mirror of https://github.com/kubeflow/examples.git
Use kubeflow/examples as base link for G-Research (#1006)
Signed-off-by: Kimonas Sotirchos <kimwnasptd@arrikto.com> Signed-off-by: Kimonas Sotirchos <kimwnasptd@arrikto.com>
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Kimonas Sotirchos
GitHub
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# Objective
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In this example we are going to convert this generic [notebook](https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/g-research-crypto-forecast-orig.ipynb) based on the [Kaggle G-Research Crypto Forecast](https://www.kaggle.com/competitions/g-research-crypto-forecasting/overview) competition into a Kubeflow pipeline.
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In this example we are going to convert this generic [notebook](./g-research-crypto-forecast-orig.ipynb) based on the [Kaggle G-Research Crypto Forecast](https://www.kaggle.com/competitions/g-research-crypto-forecasting/overview) competition into a Kubeflow pipeline.
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The objective of this task is to correctly forecast short term returns in 14 popular cryptocurrencies.
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The dataset provided contains information on historic trades for several cryptoassets, such as Bitcoin and Ethereum.
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@ -44,13 +44,13 @@ Environment:
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* Click on ‘Account’.
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/kaggle-account.PNG?raw=true" alt="kaggle-click-account"/>
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<img src="./images/kaggle-account.PNG?raw=true" alt="kaggle-click-account"/>
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</p>
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* Under ‘Account’, navigate to the ‘API’ section.
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/jpx-tokyo-stock-exchange-kaggle-competition/images/kaggle-create-new-api-token.PNG?raw=true" alt="kaggle-create-new-api-token"/>
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<img src="../jpx-tokyo-stock-exchange-kaggle-competition/images/kaggle-create-new-api-token.PNG?raw=true" alt="kaggle-create-new-api-token"/>
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</p>
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* Click ‘Create New API token’.
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@ -85,7 +85,7 @@ Environment:
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* After successfully deploying the PodDefault, create a new Notebook Server and add the `kaggle-secret` configuration to the new Notebook Server
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that runs the Kale or KFP pipeline.
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/jpx-tokyo-stock-exchange-kaggle-competition/images/notebook-ui-kaggle-config.png?raw=true" alt="notebook-ui-kaggle-config"/>
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<img src="../jpx-tokyo-stock-exchange-kaggle-competition/images/notebook-ui-kaggle-config.png?raw=true" alt="notebook-ui-kaggle-config"/>
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</p>
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## Section 3: Vanilla KFP Pipeline
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@ -116,14 +116,14 @@ Finally, the [`create_run_from_pipeline_func`](https://kubeflow-pipelines.readt
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* Set the CPU specification to 8 and RAM to 16 GB
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
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<img src="./images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
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</p>
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* Add the `kaggle-secret` configuration to the new Notebook Server.
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2. Create a new Terminal and clone this repo. After cloning, navigate to this directory.
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3. Open the [g-research-crypto-forecast-kfp](https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/g-research-crypto-forecast-kfp.ipynb) notebook
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3. Open the [g-research-crypto-forecast-kfp](./g-research-crypto-forecast-kfp.ipynb) notebook
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4. Run the g-research-crypto-forecast notebook from start to finish
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@ -132,13 +132,13 @@ Finally, the [`create_run_from_pipeline_func`](https://kubeflow-pipelines.readt
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### View Pipeline
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/g-research-crypto-forecast-kfp-pipeline.PNG?raw=true" alt="kubeflow pipeline"/>
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<img src="./images/g-research-crypto-forecast-kfp-pipeline.PNG?raw=true" alt="kubeflow pipeline"/>
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</p>
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### View Pipeline Metric
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/kfp-pipeline-metrics.PNG?raw=true" alt="kubeflow pipeline metrics"/>
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<img src="./images/kfp-pipeline-metrics.PNG?raw=true" alt="kubeflow pipeline metrics"/>
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</p>
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@ -153,26 +153,26 @@ To create a KFP pipeline using the Kale JupyterLab extension
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* Set the CPU specification to 8 and RAM to 16 GB
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
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<img src="./images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
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</p>
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* Increase the Workspace Volume to 15Gi
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/kale-volume.PNG?raw=true" alt="kale volume"/>
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<img src="./images/kale-volume.PNG?raw=true" alt="kale volume"/>
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</p>
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* Add the `kaggle-secret` configuration to the new Notebook Server.
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2. Create a new Terminal and clone this repo. After cloning, navigate to this directory.
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3. Launch the [g-research-crypto-forecast-orig.ipynb](https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/g-research-crypto-forecast-orig.ipynb) Notebook
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3. Launch the [g-research-crypto-forecast-orig.ipynb](./g-research-crypto-forecast-orig.ipynb) Notebook
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4. Install the requirements.txt file. After installation, restart the kernel.
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5. Enable the Kale extension in JupyterLab
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6. Ensure the notebook cells are annotated with Kale tags just as it is in the [g-research-crypto-forecast-kale.ipynb](https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/g-research-crypto-forecast-kale.ipynb) Notebook
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6. Ensure the notebook cells are annotated with Kale tags just as it is in the [g-research-crypto-forecast-kale.ipynb](./g-research-crypto-forecast-kale.ipynb) Notebook
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To fully understand the different Kale tags available, visit Kale [documentation](https://docs.arrikto.com/user/kale/jupyterlab/cell-types.html?highlight=pipeline%20metrics#annotate-pipeline-step-cells)
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@ -205,7 +205,7 @@ To create a KFP pipeline using the Kale JupyterLab extension
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7. Compile and run the Notebook by hitting the "Compile & Run" in Kale's left panel
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/kale-deployment-panel.PNG?raw=true" alt="kale-deployment-panel"/>
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<img src="./images/kale-deployment-panel.PNG?raw=true" alt="kale-deployment-panel"/>
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</p>
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### View Pipeline
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@ -213,15 +213,15 @@ To create a KFP pipeline using the Kale JupyterLab extension
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View Pipeline by clicking "View" in Kale's left panel
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/view-pipeline.PNG?raw=true" alt="view-pipeline"/>
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<img src="./images/view-pipeline.PNG?raw=true" alt="view-pipeline"/>
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</p>
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/g-research-crypto-forecast-kale-pipeline.PNG?raw=true" alt="kale-pipeline"/>
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<img src="./images/g-research-crypto-forecast-kale-pipeline.PNG?raw=true" alt="kale-pipeline"/>
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</p>
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### View Pipeline Metric
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<p align=center>
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<img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/kale-pipeline-metrics.PNG?raw=true" alt="kale-metrics"/>
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<img src="./images/kale-pipeline-metrics.PNG?raw=true" alt="kale-metrics"/>
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</p>
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},
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"source": [
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"# 🪙 G-Research Crypto Kale Pipeline\n",
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"\n",
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"\n",
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"\n",
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"---\n"
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]
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},
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"source": [
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"# 🪙 G-Research Crypto Kubeflow Pipeline\n",
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"\n",
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"\n",
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"\n",
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"---\n"
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]
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},
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"source": [
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"# 🪙 G-Research Crypto Original Notebook\n",
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"\n",
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"\n",
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"\n",
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"---\n"
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]
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