Artificial Intelligence

Proposed refactor

I want to add typing for the trainer pointer within the LightningModule.

Motivation

This would improve autocompletion for IDEs.

Pitch

I would like to make the following addi

https://github.com/actions recently released v3 for their actions (e.g. https://github.com/actions/setup-python/releases/tag/v3.0.0). We should upgrade our GitHub Actions config to use the new version.

Example:

https://github.com/mlflow/mlflow/blob/f9e5200a3a1e8d5ec86fd4ed9776f1c2392ce18e/.github/actions/setup-python/action.yml#L32

-      - uses: actions/setup-python@v2
+      -

Is there an existing issue for this?

• I have searched the existing issues

Environment

- Milvus version:lastest
- Deployment mode(standalone or cluster):all
- SDK version(e.g. pymilvus v2.0.0rc2):
- OS(Ubuntu or CentOS): 
- CPU/Memory: 
- GPU: 
- Others:

Current Behavior

In the current code logic, it is directly read and written through Pa

We support toml as params file. There are a few issues with our current toml parsing:

1. We use toml library, which is not toml 1.0 standard compatible. Also, the library is not being actively maintained.
2. toml dumping does not preserve existing formatting.
3. iterative/dvc#6402

We can try migrating to tomli (which

Currently, when entering epic mode the README is frozen in the last level of the tower. When you're trying to fine-tune the score for a level other than the last one, it would be helpful if we had the README for that level available. The proposal is that when entering epic mode, the README is updated with all levels, one following the other.

Example:

# Starbolt - beginner

Specs

• Leon version: latest
• OS (or browser) version: Fedora 30
• Node.js version: 10.16.3
• Complete "npm run check" output:

➡ Here is the diagnosis about your current setup
✔ Run
✔ Run modules
✔ Reply you by texting
❗ Amazon Polly text-to-speech
❗ Google Cloud text-to-speech
❗ Watson text-to-speech
❗ Offline text-to-speech
❗ Google Cloud speech-to-text
❗ Watson spee

Currently, you can do something like this: Task(Flow/RunID/StepName) and this will not result in an error but then the resulting Task object behaves in a bizarre manner where things like t.data will work but t.data.my_artifact will not for example.

We should validate the format of the pathspec passed in to each object and verify that the following are the only possible cases:

• Metaflo