)]}'
{
  "commit": "b3c7e3515308749892720bc8bc6b16ba6ca71941",
  "tree": "e978c5ce83691af8486dfab3863071b664d03e29",
  "parents": [
    "e7d7ba5e57fb51c34cc5e2a3f0041d55afe0c4cb"
  ],
  "author": {
    "name": "Stefan Krawczyk",
    "email": "stefan@idibon.com",
    "time": "Wed Jan 12 22:20:51 2022 -0800"
  },
  "committer": {
    "name": "Stefan Krawczyk",
    "email": "stefan@idibon.com",
    "time": "Wed Jan 12 22:20:51 2022 -0800"
  },
  "message": "Adds some ideas on how to proceed with task from a user API perspective\n\nI’ve come up with a few ideas. Feel free to comment on which you like better. I’m not\n100% wed to the language, so would also welcome critique there.\n\nI would like to ensure that we don’t require many decisions from people to make\nuse of Hamilton in a distributed context. (to do this well I should probably try\nto code up one for Ray or Spark)….\n\nI was also thinking about the return type. Right now it’s a pandas DF — but we should\nwhile we’re doing this change, also think about providing more flexibility there. E.g. if\nsomeone wanted to do all numpy, or return a Dask DF — they should be able to.\nThat said, distributed engines like Dask, should be more or less agnostic to the return type.\n",
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