🏦 Prep training Boursorama

prepare-labs/templates/cards.html

@@ -69,7 +69,7 @@ body {
 body {
     width: 6.75in; /* two cards wide */
     margin-left: 0.875in; /* (8.5in - 6.75in)/2 */
-    margin-top: 0; /* NOTE: we have to manually specify a top margin of e.g. 0.1875in when printing */
+    margin-top: 0.1875in; /* (11in - 5 cards)/2 */
 }
 {% endif %}
 

prepare-labs/templates/cards.yaml

@@ -8,11 +8,12 @@ backside: |
     Thanks for attending the Asynchronous Architecture Patterns workshop at QCON!
   </p>
   <p>
-    If you'd like me to send you a copy of the recording of the workshop
-    and of the training materials,
-    please scan that QR code to leave me your
-    contact information. Thank you!
+    <b>This QR code will give you my contact info</b> as well as a link to a feedback form.
   </p>
-qrcode: https://2024-11-qconsf.container.training/q
+  <p>
+    If you liked this workshop, I can train your team, in person or online, with custom
+    courses of any length and any level, on Docker, Kubernetes, and MLops.
+  </p>
+qrcode: https://2024-11-qconsf.container.training/#contact
 thing: Kubernetes cluster
-image: logo-bento.svg
+image: logo-kubernetes.png

slides/_redirects

@@ -2,7 +2,7 @@
 #/ /kube-halfday.yml.html 200!
 #/ /kube-fullday.yml.html 200!
 #/ /kube-twodays.yml.html 200!
-/ /mq.yml.html 200!
+/ /kube.yml.html 200!
 
 # And this allows to do "git clone https://container.training".
 /info/refs service=git-upload-pack https://github.com/jpetazzo/container.training/info/refs?service=git-upload-pack
@@ -13,7 +13,7 @@
 #/kubernetesmastery https://www.udemy.com/course/kubernetesmastery/?couponCode=DOCKERALLDAY
 
 # Shortlink for the QRCode
-/q https://docs.google.com/forms/d/e/1FAIpQLScYloWur4uVhKgVNIdUrfHZ8pk_mBmPcQwmbhjK2FlR9KWDCA/viewform
+/q /qrcode.html 200
 
 # Shortlinks for next training in English and French
 #/next https://www.eventbrite.com/e/livestream-intensive-kubernetes-bootcamp-tickets-103262336428
@@ -22,3 +22,5 @@
 /us https://www.ardanlabs.com/live-training-events/deploying-microservices-and-traditional-applications-with-kubernetes-march-28-2022.html
 /uk https://skillsmatter.com/workshops/827-deploying-microservices-and-traditional-applications-with-kubernetes-with-jerome-petazzoni
 
+# Survey form
+/please https://docs.google.com/forms/d/e/1FAIpQLSfIYSgrV7tpfBNm1hOaprjnBHgWKn5n-k5vtNXYJkOX1sRxng/viewform

slides/docker-compose.yaml

@@ -1,3 +1,5 @@
+version: "2"
+
 services:
   www:
     image: nginx

slides/k8s/bento-enrichment.md

@@ -1,285 +0,0 @@
-# Calling APIs from Bento
-
-- We want to ask our LLM who's the mayor of each of these cities
-
-- We'll use a prompt that will usually ensure a short answer
-
-  (so that it's faster; we don't want to wait 30 seconds per city!)
-
-- We'll test the prompt with the Ollama CLI
-
-- Then we'll craft a proper HTTP API query
-
-- Finally, we'll configure an [enrichment workflow][enrichment] in Bento
-
----
-
-## Test our prompt
-
-Assuming that our earlier Ollama Deployment is still running:
-
-```bash
-kubectl exec deployment/ollama -- \
-ollama run qwen2:1.5b "
-Who is the mayor of San Francisco?
-Just give the name by itself on a single line.
-If you don't know, don't say anything.
-"
-```
-
----
-
-## Turn the prompt into an HTTP API query
-
-Note: to install `http` in an Alpine container, run `apk add httpie`.
-
-```bash
-http http://ollama.default:11434/api/generate \
-model=qwen2:1.5b stream:=false prompt="
-Who is the mayor of Paris?
-Just give the name by itself on a single line.
-If you don't know, don't say anything.
-"
-```
-
-We get a JSON payload, and we want to use the `response` field.
-
----
-
-## Configure an enrichment workflow
-
-The [Bento documentation][enrichment] is really good!
-
-We need to set up:
-
-- a `branch` processor
-
-- a `request_map` to transform the city into an Ollama request
-
-- an `http` processor to submit the request to Ollama
-
-- a `result_map` to transform the Ollama response
-
----
-
-## Without the `branch` processor
-
-<pre class="mermaid">
-flowchart LR
-
-  CITY["
-    city: Paris
-    country: France
-    population: 1106000
-    iso2: FR
-    ...
-    "]
-
-  REQ["
-    model: qwen2:1.5b
-    stream: false
-    prompt: Who is the mayor of Paris?
-    "]
-
-  REP["
-    response: Anne Hidalgo
-    eval_count: ...
-    prompt_eval_count: ...
-    (other ollama fields)
-    "]
-
-  CITY@{ shape: card}
-  REQ@{ shape: card}
-  REP@{ shape: card}
-
-  style CITY text-align: left
-  style REQ text-align: left
-  style REP text-align: left
-
-  mapping@{ shape: diam }
-  http["http processor"]@{ shape: diam }
-
-  CITY --> mapping --> REQ --> http --> REP
-</pre>
-
-- We transform the `city` into an Ollama request
-
-- The `http` processor submits the request to Ollama
-
-- The final output is the Ollama response
-
----
-
-## With the `branch` processor
-
-<pre class="mermaid">
-flowchart LR
-
-  CITY["
-    city: Paris
-    country: France
-    population: 1106000
-    iso2: FR
-    ...
-    "]
-
-  REQ["
-    model: qwen2:1.5b
-    stream: false
-    prompt: Who is the mayor of Paris?
-    "]
-
-  REP["
-    response: Anne Hidalgo
-    eval_count: ...
-    prompt_eval_count: ...
-    (other ollama fields)
-    "]
-
-  OUT["
-    city: Paris
-    country: France
-    population: 1106000
-    iso2: FR
-    ...
-    mayor: Anne Hidalgo
-    "]
-
-  CITY@{ shape: card}
-  REQ@{ shape: card}
-  REP@{ shape: card}
-  OUT@{ shape: card}
-
-  style CITY text-align: left
-  style REQ text-align: left
-  style REP text-align: left
-  style OUT text-align: left
-
-  branch@{ shape: diam }
-  request_map@{ shape: diam }
-  result_map@{ shape: diam }
-  http["http processor"]@{ shape: diam }
-
-  CITY --> branch
-  branch --> result_map
-  branch --> request_map
-  request_map --> REQ
-  REQ --> http
-  http --> REP
-  REP --> result_map
-  result_map --> OUT
-</pre>
-
-- The `branch` processor allows doing the processing "on the side"
-
-- `request_map` and `result_map` transform the message before/after processing
-
-- Then, the result is combined with the original message (the `city`)
-
----
-
-```yaml
-input:
-  csv:
-    paths: ["cities.csv"]
-pipeline:
-  processors:
-    - branch:
-        request_map: |
-          root.model = "qwen2:1.5b"
-          root.stream = false
-          root.prompt = (
-            "Who is the mayor of %s? ".format(this.city) +
-            "Just give the name by itself on a single line. " +
-            "If you don't know, don't say anything."
-            )
-        processors:
-          - http:
-              url: http://ollama:11434/api/generate
-              verb: POST
-        result_map: |
-          root.mayor = this.response
-```
-
----
-
-## Trying it out
-
-- Save the YAML on the previous page into a configuration file
-
-- Run Bento with that configuration file
-
-- What happens?
-
---
-
-🤔 We're seeing errors due to timeouts
-
-```
-ERRO HTTP request to 'http://ollama...' failed: http://ollama...:
-Post "http://ollama...": context deadline exceeded
-(Client.Timeout exceeded while awaiting headers)
-```
-
----
-
-## 🙋 Choose your own adventure
-
-How should we address errors?
-
-- Option 1: increase the timeout in the [http][bento-http] processor
-
-- Option 2: use a [retry][bento-retry] processor in the pipeline
-
-- Option 3: use a [reject_errored][bento-reject] output
-
----
-
-## 🏗️ Let's build something!
-
-- We want to process 1000 cities with our LLM
-
-  (guessing who the mayor is, or something similar)
-
-- Store the output wherever we want
-
-  (Redis, CSV file, JSONL files...)
-
-- Deal correctly with errors
-
-  (we'll check that there are, indeed, 1000 cities in the output)
-
-- Scale out to process faster
-
-  (scale ollama to e.g. 10 replicas, enable parallelism in Bento)
-
----
-
-class: title
-
-🍱 Lunch time! 🍱
-
----
-
-## What happened?
-
-- If your Ollama pods have *resource requests*:
-
-  → your cluster may have auto-scaled
-
-- If your Ollama pods don't have *resource requests*:
-
-  → you probably have a bunch of container restarts, due to out-of-memory errors
-
-🤔 What's that about?
-
-[bento-http]: https://warpstreamlabs.github.io/bento/docs/components/processors/http/
-[bento-inputs]: https://warpstreamlabs.github.io/bento/docs/components/inputs/about/
-[bento-reject]: https://warpstreamlabs.github.io/bento/docs/components/outputs/reject_errored
-[bento-retry]: https://warpstreamlabs.github.io/bento/docs/components/processors/retry
-[bento-switch]: https://warpstreamlabs.github.io/bento/docs/components/processors/switch/
-[enrichment]: https://warpstreamlabs.github.io/bento/cookbooks/enrichments/
-[output-http-server]: https://warpstreamlabs.github.io/bento/docs/components/outputs/http_server
-[redpanda-acquires-benthos]: https://www.redpanda.com/press/redpanda-acquires-benthos
-[warpstream-forks-benthos]: https://www.warpstream.com/blog/announcing-bento-the-open-source-fork-of-the-project-formerly-known-as-benthos
-

slides/k8s/bento-hpa.md

@@ -244,7 +244,7 @@ spec:
 
 ---
 
-# Improving throughput
+## Improving throughput
 
 We're going to review multiple techniques:
 

slides/k8s/bento-intro.md

@@ -280,12 +280,22 @@ pipeline:
 
 ---
 
-## 🏗️ Let's generate the Bento configuration!
+## Generate the Bento configuration
 
 Option 1: `bento create redis_list//http_server`
 
 Option 2: [read the docs][output-http-server]
 
+---
+
+## 🙋 Choose your own adventure
+
+Do you want to try to write that configuration?
+
+Or shall we see it right away?
+
+--
+
 ⚠️ Spoilers on next slide!
 
 ---
@@ -323,3 +333,291 @@ It's also possible to batch, stream...
   ```
 
 - Check what happens after we retrive *all* the cities!
+
+---
+
+## 3️⃣ Query our LLM for each city
+
+- We want to ask our LLM who's the mayor of each of these cities
+
+- We'll use a prompt that will usually ensure a short answer
+
+  (so that it's faster; we don't want to wait 30 seconds per city!)
+
+- We'll test the prompt with the Ollama CLI
+
+- Then we'll craft a proper HTTP API query
+
+- Finally, we'll configure an [enrichment workflow][enrichment] in Bento
+
+---
+
+## Test our prompt
+
+Assuming that our earlier Ollama Deployment is still running:
+
+```bash
+kubectl exec deployment/ollama -- \
+ollama run qwen2:1.5b "
+Who is the mayor of San Francisco?
+Just give the name by itself on a single line.
+If you don't know, don't say anything.
+"
+```
+
+---
+
+## Turn the prompt into an HTTP API query
+
+Note: to install `http` in an Alpine container, run `apk add httpie`.
+
+```bash
+http http://ollama.default:11434/api/generate \
+model=qwen2:1.5b stream:=false prompt="
+Who is the mayor of Paris?
+Just give the name by itself on a single line.
+If you don't know, don't say anything.
+"
+```
+
+We get a JSON payload, and we want to use the `response` field.
+
+---
+
+## Configure an enrichment workflow
+
+The [Bento documentation][enrichment] is really good!
+
+We need to set up:
+
+- a `branch` processor
+
+- a `request_map` to transform the city into an Ollama request
+
+- an `http` processor to submit the request to Ollama
+
+- a `result_map` to transform the Ollama response
+
+---
+
+## Without the `branch` processor
+
+<pre class="mermaid">
+flowchart LR
+
+  CITY["
+    city: Paris
+    country: France
+    population: 1106000
+    iso2: FR
+    ...
+    "]
+
+  REQ["
+    model: qwen2:1.5b
+    stream: false
+    prompt: Who is the mayor of Paris?
+    "]
+
+  REP["
+    response: Anne Hidalgo
+    eval_count: ...
+    prompt_eval_count: ...
+    (other ollama fields)
+    "]
+
+  CITY@{ shape: card}
+  REQ@{ shape: card}
+  REP@{ shape: card}
+
+  style CITY text-align: left
+  style REQ text-align: left
+  style REP text-align: left
+
+  mapping@{ shape: diam }
+  http["http processor"]@{ shape: diam }
+
+  CITY --> mapping --> REQ --> http --> REP
+</pre>
+
+- We transform the `city` into an Ollama request
+
+- The `http` processor submits the request to Ollama
+
+- The final output is the Ollama response
+
+---
+
+## With the `branch` processor
+
+<pre class="mermaid">
+flowchart LR
+
+  CITY["
+    city: Paris
+    country: France
+    population: 1106000
+    iso2: FR
+    ...
+    "]
+
+  REQ["
+    model: qwen2:1.5b
+    stream: false
+    prompt: Who is the mayor of Paris?
+    "]
+
+  REP["
+    response: Anne Hidalgo
+    eval_count: ...
+    prompt_eval_count: ...
+    (other ollama fields)
+    "]
+
+  OUT["
+    city: Paris
+    country: France
+    population: 1106000
+    iso2: FR
+    ...
+    mayor: Anne Hidalgo
+    "]
+
+  CITY@{ shape: card}
+  REQ@{ shape: card}
+  REP@{ shape: card}
+  OUT@{ shape: card}
+
+  style CITY text-align: left
+  style REQ text-align: left
+  style REP text-align: left
+  style OUT text-align: left
+
+  branch@{ shape: diam }
+  request_map@{ shape: diam }
+  result_map@{ shape: diam }
+  http["http processor"]@{ shape: diam }
+
+  CITY --> branch
+  branch --> result_map
+  branch --> request_map
+  request_map --> REQ
+  REQ --> http
+  http --> REP
+  REP --> result_map
+  result_map --> OUT
+</pre>
+
+- The `branch` processor allows doing the processing "on the side"
+
+- `request_map` and `result_map` transform the message before/after processing
+
+- Then, the result is combined with the original message (the `city`)
+
+---
+
+```yaml
+input:
+  csv:
+    paths: ["cities.csv"]
+pipeline:
+  processors:
+    - branch:
+        request_map: |
+          root.model = "qwen2:1.5b"
+          root.stream = false
+          root.prompt = (
+            "Who is the mayor of %s? ".format(this.city) +
+            "Just give the name by itself on a single line. " +
+            "If you don't know, don't say anything."
+            )
+        processors:
+          - http:
+              url: http://ollama:11434/api/generate
+              verb: POST
+        result_map: |
+          root.mayor = this.response
+```
+
+---
+
+## Trying it out
+
+- Save the YAML on the previous page into a configuration file
+
+- Run Bento with that configuration file
+
+- What happens?
+
+--
+
+🤔 We're seeing errors due to timeouts
+
+```
+ERRO HTTP request to 'http://ollama...' failed: http://ollama...:
+Post "http://ollama...": context deadline exceeded
+(Client.Timeout exceeded while awaiting headers)
+```
+
+---
+
+## 🙋 Choose your own adventure
+
+How should we address errors?
+
+- Option 1: increase the timeout in the [http][bento-http] processor
+
+- Option 2: use a [retry][bento-retry] processor in the pipeline
+
+- Option 3: use a [reject_errored][bento-reject] output
+
+---
+
+## 🏗️ Let's build something!
+
+- We want to process 1000 cities with our LLM
+
+  (guessing who the mayor is, or something similar)
+
+- Store the output wherever we want
+
+  (Redis, CSV file, JSONL files...)
+
+- Deal correctly with errors
+
+  (we'll check that there are, indeed, 1000 cities in the output)
+
+- Scale out to process faster
+
+  (scale ollama to e.g. 10 replicas, enable parallelism in Bento)
+
+---
+
+class: title
+
+🍱 Lunch time! 🍱
+
+---
+
+## What happened?
+
+- If your Ollama pods have *resource requests*:
+
+  → your cluster may have auto-scaled
+
+- If your Ollama pods don't have *resource requests*:
+
+  → you probably have a bunch of container restarts, due to out-of-memory errors
+
+🤔 What's that about?
+
+[bento-http]: https://warpstreamlabs.github.io/bento/docs/components/processors/http/
+[bento-inputs]: https://warpstreamlabs.github.io/bento/docs/components/inputs/about/
+[bento-reject]: https://warpstreamlabs.github.io/bento/docs/components/outputs/reject_errored
+[bento-retry]: https://warpstreamlabs.github.io/bento/docs/components/processors/retry
+[bento-switch]: https://warpstreamlabs.github.io/bento/docs/components/processors/switch/
+[enrichment]: https://warpstreamlabs.github.io/bento/cookbooks/enrichments/
+[output-http-server]: https://warpstreamlabs.github.io/bento/docs/components/outputs/http_server
+[redpanda-acquires-benthos]: https://www.redpanda.com/press/redpanda-acquires-benthos
+[warpstream-forks-benthos]: https://www.warpstream.com/blog/announcing-bento-the-open-source-fork-of-the-project-formerly-known-as-benthos
+

slides/k8s/cluster-autoscaler.md

@@ -1,4 +1,4 @@
-# Scaling up the cluster
+# Cluster autoscaler
 
 - When the cluster is full, we need to add more nodes
 
@@ -221,9 +221,7 @@ THEN add a Node.
 
 ---
 
-# Scaling down the cluster
-
-*In theory:*
+## Scaling down in theory
 
 IF a Node has less than 50% utilization for 10 minutes,
 

slides/k8s/handson-mlops.md

@@ -110,77 +110,6 @@ class: in-person, pic
 
 ---
 
-## Our recommendation
-
-- Any managed Kubernetes cluster
-
-- Nodes with 8 GB of RAM (or more)
-
-- At least 1 node (obviously!)
-
-- Ideally, cluster autoscaling
-
-  (you can set the maximum number of nodes to 5)
-
-- Alternatively, have a cluster of at least 3 nodes
-
-  (ideally a bit more to see the effect of scaling)
-
-- Local tools: kubectl, Helm, Stern, Bento
-
-- You can also use [shpod] to get a shell on the cluster
-
-[shpod]: https://github.com/jpetazzo/shpod
-
----
-
-## Example with Linode (create cluster)
-
-- Make sure you have a [Linode account][linode-account]
-
-- Install and configure the [Linode CLI][linode-cli]
-
-- Create a cluster:
-  ```bash
-  lin lke cluster-create --label mlops \
-    --k8s_version=1.31 \
-    --node_pools.type g6-standard-4 \
-    --node_pools.count 1 \
-    --node_pools.autoscaler.enabled true \
-    --node_pools.autoscaler.min 1 \
-    --node_pools.autoscaler.max 5 \
-    #
-  ```
-
-[linode-account]: https://login.linode.com/signup
-[linode-cli]: https://www.linode.com/products/cli/
-
----
-
-## Example with Linode (retrieve kubeconfig)
-
-- Retrieve the cluster ID:
-  ```bash
-  CLUSTER_ID=$(lin  lke clusters-list --label $CLUSTER_NAME  --json | jq .[].id)
-  ```
-
-- Wait until the cluster is provisioned:
-  ```bash
-    while ! lin lke kubeconfig-view $CLUSTER_ID; do
-      sleep 10
-    done
-  ```
-
-- Retrieve the cluster kubeconfig:
-  ```bash
-    lin lke kubeconfig-view $CLUSTER_ID --json | jq -r .[].kubeconfig | 
-        base64 -d > kubeconfig.$CLUSTER_ID
-  ```
-
-- And set the `KUBECONFIG` environment variable accordingly!
-
----
-
 class: in-person
 
 ## Why don't we run containers locally?

slides/k8s/prereqs-advanced.md

@@ -1,4 +1,4 @@
-## Pre-requirements
+# Pre-requirements
 
 - Kubernetes concepts
 

slides/k8s/resource-limits.md

@@ -1,4 +1,4 @@
-# Allocating compute resources in theory
+# Resource Limits
 
 - We can attach resource indications to our pods
 
@@ -426,7 +426,7 @@ Each pod is assigned a QoS class (visible in `status.qosClass`).
 
 ---
 
-# Allocating compute resources in practice
+## Specifying resources
 
 - Resource requests are expressed at the *container* level
 
@@ -606,6 +606,246 @@ per Pod, but it's not [officially documented yet](https://github.com/kubernetes/
 
 ---
 
+# Namespace quotas
+
+- We can also set quotas per namespace
+
+- Quotas apply to the total usage in a namespace
+
+  (e.g. total CPU limits of all pods in a given namespace)
+
+- Quotas can apply to resource limits and/or requests
+
+  (like the CPU and memory limits that we saw earlier)
+
+- Quotas can also apply to other resources:
+
+  - "extended" resources (like GPUs)
+
+  - storage size
+
+  - number of objects (number of pods, services...)
+
+---
+
+## Creating a quota for a namespace
+
+- Quotas are enforced by creating a ResourceQuota object
+
+- ResourceQuota objects are namespaced, and apply to their namespace only
+
+- We can have multiple ResourceQuota objects in the same namespace
+
+- The most restrictive values are used
+
+---
+
+## Limiting total CPU/memory usage
+
+- The following YAML specifies an upper bound for *limits* and *requests*:
+  ```yaml
+    apiVersion: v1
+    kind: ResourceQuota
+    metadata:
+      name: a-little-bit-of-compute
+    spec:
+      hard:
+        requests.cpu: "10"
+        requests.memory: 10Gi
+        limits.cpu: "20"
+        limits.memory: 20Gi
+  ```
+
+These quotas will apply to the namespace where the ResourceQuota is created.
+
+---
+
+## Limiting number of objects
+
+- The following YAML specifies how many objects of specific types can be created:
+  ```yaml
+    apiVersion: v1
+    kind: ResourceQuota
+    metadata:
+      name: quota-for-objects
+    spec:
+      hard:
+        pods: 100
+        services: 10
+        secrets: 10
+        configmaps: 10
+        persistentvolumeclaims: 20
+        services.nodeports: 0
+        services.loadbalancers: 0
+        count/roles.rbac.authorization.k8s.io: 10
+  ```
+
+(The `count/` syntax allows limiting arbitrary objects, including CRDs.)
+
+---
+
+## YAML vs CLI
+
+- Quotas can be created with a YAML definition
+
+- ...Or with the `kubectl create quota` command
+
+- Example:
+  ```bash
+  kubectl create quota my-resource-quota --hard=pods=300,limits.memory=300Gi
+  ```
+
+- With both YAML and CLI form, the values are always under the `hard` section
+
+  (there is no `soft` quota)
+
+---
+
+## Viewing current usage
+
+When a ResourceQuota is created, we can see how much of it is used:
+
+```
+kubectl describe resourcequota my-resource-quota
+
+Name:                            my-resource-quota
+Namespace:                       default
+Resource                         Used  Hard
+--------                         ----  ----
+pods                             12    100
+services                         1     5
+services.loadbalancers           0     0
+services.nodeports               0     0
+```
+
+---
+
+## Advanced quotas and PriorityClass
+
+- Pods can have a *priority*
+
+- The priority is a number from 0 to 1000000000
+
+  (or even higher for system-defined priorities)
+
+- High number = high priority = "more important" Pod
+
+- Pods with a higher priority can *preempt* Pods with lower priority
+
+  (= low priority pods will be *evicted* if needed)
+
+- Useful when mixing workloads in resource-constrained environments
+
+---
+
+## Setting the priority of a Pod
+
+- Create a PriorityClass
+
+  (or use an existing one)
+
+- When creating the Pod, set the field `spec.priorityClassName`
+
+- If the field is not set:
+
+  - if there is a PriorityClass with `globalDefault`, it is used
+
+  - otherwise, the default priority will be zero
+
+---
+
+class: extra-details
+
+## PriorityClass and ResourceQuotas
+
+- A ResourceQuota can include a list of *scopes* or a *scope selector*
+
+- In that case, the quota will only apply to the scoped resources
+
+- Example: limit the resources allocated to "high priority" Pods
+
+- In that case, make sure that the quota is created in every Namespace
+
+  (or use *admission configuration* to enforce it)
+
+- See the [resource quotas documentation][quotadocs] for details
+
+[quotadocs]: https://kubernetes.io/docs/concepts/policy/resource-quotas/#resource-quota-per-priorityclass
+
+---
+
+# Limiting resources in practice
+
+- We have at least three mechanisms:
+
+  - requests and limits per Pod
+
+  - LimitRange per namespace
+
+  - ResourceQuota per namespace
+
+- Let's see one possible strategy to get started with resource limits
+
+---
+
+## Set a LimitRange
+
+- In each namespace, create a LimitRange object
+
+- Set a small default CPU request and CPU limit
+
+  (e.g. "100m")
+
+- Set a default memory request and limit depending on your most common workload
+
+  - for Java, Ruby: start with "1G"
+
+  - for Go, Python, PHP, Node: start with "250M"
+
+- Set upper bounds slightly below your expected node size
+
+  (80-90% of your node size, with at least a 500M memory buffer)
+
+---
+
+## Set a ResourceQuota
+
+- In each namespace, create a ResourceQuota object
+
+- Set generous CPU and memory limits
+
+  (e.g. half the cluster size if the cluster hosts multiple apps)
+
+- Set generous objects limits
+
+  - these limits should not be here to constrain your users
+
+  - they should catch a runaway process creating many resources
+
+  - example: a custom controller creating many pods
+
+---
+
+## Observe, refine, iterate
+
+- Observe the resource usage of your pods
+
+  (we will see how in the next chapter)
+
+- Adjust individual pod limits
+
+- If you see trends: adjust the LimitRange
+
+  (rather than adjusting every individual set of pod limits)
+
+- Observe the resource usage of your namespaces
+
+  (with `kubectl describe resourcequota ...`)
+
+- Rinse and repeat regularly
+
+---
+
 ## Underutilization
 
 - Remember: when assigning a pod to a node, the scheduler looks at *requests*

slides/k8s/service-types.md

@@ -403,7 +403,7 @@ class: extra-details
 
 - Enabled with annotation `service.kubernetes.io/topology-mode=Auto` 
 
-- Relies on node label `topology.kubernetes.io/zone`
+- Relies on node annotation `topology.kubernetes.io/zone`
 
 - Kubernetes service proxy will try to keep connections within a zone
 

slides/k8s/user-cert.md

@@ -77,7 +77,7 @@ This is the flag that we're looking for:
 
 - We only need to transfer the CSR (Certificate Signing Request) to the CA
 
-  (we never need to expose the private key)
+  (we never need to expoes the private key)
 
 .lab[
 

slides/kube.yml

@@ -0,0 +1,98 @@
+title: |
+  Kubernetes
+
+chat: "[Mattermost](https://training.enix.io/mattermost)"
+
+gitrepo: github.com/jpetazzo/container.training
+
+slides: https://2024-10-boursorama.container.training/
+
+#slidenumberprefix: "#SomeHashTag — "
+
+exclude:
+- self-paced
+
+content:
+- shared/title.md
+- logistics.md
+- k8s/intro.md
+- shared/about-slides.md
+- shared/chat-room-im.md
+#- shared/chat-room-zoom-meeting.md
+#- shared/chat-room-zoom-webinar.md
+- shared/prereqs.md
+- shared/handson.md
+#- shared/webssh.md
+- shared/connecting.md
+- shared/toc.md
+- # 1 Monday morning
+  - shared/sampleapp.md
+  - shared/composedown.md
+  - k8s/concepts-k8s.md
+  - k8s/kubectlget.md
+  - k8s/kubectl-run.md
+  - k8s/kubectlexpose.md
+  #- k8s/shippingimages.md
+  #- k8s/buildshiprun-selfhosted.md
+  - k8s/buildshiprun-dockerhub.md
+  - exercises/k8sfundamentals-details.md
+  - k8s/ourapponkube.md
+- # 2 Monday afternoon
+  - k8s/service-types.md
+  - k8s/kubenet.md
+  - k8s/labels-annotations.md
+  - k8s/kubectl-logs.md
+  - k8s/logs-cli.md
+- # 3 Tuesday morning
+  - shared/yaml.md
+  - k8s/yamldeploy.md
+  - k8s/namespaces.md
+  - shared/declarative.md
+  - k8s/declarative.md
+  - k8s/deploymentslideshow.md
+  - k8s/ingress.md
+  - k8s/cert-manager.md
+  - exercises/yaml-details.md
+  - exercises/ingress-details.md
+- # 4 Tuesday afternoon
+  - k8s/volumes.md
+  #- k8s/exercise-configmap.md
+  - k8s/configuration.md
+  - k8s/secrets.md
+- # 5 Wednesday morning
+  - k8s/scalingdockercoins.md
+  - shared/hastyconclusions.md
+  - k8s/daemonset.md
+  - k8s/rollout.md
+  - k8s/healthchecks.md
+  - exercises/healthchecks-details.md
+- # 6 Wednesday afternoon
+  - k8s/resource-limits.md
+  - k8s/metrics-server.md
+  - k8s/cluster-sizing.md
+  #- k8s/horizontal-pod-autoscaler.md
+  - exercises/reqlim-details.md
+- # 7 Thursday morning
+  - k8s/authn-authz.md
+  - k8s/admission.md
+  - k8s/cainjector.md
+  - k8s/kyverno.md
+  - exercises/rbac-details.md
+  - exercises/kyverno-ingress-domain-name-details.md
+- # 8 Thursday afternoon
+  - k8s/statefulsets.md
+  - k8s/consul.md
+  - k8s/pv-pvc-sc.md
+  - k8s/volume-claim-templates.md
+  - k8s/stateful-failover.md
+- # 9 Friday morning
+  - k8s/helm-intro.md
+  - k8s/helm-chart-format.md
+  - k8s/helm-create-basic-chart.md
+  - exercises/helm-generic-chart-details.md
+- # 10 Friday afternoon
+  - k8s/helm-create-better-chart.md
+  - k8s/helm-dependencies.md
+  - k8s/helm-values-schema-validation.md
+  - k8s/helm-secrets.md
+  - exercises/helm-umbrella-chart-details.md

slides/logistics-template.md

@@ -1,44 +1,54 @@
 ## Introductions
 
-Hello! We are:
+- Hello! I'm Jérôme Petazzoni ([@jpetazzo], [@jpetazzo@hachyderm.io], Enix SAS)
 
-- Jérôme Petazzoni ([@jpetazzo@hachyderm.io], [/in/jpetazzo][jp-linkedin])
+- Schedule: FIXME
 
-  - freelance Docker¹ / Kubernetes / MLops consultant and trainer
+- Feel free to interrupt for questions at any time
 
-- AJ Bowen ([GitHub: @soulshake][aj-github], [LinkedIn: AJ Bowen][aj-linkedin])
+- *Especially when you see full screen container pictures!*
 
-  - freelance k8s/IaC/CI/CD/devOps engineer and consultant
+- Live feedback, questions, help: @@CHAT@@
 
-  - founder of [EphemeraSearch]
+- You ~~should~~ must ask questions! Lots of questions!
 
-.footnote[¹I worked at Docker from 2011 to 2018.
-I ran containers in production before it was cool. 😎]
+  (especially when you see full screen container pictures)
 
+- Use @@CHAT@@ to ask questions, get help, etc.
+
+[@alexbuisine]: https://twitter.com/alexbuisine
 [EphemeraSearch]: https://ephemerasearch.com/
 [@jpetazzo]: https://twitter.com/jpetazzo
-[aj-github]: https://github.com/soulshake
-[aj-linkedin]: https://linkedin.com/in/ajbowen
-[jp-linkedin]: https://linkedin.com/in/jpetazzo
 [@jpetazzo@hachyderm.io]: https://hachyderm.io/@jpetazzo
+[@s0ulshake]: https://twitter.com/s0ulshake
+[Quantgene]: https://www.quantgene.com/
 
 ---
 
-## Context
+## Un petit sondage ...
 
-- This content was delivered at QCON SF in November 2024
- 
-  (in-person workshop)
+Sur une échelle de 0 à 5, où vous situez-vous avec Kubernetes ?
 
-- I recorded that workshop, but had technical issues
+0️⃣ Kuberne-quoi ? 😅
 
-- This is a re-recording of the workshop!
+1️⃣ J'ai déjà vu quelques présentations, démos ... Mais jamais utilisé
 
-- I may or may not cover everything in this video series
+2️⃣ J'ai déjà fait quelques tutos mais je ne connais pas bien les concepts
 
-- At the conference, I provided everyone with an individual cluster
+3️⃣ J'utilise occasionnellement ; j'ai (ou peut avoir) accès à un cluster (local, cloud, n'importe) ; je connais les concepts de Pod, Deployment, Service
 
-- If you want to follow along the demos and labs, you'll need your own
+4️⃣ Je déploie régulièrement sur un cluster (que ça soit dev en local ou prod) ; je sais écrire / générer et utiliser des manifests YAML
 
-  (I'll explain how to provision it)
+5️⃣ Je connais tout ça et j'ai aussi déjà manipulé au moins un concept avancé comme le RBAC, les Ingress, les requests/limits
 
+---
+
+## Exercises
+
+- In the middle of each day, there is a series of exercises
+
+- To make the most out of the training, please try the exercises!
+
+  (it will help to practice and memorize the content of the day)
+
+- We'll run polls to know how much time to spend on reviewing the exercises

slides/mlops.yml

@@ -7,16 +7,15 @@ chat: "In person!"
 
 gitrepo: github.com/jpetazzo/container.training
 
-slides: https://2024-12-mq.container.training/
+slides: https://FIXME.container.training/
 
 #slidenumberprefix: "#SomeHashTag — "
 
 exclude:
-- in-person
+- self-paced
 
 content:
 - shared/title.md
-- shared/contact.md
 - logistics.md
 - shared/about-slides.md
 #- shared/chat-room-im.md
@@ -25,7 +24,7 @@ content:
 #- shared/chat-room-zoom-webinar.md
 - k8s/prereqs-advanced.md
 - k8s/handson-mlops.md
-#- shared/connecting.md
+- shared/connecting.md
 - k8s/mlops-headsup.md
 - shared/toc.md
 -
@@ -34,11 +33,9 @@ content:
   - k8s/queue-architecture.md
   - k8s/bento-intro.md
 -
-  - k8s/bento-enrichment.md
   - k8s/resource-limits.md
   - k8s/cluster-autoscaler.md
   - k8s/ollama-reqlim.md
--
   - k8s/bento-hpa.md
   - k8s/bento-rmq.md
   - k8s/bento-cnpg.md

slides/shared/connecting.md

@@ -60,7 +60,7 @@ class: in-person
 ## Doing or re-doing the workshop on your own?
 
 - Use something like
-  [Play-With-Docker](https://labs.play-with-docker.com/) or
+  [Play-With-Docker](http://play-with-docker.com/) or
   [Play-With-Kubernetes](https://training.play-with-kubernetes.com/)
 
   Zero setup effort; but environment are short-lived and
@@ -103,13 +103,13 @@ class: self-paced
 
 .lab[
 
-- Go to https://labs.play-with-docker.com/
+- Go to http://www.play-with-docker.com/
 
 - Log in
 
 - Create your first node
 
-<!-- ```open https://labs.play-with-docker.com/``` -->
+<!-- ```open http://www.play-with-docker.com/``` -->
 
 ]
 

slides/shared/prereqs.md

@@ -1,4 +1,4 @@
-# Pre-requirements
+## Pre-requirements
 
 - Be comfortable with the UNIX command line
 

slides/shared/pwd.md

@@ -1,6 +1,6 @@
 ## Using Play-With-Docker
 
-- Open a new browser tab to [labs.play-with-docker.com](https://labs.play-with-docker.com/)
+- Open a new browser tab to [www.play-with-docker.com](http://www.play-with-docker.com/)
 
 - Confirm that you're not a robot