Forbidden S3 bucket in the example: amazon-sagemaker-examples/introduction_to_applying_machine_learning/gluon_recommender_system/gluon_recommender_system.ipynb #267
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A temporary solution I found (See this discussion) is running the |
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After fixing the S3 issue, I tested the rest of the code but the kernel (Both
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Hi @pilhokim . Could you confirm that you're running in a GPU instance? The MXNet context is currently set at mx.gpu() which will fail if you're running in a CPU instance. Thanks. |
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You are right, @djarpin! Thanks a lot for your advice. I forgot the fact that this code runs on the local machine, not deploying training instances. I tested |
akrishna1995
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* UCI heart example for step decorator with EMR step for preprocessing (#266) * UCI heart example for step decorator with EMR step for preprocessing * UCI heart example for step decorator with EMR step, after linter * UCI heart example for step decorator with EMR step, after linter * removed content_types * using prod S3 bucket * using XGBClassifier * fix code format --------- Co-authored-by: feliplp <[email protected]> Co-authored-by: Dewen Qi <[email protected]> * add notebook example on basic pipeline for batch inference using step decorator (#264) * add basic pipeline for batch inference using step decorator * change Booster to XGBClassifier; incorporating feedback from aws/amazon-sagemaker-examples-staging#264 * fix minor typos * incorporate comments from PR aws/amazon-sagemaker-examples-staging#264 * incorporate feedback from aws/amazon-sagemaker-examples-staging#264 --------- Co-authored-by: pprasety <[email protected]> * Add remote-function notebook fixes (#265) Co-authored-by: svia3 <[email protected]> * Add pipeline step decorator quick start notebook (#267) add pipeline scheduler examples Address comments and refine Add pipeline step decorator ablone notebook Address review meeting comments Update clean up sections Add rate-based schedules back Udpate notebooks for Public Beta Fix the colliding endpoint name across different executions Upgrading pandas to fix ImportError in Studio DataScience 3.0 image add scheduler-light additions to quick_start notebook add scheduler-light additions to quick_start notebook fix invalid notebook json Update notebooks for GA Add modular package for lightsaber Add a simple notebook to demonstrate mix use of training step and step deco Address comments fix pipeline delete resouce leak issue in using_step_decorator notebook dummy commit Co-authored-by: Dewen Qi <[email protected]> * remove local SDK tar and retrieve SDK from public --------- Co-authored-by: Felipe Lopez <[email protected]> Co-authored-by: feliplp <[email protected]> Co-authored-by: Dewen Qi <[email protected]> Co-authored-by: Philips Kokoh <[email protected]> Co-authored-by: pprasety <[email protected]> Co-authored-by: Stephen Via <[email protected]> Co-authored-by: svia3 <[email protected]>
akrishna1995
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* feat: Add notebooks for step decorator (#272) * UCI heart example for step decorator with EMR step for preprocessing (#266) * UCI heart example for step decorator with EMR step for preprocessing * UCI heart example for step decorator with EMR step, after linter * UCI heart example for step decorator with EMR step, after linter * removed content_types * using prod S3 bucket * using XGBClassifier * fix code format --------- Co-authored-by: feliplp <[email protected]> Co-authored-by: Dewen Qi <[email protected]> * add notebook example on basic pipeline for batch inference using step decorator (#264) * add basic pipeline for batch inference using step decorator * change Booster to XGBClassifier; incorporating feedback from aws/amazon-sagemaker-examples-staging#264 * fix minor typos * incorporate comments from PR aws/amazon-sagemaker-examples-staging#264 * incorporate feedback from aws/amazon-sagemaker-examples-staging#264 --------- Co-authored-by: pprasety <[email protected]> * Add remote-function notebook fixes (#265) Co-authored-by: svia3 <[email protected]> * Add pipeline step decorator quick start notebook (#267) add pipeline scheduler examples Address comments and refine Add pipeline step decorator ablone notebook Address review meeting comments Update clean up sections Add rate-based schedules back Udpate notebooks for Public Beta Fix the colliding endpoint name across different executions Upgrading pandas to fix ImportError in Studio DataScience 3.0 image add scheduler-light additions to quick_start notebook add scheduler-light additions to quick_start notebook fix invalid notebook json Update notebooks for GA Add modular package for lightsaber Add a simple notebook to demonstrate mix use of training step and step deco Address comments fix pipeline delete resouce leak issue in using_step_decorator notebook dummy commit Co-authored-by: Dewen Qi <[email protected]> * remove local SDK tar and retrieve SDK from public --------- Co-authored-by: Felipe Lopez <[email protected]> Co-authored-by: feliplp <[email protected]> Co-authored-by: Dewen Qi <[email protected]> Co-authored-by: Philips Kokoh <[email protected]> Co-authored-by: pprasety <[email protected]> Co-authored-by: Stephen Via <[email protected]> Co-authored-by: svia3 <[email protected]> * Notebook Job Step Example (#274) * Create README.md * Adding notebooks * Delete sagemaker-pipelines/notebook-job-step/README.md * Adding example for inference components and managed instance scaling for SageMaker real time hosting and inference (#275) * Cleaned up notebooks Cleaned up for initial push to staging * removed references to goldfinch * Updated readme * moved to proper directory * fixed session object reference * Updated session variable * Updated with logic to check store vars. Need to remove internal only code * linted notebooks and added test header and footers * Fixed prompt and parameters for codegen25 * Updated descriptions, added handling * Removed custom model shapes * Making Jumpstart notebooks Python 3.10 compatible (#269) * removed roleARN which is not needed * smart sifting notebooks (#281) * smart sifting notebooks * smart sifting notebooks updated description * Added new flow diagram --------- Co-authored-by: Arun Lokanatha <[email protected]> --------- Co-authored-by: qidewenwhen <[email protected]> Co-authored-by: Felipe Lopez <[email protected]> Co-authored-by: feliplp <[email protected]> Co-authored-by: Dewen Qi <[email protected]> Co-authored-by: Philips Kokoh <[email protected]> Co-authored-by: pprasety <[email protected]> Co-authored-by: Stephen Via <[email protected]> Co-authored-by: svia3 <[email protected]> Co-authored-by: Ram Vegiraju <[email protected]> Co-authored-by: James Park <[email protected]> Co-authored-by: Pooja Karadgi <[email protected]> Co-authored-by: Arun Lokanatha <[email protected]> Co-authored-by: Arun Lokanatha <[email protected]>
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In the example code "amazon-sagemaker-examples/introduction_to_applying_machine_learning/gluon_recommender_system/gluon_recommender_system.ipynb", I couldn't copy the data file located at "s3://amazon-reviews-pds/tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz". Please check the below code part in that Jupyter file and replace the below code with a valid S3 address:
aws s3 cp s3://amazon-reviews-pds/tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz /tmp/recsys/The text was updated successfully, but these errors were encountered: