default highlight

_quarto.yml

@@ -19,6 +19,6 @@ format:
     logo: images/HITS_RGB_eng.jpg
     footer: "ML Workflow Orchestration (Bernd Doser, HITS)"
     slide-number: true
-    highlight-style: a11y
+    # highlight-style: a11y
     # code-block-height: 650px
     # width: 1300

flyte.qmd

@@ -49,26 +49,36 @@ def mean(values: List[float]) -> float:
 
 ## Flyte Workflows
 
-- Workflows doesn’t perform computations it’s used to structure tasks 
+Workflows are used to structure the task execution graph 
 
-```python
+<!-- ```python
 workflow_outputs = typing.NamedTuple(
     "WorkflowOutputs",
     simwords=Dict[str, float],
     distance=float,
     topics=Dict[int, List[str]],
 )
+``` -->
+
+::: {style="font-size: 80%;"}
+``` {.python}
+@task
+def generate_processed_corpus() -> List[List[str]]:
 
 @workflow
-def nlp_workflow(target_word: str = "computer") -> workflow_outputs:
+def nlp_workflow(target_word: str = "computer") -> [Dict[str, float], float, Dict[int, List[str]]]:
     corpus = generate_processed_corpus()
     model_wv = train_word2vec_model(training_data=corpus, hyperparams=Word2VecModelHyperparams())
     lda_topics = train_lda_model(corpus=corpus, hyperparams=LDAModelHyperparams())
     similar_words = word_similarities(model_ser=model_wv.model, word=target_word)
     distance = word_movers_distance(model_ser=model_wv.model)
     return similar_words, distance, lda_topics
 ```
+:::
 
+::: aside
+[Source](https://docs.flyte.org/en/latest/flytesnacks/examples/nlp_processing/word2vec_and_lda.html)
+:::
 
 ## Containerized Tasks
 
@@ -77,7 +87,7 @@ A Flyte task operates within its own container and runs on a Kubernetes pod
 ![](images/union_tasks.png){fig-align="center"}
 
 
-## ImageSpec
+## Container Image Specifications
 
 - Customize the container image without a Dockerfile
 - Container image is build at registration and pushed to a container registry
@@ -101,7 +111,6 @@ custom_image = ImageSpec(
 - Clone and recover executions
 
 
-## Flyte Demo
+# [Flyte Demo](https://github.com/BerndDoser/flyte-mnist)
 
-- [GitHub repo](https://github.com/BerndDoser/flyte-mnist)
-- [PyTorch MNIST Classifier](https://docs.flyte.org/en/latest/flytesnacks/examples/mnist_classifier/pytorch_single_node_and_gpu.html)
+<!-- - [PyTorch MNIST Classifier](https://docs.flyte.org/en/latest/flytesnacks/examples/mnist_classifier/pytorch_single_node_and_gpu.html) -->

hpc-ai.qmd

@@ -1,5 +1,5 @@
 # Infrastructure for Machine Learning Workflows
-## {background-image="images/ai_vs_hpc.png"}
+## {background-image="images/ai_vs_hpc.png" transition="fade"}
 
 [The background image was generated with FLUX on fal.ai\
 using the prompt 'Visualize two worlds showing the difference between HPC and AI infrastructure']{.top_white}
@@ -10,7 +10,7 @@ using the prompt 'Visualize two worlds showing the difference between HPC and AI
 ::: {layout='[1,1]' layout-valign=center}
 ::: n1
 
-- SLURM
+- Slurm
 - MPI
 - File Storage
 

index.qmd

@@ -1,9 +1,15 @@
 ## Agenda
 
-- General Overview to Workflow Orchestration
+- Introduction to Workflow Orchestration
+
+. . .
+
 - Solutions for Workflow Orchestration
   - StreamFlow
   - Flyte
+
+. . .
+
 - Infrastructure for Machine Learning Workflows
   - HPC vs AI/ML
   - File vs Object Storage

streamflow.qmd

@@ -10,7 +10,7 @@
   - Kubernetes
 
 ::: {.callout-note}
-[BioExcel Building Blocks](https://bioexcel.eu/biobb-new/) is a **showpiece** using CWL for biomolecular simulation.
+[BioExcel Building Blocks](https://bioexcel.eu/biobb-new/) is a great example using CWL for biomolecular simulation.
 :::
 
 
@@ -27,10 +27,13 @@ From [StreamFlow documentation](https://streamflow.di.unito.it/documentation/0.2
 
 ::: {layout='[1,1]'}
 ::: n1
+::: {style="font-size: 80%;"}
 ``` {.yaml filename="workflow.cwl"}
 {{< include code/workflow.cwl >}}
 ```
 :::
+:::
+
 ::: n2
 - Declare the workflow in a YAML file
   - `inputs`
@@ -46,16 +49,21 @@ From [StreamFlow documentation](https://streamflow.di.unito.it/documentation/0.2
 
 ::: {layout='[1,1]'}
 ::: n1
+::: {style="font-size: 80%;"}
 ``` {.yaml filename="workflow.cwl"}
 {{< include code/workflow.cwl >}}
 ```
 :::
+:::
+
 ::: n2
+::: {style="font-size: 80%;"}
 ``` {.yaml filename="train.cwl"}
 {{< include code/train.cwl >}}
 ```
 :::
 :::
+:::
 
 
 ## CWL Implementations in Production
@@ -67,11 +75,13 @@ From [CWL Documentation](https://www.commonwl.org/implementations/)
 :::
 
 
-## Visualizing CWL
+## Visualizing CWL Graphs
 
 ![](images/rRNA_selection_cwl.svg){fig-align="center"}
 
-[Try it out!](https://view.commonwl.org/){top=-100px}
+::: aside
+From [CWL Viewer](https://view.commonwl.org/)
+:::
 
 
 ## StreamFlow Deployment
@@ -91,14 +101,13 @@ From [CWL Documentation](https://www.commonwl.org/implementations/)
 :::
 
 
-## StreamFlow: Cross-Facility Federated Learning
+## StreamFlow: Federated Learning
 
 ![](images/streamflow-xffl.webp){fig-align="center"}
 
-
-## StreamFlow Demo
-
-- [GitHub repo to Seminar example](https://github.com/BerndDoser/streamflow-example)
-- [Example of the developers of StreamFlow](https://github.com/alpha-unito/streamflow-fl)
+::: aside
+[GitHub](https://github.com/alpha-unito/streamflow-fl)
+:::
 
 
+# [StreamFlow Demo](https://github.com/BerndDoser/streamflow-example)

workflows.qmd

@@ -1,21 +1,24 @@
-## Motivation by [SPACE CoE](https://www.space-coe.eu/)
+## Motivated by [SPACE CoE](https://www.space-coe.eu/)
 
 ![](images/SPACE_logo.png){.absolute top=-20 right=0 width=200}
 
 ::: {.fs34}
-**Goal**: Astrophysical Simulations for Exascale Computing\
-**Challenge**: Analyze large-scale simulation data of Petabytes\
+**Goal**: Astrophysical simulations for exascale computing\
+**Challenge**: Analyze large-size simulation data (~ Petabytes)\
 **Solution**: Efficient ML-based workflows using dimensionality reduction
 :::
 
-::: {layout='[1,2.4,0.7]' layout-valign=center}
+::: {layout='[1,2.4,0.7]'}
 ::: n1
+
+**Input**
+
 ![](images/illustris.png)
 
 ::: {.fs20}
 ::: {.callout-note icon=false}
 
-## Simulation Data (~ PetaBytes)
+## Simulation Data (~ Petabytes)
 
 Time snapshots storing particle positions, velocities, and other properties, e.g. from OpenGADGET (IllustrisTNG)
 
@@ -24,10 +27,14 @@ Time snapshots storing particle positions, velocities, and other properties, e.g
 :::
 
 ::: n2
+**Workflow**
+
 ![](images/projectx.svg)
 :::
 
 ::: n3
+**Output**
+
 ![](images/P404_f2.png)
 
 ::: {.fs20}
@@ -51,14 +58,14 @@ K. L. Polsterer, B. Doser, A. Fehlner and S. Trujillo-Gomez [ADASS (2024)]().
 
 - **Reproducibility**: Ensure your experiments are reproducible by tracking the code, data, model, and environment.
 - **Resource management**: Manage resources efficiently by running tasks in parallel and optimizing resource usage.
-- **Scalability**: Scale your workflows to handle large datasets and complex pipelines.
+- **Scalability**: Scale your workflows to handle large amount of data and complex pipelines.
 - **Monitoring**: Track the progress of workflows and monitor their performance and results.
 - **Collaboration**: Enable collaboration between team members by sharing code, data, and results.
 
 :::
 
 
-## General Requirements for Workflows
+## Requirements on Workflows Orchestration
 
   - Define node requirements (e.g. CPU, memory, GPU)
   - Control runtime environment with containers