{"action":"created","issue":{"url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples/issues/314","repository_url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples","labels_url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples/issues/314/labels{/name}","comments_url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples/issues/314/comments","events_url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples/issues/314/events","html_url":"https://github.com/GoogleCloudPlatform/cloudml-samples/issues/314","id":382964037,"node_id":"MDU6SXNzdWUzODI5NjQwMzc=","number":314,"title":"Training step for Criteo_tft example does not work on google cloud","user":{"login":"PicarusW","id":43196561,"node_id":"MDQ6VXNlcjQzMTk2NTYx","avatar_url":"https://avatars2.githubusercontent.com/u/43196561?v=4","gravatar_id":"","url":"https://api.github.com/users/PicarusW","html_url":"https://github.com/PicarusW","followers_url":"https://api.github.com/users/PicarusW/followers","following_url":"https://api.github.com/users/PicarusW/following{/other_user}","gists_url":"https://api.github.com/users/PicarusW/gists{/gist_id}","starred_url":"https://api.github.com/users/PicarusW/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/PicarusW/subscriptions","organizations_url":"https://api.github.com/users/PicarusW/orgs","repos_url":"https://api.github.com/users/PicarusW/repos","events_url":"https://api.github.com/users/PicarusW/events{/privacy}","received_events_url":"https://api.github.com/users/PicarusW/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-11-21T05:42:54Z","updated_at":"2018-11-26T02:37:42Z","closed_at":null,"author_association":"NONE","body":"**Describe the bug**\r\nI am running the Criteo example, documented[ here](https://cloud.google.com/blog/products/gcp/using-google-cloud-machine-learning-to-predict-clicks-at-scale). I can run the preprocess step fine but when trying to run the training step (linear model fails) running on Google Cloud I get the following errors:\r\n\r\n> Command \"python setup.py egg_info\" failed with error code 1 in /tmp/pip-install-guzRvj/fastavro/\r\n> Command '['pip', 'install', '--user', u'trainer-1.0.tar.gz']' returned non-zero exit status 1\r\n> \r\nthe error message in Google ML engine is:\r\n load raise ImportError(\"%r has no %r attribute\" % (entry, attr)) ImportError: <module 'setuptools.dist' from '/usr/lib/python2.7/dist-packages/setuptools/dist.pyc'> has no 'check_specifier' attribute\r\n\r\n**What sample is this bug related to?**\r\n\r\nI cloned this repo a couple of days ago.\r\n\r\n**Source code / logs**\r\n\r\nSee above\r\n\r\n**To Reproduce**\r\nLocation is us-east1 (if that is relevant)\r\n\r\nJOB_ID=\"largeclicks_linear_${USER}_$(date +%Y%m%d_%H%M%S)\"\r\ncd ~/cloudml-samples/criteo_tft/\r\ngcloud beta ml-engine jobs submit  training \"$JOB_ID\" \\\r\n  --module-name trainer.task \\\r\n  --package-path trainer \\\r\n  --staging-bucket \"$BUCKET\" \\\r\n  --region \"$LOCATION\" \\\r\n  --config config-small.yaml \\\r\n  -- \\\r\n  --dataset small \\\r\n  --model_type linear \\\r\n  --ignore_crosses \\\r\n  --l2_regularization 1000 \\\r\n  --output_path \"${GCS_PATH}/output/${JOB_ID}\" \\\r\n  --metadata_path \"${GCS_PATH}/preproc/metadata.json\" \\\r\n  --eval_data_paths \"${GCS_PATH}/preproc/features_eval*\" \\\r\n  --train_data_paths \"${GCS_PATH}/preproc/features_train*\"\r\n\r\n**Expected behavior**\r\nthe job to start execution\r\n\r\n**System Information**\r\n- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Google Cloud Shell\r\n- **Framework and version (Tensorflow, scikit-learn, XGBoost)**:\r\n- **Python version**: 2.7 (both on Cloud Shell and remotely, no version specified in gcloud call) as it seems that Tensorflow-transform does not support 3.x\r\n- **Exact command to reproduce**: See above\r\n- **Tensorflow Transform environment (if applicable, see below)**: I have tried to specify many different versions in the requirements.txt, requires.txt and setup.py but does not seem to be relevant. Default values on the mentioned files do not work.\r\n- **Setuptools** is version 40.6.2\r\n\r\n**Additional context**\r\nYou can find a reduced version of  the data [here](https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz). From the train dataset, you can generate a train and eval dataset. It is 10Gb so much less than the original 1Tb.\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples/issues/comments/441502142","html_url":"https://github.com/GoogleCloudPlatform/cloudml-samples/issues/314#issuecomment-441502142","issue_url":"https://api.github.com/repos/GoogleCloudPlatform/cloudml-samples/issues/314","id":441502142,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0MTUwMjE0Mg==","user":{"login":"PicarusW","id":43196561,"node_id":"MDQ6VXNlcjQzMTk2NTYx","avatar_url":"https://avatars2.githubusercontent.com/u/43196561?v=4","gravatar_id":"","url":"https://api.github.com/users/PicarusW","html_url":"https://github.com/PicarusW","followers_url":"https://api.github.com/users/PicarusW/followers","following_url":"https://api.github.com/users/PicarusW/following{/other_user}","gists_url":"https://api.github.com/users/PicarusW/gists{/gist_id}","starred_url":"https://api.github.com/users/PicarusW/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/PicarusW/subscriptions","organizations_url":"https://api.github.com/users/PicarusW/orgs","repos_url":"https://api.github.com/users/PicarusW/repos","events_url":"https://api.github.com/users/PicarusW/events{/privacy}","received_events_url":"https://api.github.com/users/PicarusW/received_events","type":"User","site_admin":false},"created_at":"2018-11-26T02:37:42Z","updated_at":"2018-11-26T02:37:42Z","author_association":"NONE","body":"I have run the test you request. I had  already tried that with different runtime versions.\r\nBased on a bit of research it seems  it could be related to tensorflow_transform version incompatibilities. Have you tried to run it successfully?"}}
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