| Name | mlflow-go-backend JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | MLflow is an open source platform for the complete machine learning lifecycle |
| upload_time | 2025-01-28 09:25:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
mlflow
go
golang
ai
databricks
|
| VCS |
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|
[](https://pypi.org/project/mlflow-go-backend/)
# Go backend for MLflow
In order to increase the performance of the tracking server and the various stores, we propose to rewrite the server and store implementation in Go.
## Getting started
### Installation
```shell
pip install mlflow-go-backend
```
### CLI Usage
`mlflow-go-backend` is meant to be a drop-in-replacement for `mlflow`.
You can update your existing you used `mlflow` command with `mlflow-go`:
```diff
- mlflow server --backend-store-uri postgresql://postgres:postgres@localhost:5432/postgres
+ mlflow-go server --backend-store-uri postgresql://postgres:postgres@localhost:5432/postgres
```
Every existing setting of [mlflow server](https://mlflow.org/docs/latest/cli.html#mlflow-server) can be passed to `mlflow-go`.
> [!CAUTION]
> The Go implementation currently does not support file storage as a backend store. You must provide the --backend-store-uri argument pointing to a database.
### Python Usage
```py
import mlflow
import mlflow_go
# Enable the Go client implementation (disabled by default)
mlflow_go.enable_go()
# Set the tracking URI (you can also set it via the environment variable MLFLOW_TRACKING_URI)
# Currently only database URIs are supported
mlflow.set_tracking_uri("sqlite:///mlflow.db")
# Use MLflow as usual
mlflow.set_experiment("my-experiment")
with mlflow.start_run():
mlflow.log_param("param", 1)
mlflow.log_metric("metric", 2)
```
### Python Store Usage
```py
import logging
import mlflow
import mlflow_go
# Enable debug logging
logging.basicConfig()
logging.getLogger('mlflow_go').setLevel(logging.DEBUG)
# Enable the Go client implementation (disabled by default)
mlflow_go.enable_go()
# Instantiate the tracking store with a database URI
tracking_store = mlflow.tracking._tracking_service.utils._get_store('sqlite:///mlflow.db')
# Call any tracking store method
tracking_store.get_experiment(0)
# Instantiate the model registry store with a database URI
model_registry_store = mlflow.tracking._model_registry.utils._get_store('sqlite:///mlflow.db')
# Call any model registry store method
model_registry_store.get_latest_versions("model")
```
## Why bother?
For that sweet performance my friend!
Initial benchmarks show us that critical API calls are faster and Go can handle more concurrent requests.
> [!NOTE]
> These were initial results and can perhaps still be more optimized.
See [benchmarks](./benchmarks/README.md) for more information.
## Contribution
### Try it out!
We advocate this project as a drop-in replacement for `mlflow`.
Please give it a try, and let us know if anything isn't working for you!
We look forward to your feedback!
### Missing Endpoints
Hey there! We need your help! This project is not yet a full port of the current tracking server.
Not every [endpoint in the REST API](https://mlflow.org/docs/latest/rest-api.html) is implemented in Go.
Those missing are redirected to the existing Python implementation.
Please consider helping us out and implement [a missing endpoint](./docs/porting-a-new-endpoint.md).
Not yet a Go wizard? We've collected some [resources to learn Go](./docs/learning-go.md). Trust us, it will be an enriching and fun experience!
## Community
There is a dedicated Slack channel called `#mlflow-go` on the official [mlflow Slack](https://mlflow.org/slack).
Shoot us a message if you have any questions!
Raw data
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"maintainer_email": "Databricks <mlflow-oss-maintainers@googlegroups.com>",
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"author": null,
"author_email": null,
"download_url": null,
"platform": null,
"description": "[](https://pypi.org/project/mlflow-go-backend/)\n\n# Go backend for MLflow\n\nIn order to increase the performance of the tracking server and the various stores, we propose to rewrite the server and store implementation in Go.\n\n## Getting started\n\n### Installation\n\n```shell\npip install mlflow-go-backend\n```\n\n### CLI Usage\n\n`mlflow-go-backend` is meant to be a drop-in-replacement for `mlflow`.\n\nYou can update your existing you used `mlflow` command with `mlflow-go`:\n\n```diff\n- mlflow server --backend-store-uri postgresql://postgres:postgres@localhost:5432/postgres\n+ mlflow-go server --backend-store-uri postgresql://postgres:postgres@localhost:5432/postgres\n```\n\nEvery existing setting of [mlflow server](https://mlflow.org/docs/latest/cli.html#mlflow-server) can be passed to `mlflow-go`.\n\n> [!CAUTION]\n> The Go implementation currently does not support file storage as a backend store. You must provide the --backend-store-uri argument pointing to a database.\n\n### Python Usage\n\n```py\nimport mlflow\nimport mlflow_go\n\n# Enable the Go client implementation (disabled by default)\nmlflow_go.enable_go()\n\n# Set the tracking URI (you can also set it via the environment variable MLFLOW_TRACKING_URI)\n# Currently only database URIs are supported\nmlflow.set_tracking_uri(\"sqlite:///mlflow.db\")\n\n# Use MLflow as usual\nmlflow.set_experiment(\"my-experiment\")\n\nwith mlflow.start_run():\n mlflow.log_param(\"param\", 1)\n mlflow.log_metric(\"metric\", 2)\n```\n\n### Python Store Usage\n\n```py\nimport logging\nimport mlflow\nimport mlflow_go\n\n# Enable debug logging\nlogging.basicConfig()\nlogging.getLogger('mlflow_go').setLevel(logging.DEBUG)\n\n# Enable the Go client implementation (disabled by default)\nmlflow_go.enable_go()\n\n# Instantiate the tracking store with a database URI\ntracking_store = mlflow.tracking._tracking_service.utils._get_store('sqlite:///mlflow.db')\n\n# Call any tracking store method\ntracking_store.get_experiment(0)\n\n# Instantiate the model registry store with a database URI\nmodel_registry_store = mlflow.tracking._model_registry.utils._get_store('sqlite:///mlflow.db')\n\n# Call any model registry store method\nmodel_registry_store.get_latest_versions(\"model\")\n```\n\n## Why bother?\n\nFor that sweet performance my friend!\n\nInitial benchmarks show us that critical API calls are faster and Go can handle more concurrent requests.\n\n\n\n\n> [!NOTE]\n> These were initial results and can perhaps still be more optimized.\n\nSee [benchmarks](./benchmarks/README.md) for more information.\n\n\n## Contribution\n\n### Try it out!\n\nWe advocate this project as a drop-in replacement for `mlflow`.\nPlease give it a try, and let us know if anything isn't working for you!\nWe look forward to your feedback!\n\n### Missing Endpoints\n\nHey there! We need your help! This project is not yet a full port of the current tracking server.\nNot every [endpoint in the REST API](https://mlflow.org/docs/latest/rest-api.html) is implemented in Go.\nThose missing are redirected to the existing Python implementation.\nPlease consider helping us out and implement [a missing endpoint](./docs/porting-a-new-endpoint.md).\n\nNot yet a Go wizard? We've collected some [resources to learn Go](./docs/learning-go.md). Trust us, it will be an enriching and fun experience!\n\n## Community\n\nThere is a dedicated Slack channel called `#mlflow-go` on the official [mlflow Slack](https://mlflow.org/slack).\nShoot us a message if you have any questions!\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. 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In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n \n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n \n Copyright [yyyy] [name of copyright owner]\n \n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n \n http://www.apache.org/licenses/LICENSE-2.0\n \n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n ",
"summary": "MLflow is an open source platform for the complete machine learning lifecycle",
"version": "0.2.0",
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