Artificial Intelligence

🐛 Bug

DeepSpeed raises an internal error when the Trainer runs on CPU. I imagine they don't support CPU training so we should raise a MisconfigurationException in that case.

To Reproduce

Code

import os

import torch
from torch.utils.data import DataLoader, Dataset

from pytorch_lightning import LightningModule, Trainer


class RandomDataset(Dataset):
    de

Motivation

We frequently construct an MlflowException instance with error_code=INVALID_PARAMETER_VALUE:

import mlflow.exceptions from MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE

raise MlflowException(
    "error message",
    error_code=INVALID_PARAMETER_VALUE,
)

If we had a class method invalid_parameter_value:

Is there an existing issue for this?

• I have searched the existing issues

What would you like to be added?

When a user reports a bug in an issue, in addition to "Environment", "Current Behavior", "Expected Behavior", and "Steps To Reproduce", a "Milvus log" needs to be added to the issue template.

![image](https://user-images.githubusercontent.com/53512883/162382819-86855bd6-7f6

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