Comments (5)
Yeah I think a boolean on the Trainer and Evaluator as you've suggested offline makes sense to me. Will work on that after #83
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Don't mean to harp on it but... I see this being significantly less useful without ids 😬 Because then you simply can't shuffle = False
, nor drop_last = True
, in BaseSampler
, right?
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Actually, meh, nvm.
Let's say that we get 2/3 of the usefulness from just writing to disk, and the remaining 1/3 with ids.
With just the former, we can still do all the global aggregations.
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That's a great idea! However, while we're at it: would it be possible to make this more general, and more user-controllable? I.e., could we maybe have some general "results-writer" that can be triggered for some things automatically, but that could also be included in arbitrary models?
E.g., something like an automatic boolean called "log" that's a wrapper around each model s.t. the input-output pairs of the nn.module will be stored? But additionally having something like a results_writer
object?
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@cle-ros That s a sweet idea. I'll think about it after the next release!
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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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