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2
docs/apps/clearml_session.md
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@ -145,7 +145,7 @@ The Task must be connected to a git repository, since currently single script de
| Command line options | Description | Default value |
|-----|---|---|
| `--attach`| Attach to running interactive session | Previous session|
| `--project`| Set the project name to the iteractive session task| `DevOps` |
| `--project`| Set the project name to the interactive session task| `DevOps` |
| `--requirements`| Specify requirements.txt file to install when setting the interactive session. | `none` or previously used requirements (can be overridden by calling `--packages`)|
| `--packages`| Additional packages to add. Supports version numbers. Example: `--packages torch==1.7 tqdm` | Previously added packages.|
| `--git-credentials` | If `True`, local `.git-credentials` file is sent to the interactive session.| `false`|

4
docs/clearml_sdk/task_sdk.md
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@ -672,12 +672,12 @@ your task’s configuration using the [`Task.connect`](../references/sdk/task.md
to a task, ClearML automatically logs all object elements (e.g. class members, dictionary key-values pairs).
```python
class person:
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
me = person('Erik', 5)
me = Person('Erik', 5)
params_dictionary = {'epochs': 3, 'lr': 0.4}

2
docs/clearml_serving/clearml_serving_cli.md
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@ -85,7 +85,7 @@ clearml-serving metrics add [-h] --endpoint ENDPOINT [--log-freq LOG_FREQ]
|---|---|---|
|`--endpoint`|Metric endpoint name including version (e.g. `"model/1"` or a prefix `"model/*"`). Notice: it will override any previous endpoint logged metrics| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--log-freq`|Logging request frequency, between 0.0 to 1.0. Example: 1.0 means all requests are logged, 0.5 means half of the requests are logged if not specified. To use global logging frequency, see [`config --metric-log-freq`](#config)| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--variable-scalar`|Add float (scalar) argument to the metric logger, `<name>=<histogram>`. Example: with specific buckets: `"x1=0,0.2,0.4,0.6,0.8,1"` or with min/max/num_buckets `"x1=0.0/1.0/5"`. Notice: In cases where 1000s of requests per second reach the serving, it makes no sense to display every datapoint. So scalars can be divided in buckets, and for each minute for example. Then its possible to calculate how many % of the total traffic fell in bucket 1, bucket 2, bucket 3 etc. The Y axis represents the buckets, color is the value in % of traffic in that bucket, and X is time. | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--variable-scalar`|Add float (scalar) argument to the metric logger, `<name>=<histogram>`. Example: with specific buckets: `"x1=0,0.2,0.4,0.6,0.8,1"` or with min/max/num_buckets `"x1=0.0/1.0/5"`. Notice: In cases where 1000s of requests per second reach the serving, it makes no sense to display every datapoint. So scalars can be divided in buckets, and for each minute for example. Then it's possible to calculate what % of the total traffic fell in bucket 1, bucket 2, bucket 3 etc. The Y axis represents the buckets, color is the value in % of traffic in that bucket, and X is time. | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--variable-enum`|Add enum (string) argument to the metric logger, `<name>=<optional_values>`. Example: `"detect=cat,dog,sheep"` |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--variable-value`|Add non-samples scalar argument to the metric logger, `<name>`. Example: `"latency"` |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|

8
docs/getting_started/video_tutorials/hands-on_mlops_tutorials/how_clearml_is_used_by_a_data_scientist.md
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@ -29,8 +29,8 @@ You can see the overview of the code, so I'm not going to dive into the code imm
context, and then we'll go deeper from there.
So the idea is that I'm doing audio classification here. I have a client who I want to give like a proof of concept on
how well it can work, and I'm doing that on the Urbansound dataset. So the first thing I'll do, and you'll see that
later is I'll get the data from the Urbansound servers. I'm using a script called `get_data.py` for that, and then for
how well it can work, and I'm doing that on the UrbanSound dataset. So the first thing I'll do, and you'll see that
later is I'll get the data from the UrbanSound servers. I'm using a script called `get_data.py` for that, and then for
reasons I'll go further into in the video I'm actually putting all of that data into a ClearML dataset which is a special
kind of dataset task or like a special kind of ClearML task that can keep track of your data. Then the `preprocessing.py`
script will get that data and then convert the WAV files or like the audio files to spectrum images. Essentially you're
@ -60,7 +60,7 @@ of this data is flowing. It's a lot easier to use a ClearML dataset instead.
So what I'm doing here and this is actually really cool. I'm using a single link to a zip file that I made, which is a
subset of the complete data, so it only has like 120 samples or something, and then we use that to iterate really quickly.
We also have the part to the Urbansounds full dataset, which we then label as `full dataset` and that will give us the
We also have the part to the UrbanSounds full dataset, which we then label as `full dataset` and that will give us the
freedom to switch between subset and full dataset. So I will essentially create two ClearML data versions, one with the
subset, one with the full dataset, and that will allow me to very quickly change without having the whole thing, with
different versions on my desk all the time. What I used to do is then have different versions or different
@ -197,7 +197,7 @@ closer to this diagonal shape that we're trying to get to. So this is showing me
learning something, it's doing something so that actually is very interesting.
And then you have debug samples as well, which you can use to show actually whatever kind of media you need. So these
are for example, the images that I generated that are the mel spectrograms so that the preprocessing outputs uh, and you
are for example, the images that I generated that are the mel spectrogram's so that the preprocessing outputs, and you
can just show them here with the name of what the label was and what to predict it was. So I can just have a very quick
overview of how this is working, and then I can actually even do it with audio samples as well. So I can for example here
say this is labeled "dog", and it is predicted as "children playing". So then I can listen to it and get an idea on, is

6
docs/release_notes/ver_1_0.md
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@ -153,7 +153,7 @@ Arguments order changed in `Logger.report_line_plot()`, `Logger.report_plotly()`
**Features**
* Add OS environemt variable `CLEARML_DEFAULT_OUTPUT_URI` to override default `output_uri` for automatic models upload - [ClearML GitHub issue 328](https://github.com/allegroai/clearml/issues/328)
* Add OS environment variable `CLEARML_DEFAULT_OUTPUT_URI` to override default `output_uri` for automatic models upload - [ClearML GitHub issue 328](https://github.com/allegroai/clearml/issues/328)
* Add `Task.force_requirements_env_freeze()` forcing `pip freeze` instead of package analysis - [ClearML GitHub issue 304](https://github.com/allegroai/clearml/issues/304)
* Improve pipeline support
* Add Task parameters override in `ClearmlJob` - [ClearML GitHub issue 214](https://github.com/allegroai/clearml/issues/214)
@ -167,7 +167,7 @@ Arguments order changed in `Logger.report_line_plot()`, `Logger.report_plotly()`
* Add `docker_args` and `docker_bash_setup_script` arguments to `Task.create()`
* Change Logger plot iteration argument default to 0 (or `None`)
* Add task filtering in `Task.get_task()` using the `allow_archived` and `task_filter` arguments
* Add `Task.get_configuration_objects()` for reteriving configuration object as a blob of text in automation use-cases (instead of using `Task.connect_configuration()`)
* Add `Task.get_configuration_objects()` for retrieving configuration object as a blob of text in automation use-cases (instead of using `Task.connect_configuration()`)
* Improved pipeline support
* Add pipeline step caching
* Add Pipeline controller caching
@ -250,7 +250,7 @@ Arguments order changed in `Logger.report_line_plot()`, `Logger.report_plotly()`
* Add Context menu batch operations - [ClearML GitHub issue 286](https://github.com/allegroai/clearml/issues/286)
* Add global search regex support - [ClearML GitHub issue 81](https://github.com/allegroai/clearml/issues/81#issuecomment-735003956)
* Add support for extended container (docker) configuration and init script
* Add special character support ("." (dot) and "$") in Hyper Paramters
* Add special character support ("." (dot) and "$") in Hyperparameters
* Add new context menu icons
* Add deleting experiments and models through UI - ClearML GitHub issues [53](https://github.com/allegroai/clearml-server/issues/53), [61](https://github.com/allegroai/clearml-server/issues/61) and [81](https://github.com/allegroai/clearml/issues/81#issuecomment-706907718)
* Improve Project deletion: deletes project's experiments and models.

2
docs/release_notes/ver_1_1.md
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@ -255,7 +255,7 @@ This release is not backwards compatible - see notes below on upgrading
**Bug Fixes**
- Fix poltly plots converting `NaN` to `nan` instead of `null` [ClearML GitHub issue #373](https://github.com/allegroai/clearml/issues/373)
- Fix plotly plots converting `NaN` to `nan` instead of `null` [ClearML GitHub issue #373](https://github.com/allegroai/clearml/issues/373)
- Fix deprecation warning [ClearML GitHub issue #376](https://github.com/allegroai/clearml/issues/376)
- Fix plotly multi-index without index names [ClearML GitHub issue #399](https://github.com/allegroai/clearml/issues/399)
- Fix click support [ClearML GitHub issue #437](https://github.com/allegroai/clearml/issues/437)

2
docs/webapp/pipelines/webapp_pipeline_table.md
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@ -36,7 +36,7 @@ The models table contains the following columns:
Customize the table using any of the following:
* Dynamic column ordering - Drag a column title to a different position.
* Show / hide columns - Click <img src="/docs/latest/icons/ico-settings.svg" alt="Setting Gear" className="icon size-md" />
to view and select columns to show. Click **Metric** and **Hyper Parameter** to add the respective custom columns
to view and select columns to show. Click **Metric** and **Hyperparameter** to add the respective custom columns
* [Filter columns](#filtering-columns)
* Sort columns
* Resize columns - Drag the column separator to change the width of that column. Double-click the column separator for