diff --git a/examples/quickstart/quickstart.ipynb b/examples/quickstart/quickstart.ipynb index 067c262afb..a476f0ffa4 100644 --- a/examples/quickstart/quickstart.ipynb +++ b/examples/quickstart/quickstart.ipynb @@ -1,756 +1,748 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "63ab391a", - "metadata": {}, - "source": [ - "# ZenML Quickstart: Bridging Local Development and Cloud Deployment\n", - "\n", - "This repository demonstrates how ZenML streamlines the transition of machine learning workflows from local environments to cloud-scale operations.\n", - "\n", - "## Key advantages:\n", - "\n", - "Deploy to major cloud providers with minimal code changes\n", - "\n", - "* Connect directly to your existing infrastructure\n", - "* Bridge the gap between ML and Ops teams\n", - "* Gain deep insights into pipeline metadata via the ZenML Dashboard\n", - "\n", - "Unlike traditional MLOps tools, ZenML offers unparalleled flexibility and control. It integrates seamlessly with your infrastructure, allowing both ML and Ops teams to collaborate effectively without compromising on their specific requirements.\n", - "\n", - "The notebook guides you through adapting local code for cloud deployment, showcasing ZenML's ability to enhance workflow efficiency while maintaining reproducibility and auditability in production.\n", - "\n", - "Ready to unify your ML development and operations? Let's begin. The diagram below \n", - "describes what we'll show you in this example.\n", - "\n", - "\"Pipelines\n", - "\n", - "1) We have done some of the experimenting for you already and created a simple finetuning pipeline for a text-to-text task.\n", - "\n", - "2) We will run this pipeline on your machine and a verify that everything works as expected.\n", - "\n", - "3) Now we'll connect ZenML to your infrastructure and configure everything.\n", - "\n", - "4) Finally, we are ready to run our code remotely.\n", - "\n", - "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!" - ] - }, - { - "cell_type": "markdown", - "id": "8f466b16", - "metadata": {}, - "source": [ - "## Run on Colab\n", - "\n", - "You can use Google Colab to run this notebook, no local installation\n", - "required!\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](\n", - "https://colab.research.google.com/github/zenml-io/zenml/blob/main/examples/quickstart/quickstart.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "66b2977c", - "metadata": {}, - "source": [ - "# 👶 Step 0. Install Requirements\n", - "\n", - "Let's install ZenML and all requirement to get started." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4385bdb-6cc8-4a6b-8de2-a7fd658556aa", - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a cloud provider - at the end of this notebook you will run a pipeline on this cloud provider\n", - "CLOUD_PROVIDER = None # Set this to \"GCP\", \"AWS\" or \"AZURE\" as needed\n", - "\n", - "\n", - "def in_google_colab() -> bool:\n", - " \"\"\"Checks wether this notebook is run in google colab.\"\"\"\n", - " try:\n", - " import google.colab # noqa\n", - "\n", - " return True\n", - "\n", - " except ModuleNotFoundError:\n", - " return False\n", - "\n", - "\n", - "if in_google_colab():\n", - " # Pull required modules from this example\n", - " !git clone -b main https://github.com/zenml-io/zenml\n", - " !cp -r zenml/examples/quickstart/* .\n", - " !rm -rf zenml\n", - "\n", - "\n", - "# Common imports and setup\n", - "if CLOUD_PROVIDER.lower() == \"gcp\":\n", - " !pip install -r requirements_gcp.txt\n", - "\n", - "elif CLOUD_PROVIDER.lower() == \"aws\":\n", - " !pip install -r requirements_aws.txt\n", - "\n", - "elif CLOUD_PROVIDER.lower() == \"azure\":\n", - " !pip install -r requirements_azure.txt\n", - "\n", - "else: # In this case the second half of the notebook won't work for you\n", - " !pip install -r requirements.txt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f76f562e", - "metadata": {}, - "outputs": [], - "source": [ - "# Restart Kernel to ensure all libraries are properly loaded\n", - "import IPython\n", - "\n", - "IPython.Application.instance().kernel.do_shutdown(restart=True)" - ] - }, - { - "cell_type": "markdown", - "id": "3b044374", - "metadata": {}, - "source": [ - "\n", - "Please wait for the installation to complete before running subsequent cells. At\n", - "the end of the installation, the notebook kernel will restart." - ] - }, - { - "cell_type": "markdown", - "id": "966ce581", - "metadata": {}, - "source": [ - "## ☁️ Step 1: Connect to your ZenML Server\n", - "To run this quickstart you need to connect to a ZenML Server. You can deploy it [yourself on your own infrastructure](https://docs.zenml.io/getting-started/deploying-zenml) or try it out for free, no credit-card required in our [ZenML Pro managed service](https://zenml.io/pro)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2587315", - "metadata": {}, - "outputs": [], - "source": [ - "zenml_server_url = (\n", - " None # INSERT URL TO SERVER HERE in the form \"https://URL_TO_SERVER\"\n", - ")\n", - "\n", - "assert zenml_server_url\n", - "\n", - "!zenml connect --url $zenml_server_url" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f78a2f42-7a53-45f1-b45b-77bfc3762260", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize ZenML and define the root for imports and docker builds\n", - "!zenml init\n", - "\n", - "!zenml stack set default" - ] - }, - { - "cell_type": "markdown", - "id": "35e48460", - "metadata": {}, - "source": [ - "## 🥇 Step 2: Build and run your first pipeline\n", - "\n", - "In this quickstart we'll be working with a small dataset of sentences in old english paired with more modern formulations. The task is a text-to-text transformation.\n", - "\n", - "When you're getting started with a machine learning problem you'll want to break down your code into distinct functions that load your data, bring it into the correct shape and finally produce a model. This is the experimentation phase where we try to massage our data into the right format and feed it into our model training.\n", - "\n", - "\"Experimentation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3cd974d1", - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "from datasets import Dataset\n", - "from typing_extensions import Annotated\n", - "\n", - "from zenml import step\n", - "\n", - "PROMPT = \"\" # In case you want to also use a prompt you can set it here\n", - "\n", - "\n", - "def read_data_from_url(url):\n", - " \"\"\"Reads data from url.\n", - "\n", - " Assumes the individual data points are linebreak separated\n", - " and input, targets are separated by a `|` pipe.\n", - " \"\"\"\n", - " inputs = []\n", - " targets = []\n", - "\n", - " response = requests.get(url)\n", - " response.raise_for_status() # Raise an exception for bad responses\n", - "\n", - " for line in response.text.splitlines():\n", - " old, modern = line.strip().split(\"|\")\n", - " inputs.append(f\"{PROMPT}{old}\")\n", - " targets.append(modern)\n", - "\n", - " return {\"input\": inputs, \"target\": targets}\n", - "\n", - "\n", - "@step\n", - "def load_data(\n", - " data_url: str,\n", - ") -> Annotated[Dataset, \"full_dataset\"]:\n", - " \"\"\"Load and prepare the dataset.\"\"\"\n", - "\n", - " # Fetch and process the data\n", - " data = read_data_from_url(data_url)\n", - "\n", - " # Convert to Dataset\n", - " return Dataset.from_dict(data)" - ] - }, - { - "cell_type": "markdown", - "id": "b6286b67", - "metadata": {}, - "source": [ - "ZenML is built in a way that allows you to experiment with your data and build\n", - "your pipelines one step at a time. If you want to call this function to see how it\n", - "works, you can just call it directly. Here we take a look at the first few rows\n", - "of your training dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d838e2ea", - "metadata": {}, - "outputs": [], - "source": [ - "data_source = \"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\"\n", - "\n", - "dataset = load_data(data_url=data_source)\n", - "print(f\"Input: {dataset['input'][1]} - Target: {dataset['target'][1]}\")" - ] - }, - { - "cell_type": "markdown", - "id": "28c05291", - "metadata": {}, - "source": [ - "Everything looks as we'd expect and the input/output pair looks to be in the right format 🥳.\n", - "\n", - "For the sake of this quickstart we have prepared a few steps in the steps-directory. We'll now connect these together into a pipeline. To do this simply plug multiple steps together through their inputs and outputs. Then just add the `@pipeline` decorator to the function that connects the steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b50a9537", - "metadata": {}, - "outputs": [], - "source": [ - "import materializers\n", - "from steps import (\n", - " evaluate_model,\n", - " load_data,\n", - " split_dataset,\n", - " test_model,\n", - " tokenize_data,\n", - " train_model,\n", - ")\n", - "from steps.model_trainer import T5_Model\n", - "\n", - "from zenml import Model, pipeline\n", - "from zenml.client import Client\n", - "\n", - "assert materializers\n", - "\n", - "# Initialize the ZenML client to fetch objects from the ZenML Server\n", - "client = Client()\n", - "\n", - "Client().activate_stack(\n", - " \"default\"\n", - ") # We will start by using the default stack which is local\n", - "\n", - "model_name = \"YeOldeEnglishTranslator\"\n", - "model = Model(\n", - " name=\"YeOldeEnglishTranslator\",\n", - " description=\"Model to translate from old to modern english\",\n", - " tags=[\"quickstart\", \"llm\", \"t5\"],\n", - ")\n", - "\n", - "\n", - "@pipeline(model=model)\n", - "def english_translation_pipeline(\n", - " data_url: str,\n", - " model_type: T5_Model,\n", - " per_device_train_batch_size: int,\n", - " gradient_accumulation_steps: int,\n", - " dataloader_num_workers: int,\n", - " num_train_epochs: int = 5,\n", - "):\n", - " \"\"\"Define a pipeline that connects the steps.\"\"\"\n", - " full_dataset = load_data(data_url)\n", - " tokenized_dataset, tokenizer = tokenize_data(\n", - " dataset=full_dataset, model_type=model_type\n", - " )\n", - " tokenized_train_dataset, tokenized_eval_dataset, tokenized_test_dataset = (\n", - " split_dataset(\n", - " tokenized_dataset,\n", - " train_size=0.7,\n", - " test_size=0.1,\n", - " eval_size=0.2,\n", - " subset_size=0.1, # We use a subset of the dataset to speed things up\n", - " random_state=42,\n", - " )\n", - " )\n", - " model = train_model(\n", - " tokenized_dataset=tokenized_train_dataset,\n", - " model_type=model_type,\n", - " num_train_epochs=num_train_epochs,\n", - " per_device_train_batch_size=per_device_train_batch_size,\n", - " gradient_accumulation_steps=gradient_accumulation_steps,\n", - " dataloader_num_workers=dataloader_num_workers,\n", - " )\n", - " evaluate_model(model=model, tokenized_dataset=tokenized_eval_dataset)\n", - " test_model(\n", - " model=model,\n", - " tokenized_test_dataset=tokenized_test_dataset,\n", - " tokenizer=tokenizer,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "7cd73c23", - "metadata": {}, - "source": [ - "We're ready to run the pipeline now, which we can do just as with the step - by calling the\n", - "pipeline function itself:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e0aa9af", - "metadata": {}, - "outputs": [], - "source": [ - "# Run the pipeline and configure some parameters at runtime\n", - "pipeline_run = english_translation_pipeline(\n", - " data_url=\"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\",\n", - " model_type=\"t5-small\",\n", - " num_train_epochs=1, # to make this demo fast, we start at 1 epoch\n", - " per_device_train_batch_size=2,\n", - " gradient_accumulation_steps=4,\n", - " dataloader_num_workers=4,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6c42078a", - "metadata": {}, - "source": [ - "As you can see the pipeline has run successfully. Here is a sneak-peak of the dashboard view into this pipeline. The URL for this view can be found in the logs.\n", - "\n", - "\"Dashboard\n", - "\n", - "This isn't all that the ZenML Dashboard has to offer, if you navigate over to the ZenML Model control plane, you'll also find the produced model along with a lot of important metadata.\n", - "\n", - "\"Model\n", - "\n", - "Here you'll also see a collection of example Input-Output pairs. As you can see, the model is currently not performing its task well." - ] - }, - { - "cell_type": "markdown", - "id": "a037f09d", - "metadata": {}, - "source": [ - "We can now access the trained model and it's tokenizer from the ZenML Model Control Plane. This will allow us to interact with the model directly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53e514ac-1a0a-49a0-b8a4-e33cee12c765", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "# load the model object\n", - "model = client.get_model_version(model_name).get_model_artifact(\"model\").load()\n", - "tokenizer = (\n", - " client.get_model_version(model_name).get_artifact(\"tokenizer\").load()\n", - ")\n", - "\n", - "test_text = \"I do desire we may be better strangers\" # Insert your own test sentence here\n", - "\n", - "input_ids = tokenizer(\n", - " test_text,\n", - " return_tensors=\"pt\",\n", - " max_length=128,\n", - " truncation=True,\n", - " padding=\"max_length\",\n", - ").input_ids\n", - "\n", - "with torch.no_grad():\n", - " outputs = model.generate(\n", - " input_ids,\n", - " max_length=128,\n", - " num_return_sequences=1,\n", - " no_repeat_ngram_size=2,\n", - " top_k=50,\n", - " top_p=0.95,\n", - " temperature=0.7,\n", - " )\n", - "\n", - "decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", - "\n", - "print(decoded_output)" - ] - }, - { - "cell_type": "markdown", - "id": "1e653c7a-4073-424e-8a59-c69f49526b96", - "metadata": {}, - "source": [ - "## Lets recap what we've done so far\n", - "\n", - "We created a modular pipeline, this pipeline is modularly constructed from different steps. We have shown that this pipeline runs locally.\n", - "\n", - "As expected, the modcel does not yet solve its task. To train a model that can solve our task well, we would have to train a larger model for longer. For this, we'll need to move away from our local environment. " - ] - }, - { - "cell_type": "markdown", - "id": "8c28b474", - "metadata": {}, - "source": [ - "# ⌚ Step 3: Scale it up in the cloud" - ] - }, - { - "cell_type": "markdown", - "id": "a791b32b-f6be-4ae2-867c-5e628f363858", - "metadata": {}, - "source": [ - "Our last section confirmed to us, that the pipeline works. Let's now run the pipeline in the environment of your choice.\n", - "\n", - "For you to be able to try this step, you will need to have access to a cloud environment (AWS, GCP, AZURE). ZenML wraps around all the major cloud providers and orchestration tools and lets you easily deploy your code onto them.\n", - "\n", - "To do this lets head over to the `Stack` section of your ZenML Dashboard. Here you'll be able to either connect to an existing or deploy a new environment. Choose on of the options presented to you there and come back when you have a stack ready to go. \n", - "\n", - "\"Stack\n", - "\n", - "Then proceed to the section below. Also be sure that you are running with a remote ZenML server (see Step 1 above)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e7a90b5-78c3-4019-8a81-671b5d62d470", - "metadata": {}, - "outputs": [], - "source": [ - "from zenml.environment import Environment\n", - "\n", - "# Set the cloud provider here\n", - "CLOUD_PROVIDER = None # Set this to \"GCP\", \"AWS\" or \"AZURE\"\n", - "assert CLOUD_PROVIDER\n", - "\n", - "# Set the name of the stack that you created within zenml\n", - "stack_name = None # Set this\n", - "assert stack_name # Set your stack, follow instruction above\n", - "\n", - "from zenml import pipeline\n", - "from zenml.client import Client\n", - "from zenml.config import DockerSettings\n", - "\n", - "settings = {}\n", - "\n", - "# Common imports and setup\n", - "if CLOUD_PROVIDER.lower() == \"gcp\":\n", - " parent_image = (\n", - " \"zenmldocker/zenml-public-pipelines:quickstart-0.70.0-py3.11-gcp\"\n", - " )\n", - " skip_build = True\n", - "\n", - "elif CLOUD_PROVIDER.lower() == \"aws\":\n", - " from zenml.integrations.aws.flavors.sagemaker_orchestrator_flavor import (\n", - " SagemakerOrchestratorSettings,\n", - " )\n", - "\n", - " parent_image = \"339712793861.dkr.ecr.eu-central-1.amazonaws.com/zenml-public-pipelines:quickstart-0.70.0-py3.11-aws\"\n", - " skip_build = True # if you switch this to False, you need to remove the parent image\n", - "\n", - " settings[\"orchestrator.sagemaker\"] = SagemakerOrchestratorSettings(\n", - " instance_type=\"ml.m5.4xlarge\"\n", - " )\n", - "\n", - "elif CLOUD_PROVIDER.lower() == \"azure\":\n", - " parent_image = (\n", - " \"zenmldocker/zenml-public-pipelines:quickstart-0.70.0-py3.11-azure\"\n", - " )\n", - " skip_build = True\n", - "\n", - "Client().activate_stack(stack_name)\n", - "\n", - "data_source = \"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\"\n", - "\n", - "# We've prebuilt a docker image for this quickstart to speed things up, feel free to delete the DockerSettings to build from scratch\n", - "settings[\"docker\"] = DockerSettings(\n", - " parent_image=parent_image, skip_build=skip_build\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8a5cd031-0661-4073-a5ea-6aac5f989212", - "metadata": {}, - "source": [ - "If you are in a google colab you might need to rerun the cell above a second time after the runtime restarted." - ] - }, - { - "cell_type": "markdown", - "id": "95f17b7a-5a82-4975-b9bd-6a63fbb97a68", - "metadata": {}, - "source": [ - "## 🚀 Ready to launch" - ] - }, - { - "cell_type": "markdown", - "id": "df14f30c-9a8e-46ca-ba44-cf16ea715dac", - "metadata": {}, - "source": [ - "We now have configured a ZenML stack that represents your very own cloud infrastructure. For the next pipeline run, we'll be training the same t5 model (`t5_small`) on your own infrastrucutre.\n", - "\n", - "Note: The whole process may take a bit longer the first time around, as your pipeline code needs to be built into docker containers to be run in the orchestration environment of your stack. Any consecutive run of the pipeline, even with different parameters set, will not take as long again thanks to docker caching." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cfad9bd5", - "metadata": {}, - "outputs": [], - "source": [ - "# In the case that we are within a colab environment we want to remove\n", - "# these folders\n", - "if Environment.in_google_colab():\n", - " !rm -rf results\n", - " !rm -rf sample_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12e758fe-6ea3-42ff-bea8-33953135bf6b", - "metadata": {}, - "outputs": [], - "source": [ - "from pipelines import (\n", - " english_translation_pipeline,\n", - ")\n", - "\n", - "from zenml import Model\n", - "\n", - "model_name = \"YeOldeEnglishTranslator\"\n", - "model = Model(\n", - " name=\"YeOldeEnglishTranslator\",\n", - ")\n", - "\n", - "pipeline_run = english_translation_pipeline.with_options(\n", - " settings=settings, model=model\n", - ")(\n", - " data_url=\"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\",\n", - " model_type=\"t5-small\",\n", - " num_train_epochs=2,\n", - " per_device_train_batch_size=4,\n", - " gradient_accumulation_steps=4,\n", - " dataloader_num_workers=0, # Some cloud environment don't support multiple of these\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c7eef2c6-6dfb-4b67-9883-594a0df20173", - "metadata": {}, - "source": [ - "You did it! You build a pipeline locally, verified that all its parts work well together and now are running it on a production environment\n", - "\n", - "\"Pipeline\n", - "\n", - "Depending on the backend you chose, you can also go inspect your run in the orchestrator of your choice. Here is an example on GCP Vertex:\n", - "\n", - "\"Pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "1a03054e-8b3e-4edb-9d87-82ae51693d2d", - "metadata": {}, - "source": [ - "## Adding Accelerators\n", - "Each of the cloud providers allows users to add accelerators to their serverless offerings. Here's what you need to add to the pipeline settings in order to unlock gpus. Keep in mind, that you might have to increase your quotas within the cloud providers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a2f7c3c5-dcde-4824-a012-f3da224cb8d8", - "metadata": {}, - "outputs": [], - "source": [ - "from zenml.config import ResourceSettings\n", - "\n", - "if CLOUD_PROVIDER == \"GCP\":\n", - " from zenml.integrations.gcp.flavors.vertex_orchestrator_flavor import (\n", - " VertexOrchestratorSettings,\n", - " )\n", - "\n", - " # find out about your options here: https://docs.zenml.io/stack-components/orchestrators/vertex#additional-configuration\n", - "\n", - " english_translation_pipeline.with_options(\n", - " settings={\n", - " \"orchestrator.vertex\": VertexOrchestratorSettings(\n", - " node_selector_constraint=(\n", - " \"cloud.google.com/gke-accelerator\",\n", - " \"NVIDIA_TESLA_P4\",\n", - " )\n", - " ),\n", - " \"resources\": ResourceSettings(memory=\"32GB\", gpu_count=1),\n", - " }\n", - " )\n", - "if CLOUD_PROVIDER == \"AWS\":\n", - " from zenml.integrations.aws.flavors.sagemaker_orchestrator_flavor import (\n", - " SagemakerOrchestratorSettings,\n", - " )\n", - "\n", - " # find out your options here: https://docs.zenml.io/stack-components/orchestrators/sagemaker#configuration-at-pipeline-or-step-level\n", - "\n", - " english_translation_pipeline.with_options(\n", - " settings={\n", - " \"orchestrator.sagemaker\": SagemakerOrchestratorSettings(\n", - " instance_type=\"ml.p2.xlarge\"\n", - " )\n", - " }\n", - " )\n", - "if CLOUD_PROVIDER == \"AZURE\":\n", - " from zenml.integrations.azure.flavors import AzureMLOrchestratorSettings\n", - "\n", - " # find out your options here: https://docs.zenml.io/stack-components/orchestrators/azureml#settings\n", - " # The quickest way is probably to configure a compute-instance in azure ml. This instance should contain\n", - " # a gpu. Then specify the name of the compute instance here.\n", - "\n", - " compute_name = None # Insert the name of your compute instance here\n", - "\n", - " english_translation_pipeline.with_options(\n", - " settings={\n", - " \"orchestrator.azureml\": AzureMLOrchestratorSettings(\n", - " mode=\"compute-instance\", compute_name=compute_name\n", - " )\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "8231677c-1fd6-4ec3-8c8c-47fd9406072e", - "metadata": {}, - "source": [ - "## Now it's up to you" - ] - }, - { - "cell_type": "markdown", - "id": "90c31e0d-dfab-4692-a406-0dc439f25443", - "metadata": {}, - "source": [ - "You can now start worrying about making the model actually work well on our toy example or any other dataset you like.\n", - "\n", - "Here are some things that you could do:\n", - "\n", - "* Iterate on the training data and its tokenization\n", - "* You can switch out the model itself. Instead of `model_type=\"t5_small\"` you could use `model_type=\"t5_large\"` for example\n", - "* You can train for longer by increasing the `num_train_epochs=xxx`. In order to speed this up you can also add accelerators to your orchestrators. Learn more about this in the section below.\n", - "\n", - "No matter what avenue you choose to actually make the model work, we would love to see how you did it, so please reach out and share your solution with us either on [**Slack Community**](https://zenml.io/slack) or through our email hello@zenml.io." - ] - }, - { - "cell_type": "markdown", - "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", - "metadata": {}, - "source": [ - "## Further exploration\n", - "\n", - "This was just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", - "about the capabilities of ZenML. For example, you might want to:\n", - "\n", - "- [Deploy ZenML](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml) to collaborate with your colleagues.\n", - "- Run the same pipeline on a [cloud MLOps stack in production](https://docs.zenml.io/user-guide/production-guide/cloud-stack).\n", - "- Track your metrics in an experiment tracker like [MLflow](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers/mlflow).\n", - "\n", - "## What next?\n", - "\n", - "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", - "* If you want to quickly get started with ZenML, check out [ZenML Pro](https://zenml.io/pro)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c560354d-9e78-4061-aaff-2e6213229911", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "cells": [ + { + "cell_type": "markdown", + "id": "63ab391a", + "metadata": {}, + "source": [ + "# ZenML Quickstart: Bridging Local Development and Cloud Deployment\n", + "\n", + "This repository demonstrates how ZenML streamlines the transition of machine learning workflows from local environments to cloud-scale operations.\n", + "\n", + "## Key advantages:\n", + "\n", + "Deploy to major cloud providers with minimal code changes\n", + "\n", + "* Connect directly to your existing infrastructure\n", + "* Bridge the gap between ML and Ops teams\n", + "* Gain deep insights into pipeline metadata via the ZenML Dashboard\n", + "\n", + "Unlike traditional MLOps tools, ZenML offers unparalleled flexibility and control. It integrates seamlessly with your infrastructure, allowing both ML and Ops teams to collaborate effectively without compromising on their specific requirements.\n", + "\n", + "The notebook guides you through adapting local code for cloud deployment, showcasing ZenML's ability to enhance workflow efficiency while maintaining reproducibility and auditability in production.\n", + "\n", + "Ready to unify your ML development and operations? Let's begin. The diagram below \n", + "describes what we'll show you in this example.\n", + "\n", + "\"Pipelines\n", + "\n", + "1) We have done some of the experimenting for you already and created a simple finetuning pipeline for a text-to-text task.\n", + "\n", + "2) We will run this pipeline on your machine and a verify that everything works as expected.\n", + "\n", + "3) Now we'll connect ZenML to your infrastructure and configure everything.\n", + "\n", + "4) Finally, we are ready to run our code remotely.\n", + "\n", + "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!" + ] + }, + { + "cell_type": "markdown", + "id": "8f466b16", + "metadata": {}, + "source": [ + "## Run on Colab\n", + "\n", + "You can use Google Colab to run this notebook, no local installation\n", + "required!\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](\n", + "https://colab.research.google.com/github/zenml-io/zenml/blob/main/examples/quickstart/quickstart.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "66b2977c", + "metadata": {}, + "source": [ + "# 👶 Step 0. Install Requirements\n", + "\n", + "Let's install ZenML and all requirement to get started." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4385bdb-6cc8-4a6b-8de2-a7fd658556aa", + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a cloud provider - at the end of this notebook you will run a pipeline on this cloud provider\n", + "CLOUD_PROVIDER = None # Set this to \"GCP\", \"AWS\" or \"AZURE\" as needed\n", + "\n", + "\n", + "def in_google_colab() -> bool:\n", + " \"\"\"Checks wether this notebook is run in google colab.\"\"\"\n", + " try:\n", + " import google.colab # noqa\n", + "\n", + " return True\n", + "\n", + " except ModuleNotFoundError:\n", + " return False\n", + "\n", + "\n", + "if in_google_colab():\n", + " # Pull required modules from this example\n", + " !git clone -b main https://github.com/zenml-io/zenml\n", + " !cp -r zenml/examples/quickstart/* .\n", + " !rm -rf zenml\n", + "\n", + "\n", + "# Common imports and setup\n", + "if CLOUD_PROVIDER.lower() == \"gcp\":\n", + " !pip install -r requirements_gcp.txt\n", + "\n", + "elif CLOUD_PROVIDER.lower() == \"aws\":\n", + " !pip install -r requirements_aws.txt\n", + "\n", + "elif CLOUD_PROVIDER.lower() == \"azure\":\n", + " !pip install -r requirements_azure.txt\n", + "\n", + "else: # In this case the second half of the notebook won't work for you\n", + " !pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f76f562e", + "metadata": {}, + "outputs": [], + "source": [ + "# Restart Kernel to ensure all libraries are properly loaded\n", + "import IPython\n", + "\n", + "IPython.Application.instance().kernel.do_shutdown(restart=True)" + ] + }, + { + "cell_type": "markdown", + "id": "3b044374", + "metadata": {}, + "source": [ + "\n", + "Please wait for the installation to complete before running subsequent cells. At\n", + "the end of the installation, the notebook kernel will restart." + ] + }, + { + "cell_type": "markdown", + "id": "966ce581", + "metadata": {}, + "source": [ + "## ☁️ Step 1: Connect to your ZenML Server\n", + "To run this quickstart you need to connect to a ZenML Server. You can deploy it [yourself on your own infrastructure](https://docs.zenml.io/getting-started/deploying-zenml) or try it out for free, no credit-card required in our [ZenML Pro managed service](https://zenml.io/pro)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2587315", + "metadata": {}, + "outputs": [], + "source": [ + "zenml_server_url = (\n", + " None # INSERT URL TO SERVER HERE in the form \"https://URL_TO_SERVER\"\n", + ")\n", + "\n", + "assert zenml_server_url\n", + "\n", + "!zenml login $zenml_server_url" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f78a2f42-7a53-45f1-b45b-77bfc3762260", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize ZenML and define the root for imports and docker builds\n", + "!zenml init\n", + "\n", + "!zenml stack set default" + ] + }, + { + "cell_type": "markdown", + "id": "35e48460", + "metadata": {}, + "source": [ + "## 🥇 Step 2: Build and run your first pipeline\n", + "\n", + "In this quickstart we'll be working with a small dataset of sentences in old english paired with more modern formulations. The task is a text-to-text transformation.\n", + "\n", + "When you're getting started with a machine learning problem you'll want to break down your code into distinct functions that load your data, bring it into the correct shape and finally produce a model. This is the experimentation phase where we try to massage our data into the right format and feed it into our model training.\n", + "\n", + "\"Experimentation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cd974d1", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "from datasets import Dataset\n", + "from typing_extensions import Annotated\n", + "\n", + "from zenml import step\n", + "\n", + "PROMPT = \"\" # In case you want to also use a prompt you can set it here\n", + "\n", + "\n", + "def read_data_from_url(url):\n", + " \"\"\"Reads data from url.\n", + "\n", + " Assumes the individual data points are linebreak separated\n", + " and input, targets are separated by a `|` pipe.\n", + " \"\"\"\n", + " inputs = []\n", + " targets = []\n", + "\n", + " response = requests.get(url)\n", + " response.raise_for_status() # Raise an exception for bad responses\n", + "\n", + " for line in response.text.splitlines():\n", + " old, modern = line.strip().split(\"|\")\n", + " inputs.append(f\"{PROMPT}{old}\")\n", + " targets.append(modern)\n", + "\n", + " return {\"input\": inputs, \"target\": targets}\n", + "\n", + "\n", + "@step\n", + "def load_data(\n", + " data_url: str,\n", + ") -> Annotated[Dataset, \"full_dataset\"]:\n", + " \"\"\"Load and prepare the dataset.\"\"\"\n", + "\n", + " # Fetch and process the data\n", + " data = read_data_from_url(data_url)\n", + "\n", + " # Convert to Dataset\n", + " return Dataset.from_dict(data)" + ] + }, + { + "cell_type": "markdown", + "id": "b6286b67", + "metadata": {}, + "source": [ + "ZenML is built in a way that allows you to experiment with your data and build\n", + "your pipelines one step at a time. If you want to call this function to see how it\n", + "works, you can just call it directly. Here we take a look at the first few rows\n", + "of your training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d838e2ea", + "metadata": {}, + "outputs": [], + "source": [ + "data_source = \"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\"\n", + "\n", + "dataset = load_data(data_url=data_source)\n", + "print(f\"Input: {dataset['input'][1]} - Target: {dataset['target'][1]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "28c05291", + "metadata": {}, + "source": [ + "Everything looks as we'd expect and the input/output pair looks to be in the right format 🥳.\n", + "\n", + "For the sake of this quickstart we have prepared a few steps in the steps-directory. We'll now connect these together into a pipeline. To do this simply plug multiple steps together through their inputs and outputs. Then just add the `@pipeline` decorator to the function that connects the steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b50a9537", + "metadata": {}, + "outputs": [], + "source": [ + "import materializers\n", + "from steps import (\n", + " evaluate_model,\n", + " load_data,\n", + " split_dataset,\n", + " test_model,\n", + " tokenize_data,\n", + " train_model,\n", + ")\n", + "from steps.model_trainer import T5_Model\n", + "\n", + "from zenml import Model, pipeline\n", + "from zenml.client import Client\n", + "\n", + "assert materializers\n", + "\n", + "# Initialize the ZenML client to fetch objects from the ZenML Server\n", + "client = Client()\n", + "\n", + "Client().activate_stack(\n", + " \"default\"\n", + ") # We will start by using the default stack which is local\n", + "\n", + "model_name = \"YeOldeEnglishTranslator\"\n", + "model = Model(\n", + " name=\"YeOldeEnglishTranslator\",\n", + " description=\"Model to translate from old to modern english\",\n", + " tags=[\"quickstart\", \"llm\", \"t5\"],\n", + ")\n", + "\n", + "\n", + "@pipeline(model=model)\n", + "def english_translation_pipeline(\n", + " data_url: str,\n", + " model_type: T5_Model,\n", + " per_device_train_batch_size: int,\n", + " gradient_accumulation_steps: int,\n", + " dataloader_num_workers: int,\n", + " num_train_epochs: int = 5,\n", + "):\n", + " \"\"\"Define a pipeline that connects the steps.\"\"\"\n", + " full_dataset = load_data(data_url)\n", + " tokenized_dataset, tokenizer = tokenize_data(\n", + " dataset=full_dataset, model_type=model_type\n", + " )\n", + " tokenized_train_dataset, tokenized_eval_dataset, tokenized_test_dataset = (\n", + " split_dataset(\n", + " tokenized_dataset,\n", + " train_size=0.7,\n", + " test_size=0.1,\n", + " eval_size=0.2,\n", + " subset_size=0.1, # We use a subset of the dataset to speed things up\n", + " random_state=42,\n", + " )\n", + " )\n", + " model = train_model(\n", + " tokenized_dataset=tokenized_train_dataset,\n", + " model_type=model_type,\n", + " num_train_epochs=num_train_epochs,\n", + " per_device_train_batch_size=per_device_train_batch_size,\n", + " gradient_accumulation_steps=gradient_accumulation_steps,\n", + " dataloader_num_workers=dataloader_num_workers,\n", + " )\n", + " evaluate_model(model=model, tokenized_dataset=tokenized_eval_dataset)\n", + " test_model(\n", + " model=model,\n", + " tokenized_test_dataset=tokenized_test_dataset,\n", + " tokenizer=tokenizer,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "7cd73c23", + "metadata": {}, + "source": [ + "We're ready to run the pipeline now, which we can do just as with the step - by calling the\n", + "pipeline function itself:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e0aa9af", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the pipeline and configure some parameters at runtime\n", + "pipeline_run = english_translation_pipeline(\n", + " data_url=\"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\",\n", + " model_type=\"t5-small\",\n", + " num_train_epochs=1, # to make this demo fast, we start at 1 epoch\n", + " per_device_train_batch_size=2,\n", + " gradient_accumulation_steps=4,\n", + " dataloader_num_workers=4,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6c42078a", + "metadata": {}, + "source": [ + "As you can see the pipeline has run successfully. Here is a sneak-peak of the dashboard view into this pipeline. The URL for this view can be found in the logs.\n", + "\n", + "\"Dashboard\n", + "\n", + "This isn't all that the ZenML Dashboard has to offer, if you navigate over to the ZenML Model control plane, you'll also find the produced model along with a lot of important metadata.\n", + "\n", + "\"Model\n", + "\n", + "Here you'll also see a collection of example Input-Output pairs. As you can see, the model is currently not performing its task well." + ] + }, + { + "cell_type": "markdown", + "id": "a037f09d", + "metadata": {}, + "source": [ + "We can now access the trained model and it's tokenizer from the ZenML Model Control Plane. This will allow us to interact with the model directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53e514ac-1a0a-49a0-b8a4-e33cee12c765", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "# load the model object\n", + "model = client.get_model_version(model_name).get_model_artifact(\"model\").load()\n", + "tokenizer = (\n", + " client.get_model_version(model_name).get_artifact(\"tokenizer\").load()\n", + ")\n", + "\n", + "test_text = \"I do desire we may be better strangers\" # Insert your own test sentence here\n", + "\n", + "input_ids = tokenizer(\n", + " test_text,\n", + " return_tensors=\"pt\",\n", + " max_length=128,\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + ").input_ids\n", + "\n", + "with torch.no_grad():\n", + " outputs = model.generate(\n", + " input_ids,\n", + " max_length=128,\n", + " num_return_sequences=1,\n", + " no_repeat_ngram_size=2,\n", + " top_k=50,\n", + " top_p=0.95,\n", + " temperature=0.7,\n", + " )\n", + "\n", + "decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + "\n", + "print(decoded_output)" + ] + }, + { + "cell_type": "markdown", + "id": "1e653c7a-4073-424e-8a59-c69f49526b96", + "metadata": {}, + "source": [ + "## Lets recap what we've done so far\n", + "\n", + "We created a modular pipeline, this pipeline is modularly constructed from different steps. We have shown that this pipeline runs locally.\n", + "\n", + "As expected, the modcel does not yet solve its task. To train a model that can solve our task well, we would have to train a larger model for longer. For this, we'll need to move away from our local environment. " + ] + }, + { + "cell_type": "markdown", + "id": "8c28b474", + "metadata": {}, + "source": [ + "# ⌚ Step 3: Scale it up in the cloud" + ] + }, + { + "cell_type": "markdown", + "id": "a791b32b-f6be-4ae2-867c-5e628f363858", + "metadata": {}, + "source": [ + "Our last section confirmed to us, that the pipeline works. Let's now run the pipeline in the environment of your choice.\n", + "\n", + "For you to be able to try this step, you will need to have access to a cloud environment (AWS, GCP, AZURE). ZenML wraps around all the major cloud providers and orchestration tools and lets you easily deploy your code onto them.\n", + "\n", + "To do this lets head over to the `Stack` section of your ZenML Dashboard. Here you'll be able to either connect to an existing or deploy a new environment. Choose on of the options presented to you there and come back when you have a stack ready to go. \n", + "\n", + "\"Stack\n", + "\n", + "Then proceed to the section below. Also be sure that you are running with a remote ZenML server (see Step 1 above)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e7a90b5-78c3-4019-8a81-671b5d62d470", + "metadata": {}, + "outputs": [], + "source": [ + "from zenml.environment import Environment\n", + "\n", + "# Set the cloud provider here\n", + "CLOUD_PROVIDER = None # Set this to \"GCP\", \"AWS\" or \"AZURE\"\n", + "assert CLOUD_PROVIDER\n", + "\n", + "# Set the name of the stack that you created within zenml\n", + "stack_name = None # Set this\n", + "assert stack_name # Set your stack, follow instruction above\n", + "\n", + "from zenml import pipeline\n", + "from zenml.client import Client\n", + "from zenml.config import DockerSettings\n", + "\n", + "settings = {}\n", + "\n", + "# Common imports and setup\n", + "if CLOUD_PROVIDER.lower() == \"gcp\":\n", + " parent_image = (\n", + " \"zenmldocker/zenml-public-pipelines:quickstart-0.70.0-py3.11-gcp\"\n", + " )\n", + " skip_build = True\n", + "\n", + "elif CLOUD_PROVIDER.lower() == \"aws\":\n", + " from zenml.integrations.aws.flavors.sagemaker_orchestrator_flavor import (\n", + " SagemakerOrchestratorSettings,\n", + " )\n", + "\n", + " parent_image = \"339712793861.dkr.ecr.eu-central-1.amazonaws.com/zenml-public-pipelines:quickstart-0.70.0-py3.11-aws\"\n", + " skip_build = True # if you switch this to False, you need to remove the parent image\n", + "\n", + " settings[\"orchestrator.sagemaker\"] = SagemakerOrchestratorSettings(\n", + " instance_type=\"ml.m5.4xlarge\"\n", + " )\n", + "\n", + "elif CLOUD_PROVIDER.lower() == \"azure\":\n", + " parent_image = (\n", + " \"zenmldocker/zenml-public-pipelines:quickstart-0.70.0-py3.11-azure\"\n", + " )\n", + " skip_build = True\n", + "\n", + "Client().activate_stack(stack_name)\n", + "\n", + "data_source = \"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\"\n", + "\n", + "# We've prebuilt a docker image for this quickstart to speed things up, feel free to delete the DockerSettings to build from scratch\n", + "settings[\"docker\"] = DockerSettings(\n", + " parent_image=parent_image, skip_build=skip_build\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8a5cd031-0661-4073-a5ea-6aac5f989212", + "metadata": {}, + "source": [ + "If you are in a google colab you might need to rerun the cell above a second time after the runtime restarted." + ] + }, + { + "cell_type": "markdown", + "id": "95f17b7a-5a82-4975-b9bd-6a63fbb97a68", + "metadata": {}, + "source": [ + "## 🚀 Ready to launch" + ] + }, + { + "cell_type": "markdown", + "id": "df14f30c-9a8e-46ca-ba44-cf16ea715dac", + "metadata": {}, + "source": [ + "We now have configured a ZenML stack that represents your very own cloud infrastructure. For the next pipeline run, we'll be training the same t5 model (`t5_small`) on your own infrastrucutre.\n", + "\n", + "Note: The whole process may take a bit longer the first time around, as your pipeline code needs to be built into docker containers to be run in the orchestration environment of your stack. Any consecutive run of the pipeline, even with different parameters set, will not take as long again thanks to docker caching." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfad9bd5", + "metadata": {}, + "outputs": [], + "source": [ + "# In the case that we are within a colab environment we want to remove\n", + "# these folders\n", + "if Environment.in_google_colab():\n", + " !rm -rf results\n", + " !rm -rf sample_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12e758fe-6ea3-42ff-bea8-33953135bf6b", + "metadata": {}, + "outputs": [], + "source": [ + "from pipelines import (\n", + " english_translation_pipeline,\n", + ")\n", + "\n", + "from zenml import Model\n", + "\n", + "model_name = \"YeOldeEnglishTranslator\"\n", + "model = Model(\n", + " name=\"YeOldeEnglishTranslator\",\n", + ")\n", + "\n", + "pipeline_run = english_translation_pipeline.with_options(\n", + " settings=settings, model=model\n", + ")(\n", + " data_url=\"https://storage.googleapis.com/zenml-public-bucket/quickstart-files/translations.txt\",\n", + " model_type=\"t5-small\",\n", + " num_train_epochs=2,\n", + " per_device_train_batch_size=4,\n", + " gradient_accumulation_steps=4,\n", + " dataloader_num_workers=0, # Some cloud environment don't support multiple of these\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c7eef2c6-6dfb-4b67-9883-594a0df20173", + "metadata": {}, + "source": [ + "You did it! You build a pipeline locally, verified that all its parts work well together and now are running it on a production environment\n", + "\n", + "\"Pipeline\n", + "\n", + "Depending on the backend you chose, you can also go inspect your run in the orchestrator of your choice. Here is an example on GCP Vertex:\n", + "\n", + "\"Pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "1a03054e-8b3e-4edb-9d87-82ae51693d2d", + "metadata": {}, + "source": [ + "## Adding Accelerators\n", + "Each of the cloud providers allows users to add accelerators to their serverless offerings. Here's what you need to add to the pipeline settings in order to unlock gpus. Keep in mind, that you might have to increase your quotas within the cloud providers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2f7c3c5-dcde-4824-a012-f3da224cb8d8", + "metadata": {}, + "outputs": [], + "source": [ + "from zenml.config import ResourceSettings\n", + "\n", + "if CLOUD_PROVIDER == \"GCP\":\n", + " from zenml.integrations.gcp.flavors.vertex_orchestrator_flavor import (\n", + " VertexOrchestratorSettings,\n", + " )\n", + "\n", + " # find out about your options here: https://docs.zenml.io/stack-components/orchestrators/vertex#additional-configuration\n", + "\n", + " english_translation_pipeline.with_options(\n", + " settings={\n", + " \"orchestrator.vertex\": VertexOrchestratorSettings(\n", + " node_selector_constraint=(\n", + " \"cloud.google.com/gke-accelerator\",\n", + " \"NVIDIA_TESLA_P4\",\n", + " )\n", + " ),\n", + " \"resources\": ResourceSettings(memory=\"32GB\", gpu_count=1),\n", + " }\n", + " )\n", + "if CLOUD_PROVIDER == \"AWS\":\n", + " from zenml.integrations.aws.flavors.sagemaker_orchestrator_flavor import (\n", + " SagemakerOrchestratorSettings,\n", + " )\n", + "\n", + " # find out your options here: https://docs.zenml.io/stack-components/orchestrators/sagemaker#configuration-at-pipeline-or-step-level\n", + "\n", + " english_translation_pipeline.with_options(\n", + " settings={\n", + " \"orchestrator.sagemaker\": SagemakerOrchestratorSettings(\n", + " instance_type=\"ml.p2.xlarge\"\n", + " )\n", + " }\n", + " )\n", + "if CLOUD_PROVIDER == \"AZURE\":\n", + " from zenml.integrations.azure.flavors import AzureMLOrchestratorSettings\n", + "\n", + " # find out your options here: https://docs.zenml.io/stack-components/orchestrators/azureml#settings\n", + " # The quickest way is probably to configure a compute-instance in azure ml. This instance should contain\n", + " # a gpu. Then specify the name of the compute instance here.\n", + "\n", + " compute_name = None # Insert the name of your compute instance here\n", + "\n", + " english_translation_pipeline.with_options(\n", + " settings={\n", + " \"orchestrator.azureml\": AzureMLOrchestratorSettings(\n", + " mode=\"compute-instance\", compute_name=compute_name\n", + " )\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "8231677c-1fd6-4ec3-8c8c-47fd9406072e", + "metadata": {}, + "source": [ + "## Now it's up to you" + ] + }, + { + "cell_type": "markdown", + "id": "90c31e0d-dfab-4692-a406-0dc439f25443", + "metadata": {}, + "source": [ + "You can now start worrying about making the model actually work well on our toy example or any other dataset you like.\n", + "\n", + "Here are some things that you could do:\n", + "\n", + "* Iterate on the training data and its tokenization\n", + "* You can switch out the model itself. Instead of `model_type=\"t5_small\"` you could use `model_type=\"t5_large\"` for example\n", + "* You can train for longer by increasing the `num_train_epochs=xxx`. In order to speed this up you can also add accelerators to your orchestrators. Learn more about this in the section below.\n", + "\n", + "No matter what avenue you choose to actually make the model work, we would love to see how you did it, so please reach out and share your solution with us either on [**Slack Community**](https://zenml.io/slack) or through our email hello@zenml.io." + ] + }, + { + "cell_type": "markdown", + "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", + "metadata": {}, + "source": [ + "## Further exploration\n", + "\n", + "This was just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", + "about the capabilities of ZenML. For example, you might want to:\n", + "\n", + "- [Deploy ZenML](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml) to collaborate with your colleagues.\n", + "- Run the same pipeline on a [cloud MLOps stack in production](https://docs.zenml.io/user-guide/production-guide/cloud-stack).\n", + "- Track your metrics in an experiment tracker like [MLflow](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers/mlflow).\n", + "\n", + "## What next?\n", + "\n", + "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", + "* If you want to quickly get started with ZenML, check out [ZenML Pro](https://zenml.io/pro)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 }