Made-With-ML README

The project's README from github.com/GokuMohandas/Made-With-ML. As one markdown file: README.md.

 Made With ML

Design · Develop · Deploy · Iterate
Join 40K+ developers in learning how to responsibly deliver value with ML.

     
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Lessons

Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.

lessons

Overview

In this course, we'll go from experimentation (design + development) to production (deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a reliable production system.

  Be sure to watch the video below for a quick overview of what we'll be building.
Course overview video

Audience

Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person.

Set up

Be sure to go through the course for a much more detailed walkthrough of the content on this repository. We will have instructions for both local laptop and Anyscale clusters for the sections below, so be sure to toggle the ► dropdown based on what you're using (Anyscale instructions will be toggled on by default). If you do want to run this course with Anyscale, where we'll provide the structure, compute (GPUs) and community to learn everything in one day, join our next upcoming live cohort → sign up here!

Cluster

We'll start by setting up our cluster with the environment and compute configurations.

Local
Your personal laptop (single machine) will act as the cluster, where one CPU will be the head node and some of the remaining CPU will be the worker nodes. All of the code in this course will work in any personal laptop though it will be slower than executing the same workloads on a larger cluster.
Anyscale

We can create an Anyscale Workspace using the webpage UI.

- Workspace name: `madewithml`
- Project: `madewithml`
- Cluster environment name: `madewithml-cluster-env`
# Toggle `Select from saved configurations`
- Compute config: `madewithml-cluster-compute-g5.4xlarge`

Alternatively, we can use the CLI to create the workspace via anyscale workspace create ...

Other (cloud platforms, K8s, on-prem)

If you don't want to do this course locally or via Anyscale, you have the following options:

Git setup

Create a repository by following these instructions: Create a new repository → name it Made-With-ML → Toggle Add a README file (very important as this creates a main branch) → Click Create repository (scroll down)

Now we're ready to clone the repository that has all of our code:

git clone https://github.com/GokuMohandas/Made-With-ML.git .

Credentials

touch .env

# Inside .env
GITHUB_USERNAME="CHANGE_THIS_TO_YOUR_USERNAME"  # ← CHANGE THIS

source .env

Virtual environment

Local
export PYTHONPATH=$PYTHONPATH:$PWD
python3 -m venv venv  # recommend using Python 3.10
source venv/bin/activate  # on Windows: venv\Scripts\activate
python3 -m pip install --upgrade pip setuptools wheel
python3 -m pip install -r requirements.txt
pre-commit install
pre-commit autoupdate

Highly recommend using Python 3.10 and using pyenv (mac) or pyenv-win (windows).

Anyscale

Our environment with the appropriate Python version and libraries is already all set for us through the cluster environment we used when setting up our Anyscale Workspace. So we just need to run these commands:

export PYTHONPATH=$PYTHONPATH:$PWD
pre-commit install
pre-commit autoupdate

Notebook

Start by exploring the jupyter notebook to interactively walkthrough the core machine learning workloads.

Local
# Start notebook
jupyter lab notebooks/madewithml.ipynb
Anyscale

Click on the Jupyter icon    at the top right corner of our Anyscale Workspace page and this will open up our JupyterLab instance in a new tab. Then navigate to the notebooks directory and open up the madewithml.ipynb notebook.

Scripts

Now we'll execute the same workloads using the clean Python scripts following software engineering best practices (testing, documentation, logging, serving, versioning, etc.) The code we've implemented in our notebook will be refactored into the following scripts:

madewithml
├── config.py
├── data.py
├── evaluate.py
├── models.py
├── predict.py
├── serve.py
├── train.py
├── tune.py
└── utils.py

Note: Change the --num-workers, --cpu-per-worker, and --gpu-per-worker input argument values below based on your system's resources. For example, if you're on a local laptop, a reasonable configuration would be --num-workers 6 --cpu-per-worker 1 --gpu-per-worker 0.

Training

export EXPERIMENT_NAME="llm"
export DATASET_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/dataset.csv"
export TRAIN_LOOP_CONFIG='{"dropout_p": 0.5, "lr": 1e-4, "lr_factor": 0.8, "lr_patience": 3}'
python madewithml/train.py \
    --experiment-name "$EXPERIMENT_NAME" \
    --dataset-loc "$DATASET_LOC" \
    --train-loop-config "$TRAIN_LOOP_CONFIG" \
    --num-workers 1 \
    --cpu-per-worker 3 \
    --gpu-per-worker 1 \
    --num-epochs 10 \
    --batch-size 256 \
    --results-fp results/training_results.json

Tuning

export EXPERIMENT_NAME="llm"
export DATASET_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/dataset.csv"
export TRAIN_LOOP_CONFIG='{"dropout_p": 0.5, "lr": 1e-4, "lr_factor": 0.8, "lr_patience": 3}'
export INITIAL_PARAMS="[{\"train_loop_config\": $TRAIN_LOOP_CONFIG}]"
python madewithml/tune.py \
    --experiment-name "$EXPERIMENT_NAME" \
    --dataset-loc "$DATASET_LOC" \
    --initial-params "$INITIAL_PARAMS" \
    --num-runs 2 \
    --num-workers 1 \
    --cpu-per-worker 3 \
    --gpu-per-worker 1 \
    --num-epochs 10 \
    --batch-size 256 \
    --results-fp results/tuning_results.json

Experiment tracking

We'll use MLflow to track our experiments and store our models and the MLflow Tracking UI to view our experiments. We have been saving our experiments to a local directory but note that in an actual production setting, we would have a central location to store all of our experiments. It's easy/inexpensive to spin up your own MLflow server for all of your team members to track their experiments on or use a managed solution like Weights & Biases, Comet, etc.

export MODEL_REGISTRY=$(python -c "from madewithml import config; print(config.MODEL_REGISTRY)")
mlflow server -h 0.0.0.0 -p 8080 --backend-store-uri $MODEL_REGISTRY
Local

If you're running this notebook on your local laptop then head on over to http://localhost:8080/ to view your MLflow dashboard.

Anyscale

If you're on Anyscale Workspaces, then we need to first expose the port of the MLflow server. Run the following command on your Anyscale Workspace terminal to generate the public URL to your MLflow server.

APP_PORT=8080
echo https://$APP_PORT-port-$ANYSCALE_SESSION_DOMAIN

Evaluation

export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
export HOLDOUT_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/holdout.csv"
python madewithml/evaluate.py \
    --run-id $RUN_ID \
    --dataset-loc $HOLDOUT_LOC \
    --results-fp results/evaluation_results.json

{
  "timestamp": "June 09, 2023 09:26:18 AM",
  "run_id": "6149e3fec8d24f1492d4a4cabd5c06f6",
  "overall": {
    "precision": 0.9076136428670714,
    "recall": 0.9057591623036649,
    "f1": 0.9046792827719773,
    "num_samples": 191.0
  },
...

Inference

export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
python madewithml/predict.py predict \
    --run-id $RUN_ID \
    --title "Transfer learning with transformers" \
    --description "Using transformers for transfer learning on text classification tasks."

[{
  "prediction": [
    "natural-language-processing"
  ],
  "probabilities": {
    "computer-vision": 0.0009767753,
    "mlops": 0.0008223939,
    "natural-language-processing": 0.99762577,
    "other": 0.000575123
  }
}]

Serving

Local
# Start
ray start --head

# Set up
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
python madewithml/serve.py --run_id $RUN_ID

Once the application is running, we can use it via cURL, Python, etc.:

# via Python
import json
import requests
title = "Transfer learning with transformers"
description = "Using transformers for transfer learning on text classification tasks."
json_data = json.dumps({"title": title, "description": description})
requests.post("http://127.0.0.1:8000/predict", data=json_data).json()

ray stop  # shutdown
Anyscale

In Anyscale Workspaces, Ray is already running so we don't have to manually start/shutdown like we have to do locally.

# Set up
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
python madewithml/serve.py --run_id $RUN_ID

Once the application is running, we can use it via cURL, Python, etc.:

# via Python
import json
import requests
title = "Transfer learning with transformers"
description = "Using transformers for transfer learning on text classification tasks."
json_data = json.dumps({"title": title, "description": description})
requests.post("http://127.0.0.1:8000/predict", data=json_data).json()

Testing

# Code
python3 -m pytest tests/code --verbose --disable-warnings

# Data
export DATASET_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/dataset.csv"
pytest --dataset-loc=$DATASET_LOC tests/data --verbose --disable-warnings

# Model
export EXPERIMENT_NAME="llm"
export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $EXPERIMENT_NAME --metric val_loss --mode ASC)
pytest --run-id=$RUN_ID tests/model --verbose --disable-warnings

# Coverage
python3 -m pytest tests/code --cov madewithml --cov-report html --disable-warnings  # html report
python3 -m pytest tests/code --cov madewithml --cov-report term --disable-warnings  # terminal report

Production

From this point onwards, in order to deploy our application into production, we'll need to either be on Anyscale or on a cloud VM / on-prem cluster you manage yourself (w/ Ray). If not on Anyscale, the commands will be slightly different but the concepts will be the same.

If you don't want to set up all of this yourself, we highly recommend joining our upcoming live cohort{:target="_blank"} where we'll provide an environment with all of this infrastructure already set up for you so that you just focused on the machine learning.

Authentication

These credentials below are automatically set for us if we're using Anyscale Workspaces. We do not need to set these credentials explicitly on Workspaces but we do if we're running this locally or on a cluster outside of where our Anyscale Jobs and Services are configured to run.

export ANYSCALE_HOST=https://console.anyscale.com
export ANYSCALE_CLI_TOKEN=$YOUR_CLI_TOKEN  # retrieved from Anyscale credentials page

Cluster environment

The cluster environment determines where our workloads will be executed (OS, dependencies, etc.) We've already created this cluster environment for us but this is how we can create/update one ourselves.

export CLUSTER_ENV_NAME="madewithml-cluster-env"
anyscale cluster-env build deploy/cluster_env.yaml --name $CLUSTER_ENV_NAME

Compute configuration

The compute configuration determines what resources our workloads will be executes on. We've already created this compute configuration for us but this is how we can create it ourselves.

export CLUSTER_COMPUTE_NAME="madewithml-cluster-compute-g5.4xlarge"
anyscale cluster-compute create deploy/cluster_compute.yaml --name $CLUSTER_COMPUTE_NAME

Anyscale jobs

Now we're ready to execute our ML workloads. We've decided to combine them all together into one job but we could have also created separate jobs for each workload (train, evaluate, etc.) We'll start by editing the $GITHUB_USERNAME slots inside our workloads.yaml file:

runtime_env:
  working_dir: .
  upload_path: s3://madewithml/$GITHUB_USERNAME/jobs  # <--- CHANGE USERNAME (case-sensitive)
  env_vars:
    GITHUB_USERNAME: $GITHUB_USERNAME  # <--- CHANGE USERNAME (case-sensitive)

The runtime_env here specifies that we should upload our current working_dir to an S3 bucket so that all of our workers when we execute an Anyscale Job have access to the code to use. The GITHUB_USERNAME is used later to save results from our workloads to S3 so that we can retrieve them later (ex. for serving).

Now we're ready to submit our job to execute our ML workloads:

anyscale job submit deploy/jobs/workloads.yaml

Anyscale Services

And after our ML workloads have been executed, we're ready to launch our serve our model to production. Similar to our Anyscale Jobs configs, be sure to change the $GITHUB_USERNAME in serve_model.yaml.

ray_serve_config:
  import_path: deploy.services.serve_model:entrypoint
  runtime_env:
    working_dir: .
    upload_path: s3://madewithml/$GITHUB_USERNAME/services  # <--- CHANGE USERNAME (case-sensitive)
    env_vars:
      GITHUB_USERNAME: $GITHUB_USERNAME  # <--- CHANGE USERNAME (case-sensitive)

Now we're ready to launch our service:

# Rollout service
anyscale service rollout -f deploy/services/serve_model.yaml

# Query
curl -X POST -H "Content-Type: application/json" -H "Authorization: Bearer $SECRET_TOKEN" -d '{
  "title": "Transfer learning with transformers",
  "description": "Using transformers for transfer learning on text classification tasks."
}' $SERVICE_ENDPOINT/predict/

# Rollback (to previous version of the Service)
anyscale service rollback -f $SER

Made-With-ML (GokuMohandas/Made-With-ML)

natural-language-processing
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49.8K ·GitHub
Made-With-ML
What is this?Made-With-ML is a free hands-on course that teaches how to design, develop, deploy, and iterate on production-grade machine learning applications, combining ML with software engineering best practices.
Is this a library or a course with code?It is a course: lessons plus accompanying code (Jupyter notebook and Python scripts) that implement the same ML workloads, not an installable package.
How do I get the course code running?Set up a cluster (your laptop or an Anyscale Workspace), clone the repo, create a Python virtual environment (Python 3.10 recommended), then open the Jupyter notebook or run the refactored training/serving scripts.
Do I need an Anyscale account or can I just use my laptop?You can run everything locally on your laptop (it acts as the cluster) or use Anyscale Workspaces for managed compute including GPUs; both paths are supported with slightly different instructions.
Is production deployment identical whether I use Anyscale or not?The core concepts stay the same, but if you are not on Anyscale you must manage your own cloud VM or on-prem Ray cluster and the exact commands for authentication, jobs, and services will differ.
Who is this course meant for?It targets software/infra engineers, data scientists, college graduates bridging university-to-industry gaps, and product or leadership people who want a technical ML foundation.
What tools does the course teach for experiment tracking?It uses MLflow to track experiments and view results via the MLflow Tracking UI, and mentions alternatives like Weights & Biases or Comet for managed solutions.
How does the course handle scaling ML workloads?It uses Ray to scale data processing, training, tuning, and serving workloads in pure Python without needing to learn new languages, following an MLOps approach with tracking, testing, serving, and orchestration.
Can I go from development to production without changing my code?Yes, a key goal of the course is demonstrating how to move from development to production (Dev to Prod) without any changes to code or infrastructure management.
What is the easiest way to deploy a trained model as a service?On Anyscale, after running training/tuning jobs you edit a servemodel.yaml config and run 'anyscale service rollout -f deploy/services/servemodel.yaml' to launch the service, which can then be queried via cURL or Python.
What are similar projects?ray, CV, pytorch-handbook, metaflow, machine-learning-zoomcamp, fastbook, mlops-zoomcamp, nlp_course
Which topics is it in?data-quality, llmops, distributed-training, mlops, data-engineering, llms, ci-cd, natural-language-processing, course, jupyter notebook, data-science, pytorch
Creator
GokuMohandas
Size
3.4 MB
Stars
49.8K
Updated
CommitMar 04Releasev1.1.0Mar 04
Age
7y
Commits · 90 Days