ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
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Published by htahir1 over 3 years ago
This release is a big design change and refactor. It involves a significant change in the Configuration file structure, meaning this is a breaking upgrade.
For those upgrading from an older version of ZenML, we ask to please delete their old pipelines
dir and .zenml
folders and start afresh with a zenml init
.
If only working locally, this is as simple as:
cd zenml_enabled_repo
rm -rf pipelines/
rm -rf .zenml/
And then another ZenML init:
pip install --upgrade zenml
cd zenml_enabled_repo
zenml init
from zenml.core.pipelines.training_pipeline import TrainingPipeline
A user can simple do:
from zenml.pipelines import TrainingPipeline
The caveat is of course that this might involve a re-write of older ZenML code imports.
Note: Future releases are also expected to be breaking. Until announced, please expect that upgrading ZenML versions may cause older-ZenML generated pipelines to behave unexpectedly.
Special shout-out to @nicholasmaiot for major contributions to this release!
Published by bcdurak over 3 years ago
This release is a significant one as it includes the first version of the AWS integration. It allows you to use ZenML to launch an EC2 instance as an orchestrator and execute a ZenML pipeline possibly coupled with an S3 artifact store and RDS metadata store.
It is a new feature and it does not include any breaking changes.
In order to install ZenML with the AWS integration attached, you can follow:
pip install --upgrade zenml[aws]
zenml init
Published by htahir1 over 3 years ago
Earlier release to get the PostgreSQL datasource out quicker.
To upgrade:
pip install --upgrade zenml
Published by htahir1 over 3 years ago
This release is a big design change and refactor. It involves a significant change in the Configuration file structure, meaning this is a breaking upgrade. For those upgrading from 0.2.0, we ask to please delete their old pipelines
dir and .zenml
folders and start afresh with a zenml init
.
If only working locally, this is as simple as:
cd zenml_enabled_repo
rm -rf pipelines/
rm -rf .zenml/
And then another init:
pip install --upgrade zenml
zenml init
DeployPipeline
added to deploy a pipeline directly without having to create a TrainingPipeline
.Datasource
and Data Step
refined.Note: Future releases are also expected to be breaking. Until announced, please expect that upgrading ZenML versions may cause older-ZenML generated pipelines to behave unexpectedly.
Published by htahir1 over 3 years ago
This new release is a major one. Its the first to introduce our new integrations system, which is meant to be used to extend ZenML with various other ML/MLOps libraries easily. The first big advantage one gets is 🚀 PyTorch Support 🚀!
pip install --upgrade zenml
And to enable the PyTorch extension:
pip install zenml[pytorch]
TorchBaseTrainer
example.TrainerStep
, followed by TFBaseTrainerStep
and TorchBaseTrainerStep
.input_fn
of the TorchTrainer have implemented in a way that it can ingest from a tfrecords file. This marks one of the few projects out thereRepository.get_zenml_dir()
that caused any pipeline creates below root level to fail on creation.The docs are almost complete! We are at 80% completion. Keep an eye out as we update with more details on how to use/extend ZenML and let us know via slack if there is something missing!
Published by htahir1 over 3 years ago
step
, datasource
and pipelines
. E.g. zenml pipeline list
gives list of pipelines in current repo.pipelines_dir
, in reference to concerns raised in #13.Published by htahir1 almost 4 years ago
docs/book
folder.Published by hamzamaiot almost 4 years ago