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pollfly
2023-09-04 15:40:42 +03:00
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docs/getting_started/architecture.md
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@@ -14,6 +14,6 @@ Solutions combined with the clearml-server control plane.
## YouTube Playlist
The first video in our YouTube Getting Started playlist covers these modules in more detail, feel free to check out the video below.
The first video in the ClearML YouTube **Getting Started** playlist covers these modules in more detail, feel free to check out the video below.
[![Watch the video](https://img.youtube.com/vi/s3k9ntmQmD4/hqdefault.jpg)](https://www.youtube.com/watch?v=s3k9ntmQmD4&list=PLMdIlCuMqSTnoC45ME5_JnsJX0zWqDdlO&index=1)

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docs/getting_started/ds/best_practices.md
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@@ -41,8 +41,8 @@ yields the best performing model for your task!
- You should continue coding while experiments are being executed without interrupting them.
- Stop optimizing your code because your machine struggles, and run it on a beefier machine (cloud / on-prem).
Visualization and comparisons dashboards keep your sanity at bay! In this stage we usually have a docker container with all the binaries
that we need.
Visualization and comparisons dashboards keep your sanity at bay! In this stage you usually have a docker container with all the binaries
that you need.
- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) ensures that all the metrics, parameters and Models are automatically logged and can later be
accessed, [compared](../../webapp/webapp_exp_comparing.md) and [tracked](../../webapp/webapp_exp_track_visual.md).
- [ClearML Agent](../../clearml_agent.md) does the heavy lifting. It reproduces the execution environment, clones your code,

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docs/getting_started/mlops/mlops_second_steps.md
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@@ -66,11 +66,11 @@ When you access the Dataset, it automatically merges the files from all parent v
in a fully automatic and transparent process, as if the files were always part of the requested Dataset.
### Training
We can now train our model with the **latest** Dataset we have in the system.
We will do that by getting the instance of the Dataset based on the `latest` tag
(if by any chance we have two Datasets with the same tag we will get the newest).
Once we have the dataset we can request a local copy of the data. All local copy requests are cached,
which means that if we are accessing the same dataset multiple times we will not have any unnecessary downloads.
You can now train your model with the **latest** Dataset you have in the system, by getting the instance of the Dataset
based on the `latest` tag
(if by any chance you have two Datasets with the same tag you will get the newest).
Once you have the dataset you can request a local copy of the data. All local copy requests are cached,
which means that if you access the same dataset multiple times you will not have any unnecessary downloads.
```python
# create a task for the model training
@@ -87,7 +87,7 @@ dataset_folder = dataset.get_local_copy()
## Building the Pipeline
Now that we have the data creation step, and the data training step, let's create a pipeline that when executed,
Now that you have the data creation step, and the data training step, create a pipeline that when executed,
will first run the first and then run the second.
It is important to remember that pipelines are Tasks by themselves and can also be automated by other pipelines (i.e. pipelines of pipelines).