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ML Metadata

PythonPyPI

ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows.

NOTE: ML Metadata may be backwards incompatible before version 1.0.

Getting Started

For more background on MLMD and instructions on using it, see the getting started guide

Installing from PyPI

The recommended way to install ML Metadata is to use the PyPI package:

pip install ml-metadata

Then import the relevant packages:

fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2

Nightly Packages

ML Metadata (MLMD) also hosts nightly packages at https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest nightly package, please use the following command:

pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata

Installing with Docker

This is the recommended way to build ML Metadata under Linux, and is continuously tested at Google.

Please first install docker and docker-compose by following the directions: docker; docker-compose.

Then, run the following at the project root:

DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}

where PY_VERSION is one of {36, 37, 38}.

A wheel will be produced under dist/, and installed as follows:

pip install dist/*.whl

Installing from source

1. Prerequisites

To compile and use ML Metadata, you need to set up some prerequisites.

Install Bazel

If Bazel is not installed on your system, install it now by following these directions.

Install cmake

If cmake is not installed on your system, install it now by following these directions.

2. Clone ML Metadata repository

git clone https://github.com/google/ml-metadata
cd ml-metadata

Note that these instructions will install the latest master branch of ML Metadata. If you want to install a specific branch (such as a release branch), pass -b <branchname> to the git clone command.

3. Build the pip package

ML Metadata uses Bazel to build the pip package from source:

python setup.py bdist_wheel

You can find the generated .whl file in the dist subdirectory.

4. Install the pip package

pip install dist/*.whl

5.(Optional) Build the grpc server

ML Metadata uses Bazel to build the c++ binary from source:

bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server

Supported platforms

MLMD is built and tested on the following 64-bit operating systems:

  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.

About

For recording and retrieving metadata associated with ML developer and data scientist workflows.

www.tensorflow.org/tfx/guide/mlmd

Resources

Readme
Apache-2.0 license

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

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GitHub - jay90099/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows. · GitHub
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ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
Report repository
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GitHub - jay90099/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows. · GitHub
Skip to content
BranchesTags
Repository files navigation
ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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GitHub - jay90099/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows. · GitHub
Skip to content
BranchesTags
Repository files navigation
ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - jay90099/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows. · GitHub
Skip to content
BranchesTags
Repository files navigation
ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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GitHub - jay90099/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows. · GitHub
Skip to content
BranchesTags
Repository files navigation
ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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GitHub - jay90099/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows. · GitHub
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ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
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ML Metadata
PythonPyPI
ML Metadata (MLMD) is a library for recording and retrieving metadata
associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
Getting Started
For more background on MLMD and instructions on using it, see the
getting started guide
Installing from PyPI
The recommended way to install ML Metadata is to use the
PyPI package:
pip install ml-metadata
Then import the relevant packages:
fromml_metadataimportmetadata_storefromml_metadata.protoimportmetadata_store_pb2
Nightly Packages
ML Metadata (MLMD) also hosts nightly packages at
https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest
nightly package, please use the following command:
pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata
Installing with Docker
This is the recommended way to build ML Metadata under Linux, and is
continuously tested at Google.
Please first install docker and docker-compose by following the directions:
docker;
docker-compose.
Then, run the following at the project root:
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
where PY_VERSION is one of {36, 37, 38}.
A wheel will be produced under dist/, and installed as follows:
pip install dist/*.whl
Installing from source
1. Prerequisites
To compile and use ML Metadata, you need to set up some prerequisites.
Install Bazel
If Bazel is not installed on your system, install it now by following these
directions.
Install cmake
If cmake is not installed on your system, install it now by following these
directions.
2. Clone ML Metadata repository
git clone https://github.com/google/ml-metadata
cd ml-metadata
Note that these instructions will install the latest master branch of ML
Metadata. If you want to install a specific branch (such as a release branch),
pass -b <branchname> to the git clone command.
3. Build the pip package
ML Metadata uses Bazel to build the pip package from source:
python setup.py bdist_wheel
You can find the generated .whl file in the dist subdirectory.
4. Install the pip package
pip install dist/*.whl
5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
bazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server
Supported platforms
MLMD is built and tested on the following 64-bit operating systems:
  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.
About
For recording and retrieving metadata associated with ML developer and data scientist workflows.
www.tensorflow.org/tfx/guide/mlmd
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages