker that runs the model directly in **your Python environment**, with all the dependencies required by your model. You can either execute the ML worker:
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- From your **local notebook** within the kernel that contains all the dependencies of your model
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- From **Google Colab** within the kernel that contains all the dependencies of your model
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- Or from **your terminal** within the Python environment that contains all the dependencies of your model
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:::{note}
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If you plan to use LLM-assisted tests or transformations, don’t forget to set the ``OPENAI_API_KEY`` environment
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variable before starting the Giskard worker.
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:::
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:::::::{tab-set}
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::::::{tab-item} From your local notebook
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To start the ML worker from your notebook, run the following code in your notebook:
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```
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!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::
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::::::{tab-item} From Colab notebook
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To start the ML worker from your Colab notebook, run in your Colab cell:
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```
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!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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Giskard executes your model using an ML worker that runs the model. The worker is created along with your project, using the dependencies in your current environment. You can start the worker on Giskard Hub, if it is not started automatically.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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For advanced and flexible usages, please check [our doc for ML worker](../../mlworker/index.md).
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::
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::::::{tab-item} From your terminal
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* Run the following command **within the Python environment that contains all the dependencies of your model**:
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```
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giskard worker start -k YOUR_KEY -u http://<your IP address>:19000/
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```
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The API Access Key (`YOUR_KEY`) can be found in the Settings tab of the Giskard Hub.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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::::::
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:::::::

‎docs/giskard_hub/installation_hub/install_hfs/index.md‎

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### 4. Start the ML worker
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Giskard executes your model using a worker that runs the model directly in **your Python environment**, with all the dependencies required by your model. You can either execute the ML worker:
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Giskard executes your model using an ML worker that runs the model. The worker is created along with your project, using the dependencies in your current environment. You can start the worker on Giskard Hub, if it is not started automatically.
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- From your **local notebook** within the kernel that contains all the dependencies of your model
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- From **Google Colab** within the kernel that contains all the dependencies of your model
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- Or from **your terminal** within the Python environment that contains all the dependencies of your model
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For advanced and flexible usages, please check [our doc for ML worker](../mlworker/index.md).
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:::{note}
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If you plan to use LLM-assisted tests or transformations, don’t forget to set the ``OPENAI_API_KEY`` environment
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variable before starting the Giskard worker.
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:::
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:::::::{tab-set}
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::::::{tab-item} From your local notebook
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To start the ML worker from your notebook, run the following code in your notebook:
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```
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!giskard worker start -d -k YOUR_KEY -u https://XXX.hf.space -t HF-TOKEN
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```
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To find the **exact** command with the right API Access Key (`YOUR_KEY`) and HuggingFace token (`HF-TOKEN`), go to the "Ml Worker" section in the Settings tab in the Giskard Hub that you install in HF Space.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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::::::
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::::::{tab-item} From Colab notebook
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To start the ML worker from your Colab notebook, run in your Colab cell:
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```
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!giskard worker start -d -k YOUR_KEY -u https://XXX.hf.space -t HF-TOKEN
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```
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To find the **exact** command with the right API Access Key (`YOUR_KEY`) and HuggingFace token (`HF-TOKEN`), go to the "Ml Worker" section in the Settings tab in the Giskard Hub that you install in HF Space.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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::::::
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::::::{tab-item} From your terminal
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* Run the following command **within the Python environment that contains all the dependencies of your model**:
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```
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giskard worker start -d -k YOUR_KEY -u https://XXX.hf.space -t HF-TOKEN
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```
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To find the **exact** command with the right API Access Key (`YOUR_KEY`) and HuggingFace token (`HF-TOKEN`), go to the "Ml Worker" section in the Settings tab in the Giskard Hub that you install in HF Space.
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> ### ⚠️ Warning
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> To see the available commands of the worker, you can execute:
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>```
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>!giskard worker --help
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>```
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::::::
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:::::::
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You're all set to try Giskard in action. Upload your first model, dataset or test suite by following the [upload an object](../../../upload/index.md) page.
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### 5. Upload your test suite by creating a Giskard Client for your HF Space
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