https://github.com/cloudacademy/ml-engine-intro
https://github.com/cloudacademy/ml-engine-intro
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/cloudacademy/ml-engine-intro
- Owner: cloudacademy
- Created: 2017-07-05T21:15:06.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2018-11-16T18:14:51.000Z (over 7 years ago)
- Last Synced: 2025-05-06T11:35:33.733Z (about 1 year ago)
- Language: Python
- Size: 10.7 KB
- Stars: 2
- Watchers: 4
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## This repository is outdated and has been replaced by https://github.com/cloudacademy/mlengine-intro.
### TensorFlow
TensorFlow website: https://www.tensorflow.org
High-level API (tf.contrib.learn) tutorial: https://www.tensorflow.org/get_started/tflearn
TensorFlow installation: https://www.tensorflow.org/install
```
sudo -H pip install tensorflow
```
Add to iris.py:
```
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.logging.set_verbosity(tf.logging.ERROR)
import shutil
shutil.rmtree('/tmp/iris_model', ignore_errors=True)
```
```
python iris.py
```
### Training a Model with ML Engine
Google Cloud SDK installation: https://cloud.google.com/sdk
```
mkdir trainer
mv iris.py trainer
touch trainer/__init__.py
gcloud ml-engine local train --module-name trainer.iris --package-path trainer
```
```
BUCKET=gs://[ProjectID]-ml # Replace [ProjectID] with your Google Cloud Project ID
REGION=[Region] # Replace [Region] with a Google Cloud Platform region, such as us-central1
```
```
gcloud ml-engine jobs submit training job1 \
--module-name trainer.iris \
--package-path trainer \
--staging-bucket $BUCKET \
--region $REGION
```
### Feature Engineering
TensorFlow Linear Model Tutorial: https://www.tensorflow.org/tutorials/wide
```
sudo -H pip install pandas
```
```
python wide_n_deep_tutorial.py --model_type=wide
```
### A Wide and Deep Model
```
python wide_n_deep_tutorial.py --model_type=deep
```
### Distributed Training on ML Engine
Sample code: https://github.com/GoogleCloudPlatform/cloudml-samples/archive/master.zip
```
cd Downloads/cloudml-samples-master/census/estimator
ls trainer
```
Hyperparameter Tuning: https://cloud.google.com/ml-engine/docs/concepts/hyperparameter-tuning-overview
```
gsutil cp -r gs://cloudml-public/census/data $BUCKET
TRAIN_DATA=$BUCKET/data/adult.data.csv
EVAL_DATA=$BUCKET/data/adult.test.csv
JOB=census1
```
```
gcloud ml-engine jobs submit training $JOB \
--job-dir $BUCKET/$JOB \
--runtime-version 1.2 \
--module-name trainer.task \
--package-path trainer \
--region $REGION \
--scale-tier STANDARD_1 \
-- \
--train-files $TRAIN_DATA \
--eval-files $EVAL_DATA \
--train-steps 1000
```
### Deploying a Model on ML Engine
```
gcloud ml-engine models create census --regions=$REGION
gsutil ls -r $BUCKET/census1/export
```
```
# Note: Replace [Path-to-model] below with your Cloud Storage path
gcloud ml-engine versions create v1 \
--model census \
--origin [Path-to-model] \
```
```
--runtime-version 1.2
```
```
gcloud ml-engine predict \
--model census \
--version v1 \
--json-instances \
../test.json
```
### Conclusion
Cloud Machine Learning Engine documentation: https://cloud.google.com/ml-engine/docs