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Small edits (#812)

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pollfly
2024-03-27 11:56:21 +02:00
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parent 57be45d2a8
commit fb6270ff23
4 changed files with 15 additions and 13 deletions
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8
docs/getting_started/ds/best_practices.md
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@@ -7,7 +7,7 @@ While ClearML was designed to fit into any workflow, the practices described bel
to preparing it to scale in the long term.
:::important
The below is only our opinion. ClearML was designed to fit into any workflow whether it conforms to our way or not!
The following is only an opinion. ClearML is designed to accommodate any workflow whether it conforms to our way or not!
:::
## Develop Locally
@@ -16,9 +16,9 @@ The below is only our opinion. ClearML was designed to fit into any workflow whe
During early stages of model development, while code is still being modified heavily, this is the usual setup we'd expect to see used by data scientists:
- A local development machine, usually a laptop (and usually using only CPU) with a fraction of the dataset for faster
iterations - Use a local machine for writing, training, and debugging pipeline code.
- A workstation with a GPU, usually with a limited amount of memory for small batch-sizes - Use this workstation to train
- **Local development machine**, usually a laptop (and usually using only CPU) with a fraction of the dataset for faster
iterations. Use a local machine for writing, training, and debugging pipeline code.
- **Workstation with a GPU**, usually with a limited amount of memory for small batch-sizes. Use this workstation to train
the model and ensure that you choose a model that makes sense, and the training procedure works. Can be used to provide initial models for testing.
The abovementioned setups might be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome!

4
docs/getting_started/mlops/mlops_second_steps.md
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@@ -51,8 +51,8 @@ new_dataset = Dataset.create(
dataset_project='data',
dataset_name='dataset_v2',
parent_datasets=[dataset],
use_current_task=True,
# this will make sure we have the creation code and the actual dataset artifacts on the same Task
# this will make sure we have the creation code and the actual dataset artifacts on the same Task
use_current_task=True,
)
new_dataset.sync_folder(local_path=dataset_folder)
new_dataset.upload()