Adds neptune plugin example #1723
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Signed-off-by: Thomas J. Fan <thomasjpfan@gmail.com>
Signed-off-by: Thomas J. Fan <thomasjpfan@gmail.com>
neverett
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Aug 16, 2024
examples/neptune_plugin/README.md
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| .. tags:: Integration, Data, Metrics, Intermediate | ||
| ``` | ||
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| Neptune is the MLOps stack component for experiment tracking. It offers a single place to log, compare, store, and collaborate on experiments and models. This plugin enables seamless use of Neptune within Flyte by configuring links between the two platforms. |
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I would link to Neptune docs here (assuming that's https://docs.neptune.ai/)
examples/neptune_plugin/README.md
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| pip install flytekitplugins-neptune | ||
| ``` | ||
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| To enable dynamic log links, add plugin to Flyte's configuration file: |
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| To enable dynamic log links, add plugin to Flyte's configuration file: | |
| To enable dynamic log links, add the plugin to Flyte's configuration file: |
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| (neptune)= | |||
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| # Neptune | |||
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I recommend formatting this README like the other agent READMEs with Installation, Example usage, Local testing, and Flyte deployment configuration sections -- see https://docs.flyte.org/en/latest/flytesnacks/examples/openai_batch_agent/index.html for an example.
| # %% [markdown] | ||
| # (neptune_example)= | ||
| # | ||
| # # Neptune Example |
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| # # Neptune Example | |
| # # Neptune example |
| from flytekitplugins.neptune import neptune_init_run | ||
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| # %% [markdown] | ||
| # First, we specify the neptune project that we will use with Neptune |
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| # First, we specify the neptune project that we will use with Neptune | |
| # First, we specify the Neptune project that we will use with Neptune. |
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| # %% [mardkwon] | ||
| # Next, we use `ImageSpec` to construct a container with the dependencies for our | ||
| # XGBoost training task. Please set the `REGISTRY` to an registry that your cluster can access; |
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| # XGBoost training task. Please set the `REGISTRY` to an registry that your cluster can access; | |
| # XGBoost training task. Please set the `REGISTRY` to a registry that your cluster can access. |
| # %% | ||
| # Next, we use the `neptune_init_run` decorator to configure Flyte to train an XGBoost | ||
| # model. The decorator requires an `api_key` secret to authenticate with Neptune and | ||
| # the task definition needs to requests the same `api_key` secret. In the training |
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| # the task definition needs to requests the same `api_key` secret. In the training | |
| # the task definition needs to request the same `api_key` secret. In the training |
| # %% [markdown] | ||
| # First, we specify the neptune project that we will use with Neptune | ||
| # Please update `NEPTUNE_PROJECT` to the value associated with your account. | ||
| WANDB_PROJECT = "username/project" |
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Should this be NEPTUNE_PROJECT instead of WANDB_PROJECT?
| secret_requests=[api_key], | ||
| requests=Resources(cpu="2", mem="4Gi"), | ||
| ) | ||
| @neptune_init_run(project=WANDB_PROJECT, secret=api_key) |
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If WANDB_PROJECT is changed above, this should also be updated.
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| # %% | ||
| # With Flyte's dynamic workflows, we scale up multiple training jobs with different |
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| # With Flyte's dynamic workflows, we scale up multiple training jobs with different | |
| # With Flyte's dynamic workflows, we can scale up multiple training jobs with different |
Signed-off-by: Thomas J. Fan <thomasjpfan@gmail.com>
Signed-off-by: Thomas J. Fan <thomasjpfan@gmail.com>
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This PR adds documentation for the neptune plugin.