Meta tags:
description=Learn how to develop, deploy and iterate on production-grade ML applications. - GokuMohandas/Made-With-ML;

Headings (most frequently used words):

navigation, uh, oh, saved, searches, made, with, ml, files, notebook, footer, cluster, environment, anyscale, search, code, repositories, users, issues, pull, requests, provide, feedback, gokumohandas, menu, use, to, filter, your, results, more, quickly, folders, and, latest, commit, history, repository, lessons, overview, audience, set, up, scripts, production, faq, about, contributors, languages, git, setup, credentials, virtual, training, tuning, experiment, tracking, evaluation, inference, serving, testing, authentication, compute, configuration, jobs, services, ci, cd, continual, learning, jupyter, kernels, topics, resources, license, stars, watchers, forks,

Text of the page (most frequently used words):
the (72), our (46), and (39), #anyscale (35), this (33), with (32), madewithml (30), export (28), code (24), python (23), github (22), for (22), you (20), learning (20), name (20), will (20), your (20), can (18), all (18), deploy (17), cluster (17), change (14), workloads (14), experiment_name (14), run_id (13), that (12), data (12), com (12), production (12), github_username (12), made (12), create (12), using (12), run (12), git (11), https (11), predict (11), security (10), jupyter (10), machine (10), now (10), use (10), environment (10), results (10), start (10), from (10), commit (10), json (10), notebook (9), repository (9), etc (9), experiment (9), main (9), config (9), set (9), have (9), compute (9), gokumohandas (9), local (9), view (9), worker (9), not (8), learn (8), how (8), but (8), yaml (8), description (8), transformers (8), transfer (8), title (8), best (8), per (8), develop (7), venv (7), python3 (7), application (7), new (7), service (7), jobs (7), username (7), credentials (7), services (7), tests (7), dataset (7), llm (7), requests (7), via (7), mlflow (7), workspace (7), num (7), personal (6), community (6), reload (6), page (6), ray (6), grade (6), open (6), following (6), add (6), workflow (6), any (6), branch (6), serve (6), get (6), actions (6), train (6), env (6), dataset_loc (6), datasets (6), cpu (6), pre (6), enterprise (6), navigation (5), report (5), license (5), readme (5), resources (5), quality (5), install (5), issues (5), notebooks (5), what (5), into (5), token (5), these (5), toggle (5), need (5), execute (5), ready (5), case (5), workers (5), one (5), already (5), build (5), where (5), running (5), pytest (5), disable (5), warnings (5), metric (5), val_loss (5), mode (5), asc (5), import (5), laptop (5), experiments (5), train_loop_config (5), software (5), course (5), search (5), mlops (4), engineering (4), iterate (4), kernel (4), manually (4), model (4), access (4), dev (4), changes (4), first (4), settings (4), text (4), classification (4), tasks (4), sensitive (4), sure (4), job (4), serving (4), configuration (4), below (4), workspaces (4), want (4), loc (4), cov (4), raw (4), githubusercontent (4), csv (4), json_data (4), server (4), models (4), gpu (4), scripts (4), documentation (4), tab (4), pythonpath (4), source (4), saved (4), more (4), sign (4), industry (4), workflows (4), support (4), manage (3), docs (3), other (3), there (3), was (3), error (3), while (3), loading (3), please (3), stars (3), training (3), natural (3), language (3), processing (3), topics (3), applications (3), top (3), menu (3), user (3), also (3), through (3), evaluation (3), like (3), merge (3), rollout (3), click (3), when (3), email (3), push (3), message (3), them (3), before (3), console (3), every (3), serve_model (3), curl (3), post (3), content (3), type (3), working_dir (3), runtime_env (3), submit (3), here (3), created (3), evaluate (3), file (3), locally (3), don (3), recommend (3), provide (3), same (3), prem (3), verbose (3), testing (3), http (3), head (3), 8080 (3), then (3), model_registry (3), store (3), track (3), setting (3), tracking (3), tune (3), size (3), system (3), practices (3), pip (3), requirements (3), txt (3), instructions (3), developers (3), development (3), design (3), repositories (3), files (3), insights (3), pull (3), signed (3), another (3), window (3), refresh (3), session (3), feedback (3), features (3), copilot (3), platform (3), solutions (3), explore (3), time (2), footer (2), languages (2), forks (2), mit (2), distributed (2), about (2), kernelspec (2), default (2), virtual (2), focus (2), easy (2), foundation (2), connect (2), runs (2), easily (2), additional (2), directly (2), trigger (2), prompted (2), password (2), scroll (2), down (2), generate (2), global (2), example (2), origin (2), make (2), order (2), anyscale_host (2), anyscale_cli_token (2), retrieved (2), your_cli_token (2), url (2), save (2), instead (2), automate (2), rollback (2), terminate (2), service_name (2), version (2), query (2), launch (2), env_vars (2), upload_path (2), been (2), executed (2), used (2), later (2), combine (2), separate (2), inside (2), cluster_compute_name (2), 4xlarge (2), determines (2), ourselves (2), cluster_env_name (2), are (2), yourself (2), highly (2), just (2), upcoming (2), live (2), cohort (2), commands (2), cloud (2), html (2), terminal (2), 127 (2), 8000 (2), dumps (2), once (2), shutdown (2), stop (2), holdout_loc (2), app_port (2), port (2), public (2), directory (2), note (2), would (2), initial_params (2), epochs (2), batch (2), 256 (2), dropout_p (2), lr_factor (2), lr_patience (2), input (2), based (2), ipynb (2), lab (2), walkthrough (2), autoupdate (2), pwd (2), windows (2), pyenv (2), activate (2), clone (2), very (2), platforms (2), supported (2), project (2), options (2), configurations (2), work (2), join (2), they (2), reliable (2), products (2), powered (2), skills (2), infra (2), way (2), better (2), quickly (2), without (2), components (2), end (2), scale (2), jump (2), principles (2), overview (2), lessons (2), pyproject (2), toml (2), mkdocs (2), yml (2), makefile (2), gitignore (2), commits (2), last (2), projects (2), appearance (2), cancel (2), see (2), available (2), searches (2), business (2), advanced (2), developer (2), customer (2), devops (2), app (2), perform, action, share, information, cookies, contact, status, privacy, terms, 2026, inc, contributors, watching, watchers, activity, llms, pytorch, deep, science, list, uninstall, bar, ever, delete, ipykernel, configuring, kernels, faq, place, continually, improving, becomes, really, extend, scheduled, cron, drift, detected, context, such, comparing, currently, even, online, monitoring, pipelines, continual, which, succeeds, produce, comments, enter, follow, steps, copy, paste, repo, authenticate, pushing, adding, necessary, secrets, remote, checkout, going, process, service_config, previous, service_endpoint, authorization, bearer, secret_token, entrypoint, import_path, ray_serve_config, after, similar, configs, specifies, should, upload, current, bucket, retrieve, decided, together, could, each, workload, editing, slots, cluster_compute, executes, cluster_env, dependencies, update, explicitly, outside, configured, automatically, authentication, joining, target, _blank, infrastructure, focused, point, onwards, either, concepts, slightly, different, term, coverage, 000575123, 99762577, 0008223939, 0009767753, computer, vision, probabilities, prediction, inference, 191, num_samples, 9046792827719773, 9057591623036649, recall, 9076136428670714, precision, overall, 6149e3fec8d24f1492d4a4cabd5c06f6, june, 2023, timestamp, evaluation_results, holdout, anyscale_session_domain, echo, expose, command, over, dashboard, localhost, backend, uri, print, saving, actual, central, location, inexpensive, spin, own, team, members, their, managed, solution, comet, weights, biases, initial, params, tuning_results, tuning, loop, training_results, argument, values, reasonable, utils, clean, logging, versioning, implemented, refactored, icon, right, corner, jupyterlab, instance, navigate, exploring, interactively, core, appropriate, libraries, mac, win, bin, upgrade, setuptools, wheel, change_this_to_your_username, touch, has, creates, important, setup, onto, listed, officially, kuberay, kubernetes, azure, aliyun, integrations, exist, aws, gcp, k8s, alternatively, cli, select, webpage, single, act, node, some, remaining, nodes, though, slower, than, executing, larger, much, detailed, both, clusters, sections, dropdown, toggled, everything, day, next, gpus, structure, who, technical, amazing, product, leadership, practical, required, bridge, gap, between, university, curriculum, expects, college, graduates, whether, engineer, scientist, increasingly, becoming, key, part, developing, powerful, thinking, reserved, person, audience, mature, continuously, modular, integrates, stack, reliably, management, prod, orchestration, having, completely, implement, straight, understanding, concept, watch, video, quick, building, experimentation, deployment, iteration, iteratively, motivating, enable, among, 40k, responsibly, deliver, value, items, history, latest, date, folders, tags, branches, star, fork, must, notification, notifications, dismiss, alert, switched, accounts, out, resetting, qualifiers, filter, include, address, contacted, read, piece, take, seriously, syntax, tips, clear, users, pricing, premium, ons, collections, trending, archive, program, accelerator, maintainer, programs, fund, sponsors, partners, trust, center, forum, ebooks, reports, events, webinars, stories, topic, industries, government, manufacturing, financial, healthcare, cases, devsecops, modernization, nonprofits, startups, small, medium, teams, enterprises, company, marketplace, changelog, blog, why, leaks, secret, protection, secure, find, fix, vulnerabilities, enforce, review, plan, instant, environments, codespaces, integrate, external, tools, mcp, registry, direct, agents, issue, write, creation, skip,

Text of the page (random words):
ng focus you signed in with another tab or window reload to refresh your session you signed out in another tab or window reload to refresh your session you switched accounts on another tab or window reload to refresh your session dismiss alert message gokumohandas made with ml public notifications you must be signed in to change notification settings fork 7 7k star 48 9k code issues 18 pull requests 9 actions projects security and quality 0 insights additional navigation options code issues pull requests actions projects security and quality insights gokumohandas made with ml main branches tags go to file code open more actions menu folders and files name name last commit message last commit date latest commit history 19 commits 19 commits github workflows github workflows datasets datasets deploy deploy docs docs madewithml madewithml notebooks notebooks tests tests gitignore gitignore pre commit config yaml pre commit config yaml license license makefile makefile readme md readme md mkdocs yml mkdocs yml pyproject toml pyproject toml requirements txt requirements txt view all files repository files navigation readme mit license more items made with ml design develop deploy iterate join 40k developers in learning how to responsibly deliver value with ml among the top ml repositories on github lessons learn how to combine machine learning with software engineering to design develop deploy and iterate on production grade ml applications lessons https madewithml com code gokumohandas made with ml 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 first principles before we jump straight into the code we develop a first principles understanding for every machine learning concept best practices implement software engineering best practices as we develop and deploy our machine learning models scale easily scale ml workloads data train tune serve in python without having to learn completely new languages ️ mlops connect mlops components tracking testing serving orchestration etc as we build an end to end machine learning system dev to prod learn how to quickly and reliably go from development to production without any changes to our code or infra management ci cd learn how to create mature ci cd workflows to continuously train and deploy better models in a modular way that integrates with any stack 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 all developers whether software infra engineer or data scientist ml is increasingly becoming a key part of the products that you ll be developing college graduates learn the practical skills required for industry and bridge gap between the university curriculum and what industry expects product leadership who want to develop a technical foundation so that they can build amazing and reliable products powered by machine learning 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 on aws and gcp community supported azure and aliyun integrations also exist on kubernetes via the officially supported kuberay project deploy ray manually on prem or onto platforms not listed here 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 tr...