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pollfly
2023-02-16 12:17:53 +02:00
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docs/getting_started/mlops/mlops_best_practices.md
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@@ -17,11 +17,11 @@ If you are afraid of clutter, use the archive option, and set up your own [clean
These metrics can later be part of your own in-house monitoring solution, don't let good data go to waste :)
## Clone Tasks
In order to define a Task in ClearML we have two options
Define a ClearML Task with one of the following options:
- Run the actual code with `Task.init` call. This will create and auto-populate the Task in CleaML (including Git Repo / Python Packages / Command line etc.).
- Register local / remote code repository with `clearml-task`. See [details](../../apps/clearml_task.md).
Once we have a Task in ClearML, we can clone and edit its definitions in the UI, then launch it on one of our nodes with [ClearML Agent](../../clearml_agent.md).
Once you have a Task in ClearML, you can clone and edit its definitions in the UI, then launch it on one of your nodes with [ClearML Agent](../../clearml_agent.md).
## Advanced Automation
- Create daily / weekly cron jobs for retraining best performing models on.

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docs/getting_started/mlops/mlops_first_steps.md
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@@ -164,7 +164,7 @@ and [pipeline](../../pipelines/pipelines.md) solutions.
Logging models into the model repository is the easiest way to integrate the development process directly with production.
Any model stored by a supported framework (Keras / TensorFlow / PyTorch / Joblib etc.) will be automatically logged into ClearML.
ClearML also offers methods to explicitly log models. Models can be automatically stored on a preferred storage medium
ClearML also supports methods to explicitly log models. Models can be automatically stored on a preferred storage medium
(s3 bucket, google storage, etc.).
#### Log Metrics
@@ -208,7 +208,7 @@ tasks = Task.get_tasks(
Data is probably one of the biggest factors that determines the success of a project. Associating a model’s data with
the model's configuration, code, and results (such as accuracy) is key to deducing meaningful insights into model behavior.
[ClearML Data](../../clearml_data/clearml_data.md) allows you to version your data, so it's never lost, fetch it from every
[ClearML Data](../../clearml_data/clearml_data.md) lets you version your data, so it's never lost, fetch it from every
machine with minimal code changes, and associate data to experiment results.
Logging data can be done via command line, or programmatically. If any preprocessing code is involved, ClearML logs it

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docs/getting_started/mlops/mlops_second_steps.md
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@@ -16,19 +16,19 @@ The sections below describe the following scenarios:
## Building Tasks
### Dataset Creation
Let's assume we have some code that extracts data from a production database into a local folder.
Our goal is to create an immutable copy of the data to be used by further steps:
Let's assume you have some code that extracts data from a production database into a local folder.
Your goal is to create an immutable copy of the data to be used by further steps:
```bash
clearml-data create --project data --name dataset
clearml-data sync --folder ./from_production
```
We could also add a tag `latest` to the Dataset, marking it as the latest version.
You can add a tag `latest` to the Dataset, marking it as the latest version.
### Preprocessing Data
The second step is to preprocess the data. First we need to access it, then we want to modify it,
and lastly we want to create a new version of the data.
The second step is to preprocess the data. First access the data, then modify it,
and lastly create a new version of the data.
```python
# create a task for the data processing part
@@ -59,10 +59,10 @@ dataset.tags = []
new_dataset.tags = ['latest']
```
We passed the `parents` argument when we created v2 of the Dataset, which inherits all the parent's version content.
This not only helps trace back dataset changes with full genealogy, but also makes our storage more efficient,
The new dataset inherits the contents of the datasets specified in `Dataset.create`'s `parents` argument.
This not only helps trace back dataset changes with full genealogy, but also makes the storage more efficient,
since it only stores the changed and / or added files from the parent versions.
When we access the Dataset, it automatically merges the files from all parent versions
When you access the Dataset, it automatically merges the files from all parent versions
in a fully automatic and transparent process, as if the files were always part of the requested Dataset.
### Training