📚 Add training from a checkpoint example

docs/source/markdown/get_started/anomalib.md

@@ -17,7 +17,7 @@ The installer can be installed using the following commands:
 :::{tab-item} API
 :sync: label-1
 
-```{literalinclude} ../../snippets/install/pypi.txt
+```{literalinclude} /snippets/install/pypi.txt
 :language: bash
 ```
 
@@ -26,7 +26,7 @@ The installer can be installed using the following commands:
 :::{tab-item} Source
 :sync: label-2
 
-```{literalinclude} ../../snippets/install/source.txt
+```{literalinclude} /snippets/install/source.txt
 :language: bash
 ```
 
@@ -42,22 +42,22 @@ The next section demonstrates how to install the full package using the CLI inst
 :::::{dropdown} Installing the Full Package
 After installing anomalib, you can install the full package using the following commands:
 
-```{literalinclude} ../../snippets/install/anomalib_help.txt
+```{literalinclude} /snippets/install/anomalib_help.txt
 :language: bash
 ```
 
 As can be seen above, the only available sub-command is `install` at the moment.
 The `install` sub-command has options to install either the full package or the
 specific components of the package.
 
-```{literalinclude} ../../snippets/install/anomalib_install_help.txt
+```{literalinclude} /snippets/install/anomalib_install_help.txt
 :language: bash
 ```
 
 By default the `install` sub-command installs the full package. If you want to
 install only the specific components of the package, you can use the `--option` flag.
 
-```{literalinclude} ../../snippets/install/anomalib_install.txt
+```{literalinclude} /snippets/install/anomalib_install.txt
 :language: bash
 ```
 
@@ -66,21 +66,23 @@ After following these steps, your environment will be ready to use anomalib!
 
 ## {octicon}`mortar-board` Training
 
-Anomalib supports both API and CLI-based training. The API is more flexible and allows for more customization, while the CLI training utilizes command line interfaces, and might be easier for those who would like to use anomalib off-the-shelf.
+Anomalib supports both API and CLI-based training. The API is more flexible
+and allows for more customization, while the CLI training utilizes command line
+interfaces, and might be easier for those who would like to use anomalib off-the-shelf.
 
 ::::{tab-set}
 
 :::{tab-item} API
 
-```{literalinclude} ../../snippets/train/api/default.txt
+```{literalinclude} /snippets/train/api/default.txt
 :language: python
 ```
 
 :::
 
 :::{tab-item} CLI
 
-```{literalinclude} ../../snippets/train/cli/default.txt
+```{literalinclude} /snippets/train/cli/default.txt
 :language: bash
 ```
 
@@ -100,7 +102,7 @@ Anomalib includes multiple inferencing scripts, including Torch, Lightning, Grad
 :::{tab-item} API
 :sync: label-1
 
-```{literalinclude} ../../snippets/inference/api/lightning.txt
+```{literalinclude} /snippets/inference/api/lightning.txt
 :language: python
 ```
 
@@ -109,7 +111,7 @@ Anomalib includes multiple inferencing scripts, including Torch, Lightning, Grad
 :::{tab-item} CLI
 :sync: label-2
 
-```{literalinclude} ../../snippets/inference/cli/lightning.txt
+```{literalinclude} /snippets/inference/cli/lightning.txt
 :language: bash
 ```
 
@@ -201,15 +203,15 @@ Anomalib supports hyper-parameter optimization using [wandb](https://wandb.ai/)
 
 :::{tab-item} CLI
 
-```{literalinclude} ../../snippets/pipelines/hpo/cli.txt
+```{literalinclude} /snippets/pipelines/hpo/cli.txt
 :language: bash
 ```
 
 :::
 
 :::{tab-item} API
 
-```{literalinclude} ../../snippets/pipelines/hpo/api.txt
+```{literalinclude} /snippets/pipelines/hpo/api.txt
 :language: bash
 ```
 
@@ -233,15 +235,15 @@ To run a training experiment with experiment tracking, you will need the followi
 
 By using the configuration file above, you can run the experiment with the following command:
 
-```{literalinclude} ../../snippets/logging/cli.txt
+```{literalinclude} /snippets/logging/cli.txt
 :language: bash
 ```
 
 :::
 
 :::{tab-item} API
 
-```{literalinclude} ../../snippets/logging/api.txt
+```{literalinclude} /snippets/logging/api.txt
 :language: bash
 ```
 

docs/source/snippets/train/api/default.txt

@@ -1,12 +1,15 @@
 # Import the required modules
 from anomalib.data import MVTec
-from anomalib.models import Patchcore
 from anomalib.engine import Engine
+from anomalib.models import EfficientAd
 
 # Initialize the datamodule, model and engine
-datamodule = MVTec()
-model = Patchcore()
-engine = Engine()
+datamodule = MVTec(train_batch_size=1)
+model = EfficientAd()
+engine = Engine(max_epochs=5)
 
 # Train the model
 engine.fit(datamodule=datamodule, model=model)
+
+# Continue from a checkpoint
+engine.fit(datamodule=datamodule, model=model, ckpt_path="path/to/checkpoint.ckpt")

docs/source/snippets/train/cli/default.txt

@@ -2,10 +2,13 @@
 anomalib train -h
 
 # Train by using the default values.
-anomalib train --model Patchcore --data anomalib.data.MVTec
+anomalib train --model EfficientAd --data anomalib.data.MVTec --data.train_batch_size 1
 
 # Train by overriding arguments.
-anomalib train --model Patchcore --data anomalib.data.MVTec --data.category transistor
+anomalib train --model EfficientAd --data anomalib.data.MVTec --data.train_batch_size 1 --data.category transistor
 
 # Train by using a config file.
 anomalib train --config <path/to/config>
+
+# Continue training from a checkpoint
+anomalib train --config <path/to/config> --ckpt_path <path/to/checkpoint.ckpt>