Merge branch 'main' into cli

s10_extra/exercise_files/onnx_benchmark.py

@@ -28,19 +28,16 @@ def wrapper(*args, **kwargs):
 model = torchvision.models.resnet18()
 model.eval()
 
-
+dummy_input = torch.randn(1, 3, 224, 224)
 if sys.platform == "win32":
  # Windows doesn't support the new TorchDynamo-based ONNX Exporter
  torch.onnx.export(
  model,
- torch.randn(1, 3, 224, 224),
+ dummy_input,
  "resnet18.onnx",
  input_names=["input.1"],
  dynamic_axes={"input.1": {0: "batch_size", 2: "height", 3: "width"}},
  )
-
- compiled_model = torch.compile(model)
-
 else:
  torch.onnx.dynamo_export(model, dummy_input).save("resnet18.onnx")
 

s10_extra/exercise_files/onnx_fastapi.py

@@ -0,0 +1,20 @@
+import numpy as np
+import onnxruntime
+from fastapi import FastAPI
+
+app = FastAPI()
+
+
+@app.get("/predict")
+def predict():
+ """Predict using ONNX model."""
+ # Load the ONNX model
+ model = onnxruntime.InferenceSession("model.onnx")
+
+ # Prepare the input data
+ input_data = {"input": np.random.rand(1, 3).astype(np.float32)}
+
+ # Run the model
+ output = model.run(None, input_data)
+
+ return {"output": output[0].tolist()}

s10_extra/ml_deployment.md

@@ -367,6 +367,14 @@ an developer friendly framework, however it has historically been slow to run in
 8. (Optional) Assuming you have completed the module on [FastAPI](../s7_deployment/apis.md) try creating a small
  FastAPI application that serves a model using the ONNX runtime.
 
+ ??? success "Solution"
+
+ Here is a simple example of how to create a FastAPI application that serves a model using the ONNX runtime.
+
+ ```python linenums="1" title="onnx_fastapi.py"
+ --8<-- "s10_extra/exercise_files/onnx_fastapi.py"
+ ```
+
 This completes the exercises on the ONNX format. Do note that one limitation of the ONNX format is that is is based on
 [ProtoBuf](https://protobuf.dev/), which is a binary format. A protobuf file can have a maximum size of 2GB, which means
 that the `.onnx` format is not enough for very large models. However, through the use of

s1_development_environment/package_manager.md

@@ -185,24 +185,61 @@ in the exercise folder.
   because the latest version of Python is often not supported by all dependencies. You can always check the status
   of different Python version support [here](https://devguide.python.org/versions/).
 
+ ??? success "Solution"
+
+  ```bash
+  conda create --name my_environment python=3.11
+  ```
+
 4. Which `conda` command gives you a list of all the environments that you have created?
 
+ ??? success "Solution"
+
+  ```bash
+  conda env list
+  ```
+
 5. Which `conda` command gives you a list of the packages installed in the current environment?
 
+ ??? success "Solution"
+
+  ```bash
+  conda list
+  ```
+
  1. How do you easily export this list to a text file? Do this, and make sure you export it to
  a file called `environment.yaml`, as conda uses another format by default than `pip`.
 
+ ??? success "Solution"
+
+  ```bash
+  conda list --explicit > environment.yaml
+  ```
+
  2. Inspect the file to see what is in it.
 
  3. The `environment.yaml` file you have created is one way to secure *reproducibility* between users because
  anyone should be able to get an exact copy of your environment if they have your `environment.yaml` file.
  Try creating a new environment directly from your `environment.yaml` file and check that the packages being
  installed exactly match what you originally had.
 
+ ??? success "Solution"
+
+  ```bash
+  conda env create --file environment.yaml
+  ```
+
 6. As the introduction states, it is fairly safe to use `pip` inside `conda` today. What is the corresponding `pip`
  command that gives you a list of all `pip` installed packages? And how do you export this to `requirements.txt`
  file?
 
+ ??? success "Solution"
+
+  ```bash
+  pip list # List all installed packages
+  pip freeze > requirements.txt # Export all installed packages to a requirements.txt file
+  ```
+
 7. If you look through the requirements that both `pip` and `conda` produce then you will see that it
  is often filled with a lot more packages than what you are using in your project. What you are interested in are the
  packages that you import in your code: `from package import module`. One way to get around this is to use the

s2_organisation_and_version_control/git.md

@@ -114,16 +114,16 @@ working together on the same project.
 
  2. Move/copy the three files from yesterday into the repository (and any other that you made)
 
- 3. Add the files to a commit by using `git add` command (1)
- { .annotate }
+ 3. Add the files to a commit by using `git add` command
+
+ 4. Commit the files using `git commit` command where you use the `-m` argument to provide a commit message (1).
+ { .annotate }
 
  1. :man_raising_hand: Writing good commit message is a skill in itself. A commit message should be short but
  informative about the work you are trying to commit. Try to practise writing good commit messages
  throughout the course. You can see
  [this guideline](https://github.com/joelparkerhenderson/git-commit-message) for help.
 
- 4. Commit the files using `git commit`
-
  5. Finally push the files to your repository using `git push`. Make sure to check online that the files have been
  updated in your repository.
 
@@ -139,10 +139,15 @@ working together on the same project.
  git checkout -b <my_branch_name>
  ```
 
- Afterwards, you can use `git checkout` to change between branches (remember to commit your work!)
+ Afterwards, you can use `git checkout` (1) to change between branches (remember to commit your work!)
  Try adding something (a file, a new line of code etc.) to the newly created branch, commit it and
  try changing back to master afterwards. You should hopefully see whatever you added on the branch
  is not present on the main branch.
+ { .annotate}
+
+ 1. :man_raising_hand: The `git checkout` command is used for a lot of different things in git. It can be used to
+ change branches, to revert changes and to create new branches. An alternative is using `git switch` and
+ `git restore` which are more modern commands.
 
 3. If you do not already have a cloned version of this repository belonging to the course, make sure to make one!
  I am continuously updating/changing some of the material during the course and I therefore recommend that you
@@ -182,10 +187,24 @@ working together on the same project.
  [page](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/configuring-a-remote-repository-for-a-fork)
  , and set a remote upstream for the repository you just forked.
 
+ ??? success "Solution"
+
+ ```bash
+ git remote add upstream <url-to-original-repo>
+ ```
+
 6. After setting the upstream branch, we need to pull and merge any update. Take a look on this
  [page](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork)
  and figure out how to do this.
 
+ ??? success "Solution"
+
+ ```bash
+ git fetch upstream
+ git checkout main
+ git merge upstream/main
+ ```
+
 7. As a final exercise we want to simulate a *merge conflict*, which happens when two users try to commit changes
  to exactly same lines of code in the codebase, and git is not able to resolve how the different commits should be
  integrated.