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kandi X-RAY | cloudml-samples Summary
kandi X-RAY | cloudml-samples Summary
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Top functions reviewed by kandi - BETA
- Wrapper for resnet_v1
- Calculate learning rate based on current epoch
- Resnet model
- A generator for resnetv1
- Run training
- Create a feed_dict for the next batch
- Runs evaluation
- Generate next batch of images
- Run MNIST training
- Construct an ensemble
- Get command line arguments
- Returns the input_fn
- Returns a function that creates a training experiment
- Process command line arguments
- Parse command line arguments
- Generate standard sql for table_name
- Create a tf train experiment
- Add default arguments to the command line
- Run the command line interface
- Transform train and test data
- Create a profiling function
- Train a trainer
- Build an estimator
- Create argument parser
- Embed model
- Convert a py file to ipynb
cloudml-samples Key Features
cloudml-samples Examples and Code Snippets
Community Discussions
Trending Discussions on cloudml-samples
QUESTION
I trained a XGBoost model using AI Platform as here.
Now I have the choice in the Console to download the model, as follows (but not Deploy it, since "Only models trained with built-in algorithms can be deployed from this page"). So, I click to download.
However, in the bucket the only file I see is a tar, as follows.
That tar (directory tree follows) holds only some training code, and not a model.bst, model.pkl, or model.joblib, or other such model file.
Where do I find model.bst or the like, which I can deploy?
EDIT:
Following the answer, below, we see that the "Download model" button is misleading as it sends us to the job directory, not the output directory (which is set arbitrarily in the codel the model is at census_data_20210527_215945/model.bst )
ANSWER
Answered 2021-May-28 at 05:48Only in-build algorithms automatically store the model in Google Cloud storage.
In your case, you have a custom training application. You have to take care of saving the model on your own.
Referring to your example this is implemented as listed here.
The model is uploaded to Google Cloud Storage using the cloud storage client.
QUESTION
I'm trying to launch a training job on Google AI Platform with a custom container. As I want to use GPUs for the training, the base image I've used for my container is:
...ANSWER
Answered 2021-Mar-11 at 01:05The suggested way to build the most reliable container is to use the officially maintained 'Deep Learning Containers'. I would suggest pulling 'gcr.io/deeplearning-platform-release/tf2-gpu.2-4'. This should already have CUDA, CUDNN, GPU Drivers, and TF 2.4 installed & tested. You'll just need to add your code into it.
- https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
- https://console.cloud.google.com/gcr/images/deeplearning-platform-release?project=deeplearning-platform-release
- https://cloud.google.com/ai-platform/deep-learning-containers/docs/getting-started-local#create_your_container
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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