Discover and read the best of Twitter Threads about #TensorBoard
Most recents (2)
👨🏻💻 Setup CI/CD in your machine learning projects using these simple yet powerful “♾️ CML commands”:
🔄 ci
🏃♂️ runner
⤴️ pr
💬 comment
🧑🏼🏫 tensorboard
@Iterativeai @DVCorg
#cml #tensorboard #opensource
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🔄 ci
🏃♂️ runner
⤴️ pr
💬 comment
🧑🏼🏫 tensorboard
@Iterativeai @DVCorg
#cml #tensorboard #opensource
🧵[1/7]
🔄 ci
The “cml ci” command prepares the whole repository for CML operations after creating the cml.yaml file
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The “cml ci” command prepares the whole repository for CML operations after creating the cml.yaml file
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🏃♂️ runner
When a workflow requires computational resources, CML can automatically allocate cloud instances using “cml runner”. You can spin up instances on AWS, Azure, GCP, Kubernetes or any other provider. We can start a runner using “cml runner launch” command.
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When a workflow requires computational resources, CML can automatically allocate cloud instances using “cml runner”. You can spin up instances on AWS, Azure, GCP, Kubernetes or any other provider. We can start a runner using “cml runner launch” command.
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✨🧠 The ecosystem that has grown up around @TensorFlow in the last few years blows my mind. There's just so much functionality, compared to some of the other, newer frameworks.
👉Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
👉Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
1) @TensorFlow Extended (TFX)
It's no secret that I 💕 #TFX and all of its tooling for deploying machine learning models into production. If you care about keeping your models up-to-date and monitoring them, you should check out the product + its paper.
tensorflow.org/tfx/?hl=zh-cn
It's no secret that I 💕 #TFX and all of its tooling for deploying machine learning models into production. If you care about keeping your models up-to-date and monitoring them, you should check out the product + its paper.
tensorflow.org/tfx/?hl=zh-cn
2) @TensorFlow Hub
If you want to train your model on a small data set, or improve generalization, you'll need to use something called transfer learning. #TFHub modules make it easy—and are available in an #OSS marketplace: tfhub.dev.
site: tensorflow.org/hub/
If you want to train your model on a small data set, or improve generalization, you'll need to use something called transfer learning. #TFHub modules make it easy—and are available in an #OSS marketplace: tfhub.dev.
site: tensorflow.org/hub/
