fix(docs) Fixed the rendering issues (#6015)

* fix the rendering issues * fixed the format * check if mermaid works * Update docs-gb/getting-started/kubernetes-installation/README.md Co-authored-by: Lucian Carata <[email protected]> * Update experiment-versions.md changed the format to outputs * changed the format to outputs * changed the url to GitHub url instead of the yaml file * changed to the yaml format because line range does not work * Update the format * referenced the GitHub files * referenced the GitHub file * referenced the GitHub file * referenced the GitHub file * Referenced GitHub file * added the line range --------- Co-authored-by: Rakavitha Kodhandapani <[email protected]> Co-authored-by: Lucian Carata <[email protected]>
SeldonIO · Nov 10, 2024 · 2ac0924 · 2ac0924
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diff --git a/docs-gb/SUMMARY.md b/docs-gb/SUMMARY.md
@@ -49,7 +49,6 @@
   * [Production income classifier with drift, outlier and explanations](examples/income.md)
   * [Conditional pipeline with pandas query model](examples/pandasquery.md)
   * [Kubernetes Server with PVC](examples/k8s-pvc.md)
-  * [Local Overcommit](examples/k8s-pvc.md)
 * [Kubernetes](kubernetes/README.md)
   * [Scaling](kubernetes/scaling.md)
   * [Autoscaling](kubernetes/autoscaling.md)

diff --git a/docs-gb/architecture/dataflow.md b/docs-gb/architecture/dataflow.md
@@ -42,9 +42,9 @@ Finally, inference graph can now be extended with adding new nodes at arbitrary
 ## References
 
 More details and information on data-centric AI and data flow paradigm can be found in these resources:
-* Data-centric AI Resource Hub: https://datacentricai.org/
-* Stanford MLSys seminar "What can Data-Centric AI Learn from Data and ML Engineering?": https://www.youtube.com/watch?v=cqDgxP8DcJA
-* A paper that explores data flow in ML deployment context: https://arxiv.org/abs/2204.12781
-* Introduction to flow based programming from its creator J.P. Morrison: https://jpaulm.github.io/fbp/index.html
-* Pathways: Asynchronous Distributed Dataflow for ML (research work from Google on the design and implementation of data flow based orchestration layer for accelerators): https://arxiv.org/abs/2203.12533
-* Better understanding of data requires tracking its history and context: https://queue.acm.org/detail.cfm?id=2602651
+* [Data-centric AI Resource Hub](https://datacentricai.org/)
+* Stanford MLSys seminar ["What can Data-Centric AI Learn from Data and ML Engineering?"](https://www.youtube.com/watch?v=cqDgxP8DcJA)
+* A paper that explores [data flow in ML deployment context](https://arxiv.org/abs/2204.12781)
+* [Introduction to flow based programming](https://jpaulm.github.io/fbp/index.html) from its creator J.P. Morrison: 
+* Pathways: Asynchronous Distributed Dataflow for [ML research work](https://arxiv.org/abs/2203.12533)from Google on the design and implementation of data flow based orchestration layer for accelerators
+* Better understanding of data requires tracking its [history and context](https://queue.acm.org/detail.cfm?id=2602651)
diff --git a/docs-gb/examples/batch-examples-local.md b/docs-gb/examples/batch-examples-local.md
@@ -3,7 +3,7 @@
 This example runs you through a series of batch inference requests made to both models and pipelines running on Seldon Core locally.
 
 {% hint style="warning" %}
-Deprecated: The MLServer CLI `infer` feature is experimental and will be removed in future work.
+**Deprecated**: The MLServer CLI `infer` feature is experimental and will be removed in future work.
 {% endhint %}
 
 ## Setup
@@ -12,7 +12,7 @@ If you haven't already, you'll need to [clone the Seldon Core repository and run
 before you run through this example.
 
 {% hint style="info" %}
-By default, the CLI will expect your inference endpoint to be at `0.0.0.0:9000`. If you have
+**Note**: By default, the CLI will expect your inference endpoint to be at `0.0.0.0:9000`. If you have
 customized this, you'll need to [redirect the CLI](../cli/README.md).
 {% endhint %}
 
@@ -508,7 +508,7 @@ As before, we can run the inference batch job using the `mlserver infer` command
 mlserver infer -u localhost:9000 -m tfsimple1 -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-output.txt --workers 10
 
 ```
-```
+```output
 2023-01-23 14:56:10,870 [mlserver] INFO - Using asyncio event-loop policy: uvloop
 2023-01-23 14:56:10,872 [mlserver] INFO - server url: localhost:9000
 2023-01-23 14:56:10,872 [mlserver] INFO - model name: tfsimple1

diff --git a/docs-gb/examples/experiment-versions.md b/docs-gb/examples/experiment-versions.md
@@ -41,7 +41,7 @@ seldon model infer iris -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris_1::50]
 
 ```
@@ -51,7 +51,7 @@ seldon model infer iris2 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::50]
 
 ```
@@ -61,7 +61,7 @@ seldon model infer iris3 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris3_1::50]
 
 ```
@@ -119,7 +119,7 @@ seldon model infer iris -i 100 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::48 :iris_1::52]
 
 ```
@@ -177,7 +177,7 @@ seldon model infer iris -i 100 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris3_1::42 :iris_1::58]
 
 ```
@@ -198,7 +198,7 @@ seldon model infer iris -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris_1::50]
 
 ```
@@ -208,7 +208,7 @@ seldon model infer iris2 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::50]
 
 ```
@@ -218,7 +218,7 @@ seldon model infer iris3 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris3_1::50]
 
 ```
@@ -273,7 +273,7 @@ seldon model infer iris -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris_1::50]
 
 ```
@@ -283,7 +283,7 @@ seldon model infer iris2 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::50]
 
 ```
@@ -293,7 +293,7 @@ seldon model infer iris3 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris3_1::50]
 
 ```
@@ -351,7 +351,7 @@ seldon model infer iris -i 100 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::51 :iris_1::49]
 
 ```
@@ -409,7 +409,7 @@ seldon model infer iris -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris_1::50]
 
 ```
@@ -419,7 +419,7 @@ seldon model infer iris3 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::25 :iris3_1::25]
 
 ```
@@ -429,7 +429,7 @@ seldon model infer iris2 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::50]
 
 ```
@@ -450,7 +450,7 @@ seldon model infer iris -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris_1::50]
 
 ```
@@ -460,7 +460,7 @@ seldon model infer iris2 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris2_1::50]
 
 ```
@@ -470,7 +470,7 @@ seldon model infer iris3 -i 50 \
   '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
 ```
 
-```
+```outputs
 Success: map[:iris3_1::50]
 
 ```