diff --git a/README.md b/README.md index 162c95f97..e523c059d 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,10 @@ src="https://img.shields.io/pypi/v/hopsworks?color=blue" alt="PyPiStatus" /> + Scala/Java Artifacts Downloads *hopsworks* is the python API for interacting with a Hopsworks cluster. Don't have a Hopsworks cluster just yet? Register an account on [Hopsworks Serverless](https://app.hopsworks.ai/) and get started for free. Once connected to your project, you can: - - Insert dataframes into the online or offline Store, create training datasets or *serve real-time* feature vectors in the Feature Store via the [Feature Store API](https://github.com/logicalclocks/feature-store-api). Already have data somewhere you want to import, checkout our [Storage Connectors](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/) documentation. - - register ML models in the model registry and *deploy* them via model serving via the [Machine Learning API](https://gitub.com/logicalclocks/machine-learning-api). - - manage environments, executions, kafka topics and more once you deploy your own Hopsworks cluster, either on-prem or in the cloud. Hopsworks is open-source and has its own [Community Edition](https://github.com/logicalclocks/hopsworks). + +- Insert dataframes into the online or offline Store, create training datasets or *serve real-time* feature vectors in the Feature Store via the Feature Store API. Already have data somewhere you want to import, checkout our [Storage Connectors](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/) documentation. +- register ML models in the model registry and *deploy* them via model serving via the Machine Learning API. +- manage environments, executions, kafka topics and more once you deploy your own Hopsworks cluster, either on-prem or in the cloud. Hopsworks is open-source and has its own [Community Edition](https://github.com/logicalclocks/hopsworks). Our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials) cover a wide range of use cases and example of what *you* can build using Hopsworks. @@ -43,16 +48,19 @@ Our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials) cover a wi Once you created a project on [Hopsworks Serverless](https://app.hopsworks.ai) and created a new [Api Key](https://docs.hopsworks.ai/latest/user_guides/projects/api_key/create_api_key/), just use your favourite virtualenv and package manager to install the library: ```bash -pip install hopsworks +pip install "hopsworks[python]" ``` Fire up a notebook and connect to your project, you will be prompted to enter your newly created API key: + ```python import hopsworks project = hopsworks.login() ``` +### Feature Store API + Access the Feature Store of your project to use as a central repository for your feature data. Use *your* favourite data engineering library (pandas, polars, Spark, etc...) to insert data into the Feature Store, create training datasets or serve real-time feature vectors. Want to predict likelyhood of e-scooter accidents in real-time? Here's how you can do it: ```python @@ -60,9 +68,9 @@ fs = project.get_feature_store() # Write to Feature Groups bike_ride_fg = fs.get_or_create_feature_group( - name="bike_rides", - version=1, - primary_key=["ride_id"], + name="bike_rides", + version=1, + primary_key=["ride_id"], event_time="activation_time", online_enabled=True, ) @@ -73,13 +81,13 @@ fg.insert(bike_rides_df) profile_fg = fs.get_feature_group("user_profile", version=1) bike_ride_fv = fs.get_or_create_feature_view( - name="bike_rides_view", - version=1, + name="bike_rides_view", + version=1, query=bike_ride_fg.select_except(["ride_id"]).join(profile_fg.select(["age", "has_license"]), on="user_id") ) bike_rides_Q1_2021_df = bike_ride_fv.get_batch_data( - start_date="2021-01-01", + start_date="2021-01-01", end_date="2021-01-31" ) @@ -97,22 +105,68 @@ bike_ride_fv.init_serving() while True: new_ride_vector = poll_ride_queue() feature_vector = bike_ride_fv.get_online_feature_vector( - {"user_id": new_ride_vector["user_id"]}, + {"user_id": new_ride_vector["user_id"]}, passed_features=new_ride_vector ) accident_probability = model.predict(feature_vector) ``` -Or you can use the Machine Learning API to register models and deploy them for serving: +The API enables interaction with the Hopsworks Feature Store. It makes creating new features, feature groups and training datasets easy. + +The API is environment independent and can be used in two modes: + +- Spark mode: For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides bindings both for Python and JVM languages. + +- Python mode: For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker or KubeFlow. + +Scala API is also available, here is a short sample of it: + +```scala +import com.logicalclocks.hsfs._ +val connection = HopsworksConnection.builder().build() +val fs = connection.getFeatureStore(); +val attendances_features_fg = fs.getFeatureGroup("games_features", 1); +attendances_features_fg.show(1) +``` + +### Machine Learning API + +Or you can use the Machine Learning API to interact with the Hopsworks Model Registry and Model Serving. The API makes it easy to export, manage and deploy models. For example, to register models and deploy them for serving you can do: + ```python mr = project.get_model_registry() # or -ms = project.get_model_serving() +ms = connection.get_model_serving() + +# Create a new model: +model = mr.tensorflow.create_model(name="mnist", + version=1, + metrics={"accuracy": 0.94}, + description="mnist model description") +model.save("/tmp/model_directory") # or /tmp/model_file + +# Download a model: +model = mr.get_model("mnist", version=1) +model_path = model.download() + +# Delete the model: +model.delete() + +# Get the best-performing model +best_model = mr.get_best_model('mnist', 'accuracy', 'max') + +# Deploy the model: +deployment = model.deploy() +deployment.start() + +# Make predictions with a deployed model +data = { "instances": [ model.input_example ] } +predictions = deployment.predict(data) ``` ## Tutorials -Need more inspiration or want to learn more about the Hopsworks platform? Check out our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials). +Need more inspiration or want to learn more about the Hopsworks platform? Check out our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials). ## Documentation @@ -124,7 +178,17 @@ For general questions about the usage of Hopsworks and the Feature Store please Please report any issue using [Github issue tracking](https://github.com/logicalclocks/hopsworks-api/issues). +### Related to Feautre Store API + +Please attach the client environment from the output below to your issue, if it is related to Feature Store API: + +```python +import hopsworks +import hsfs +hopsworks.login().get_feature_store() +print(hsfs.get_env()) +``` + ## Contributing If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md). - diff --git a/hsfs/LICENSE b/hsfs/LICENSE deleted file mode 100644 index 261eeb9e9..000000000 --- a/hsfs/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - 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/hsfs/README.md b/hsfs/README.md deleted file mode 100644 index a13ea2ce5..000000000 --- a/hsfs/README.md +++ /dev/null @@ -1,201 +0,0 @@ -# Hopsworks Feature Store - -

- Hopsworks Community - Hopsworks Feature Store Documentation - python - PyPiStatus - Scala/Java Artifacts - Downloads - Ruff - License -

- -HSFS is the library to interact with the Hopsworks Feature Store. The library makes creating new features, feature groups and training datasets easy. - -The library is environment independent and can be used in two modes: - -- Spark mode: For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides bindings both for Python and JVM languages. - -- Python mode: For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker or KubeFlow. - -The library automatically configures itself based on the environment it is run. -However, to connect from an external environment such as Databricks or AWS Sagemaker, -additional connection information, such as host and port, is required. For more information checkout the [Hopsworks documentation](https://docs.hopsworks.ai/latest/). - -## Getting Started On Hopsworks - -Get started easily by registering an account on [Hopsworks Serverless](https://app.hopsworks.ai/). Create your project and a [new Api key](https://docs.hopsworks.ai/latest/user_guides/projects/api_key/create_api_key/). In a new python environment with Python 3.8 or higher, install the [client library](https://docs.hopsworks.ai/latest/user_guides/client_installation/) using pip: - -```bash -# Get all Hopsworks SDKs: Feature Store, Model Serving and Platform SDK -pip install hopsworks -# or minimum install with the Feature Store SDK -pip install hsfs[python] -# if using zsh don't forget the quotes -pip install 'hsfs[python]' -``` - -You can start a notebook and instantiate a connection and get the project feature store handler. - -```python -import hopsworks - -project = hopsworks.login() # you will be prompted for your api key -fs = project.get_feature_store() -``` - -or using `hsfs` directly: - -```python -import hsfs - -connection = hsfs.connection( - host="c.app.hopsworks.ai", # - project="your-project", - api_key_value="your-api-key", -) -fs = connection.get_feature_store() -``` - -Create a new feature group to start inserting feature values. -```python -fg = fs.create_feature_group("rain", - version=1, - description="Rain features", - primary_key=['date', 'location_id'], - online_enabled=True) - -fg.save(dataframe) -``` - -Upsert new data in to the feature group with `time_travel_format="HUDI"`". -```python -fg.insert(upsert_df) -``` - -Retrieve commit timeline metdata of the feature group with `time_travel_format="HUDI"`". -```python -fg.commit_details() -``` - -"Reading feature group as of specific point in time". -```python -fg = fs.get_feature_group("rain", 1) -fg.read("2020-10-20 07:34:11").show() -``` - -Read updates that occurred between specified points in time. -```python -fg = fs.get_feature_group("rain", 1) -fg.read_changes("2020-10-20 07:31:38", "2020-10-20 07:34:11").show() -``` - -Join features together -```python -feature_join = rain_fg.select_all() - .join(temperature_fg.select_all(), on=["date", "location_id"]) - .join(location_fg.select_all()) -feature_join.show(5) -``` - -join feature groups that correspond to specific point in time -```python -feature_join = rain_fg.select_all() - .join(temperature_fg.select_all(), on=["date", "location_id"]) - .join(location_fg.select_all()) - .as_of("2020-10-31") -feature_join.show(5) -``` - -join feature groups that correspond to different time -```python -rain_fg_q = rain_fg.select_all().as_of("2020-10-20 07:41:43") -temperature_fg_q = temperature_fg.select_all().as_of("2020-10-20 07:32:33") -location_fg_q = location_fg.select_all().as_of("2020-10-20 07:33:08") -joined_features_q = rain_fg_q.join(temperature_fg_q).join(location_fg_q) -``` - -Use the query object to create a training dataset: -```python -td = fs.create_training_dataset("rain_dataset", - version=1, - data_format="tfrecords", - description="A test training dataset saved in TfRecords format", - splits={'train': 0.7, 'test': 0.2, 'validate': 0.1}) - -td.save(feature_join) -``` - -A short introduction to the Scala API: -```scala -import com.logicalclocks.hsfs._ -val connection = HopsworksConnection.builder().build() -val fs = connection.getFeatureStore(); -val attendances_features_fg = fs.getFeatureGroup("games_features", 1); -attendances_features_fg.show(1) -``` - -You can find more examples on how to use the library in our [hops-examples](https://github.com/logicalclocks/hops-examples) repository. - -## Usage - -Usage data is collected for improving quality of the library. It is turned on by default if the backend -is "c.app.hopsworks.ai". To turn it off, use one of the following way: -```python -# use environment variable -import os -os.environ["ENABLE_HOPSWORKS_USAGE"] = "false" - -# use `disable_usage_logging` -import hsfs -hsfs.disable_usage_logging() -``` - -The source code can be found in python/hsfs/usage.py. - -## Documentation - -Documentation is available at [Hopsworks Feature Store Documentation](https://docs.hopsworks.ai/). - -## Issues - -For general questions about the usage of Hopsworks and the Feature Store please open a topic on [Hopsworks Community](https://community.hopsworks.ai/). - -Please report any issue using [Github issue tracking](https://github.com/logicalclocks/feature-store-api/issues). - -Please attach the client environment from the output below in the issue: -```python -import hopsworks -import hsfs -hopsworks.login().get_feature_store() -print(hsfs.get_env()) -``` - -## Contributing - -If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md). diff --git a/hsml/LICENSE b/hsml/LICENSE deleted file mode 100644 index 261eeb9e9..000000000 --- a/hsml/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. 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Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. 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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/hsml/README.md b/hsml/README.md deleted file mode 100644 index ee835ddc7..000000000 --- a/hsml/README.md +++ /dev/null @@ -1,141 +0,0 @@ -# Hopsworks Model Management - -

- Hopsworks Community - Hopsworks Model Management Documentation - python - PyPiStatus - Scala/Java Artifacts - Downloads - Ruff - License -

- -HSML is the library to interact with the Hopsworks Model Registry and Model Serving. The library makes it easy to export, manage and deploy models. - -However, to connect from an external Python environment additional connection information, such as host and port, is required. - -## Getting Started On Hopsworks - -Get started easily by registering an account on [Hopsworks Serverless](https://app.hopsworks.ai/). Create your project and a [new Api key](https://docs.hopsworks.ai/latest/user_guides/projects/api_key/create_api_key/). In a new python environment with Python 3.8 or higher, install the [client library](https://docs.hopsworks.ai/latest/user_guides/client_installation/) using pip: - -```bash -# Get all Hopsworks SDKs: Feature Store, Model Serving and Platform SDK -pip install hopsworks -# or just the Model Registry and Model Serving SDK -pip install hsml -``` - -You can start a notebook and instantiate a connection and get the project feature store handler. - -```python -import hopsworks - -project = hopsworks.login() # you will be prompted for your api key - -mr = project.get_model_registry() -# or -ms = project.get_model_serving() -``` - -or using `hsml` directly: - -```python -import hsml - -connection = hsml.connection( - host="c.app.hopsworks.ai", # - project="your-project", - api_key_value="your-api-key", -) - -mr = connection.get_model_registry() -# or -ms = connection.get_model_serving() -``` - -Create a new model -```python -model = mr.tensorflow.create_model(name="mnist", - version=1, - metrics={"accuracy": 0.94}, - description="mnist model description") -model.save("/tmp/model_directory") # or /tmp/model_file -``` - -Download a model -```python -model = mr.get_model("mnist", version=1) - -model_path = model.download() -``` - -Delete a model -```python -model.delete() -``` - -Get best performing model -```python -best_model = mr.get_best_model('mnist', 'accuracy', 'max') - -``` - -Deploy a model -```python -deployment = model.deploy() -``` - -Start a deployment -```python -deployment.start() -``` - -Make predictions with a deployed model -```python -data = { "instances": [ model.input_example ] } - -predictions = deployment.predict(data) -``` - -# Tutorials - -You can find more examples on how to use the library in our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials). - -## Documentation - -Documentation is available at [Hopsworks Model Management Documentation](https://docs.hopsworks.ai/). - -## Issues - -For general questions about the usage of Hopsworks Machine Learning please open a topic on [Hopsworks Community](https://community.hopsworks.ai/). -Please report any issue using [Github issue tracking](https://github.com/logicalclocks/machine-learning-api/issues). - - -## Contributing - -If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).