From c9fdd4105b067abbdf2547e4eeb26d7df435c80e Mon Sep 17 00:00:00 2001 From: zeryx <1892175+zeryx@users.noreply.github.com> Date: Thu, 25 May 2023 12:58:39 -0400 Subject: [PATCH 1/4] WIP, incomplete Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com> --- imagespec-example/cookiecutter.json | 3 + .../{{cookiecutter.project_name}}/.gitignore | 138 ++++++++++++ .../{{cookiecutter.project_name}}/LICENSE | 201 ++++++++++++++++++ .../{{cookiecutter.project_name}}/README.md | 25 +++ .../requirements.txt | 2 + .../workflows/__init__.py | 1 + .../workflows/example.py | 79 +++++++ .../workflows/mnist_training_example.py | 7 +- 8 files changed, 452 insertions(+), 4 deletions(-) create mode 100644 imagespec-example/cookiecutter.json create mode 100644 imagespec-example/{{cookiecutter.project_name}}/.gitignore create mode 100644 imagespec-example/{{cookiecutter.project_name}}/LICENSE create mode 100644 imagespec-example/{{cookiecutter.project_name}}/README.md create mode 100644 imagespec-example/{{cookiecutter.project_name}}/requirements.txt create mode 100644 imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py create mode 100644 imagespec-example/{{cookiecutter.project_name}}/workflows/example.py diff --git a/imagespec-example/cookiecutter.json b/imagespec-example/cookiecutter.json new file mode 100644 index 0000000..aea629b --- /dev/null +++ b/imagespec-example/cookiecutter.json @@ -0,0 +1,3 @@ +{ + "project_name": "flyte_example" +} diff --git a/imagespec-example/{{cookiecutter.project_name}}/.gitignore b/imagespec-example/{{cookiecutter.project_name}}/.gitignore new file mode 100644 index 0000000..77d9962 --- /dev/null +++ b/imagespec-example/{{cookiecutter.project_name}}/.gitignore @@ -0,0 +1,138 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# IDE +.idea/* + +# Flyte serialization artifacts +_pb_output +_pb_output/* + +flyte-package.tgz diff --git a/imagespec-example/{{cookiecutter.project_name}}/LICENSE b/imagespec-example/{{cookiecutter.project_name}}/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/imagespec-example/{{cookiecutter.project_name}}/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/imagespec-example/{{cookiecutter.project_name}}/README.md b/imagespec-example/{{cookiecutter.project_name}}/README.md new file mode 100644 index 0000000..a5417e1 --- /dev/null +++ b/imagespec-example/{{cookiecutter.project_name}}/README.md @@ -0,0 +1,25 @@ +# {{ cookiecutter.project_name }} + +A template for the recommended layout of a Flyte enabled repository for code written in python using [flytekit](https://docs.flyte.org/projects/flytekit/en/latest/). + +## Usage + +To get up and running with your Flyte project, we recommend following the +[Flyte getting started guide](https://docs.flyte.org/en/latest/getting_started.html). + +This project includes a script `docker_build.sh` that you can use to build a +Docker image for your Flyte project. + +``` +# help +./docker_build.sh -h + +# build an image with defaults +./docker_build.sh + +# build an image with custom values +./docker_build.sh -p {{ cookiecutter.project_name }} -r -v +``` + +We recommend using a git repository to version this project, so that you can +use the git sha to version your Flyte workflows. diff --git a/imagespec-example/{{cookiecutter.project_name}}/requirements.txt b/imagespec-example/{{cookiecutter.project_name}}/requirements.txt new file mode 100644 index 0000000..016297c --- /dev/null +++ b/imagespec-example/{{cookiecutter.project_name}}/requirements.txt @@ -0,0 +1,2 @@ +flytekit>=1.6.0 +flytekitplugins-envd diff --git a/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py b/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py new file mode 100644 index 0000000..af0a508 --- /dev/null +++ b/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py @@ -0,0 +1 @@ +from .example import wf as wf \ No newline at end of file diff --git a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py new file mode 100644 index 0000000..29bb18b --- /dev/null +++ b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py @@ -0,0 +1,79 @@ +""" +Building Docker Images without a Dockerfile +------------------------------------------- + +.. tags:: Containerization, Intermediate + +Image Spec is a way to specify how to build a container image without a Dockerfile. The image spec by default will be +converted to an `Envd `__ config, and the `Envd builder +`__ will build the image for you. However, you can also register your own builder to build +the image using other tools. + +For every :py:class:`flytekit.PythonFunctionTask` task or a task decorated with the ``@task`` decorator, +you can specify rules for binding container images. By default, flytekit binds a single container image, i.e., +the `default Docker image `__, to all tasks. To modify this behavior, +use the ``container_image`` parameter available in the :py:func:`flytekit.task` decorator, and pass an +``ImageSpec``. + +Before building the image, Flytekit checks the container registry first to see if the image already exists. By doing +so, it avoids having to rebuild the image over and over again. If the image does not exist, flytekit will build the +image before registering the workflow, and replace the image name in the task template with the newly built image name. +""" + +import typing +from flytekit import task, workflow, ImageSpec + + +pandas_image_spec = ImageSpec( + base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.0", + packages=["pandas", "numpy"], + python_version="3.9", + apt_packages=["git"], + env={"Debug": "True"}, +) + +sklearn_image_spec = ImageSpec( + base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.0", + packages=["tensorflow"], +) + +@task +def say_hello(name: str) -> str: + """A simple Flyte task to say "hello". + + The @task decorator allows Flyte to use this function as a Flyte task, which + is executed as an isolated, containerized unit of compute. + """ + return f"hello {name}!" + + +@task +def greeting_length(greeting: str) -> int: + """A task the counts the length of a greeting.""" + return len(greeting) + +@workflow +def wf(name: str = "union") -> typing.Tuple[str, int]: + """Declare workflow called `wf`. + + The @workflow decorator defines an execution graph that is composed of tasks + and potentially sub-workflows. In this simple example, the workflow is + composed of just one task. + + There are a few important things to note about workflows: + - Workflows are a domain-specific language (DSL) for creating execution + graphs and therefore only support a subset of Python's behavior. + - Tasks must be invoked with keyword arguments + - The output variables of tasks are Promises, which are placeholders for + values that are yet to be materialized, not the actual values. + """ + greeting = say_hello(name=name) + greeting_len = greeting_length(greeting=greeting) + return greeting, greeting_len + + +if __name__ == "__main__": + # Execute the workflow, simply by invoking it like a function and passing in + # the necessary parameters + print(f"Running wf() { wf(name='passengers') }") diff --git a/mnist-training/{{cookiecutter.project_name}}/workflows/mnist_training_example.py b/mnist-training/{{cookiecutter.project_name}}/workflows/mnist_training_example.py index 56e0c7b..d2e48b6 100644 --- a/mnist-training/{{cookiecutter.project_name}}/workflows/mnist_training_example.py +++ b/mnist-training/{{cookiecutter.project_name}}/workflows/mnist_training_example.py @@ -26,7 +26,7 @@ def get_dataset(training: bool, gpu: bool = False) -> DataLoader: return dataloader -@task(requests=Resources(cpu="2", mem="10Gi")) +@task(requests=Resources(cpu="2", mem="10Gi", ephemeral_storage="500M")) def train_cpu(dataset: DataLoader, n_epochs: int) -> th.nn.Sequential: """ This task trains the model for the specified number of epochs. @@ -36,7 +36,7 @@ def train_cpu(dataset: DataLoader, n_epochs: int) -> th.nn.Sequential: return train_model(model=model, optim=optim, dataset=dataset, n_epochs=n_epochs) -@task(requests=Resources(gpu="1", mem="10Gi")) +@task(requests=Resources(gpu="1", mem="10Gi", ephemeral_storage="500M")) def train_gpu(dataset: DataLoader, n_epochs: int) -> th.nn.Sequential: """ This task trains the model for the specified number of epochs. @@ -67,7 +67,6 @@ def validation_loss(model: th.nn.Sequential, dataset: DataLoader) -> str: return "NLL model loss in test set: " + str(loss) - """ General Functions, used by Tasks """ @@ -94,6 +93,7 @@ def train_model(model: th.nn.Sequential, optim: th.optim.Optimizer, dataset: Dat optim.step() return model + def get_model_architecture() -> (th.nn.Sequential, th.optim.Optimizer): model = nn.Sequential( nn.Conv2d(1, 16, kernel_size=3, padding=1), @@ -111,7 +111,6 @@ def get_model_architecture() -> (th.nn.Sequential, th.optim.Optimizer): return model, optimizer - @workflow def mnist_workflow_cpu(n_epoch: int = 10) -> str: """Declare workflow called `wf`. From 57440b9e0d14d43ee755e2de852dbd82d89320c3 Mon Sep 17 00:00:00 2001 From: zeryx <1892175+zeryx@users.noreply.github.com> Date: Fri, 26 May 2023 13:40:33 -0400 Subject: [PATCH 2/4] initial imagespec commit, imagespec building is working Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com> --- .../workflows/__init__.py | 2 +- .../workflows/example.py | 76 +++++++++---------- 2 files changed, 39 insertions(+), 39 deletions(-) diff --git a/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py b/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py index af0a508..cf0751d 100644 --- a/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py +++ b/imagespec-example/{{cookiecutter.project_name}}/workflows/__init__.py @@ -1 +1 @@ -from .example import wf as wf \ No newline at end of file +from .example import imagespec_workflow as wf \ No newline at end of file diff --git a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py index 29bb18b..16d4de8 100644 --- a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py +++ b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py @@ -21,12 +21,13 @@ image before registering the workflow, and replace the image name in the task template with the newly built image name. """ -import typing -from flytekit import task, workflow, ImageSpec +import pandas as pd +from flytekit import task, workflow, ImageSpec, Resources pandas_image_spec = ImageSpec( base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.0", + registry="docker", packages=["pandas", "numpy"], python_version="3.9", apt_packages=["git"], @@ -35,45 +36,44 @@ sklearn_image_spec = ImageSpec( base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.0", + registry="docker", packages=["tensorflow"], ) -@task -def say_hello(name: str) -> str: - """A simple Flyte task to say "hello". - - The @task decorator allows Flyte to use this function as a Flyte task, which - is executed as an isolated, containerized unit of compute. - """ - return f"hello {name}!" +# ``is_container`` is used to determine whether the task is utilizing the image constructed from the ``ImageSpec``. +# If the task is indeed using the image built from the ``ImageSpec``, it will then import Tensorflow. +# This approach helps minimize module loading time and prevents unnecessary dependency installation within a single image. +if sklearn_image_spec.is_container(): + from sklearn.linear_model import LogisticRegression +# To enable tasks to utilize the images built with ``ImageSpec``, you can specify the ``container_image`` parameter +# for those tasks. +# @task(container_image=pandas_image_spec) @task -def greeting_length(greeting: str) -> int: - """A task the counts the length of a greeting.""" - return len(greeting) - -@workflow -def wf(name: str = "union") -> typing.Tuple[str, int]: - """Declare workflow called `wf`. - - The @workflow decorator defines an execution graph that is composed of tasks - and potentially sub-workflows. In this simple example, the workflow is - composed of just one task. - - There are a few important things to note about workflows: - - Workflows are a domain-specific language (DSL) for creating execution - graphs and therefore only support a subset of Python's behavior. - - Tasks must be invoked with keyword arguments - - The output variables of tasks are Promises, which are placeholders for - values that are yet to be materialized, not the actual values. - """ - greeting = say_hello(name=name) - greeting_len = greeting_length(greeting=greeting) - return greeting, greeting_len - - -if __name__ == "__main__": - # Execute the workflow, simply by invoking it like a function and passing in - # the necessary parameters - print(f"Running wf() { wf(name='passengers') }") +def get_pandas_dataframe() -> (pd.DataFrame, pd.Series): + df = pd.read_csv("https://storage.googleapis.com/download.tensorflow.org/data/heart.csv") + print(df.head()) + return df[['age', 'thalach', 'trestbps', 'chol', 'oldpeak']], df.pop('target') + + +@task(container_image=sklearn_image_spec, requests=Resources(cpu="1", mem="1Gi")) +# @task +def get_model(max_iter: int, multi_class: str) -> LogisticRegression: + return LogisticRegression(max_iter=max_iter, multi_class=multi_class) + + +# Get a basic model to train. +@task(container_image=sklearn_image_spec, requests=Resources(cpu="1", mem="1Gi")) +# @task +def train_model(model: LogisticRegression, feature: pd.DataFrame, target: pd.Series) -> LogisticRegression: + model.fit(feature, target) + return model +# +# +# # Lastly, let's define a workflow to capture the dependencies between the tasks. +@workflow() +def imagespec_workflow() -> LogisticRegression: + feature, target = get_pandas_dataframe() + model = get_model(max_iter=3000, multi_class="auto") + return train_model(model=model, feature=feature, target=target) From c386e60b92d8a248a11811a014acab8071ee19e8 Mon Sep 17 00:00:00 2001 From: zeryx <1892175+zeryx@users.noreply.github.com> Date: Fri, 26 May 2023 18:04:55 -0400 Subject: [PATCH 3/4] added imagespec standard (ghcr.io/unionai-oss/flytekit) as the standard example image repository for templates. Currently getting 403 errors, working on that still Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com> --- .../workflows/example.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py index 16d4de8..40cec11 100644 --- a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py +++ b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py @@ -26,17 +26,18 @@ from flytekit import task, workflow, ImageSpec, Resources pandas_image_spec = ImageSpec( + name="flytekit", base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.0", - registry="docker", + registry="ghcr.io/unionai-oss", packages=["pandas", "numpy"], python_version="3.9", - apt_packages=["git"], env={"Debug": "True"}, ) sklearn_image_spec = ImageSpec( + name="flytekit", base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.0", - registry="docker", + registry="ghcr.io/unionai-oss", packages=["tensorflow"], ) @@ -58,19 +59,16 @@ def get_pandas_dataframe() -> (pd.DataFrame, pd.Series): @task(container_image=sklearn_image_spec, requests=Resources(cpu="1", mem="1Gi")) -# @task def get_model(max_iter: int, multi_class: str) -> LogisticRegression: return LogisticRegression(max_iter=max_iter, multi_class=multi_class) # Get a basic model to train. @task(container_image=sklearn_image_spec, requests=Resources(cpu="1", mem="1Gi")) -# @task def train_model(model: LogisticRegression, feature: pd.DataFrame, target: pd.Series) -> LogisticRegression: model.fit(feature, target) return model -# -# + # # Lastly, let's define a workflow to capture the dependencies between the tasks. @workflow() def imagespec_workflow() -> LogisticRegression: From 8aaff89ad99d2bea0cc321a4946495edd3b1efe8 Mon Sep 17 00:00:00 2001 From: zeryx <1892175+zeryx@users.noreply.github.com> Date: Tue, 30 May 2023 10:33:49 -0400 Subject: [PATCH 4/4] removed unnecessary () from decorator Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com> --- .../{{cookiecutter.project_name}}/workflows/example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py index 40cec11..62019f7 100644 --- a/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py +++ b/imagespec-example/{{cookiecutter.project_name}}/workflows/example.py @@ -70,7 +70,7 @@ def train_model(model: LogisticRegression, feature: pd.DataFrame, target: pd.Ser return model # # Lastly, let's define a workflow to capture the dependencies between the tasks. -@workflow() +@workflow def imagespec_workflow() -> LogisticRegression: feature, target = get_pandas_dataframe() model = get_model(max_iter=3000, multi_class="auto")