Comments (9)
The big question for me here is: do we need to add any code to Flambé? Seems like we can just provide a small tutorial that explains how you can use Jinja to do this
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I like the idea of templating, but I don't really think this requires additional code, which is kind of cool :)
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Why not include the function I wrote above?
All of the code is super boilerplate. Plus, you can control how the config is written back to disk (removing spaces, comments, etc.).
For me, the question is: does this help people to get started with/fall more in love with flambé, and at what cost?
The answer is: yes, and at a very small one of a single helper.
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Whether we add this snippet to the code or the docs what does it change?
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They will still need to run a script on their template
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Just to clarify I do want to keep your snippet! I just think it's so small it can just go in a tutorial directly. Do you think there is a strong reason to put it in the repo? If so, we can PR it in flambe.utils
:)
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I think it would be fine to add to utils - but there is one cost: we add jinja as a dependency
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@nmatthews-asapp it's already a flask dependency, which is a dependency of ours (at least until we make the website a different repo or something)
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@williamabrwolf want to make a PR into flambe.utils?
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Related Issues (20)
- `Script` should support positional arguments HOT 2
- Cannot add custom attributes to a flambe module HOT 1
- Relative paths no longer work for local resources
- Reporting server not logging requests when tensorboard is installed.
- More useful stage dependency graph
- Check cluster compatibility of the yaml config before starting the cluster
- AvgPooling fails even when input dimensions match expected ones. HOT 1
- Richer debug mode HOT 1
- Separate embedder and encoder in flambe.nn.embedder HOT 3
- Warning when copying a tensor in the base sampler
- Reporting script can die on the orchestrator HOT 1
- Out-of-the-box experiments with standard datasets HOT 1
- Make `extra_validation_metrics` a key-value mapping rather than a list HOT 1
- RNN (sru) is incompatible with torch 1.3
- Make all Flambe base-models torchscript compatible HOT 1
- No in-built functionality for tracking of metrics during training HOT 3
- Allow pickle for saving checkpoints in Experiment HOT 1
- TaggedScalar instead of Schema when using make_component HOT 1
- Early checks for constructor argument incompatibilities HOT 2
- Better data handling for datasets HOT 1
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