From a184d311b2ea9ea1602d737edfe35e661ef1598d Mon Sep 17 00:00:00 2001 From: ThomasFaria Date: Mon, 29 Jul 2024 15:21:22 +0000 Subject: [PATCH] renaming --- slides/en/applications/_application4.qmd | 78 ----------------------- slides/en/applications/_application4a.qmd | 9 +++ slides/en/applications/_application4b.qmd | 9 +++ slides/en/applications/_application5.qmd | 73 ++++++++++++++++++++- slides/fr/applications/_application3.qmd | 1 + 5 files changed, 91 insertions(+), 79 deletions(-) delete mode 100644 slides/en/applications/_application4.qmd create mode 100644 slides/en/applications/_application4a.qmd create mode 100644 slides/en/applications/_application4b.qmd diff --git a/slides/en/applications/_application4.qmd b/slides/en/applications/_application4.qmd deleted file mode 100644 index fad7e0e..0000000 --- a/slides/en/applications/_application4.qmd +++ /dev/null @@ -1,78 +0,0 @@ -:::{.nonincremental} -:::: {.callout-tip collapse="true" icon=false} -## Part 1 : introduction to `Argo Workflows` - - -1. Launch an `Argo Workflows` service by clicking [this URL](https://datalab.sspcloud.fr/launcher/automation/argo-workflows?autoLaunch=true). Open the service and input the service password (either automatically copied or available in the `README` of the service) -2. In `VSCode`, create a file `hello_world.yaml` at the root of the project with the following content: - -```{.yml filename="hello_world.yml"} -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: hello-world- - labels: - workflows.argoproj.io/archive-strategy: "false" - annotations: - workflows.argoproj.io/description: | - This is a simple hello world example. - You can also run it in Python: https://couler-proj.github.io/couler/examples/#hello-world -spec: - entrypoint: whalesay - templates: - - name: whalesay - container: - image: docker/whalesay:latest - command: [cowsay] - args: ["hello world"] - -``` - -3. Submit the `Hello world` workflow via a terminal in `VSCode` : - -```shell -argo submit formation-mlops/hello_world.yaml -``` -4. Open the UI of `Argo Workflows`. Find the logs of the workflow you just launched. You should see the Docker logo . - -:::: -::: - - - -## Application 4 - -:::{.nonincremental} -:::: {.callout-tip collapse="true" icon=false} -## Part 2 : distributing the hyperparameters optimization - -1. Take a look at the `argo_workflows/workflow.yml` file. What do you expect will happen when we submit this workflow ? -2. Modify the highlighted line in the same manner as in application 3. - -```{.yml code-line-numbers="4" filename="workflow.yml"} -parameters: - # The MLflow tracking server is responsable to log the hyper-parameter and model metrics. - - name: mlflow-tracking-uri - value: https://user--.user.lab.sspcloud.fr - - name: mlflow-experiment-name - value: nace-prediction -``` - -3. Submit the workflow and look at the jobs completing live in the UI. - -
- - Click to see the command - - -```shell -argo submit formation-mlops/argo_workflows/workflow.yml -``` - -
- -4. Once all jobs are completed, visualize the logs of the whole workflow. -5. Finally, open the `MLflow` UI to check what has been done. -:::: - -::: diff --git a/slides/en/applications/_application4a.qmd b/slides/en/applications/_application4a.qmd new file mode 100644 index 0000000..ca3977b --- /dev/null +++ b/slides/en/applications/_application4a.qmd @@ -0,0 +1,9 @@ +:::{.callout-tip collapse="true" icon=false} +## Introduction to MLflow concepts + +:::{.incremental} +1. In `JupyterLab`, open the notebook located at `formation-mlops/notebooks/mlflow-introduction.ipynb` +2. Execute the notebook cell by cell. If you are finished early, explore the `MLflow` UI and try to build your own experiments from the example code provided in the notebook. +::: + +::: diff --git a/slides/en/applications/_application4b.qmd b/slides/en/applications/_application4b.qmd new file mode 100644 index 0000000..ca3977b --- /dev/null +++ b/slides/en/applications/_application4b.qmd @@ -0,0 +1,9 @@ +:::{.callout-tip collapse="true" icon=false} +## Introduction to MLflow concepts + +:::{.incremental} +1. In `JupyterLab`, open the notebook located at `formation-mlops/notebooks/mlflow-introduction.ipynb` +2. Execute the notebook cell by cell. If you are finished early, explore the `MLflow` UI and try to build your own experiments from the example code provided in the notebook. +::: + +::: diff --git a/slides/en/applications/_application5.qmd b/slides/en/applications/_application5.qmd index c9ad562..fad7e0e 100644 --- a/slides/en/applications/_application5.qmd +++ b/slides/en/applications/_application5.qmd @@ -1,7 +1,78 @@ :::{.nonincremental} :::: {.callout-tip collapse="true" icon=false} -## Logging +## Part 1 : introduction to `Argo Workflows` + + +1. Launch an `Argo Workflows` service by clicking [this URL](https://datalab.sspcloud.fr/launcher/automation/argo-workflows?autoLaunch=true). Open the service and input the service password (either automatically copied or available in the `README` of the service) +2. In `VSCode`, create a file `hello_world.yaml` at the root of the project with the following content: + +```{.yml filename="hello_world.yml"} +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: hello-world- + labels: + workflows.argoproj.io/archive-strategy: "false" + annotations: + workflows.argoproj.io/description: | + This is a simple hello world example. + You can also run it in Python: https://couler-proj.github.io/couler/examples/#hello-world +spec: + entrypoint: whalesay + templates: + - name: whalesay + container: + image: docker/whalesay:latest + command: [cowsay] + args: ["hello world"] + +``` + +3. Submit the `Hello world` workflow via a terminal in `VSCode` : + +```shell +argo submit formation-mlops/hello_world.yaml +``` +4. Open the UI of `Argo Workflows`. Find the logs of the workflow you just launched. You should see the Docker logo . :::: ::: + + +## Application 4 + +:::{.nonincremental} +:::: {.callout-tip collapse="true" icon=false} +## Part 2 : distributing the hyperparameters optimization + +1. Take a look at the `argo_workflows/workflow.yml` file. What do you expect will happen when we submit this workflow ? +2. Modify the highlighted line in the same manner as in application 3. + +```{.yml code-line-numbers="4" filename="workflow.yml"} +parameters: + # The MLflow tracking server is responsable to log the hyper-parameter and model metrics. + - name: mlflow-tracking-uri + value: https://user--.user.lab.sspcloud.fr + - name: mlflow-experiment-name + value: nace-prediction +``` + +3. Submit the workflow and look at the jobs completing live in the UI. + +
+ + Click to see the command + + +```shell +argo submit formation-mlops/argo_workflows/workflow.yml +``` + +
+ +4. Once all jobs are completed, visualize the logs of the whole workflow. +5. Finally, open the `MLflow` UI to check what has been done. +:::: + +::: diff --git a/slides/fr/applications/_application3.qmd b/slides/fr/applications/_application3.qmd index c60b2b5..e3901b7 100644 --- a/slides/fr/applications/_application3.qmd +++ b/slides/fr/applications/_application3.qmd @@ -217,3 +217,4 @@ python formation-mlops/src/predict_api.py Nous avons ici réalisé une succession de requêtes GET car nous avons un seul point d'entrée vers notre API. Pour réaliser des requêtes en `batch` il est préférable de réaliser des requêtes POST. :::: +:::