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@ -45,7 +45,7 @@ of the optimization results in table and graph forms.
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|`--args`| List of `<argument>=<value>` strings to pass to the remote execution. Currently only argparse/click/hydra/fire arguments are supported. Example: `--args lr=0.003 batch_size=64`|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--local`| If set, run the experiments locally. Notice that no new python environment will be created. The `--script` parameter must point to a local file entry point and all arguments must be passed with `--args`| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--save-top-k-tasks-only`| Keep only the top \<k\> performing tasks, and archive the rest of the experiments. Input `-1` to keep all tasks. Default: `10`.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--time-limit-per-job`|Maximum execution time per single job in minutes. When time limit is exceeded, the job is aborted. Default: no time limit.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--time-limit-per-job`|Maximum execution time per single job in minutes. When the time limit is exceeded, the job is aborted. Default: no time limit.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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</div>
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@ -48,13 +48,13 @@ As can be seen, the `clearml-data sync` command creates the dataset, then upload
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## Modifying Synced Folder
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Now we'll modify the folder:
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Modify the data folder:
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1. Add another line to one of the files in the `data_samples` folder.
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1. Add a file to the sample_data folder.<br/>
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Run `echo "data data data" > data_samples/new_data.txt` (this will create the file `new_data.txt` and put it in the `data_samples` folder)
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We'll repeat the process of creating a new dataset with the previous one as its parent, and syncing the folder.
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Repeat the process of creating a new dataset with the previous one as its parent, and syncing the folder.
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```bash
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clearml-data sync --project datasets --name second_ds --parents a1ddc8b0711b4178828f6c6e6e994b7c --folder data_samples
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@ -126,7 +126,7 @@ You'll need to input the Dataset ID you received when created the dataset above
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1 file added
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```
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1. Remove a file. We'll need to specify the file's full path (within the dataset, not locally) to remove it.
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1. Remove a file. You need to specify the file's full path (within the dataset, not locally) to remove it.
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```bash
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clearml-data remove --files data_samples/dancing.jpg
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@ -31,6 +31,8 @@ The second step is to preprocess the data. First access the data, then modify it
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and lastly create a new version of the data.
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```python
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from clearml import Task, Dataset
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# create a task for the data processing part
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task = Task.init(project_name='data', task_name='create', task_type='data_processing')
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@ -93,6 +95,8 @@ will first run the first and then run the second.
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It is important to remember that pipelines are Tasks by themselves and can also be automated by other pipelines (i.e. pipelines of pipelines).
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```python
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from clearml import PipelineController
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pipe = PipelineController(
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project='data',
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name='pipeline demo',
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@ -112,6 +116,6 @@ pipe.add_step(
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)
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```
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We can also pass the parameters from one step to the other (for example `Task.id`).
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In addition to pipelines made up of Task steps, ClearML also supports pipelines consisting of function steps. See more in the
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full pipeline documentation [here](../../pipelines/pipelines.md).
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You can also pass the parameters from one step to the other (for example `Task.id`).
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In addition to pipelines made up of Task steps, ClearML also supports pipelines consisting of function steps. For more
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information, see the [full pipeline documentation](../../pipelines/pipelines.md).
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@ -105,7 +105,7 @@ Switch on the **Show row extremes** toggle to highlight each variant's maximum a
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The **Hyperparameters** tab's **Parallel Coordinates** comparison shows experiments' hyperparameter impact on specified
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metrics:
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1. Under **Performance Metrics**, select a metrics to compare for
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1. Under **Performance Metrics**, select metrics to compare for
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1. Select the values to use for each metric in the plot (can select multiple):
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* LAST - The final value, or the most recent value, for currently running experiments
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* MIN - Minimal value
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