From 4702fbbd65165e1e7377fa4fe1b176628fd3a349 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joa=CC=83o=20Fonseca?= Date: Mon, 17 Jun 2024 09:22:55 +0100 Subject: [PATCH] feat: homework 4 local deployment --- homework/04-deployment/homework.md | 208 ++++++++- homework/04-deployment/homework/Dockerfile | 13 + homework/04-deployment/homework/Pipfile | 15 + homework/04-deployment/homework/Pipfile.lock | 418 ++++++++++++++++++ homework/04-deployment/homework/model.bin | Bin 0 -> 17376 bytes .../04-deployment/homework/prediction_output | Bin 0 -> 66211487 bytes homework/04-deployment/homework/starter.ipynb | 219 +++++++++ homework/04-deployment/homework/starter.py | 52 +++ homework/04-deployment/image.png | Bin 0 -> 59556 bytes 9 files changed, 924 insertions(+), 1 deletion(-) create mode 100644 homework/04-deployment/homework/Dockerfile create mode 100644 homework/04-deployment/homework/Pipfile create mode 100644 homework/04-deployment/homework/Pipfile.lock create mode 100644 homework/04-deployment/homework/model.bin create mode 100644 homework/04-deployment/homework/prediction_output create mode 100644 homework/04-deployment/homework/starter.ipynb create mode 100644 homework/04-deployment/homework/starter.py create mode 100644 homework/04-deployment/image.png diff --git a/homework/04-deployment/homework.md b/homework/04-deployment/homework.md index 36a00d7..31d89eb 100644 --- a/homework/04-deployment/homework.md +++ b/homework/04-deployment/homework.md @@ -1 +1,207 @@ -TBA. Come back later \ No newline at end of file +## Homework + +In this homework, we'll deploy the ride duration model in batch mode. Like in homework 1, we'll use the Yellow Taxi Trip Records dataset. + +You'll find the starter code in the [homework](homework) directory. + + +## Q1. Notebook + +We'll start with the same notebook we ended up with in homework 1. +We cleaned it a little bit and kept only the scoring part. You can find the initial notebook [here](homework/starter.ipynb). + +Run this notebook for the March 2023 data. + +What's the standard deviation of the predicted duration for this dataset? + +* 1.24 +* 6.24 [x] +* 12.28 +* 18.28 + + +## Q2. Preparing the output + +Like in the course videos, we want to prepare the dataframe with the output. + +First, let's create an artificial `ride_id` column: + +```python +df['ride_id'] = f'{year:04d}/{month:02d}_' + df.index.astype('str') +``` + +Next, write the ride id and the predictions to a dataframe with results. + +Save it as parquet: + +```python +df_result.to_parquet( + output_file, + engine='pyarrow', + compression=None, + index=False +) +``` + +What's the size of the output file? + +* 36M +* 46M +* 56M +* 66M [x] + +__Note:__ Make sure you use the snippet above for saving the file. It should contain only these two columns. For this question, don't change the +dtypes of the columns and use `pyarrow`, not `fastparquet`. + + +## Q3. Creating the scoring script + +Now let's turn the notebook into a script. + +Which command you need to execute for that? + +``` +jupyter nbconvert --to script starter.ipynb +``` + + +## Q4. Virtual environment + +Now let's put everything into a virtual environment. We'll use pipenv for that. + +Install all the required libraries. Pay attention to the Scikit-Learn version: it should be the same as in the starter +notebook. + +After installing the libraries, pipenv creates two files: `Pipfile` +and `Pipfile.lock`. The `Pipfile.lock` file keeps the hashes of the +dependencies we use for the virtual env. + +What's the first hash for the Scikit-Learn dependency? + +R: +``` +sha256:057b991ac64b3e75c9c04b5f9395eaf19a6179244c089afdebaad98264bff37c +``` + +## Q5. Parametrize the script + +Let's now make the script configurable via CLI. We'll create two +parameters: year and month. + +Run the script for April 2023. + +What's the mean predicted duration? + +* 7.29 +* 14.29 [x] +* 21.29 +* 28.29 + +Hint: just add a print statement to your script. + + +## Q6. Docker container + +Finally, we'll package the script in the docker container. +For that, you'll need to use a base image that we prepared. + +This is what the content of this image is: +``` +FROM python:3.10.13-slim + +WORKDIR /app +COPY [ "model2.bin", "model.bin" ] +``` + +Note: you don't need to run it. We have already done it. + +It is pushed it to [`agrigorev/zoomcamp-model:mlops-2024-3.10.13-slim`](https://hub.docker.com/layers/agrigorev/zoomcamp-model/mlops-2024-3.10.13-slim/images/sha256-f54535b73a8c3ef91967d5588de57d4e251b22addcbbfb6e71304a91c1c7027f?context=repo), +which you need to use as your base image. + +That is, your Dockerfile should start with: + +```docker +FROM agrigorev/zoomcamp-model:mlops-2024-3.10.13-slim + +RUN pip install -U pip +RUN pip install pipenv + +WORKDIR /app + +COPY ["/data/yellow_tripdata_2023-05.parquet", "/app/data/"] + +COPY ["Pipfile", "Pipfile.lock", "starter.py", "./"] + +RUN pipenv install --system --deploy + +ENTRYPOINT ["python", "starter.py"] +``` + +This image already has a pickle file with a dictionary vectorizer +and a model. You will need to use them. + +Important: don't copy the model to the docker image. You will need +to use the pickle file already in the image. + +Now run the script with docker. What's the mean predicted duration +for May 2023? + +* 0.19 [x] +* 7.24 +* 14.24 +* 21.19 + +### response + +- Using just year and month as args seems to little especially in this type of "quick demo deployment" with local data where the data goes within the docker image. +- So it was also added input directory as arg and output + + +## Bonus: upload the result to the cloud (Not graded) + +Just printing the mean duration inside the docker image +doesn't seem very practical. Typically, after creating the output +file, we upload it to the cloud storage. + +Modify your code to upload the parquet file to S3/GCS/etc. + + +- Quite simple to do so. For S3 for example: + + + + +## Bonus: Use Mage for batch inference + +Here we didn't use any orchestration. In practice we usually do. + +* Split the code into logical code blocks +* Use Mage to orchestrate the execution + + +1. Using the Mage setup from previous homework. cd to mlops folder again +2. Run `.scripts/start.sh` +3. Access `http://localhost:6789/` for mage UI +4. Add args to pipeline + +![global vars to setup year, month of data](image.png) + +## Publishing the image to dockerhub + +This is how we published the image to Docker hub: + +```bash +docker build -t mlops-zoomcamp-model:2024-3.10.13-slim . +docker tag mlops-zoomcamp-model:2024-3.10.13-slim agrigorev/zoomcamp-model:mlops-2024-3.10.13-slim + +docker login --username USERNAME +docker push agrigorev/zoomcamp-model:mlops-2024-3.10.13-slim +``` + +This is just for your reference, you don't need to do it. + + +## Submit the results + +* Submit your results here: https://courses.datatalks.club/mlops-zoomcamp-2024/homework/hw4 +* It's possible that your answers won't match exactly. If it's the case, select the closest one. \ No newline at end of file diff --git a/homework/04-deployment/homework/Dockerfile b/homework/04-deployment/homework/Dockerfile new file mode 100644 index 0000000..c1e0fa9 --- /dev/null +++ b/homework/04-deployment/homework/Dockerfile @@ -0,0 +1,13 @@ +FROM agrigorev/zoomcamp-model:mlops-2024-3.10.13-slim +RUN pip install -U pip +RUN pip install pipenv + +WORKDIR /app + +COPY ["/data/yellow_tripdata_2023-05.parquet", "/app/data/"] + +COPY ["Pipfile", "Pipfile.lock", "starter.py", "./"] + +RUN pipenv install --system --deploy + +ENTRYPOINT ["python", "starter.py"] \ No newline at end of file diff --git a/homework/04-deployment/homework/Pipfile b/homework/04-deployment/homework/Pipfile new file mode 100644 index 0000000..3dc5edc --- /dev/null +++ b/homework/04-deployment/homework/Pipfile @@ -0,0 +1,15 @@ +[[source]] +url = "https://pypi.org/simple" +verify_ssl = true +name = "pypi" + +[packages] +scikit-learn = "==1.5.0" +pandas = "*" +pyarrow = 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