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Add py4j to requirements.txt and minor training README fix #9

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Add py4j to requirements.txt and minor training README fix #9

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4d51513
Add py4j import
djalova Jul 31, 2020
df1afb8
Update old file name in README
djalova Jul 31, 2020
19480fd
Merge branch 'master' into update
bdwyer2 Nov 10, 2020
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14 changes: 7 additions & 7 deletions training/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ If you wish to train the model on data contained in the file `ratings.csv` for 5
2. Run `setup_max_model_training` and follow the prompts to configure model training.

```
$ ./setup_max_model_training max-ncf-training-config.yaml
$ ./setup_max_model_training max-recommender-training-config.yaml
...
------------------------------------------------------------------------------
Model training setup is complete and your configuration file was updated.
Expand All @@ -129,7 +129,7 @@ If you wish to train the model on data contained in the file `ratings.csv` for 5
Compute configuration : k80
```

> On Microsoft Windows run `python setup_max_model_training max-ncf-training-config.yaml`.
> On Microsoft Windows run `python setup_max_model_training max-recommender-training-config.yaml`.

The setup script updates the training configuration file using the information you've provided. For security reasons, confidential information, such as API keys or passwords, are _not_ stored in this file. Instead the script displays a set of environment variables that you must define to make this information available to the training script.

Expand Down Expand Up @@ -169,7 +169,7 @@ Complete the following steps in the terminal window where the earlier mentioned
1. Verify that the training preparation steps complete successfully.

```
$ python train_max_model.py max-ncf-training-config.yaml prepare
$ python train_max_model.py max-recommender-training-config.yaml prepare
...
# --------------------------------------------------------
# Checking environment variables ...
Expand All @@ -180,13 +180,13 @@ Complete the following steps in the terminal window where the earlier mentioned
If preparation completed successfully:

- Training data is present in the Cloud Object Storage bucket that WML will access during model training.
- Model training code is packaged `max-ncf-model-building-code.zip`
- Model training code is packaged `max-recommender-model-building-code.zip`


2. Start model training.

```
$ python train_max_model.py max-ncf-training-config.yaml package
$ python train_max_model.py max-recommender-training-config.yaml package
...
# --------------------------------------------------------
# Starting model training ...
Expand All @@ -195,7 +195,7 @@ Complete the following steps in the terminal window where the earlier mentioned
Training run name : train-max-...
Training data bucket : ...
Results bucket : ...
Model-building archive: max-ncf-model-building-code.zip
Model-building archive: max-recommender-model-building-code.zip
Model training was started. Training id: model-...
...
```
Expand All @@ -216,7 +216,7 @@ Complete the following steps in the terminal window where the earlier mentioned
To **restart** monitoring, run the following command, replacing `<training-id>` with the id that was displayed when you started model training.

```
python train_max_model.py max-ncf-training-config.yaml package <training-id>
python train_max_model.py max-recommender-training-config.yaml package <training-id>
```

To **cancel** the training run, press `Ctrl+C` twice.
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1 change: 1 addition & 0 deletions training/training_code/training_requirements.txt
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@@ -1,4 +1,5 @@
pandas>=0.23.4
numpy>=1.13.3
py4j>=0.10.0
pyspark>=2.3.1
pyarrow>=0.8.0