Comments (1)
Hi there,
I understand your concern regarding the current status of the project. While the recent lack of releases or merged PRs might indicate a temporary slowdown, I encourage exploration of alternative pathways for enabling distributed learning in the cloud.
Here are a few suggested pathways for users seeking to leverage distributed learning:
TensorFlow Extended (TFX): TFX is a powerful framework specifically designed to orchestrate end-to-end ML pipelines, including distributed training and serving components.
Apache Spark MLlib: An excellent choice for distributed machine learning, Spark MLlib offers scalable algorithms and tools for large-scale data processing and training.
PyTorch Distributed: PyTorch also provides capabilities for distributed training, enabling users to leverage cloud infrastructure efficiently.
Ray and Ray Tune: Ray is a flexible, high-performance distributed execution framework, while Ray Tune focuses on hyperparameter tuning and distributed experimentation.
Dask: Dask provides scalable computing in Python, including distributed machine learning functionalities.
I recommend evaluating these alternatives based on your specific use case and requirements. Additionally, staying tuned to this repository for updates is always valuable, as the project may undergo future enhancements or renewed activity.
Thank you for your patience and understanding.
from cloud.
Related Issues (20)
- Integration with ide.cloud.google.com
- Add support for preemptible instances HOT 3
- entry_point and requirements files are expected to be current directory HOT 1
- Tensorflow Cloud Tutorial failed to train model HOT 3
- version compatibility HOT 1
- Can't use google-colab HOT 1
- Examples are somehow all missing
- Performance issues in src/python/tensorflow_cloud/core/tests/testdata/keras_tuner_cifar_example.py(P2)
- Performance issues in /src/python/tensorflow_cloud/core (by P3) HOT 3
- Job was exited because of error in module pyparsing
- Unable to pip install tensorflow_cloud in Colab HOT 2
- Suggestion for easy patch to support custom wheel files
- Gcp region is set hard to "us-central1" HOT 2
- Schau dir "Berlin Kidz 2 - Musik Playlist" auf YouTube an
- Running into "Internal error occurred for the current attempt" problem HOT 1
- Update TensorFlow Cloud to run on Vertex AI HOT 1
- List index error while _get_colab_notebook_content() HOT 1
- tfc.remote() == False if python file entrypoint and distribution_strategy is None
- Step 2 of install procedure fails HOT 2
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