From c13d601a79a13c495c912ecedd53bac91e6a3372 Mon Sep 17 00:00:00 2001 From: Samhita Alla Date: Fri, 22 Mar 2024 11:47:10 +0530 Subject: [PATCH] sagemaker agent backend setup documentation (#5064) * sagemaker agent backend setup doc Signed-off-by: Samhita Alla * update requirement and remove debugging code Signed-off-by: Samhita Alla * add python-kubernetes Signed-off-by: Samhita Alla * incorporate suggestions by Nikki Signed-off-by: Samhita Alla --------- Signed-off-by: Samhita Alla --- docs/_ext/import_projects.py | 24 +- docs/core_use_cases/machine_learning.md | 5 +- docs/deployment/agents/index.md | 3 + .../deployment/agents/sagemaker_inference.rst | 126 + docs/flyte_fundamentals/extending_flyte.md | 2 +- docs/flyte_fundamentals/optimizing_tasks.md | 3 +- docs/user_guide/basics/workflows.md | 5 +- docs/user_guide/extending/backend_plugins.md | 7 +- .../configuring_logging_links_in_the_ui.md | 4 +- monodocs-environment.lock.yaml | 4887 ++++++++++------- monodocs-environment.yaml | 3 +- 11 files changed, 2955 insertions(+), 2114 deletions(-) create mode 100644 docs/deployment/agents/sagemaker_inference.rst diff --git a/docs/_ext/import_projects.py b/docs/_ext/import_projects.py index 1e32aa0d8c..da551ae374 100644 --- a/docs/_ext/import_projects.py +++ b/docs/_ext/import_projects.py @@ -84,10 +84,11 @@ def parse(self): def update_sys_path_for_flytekit(import_project_config: ImportProjectsConfig): # create flytekit/_version.py file manually - with open(f"{import_project_config.flytekit_api_dir}/flytekit/_version.py", "w") as f: + with open( + f"{import_project_config.flytekit_api_dir}/flytekit/_version.py", "w" + ) as f: f.write(f'__version__ = "dev"') - # add flytekit to python path flytekit_dir = os.path.abspath(import_project_config.flytekit_api_dir) flytekit_src_dir = os.path.abspath(os.path.join(flytekit_dir, "flytekit")) @@ -151,7 +152,7 @@ def import_projects(app: Sphinx, config: Config): if repo: tags = sorted( [t for t in repo.tags if re.match(VERSION_PATTERN, t.name)], - key=lambda t: t.commit.committed_datetime + key=lambda t: t.commit.committed_datetime, ) if not tags or import_projects_config.dev_build: # If dev_build is specified or the tags don't exist just use the @@ -187,7 +188,9 @@ def import_projects(app: Sphinx, config: Config): update_sys_path_for_flytekit(import_projects_config) # add functions to clean up source and docstring refs - for i, (patt, repl) in enumerate(import_projects_config.source_regex_mapping.items()): + for i, (patt, repl) in enumerate( + import_projects_config.source_regex_mapping.items() + ): app.connect( "source-read", partial(replace_refs_in_files, patt, repl), @@ -200,7 +203,9 @@ def import_projects(app: Sphinx, config: Config): ) -def replace_refs_in_files(patt: str, repl: str, app: Sphinx, docname: str, source: List[str]): +def replace_refs_in_files( + patt: str, repl: str, app: Sphinx, docname: str, source: List[str] +): text = source[0] if re.search(patt, text): @@ -211,7 +216,14 @@ def replace_refs_in_files(patt: str, repl: str, app: Sphinx, docname: str, sourc def replace_refs_in_docstrings( - patt: str, repl: str, app: Sphinx, what: str, name: str, obj: str, options: dict, lines: List[str], + patt: str, + repl: str, + app: Sphinx, + what: str, + name: str, + obj: str, + options: dict, + lines: List[str], ): replace = {} for i, text in enumerate(lines): diff --git a/docs/core_use_cases/machine_learning.md b/docs/core_use_cases/machine_learning.md index 78925af26f..489b8b05f9 100644 --- a/docs/core_use_cases/machine_learning.md +++ b/docs/core_use_cases/machine_learning.md @@ -108,9 +108,8 @@ There are many ways to extend your workloads: [Ray Tune](https://docs.ray.io/en/latest/tune/index.html) for hyperparameter optimization, all orchestrated by Flyte as ephemerally-provisioned Ray clusters. * - **📦 Ephemeral Cluster Resources** - - Use the {ref}`MPI Operator `, {ref}`Sagemaker `, - {ref}`Kubeflow Tensorflow `, {ref}`Kubeflow Pytorch` - and {doc}`more <_tags/DistributedComputing>` to do distributed training. + - Use the {ref}`MPI Operator `, {ref}`Kubeflow Tensorflow `, + {ref}`Kubeflow Pytorch` and {doc}`more <_tags/DistributedComputing>` to do distributed training. * - **🔎 Experiment Tracking** - Auto-capture training logs with the {py:func}`~flytekitplugins.mlflow.mlflow_autolog` decorator, which can be viewed as Flyte Decks with `@task(disable_decks=False)`. diff --git a/docs/deployment/agents/index.md b/docs/deployment/agents/index.md index 7c7f3db92d..c79a5ecd53 100644 --- a/docs/deployment/agents/index.md +++ b/docs/deployment/agents/index.md @@ -27,6 +27,8 @@ If you are using a managed deployment of Flyte, you will need to contact your de - Configuring your Flyte deployment for the MMCloud agent. * - {ref}`Sensor Agent ` - Configuring your Flyte deployment for the sensor agent. +* - {ref}`SageMaker Inference ` + - Deploy models and create, as well as trigger inference endpoints on SageMaker. ``` ```{toctree} @@ -39,6 +41,7 @@ chatgpt databricks bigquery mmcloud +sagemaker_inference sensor snowflake ``` diff --git a/docs/deployment/agents/sagemaker_inference.rst b/docs/deployment/agents/sagemaker_inference.rst new file mode 100644 index 0000000000..be9188d873 --- /dev/null +++ b/docs/deployment/agents/sagemaker_inference.rst @@ -0,0 +1,126 @@ +.. _deployment-agent-setup-sagemaker-inference: + +SageMaker Inference Agent +========================= + +This guide provides an overview of how to set up the SageMaker inference agent in your Flyte deployment. + +Specify agent configuration +--------------------------- + +.. tabs:: + + .. group-tab:: Flyte binary + + Edit the relevant YAML file to specify the agent. + + .. code-block:: bash + + kubectl edit configmap flyte-sandbox-config -n flyte + + .. code-block:: yaml + :emphasize-lines: 7,11-12,16-17 + + tasks: + task-plugins: + enabled-plugins: + - container + - sidecar + - k8s-array + - agent-service + default-for-task-types: + - container: container + - container_array: k8s-array + - boto: agent-service + - sagemaker-endpoint: agent-service + plugins: + agent-service: + supportedTaskTypes: + - boto + - sagemaker-endpoint + + .. group-tab:: Flyte core + + Create a file named ``values-override.yaml`` and add the following configuration to it: + + .. code-block:: yaml + :emphasize-lines: 9,14-15,19-20 + + configmap: + enabled_plugins: + tasks: + task-plugins: + enabled-plugins: + - container + - sidecar + - k8s-array + - agent-service + default-for-task-types: + container: container + sidecar: sidecar + container_array: k8s-array + boto: agent-service + sagemaker-endpoint: agent-service + plugins: + agent-service: + supportedTaskTypes: + - boto + - sagemaker-endpoint + +Add the AWS credentials +----------------------- + +1. Install the flyteagent pod using helm: + +.. code-block:: + + helm repo add flyteorg https://flyteorg.github.io/flyte + helm install flyteagent flyteorg/flyteagent --namespace flyte + +2. Get the base64 value of your AWS credentials: + +.. code-block:: + + echo -n "" | base64 + +3. Edit the flyteagent secret: + +.. code-block:: bash + + kubectl edit secret flyteagent -n flyte + +.. code-block:: yaml + :emphasize-lines: 3-5 + + apiVersion: v1 + data: + aws-access-key: + aws-secret-access-key: + aws-session-token: + kind: Secret + +Upgrade the Flyte Helm release +------------------------------ + +.. tabs:: + + .. group-tab:: Flyte binary + + .. code-block:: bash + + helm upgrade flyteorg/flyte-binary -n --values + + Replace ```` with the name of your release (e.g., ``flyte-backend``), + ```` with the name of your namespace (e.g., ``flyte``), + and ```` with the name of your YAML file. + + .. group-tab:: Flyte core + + .. code-block:: bash + + helm upgrade flyte/flyte-core -n --values values-override.yaml + + Replace ```` with the name of your release (e.g., ``flyte``) + and ```` with the name of your namespace (e.g., ``flyte``). + +You can refer to the documentation `here `__. diff --git a/docs/flyte_fundamentals/extending_flyte.md b/docs/flyte_fundamentals/extending_flyte.md index 4977e6ae25..7f0d7e475b 100644 --- a/docs/flyte_fundamentals/extending_flyte.md +++ b/docs/flyte_fundamentals/extending_flyte.md @@ -151,7 +151,7 @@ many more ways to customize Flyte tasks: in other languages outside of the `flytekit` SDK language. * - {ref}`Backend Plugins ` - These tasks plugins require implementing a backend plugin to leverage - external services like Sagemaker, Snowflake, BigQuery, etc. + external services like SageMaker, Snowflake, BigQuery, etc. ``` ## What's next? diff --git a/docs/flyte_fundamentals/optimizing_tasks.md b/docs/flyte_fundamentals/optimizing_tasks.md index 00c27c881f..9f3124e270 100644 --- a/docs/flyte_fundamentals/optimizing_tasks.md +++ b/docs/flyte_fundamentals/optimizing_tasks.md @@ -242,8 +242,7 @@ at the most granular level of your workflow! When this task is executed on a Flyte cluster, it automatically provisions all of the resources that you need. In this case, that need is distributed training, but Flyte also provides integrations for {ref}`Spark `, -{ref}`Ray `, {ref}`MPI `, {ref}`Sagemaker `, -{ref}`Snowflake `, and more. +{ref}`Ray `, {ref}`MPI `, {ref}`Snowflake `, and more. Even though Flyte itself is a powerful compute engine and orchestrator for data engineering, machine learning, and analytics, perhaps you have existing diff --git a/docs/user_guide/basics/workflows.md b/docs/user_guide/basics/workflows.md index 1f750c9da8..d5f46be04e 100644 --- a/docs/user_guide/basics/workflows.md +++ b/docs/user_guide/basics/workflows.md @@ -29,7 +29,7 @@ Workflows link multiple tasks together. They can be written as Python functions, but it's important to distinguish tasks and workflows. A task's body executes at run-time on a Kubernetes cluster, in a Query Engine like BigQuery, -or on hosted services like AWS Batch or Sagemaker. +or on hosted services like AWS Batch or SageMaker. In contrast, a workflow's body doesn't perform computations; it's used to structure tasks. A workflow's body executes at registration time, during the workflow's registration process. @@ -116,6 +116,7 @@ if __name__ == "__main__": +++ {"lines_to_next_cell": 0} To run the workflow locally, you can use the following `pyflyte run` command: + ``` pyflyte run \ https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/basics/basics/workflow.py \ @@ -124,6 +125,7 @@ pyflyte run \ If you want to run it remotely on the Flyte cluster, simply add the `--remote flag` to the `pyflyte run` command: + ``` pyflyte run --remote \ https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/basics/basics/workflow.py \ @@ -138,6 +140,7 @@ However, {ref}`executing an individual task ` independent without the confines of a workflow, offers a convenient approach for iterating on task logic effortlessly. ## Use `partial` to provide default arguments to tasks + You can use the {py:func}`functools.partial` function to assign default or constant values to the parameters of your tasks. ```{code-cell} diff --git a/docs/user_guide/extending/backend_plugins.md b/docs/user_guide/extending/backend_plugins.md index 876ef8019e..bbee56ea46 100644 --- a/docs/user_guide/extending/backend_plugins.md +++ b/docs/user_guide/extending/backend_plugins.md @@ -30,15 +30,15 @@ Flyte. To recap, here are a few examples of why you would want to implement a backend plugin: 1. We want to add a new capability to the Flyte Platform, for example we might want to: - - Talk to a new service like AWS Sagemaker, Snowflake, Redshift, Athena, BigQuery, etc. + - Talk to a new service like AWS SageMaker, Snowflake, Redshift, Athena, BigQuery, etc. - Orchestrate a set of containers in a new way like Spark, Flink, Distributed training on Kubernetes (usually using a Kubernetes operator). - Use a new container orchestration engine like AWS Batch/ECS, Hashicorp' Nomad - Use a completely new runtime like AWS Lambda, KNative, etc. -3. You want to retain the capability to update the plugin implementation and roll +2. You want to retain the capability to update the plugin implementation and roll out new changes and fixes without affecting the users code or requiring them to update versions of their plugins. -4. You want the same plugin to be accessible across multiple language SDK's. +3. You want the same plugin to be accessible across multiple language SDK's. ```{note} Talking to a new service can be done using flytekit extensions and usually is the better way to get started. But, once matured, most of these extensions are better to be migrated to the backend. For the rest of the cases, it is possible to extend flytekit to achieve these scenarios, but this is less desirable, because of the associated overhead of first launching a container that launches these jobs downstream. @@ -85,6 +85,7 @@ The backend plugin is where the actual logic of the execution is implemented. Th 1. [Kubernetes operator Plugin](https://pkg.go.dev/github.com/lyft/flyteplugins@v0.5.26/go/tasks/pluginmachinery/k8s#Plugin): The demo in the video below shows two examples of K8s backend plugins: flytekit `Athena` & `Spark`, and Flyte K8s `Pod` & `Spark`. ```{youtube} oK2RGQuP94k + ``` 2. **A Web API plugin:** [Async](https://pkg.go.dev/github.com/lyft/flyteplugins@v0.5.26/go/tasks/pluginmachinery/webapi#AsyncPlugin) or [Sync](https://pkg.go.dev/github.com/lyft/flyteplugins@v0.5.26/go/tasks/pluginmachinery/webapi#SyncPlugin). diff --git a/docs/user_guide/productionizing/configuring_logging_links_in_the_ui.md b/docs/user_guide/productionizing/configuring_logging_links_in_the_ui.md index 57502a8c77..3091e1bc82 100644 --- a/docs/user_guide/productionizing/configuring_logging_links_in_the_ui.md +++ b/docs/user_guide/productionizing/configuring_logging_links_in_the_ui.md @@ -105,7 +105,7 @@ Flytepropeller pod would be created as: ::: This code snippet will output two logs per task that use the log plugin. -However, not all task types use the log plugin; for example, the SageMaker plugin uses the log output provided by Sagemaker, and the Snowflake plugin will use a link to the snowflake console. +However, not all task types use the log plugin; for example, the Snowflake plugin will use a link to the Snowflake console. ## Datadog integration @@ -128,7 +128,7 @@ If you're using environment variables, use the following config: DD_LOGS_ENABLED: "false" DD_LOGS_CONFIG_CONTAINER_COLLECT_ALL: "true" DD_LOGS_CONFIG_K8S_CONTAINER_USE_FILE: "true" -DD_CONTAINER_EXCLUDE_LOGS: "name:datadog-agent" # This is to avoid tracking logs produced by the datadog agent itself +DD_CONTAINER_EXCLUDE_LOGS: "name:datadog-agent" # This is to avoid tracking logs produced by the datadog agent itself ``` :::{warning} diff --git a/monodocs-environment.lock.yaml b/monodocs-environment.lock.yaml index 9348bb40f6..d6e0f711de 100644 --- a/monodocs-environment.lock.yaml +++ b/monodocs-environment.lock.yaml @@ -5,7 +5,7 @@ # available, unless you explicitly update the lock file. # # Install this environment as "YOURENV" with: -# conda-lock install -n YOURENV --file monodocs-environment.lock.yaml +# conda-lock install -n YOURENV monodocs-environment.lock.yaml # To update a single package to the latest version compatible with the version constraints in the source: # conda-lock lock --lockfile monodocs-environment.lock.yaml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: @@ -13,8 +13,8 @@ version: 1 metadata: content_hash: - 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version: 2.11.1 + version: 2.12.1 manager: conda platform: osx-arm64 dependencies: + aiohttp: '>=3.7.4.post0,<4.0.0' + aioitertools: '>=0.5.1,<1.0.0' + botocore: '>=1.34.41,<1.34.52' python: '>=3.8' wrapt: '>=1.10.10,<2.0.0' - aioitertools: '>=0.5.1,<1.0.0' - aiohttp: '>=3.7.4.post0,<4.0.0' - botocore: '>=1.33.2,<1.34.28' - url: https://conda.anaconda.org/conda-forge/noarch/aiobotocore-2.11.1-pyhd8ed1ab_1.conda + url: https://conda.anaconda.org/conda-forge/noarch/aiobotocore-2.12.1-pyhd8ed1ab_0.conda hash: - md5: 9f219d2e85fcb24a70961f1e4671e194 - sha256: 3a9b915c992ec4b7b95abdbc7392c4be38ee7a51983d11aabd1cbdad29bc33c6 + md5: 045560daef58c75a759c2638fbdb5c6a + sha256: e7d43681c7c2957b535aac2b3ad90013bcb39ed5514cea3cec52302f624d457a category: main optional: false - name: aiohttp - version: 3.9.1 + version: 3.9.3 manager: conda platform: linux-64 dependencies: @@ -186,14 +186,14 @@ package: python: '>=3.9,<3.10.0a0' python_abi: 3.9.* yarl: '>=1.0,<2.0' - url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.1-py39hd1e30aa_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.3-py39hd1e30aa_1.conda hash: - 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python: '>=3.7' frozenlist: '>=1.1.0' + python: '>=3.7' url: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2 hash: md5: d1e1eb7e21a9e2c74279d87dafb68156 @@ -344,13 +344,13 @@ package: manager: conda platform: osx-arm64 dependencies: - jinja2: '' - toolz: '' entrypoints: '' - python: '>=3.7' - pandas: '>=0.18' + jinja2: '' jsonschema: '>=3.0' numpy: '>=0.18' + pandas: '>=0.18' + python: '>=3.7' + toolz: '' url: https://conda.anaconda.org/conda-forge/noarch/altair-4.2.2-pyhd8ed1ab_0.conda hash: md5: afca9c6a93335c55bbc84072011e86dc @@ -375,8 +375,8 @@ package: manager: conda platform: osx-arm64 dependencies: - textwrap3: '' python: <3.12.0a0 + textwrap3: '' url: https://conda.anaconda.org/conda-forge/noarch/ansiwrap-0.8.4-py_0.tar.bz2 hash: md5: f09557e2a7cbd2470a2ab6353000cebd @@ -384,7 +384,7 @@ package: category: main optional: false - name: anyio - version: 4.2.0 + version: 4.3.0 manager: conda platform: linux-64 dependencies: @@ -393,26 +393,26 @@ package: python: '>=3.8' sniffio: '>=1.1' typing_extensions: '>=4.1' - 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- retry # aws sagemaker - botocore # fsspec - fsspec # fsspec - google-cloud-bigquery # bigquery @@ -38,6 +37,7 @@ dependencies: - great-expectations # greatexpectations - datasets # huggingface - kubernetes # k8s-pod + - python-kubernetes # k8s-pod - modin # modin - pandera # pandera - pydantic # vaex, pydantic @@ -75,6 +75,7 @@ dependencies: - whylabs-client # whylogs - ray==2.6.3 - duckdb + - aioboto3>=12.3.0 # aws sagemaker inference platforms: - linux-64